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similarity index 71%
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index 68d96fb..d85e856 100644
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t=e.languages.javascript,n=/\{(?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})+\}/.source,r="(@(?:arg|argument|param|property)\\s+(?:"+n+"\\s+)?)";e.languages.jsdoc=e.languages.extend("javadoclike",{parameter:{pattern:RegExp(r+/(?:(?!\s)[$\w\xA0-\uFFFF.])+(?=\s|$)/.source),lookbehind:!0,inside:{punctuation:/\./}}}),e.languages.insertBefore("jsdoc","keyword",{"optional-parameter":{pattern:RegExp(r+/\[(?:(?!\s)[$\w\xA0-\uFFFF.])+(?:=[^[\]]+)?\](?=\s|$)/.source),lookbehind:!0,inside:{parameter:{pattern:/(^\[)[$\w\xA0-\uFFFF\.]+/,lookbehind:!0,inside:{punctuation:/\./}},code:{pattern:/(=)[\s\S]*(?=\]$)/,lookbehind:!0,inside:t,alias:"language-javascript"},punctuation:/[=[\]]/}},"class-name":[{pattern:RegExp(/(@(?:augments|class|extends|interface|memberof!?|template|this|typedef)\s+(?:<TYPE>\s+)?)[A-Z]\w*(?:\.[A-Z]\w*)*/.source.replace(/<TYPE>/g,function(){return 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e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(C),C.languages.c=C.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),C.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),C.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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C.languages.c.boolean,C.languages.objectivec=C.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete C.languages.objectivec["class-name"],C.languages.objc=C.languages.objectivec,C.languages.reason=C.languages.extend("clike",{string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^\\\r\n"])*"/,greedy:!0},"class-name":/\b[A-Z]\w*/,keyword:/\b(?:and|as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|method|module|mutable|new|nonrec|object|of|open|or|private|rec|sig|struct|switch|then|to|try|type|val|virtual|when|while|with)\b/,operator:/\.{3}|:[:=]|\|>|->|=(?:==?|>)?|<=?|>=?|[|^?'#!~`]|[+\-*\/]\.?|\b(?:asr|land|lor|lsl|lsr|lxor|mod)\b/}),C.languages.insertBefore("reason","class-name",{char:{pattern:/'(?:\\x[\da-f]{2}|\\o[0-3][0-7][0-7]|\\\d{3}|\\.|[^'\\\r\n])'/,greedy:!0},constructor:/\b[A-Z]\w*\b(?!\s*\.)/,label:{pattern:/\b[a-z]\w*(?=::)/,alias:"symbol"}}),delete C.languages.reason.function,function(e){for(var t=/\/\*(?:[^*/]|\*(?!\/)|\/(?!\*)|<self>)*\*\//.source,n=0;n<2;n++)t=t.replace(/<self>/g,function(){return 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C.languages.go["class-name"],function(e){var t=/\b(?:alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|char8_t|class|co_await|co_return|co_yield|compl|concept|const|const_cast|consteval|constexpr|constinit|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|final|float|for|friend|goto|if|import|inline|int|int16_t|int32_t|int64_t|int8_t|long|module|mutable|namespace|new|noexcept|nullptr|operator|override|private|protected|public|register|reinterpret_cast|requires|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|uint16_t|uint32_t|uint64_t|uint8_t|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,n=/\b(?!<keyword>)\w+(?:\s*\.\s*\w+)*\b/.source.replace(/<keyword>/g,function(){return 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256&t.flags?(t.flags&=-257,$s(e,t,s,r=ds(Error(o(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(i=r.fallback,a=t.mode,r=Mc({mode:"visible",children:r.children},a,0,null),(i=Fc(i,a,s,null)).flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,1&t.mode&&Qo(t,e.child,null,s),t.child.memoizedState=Ms(s),t.memoizedState=Fs,i);if(!(1&t.mode))return $s(e,t,s,null);if("$!"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,$s(e,t,s,r=ds(i=Error(o(419)),r,void 0))}if(l=0!==(s&e.childLanes),ws||l){if(null!==(r=Pl)){switch(s&-s){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 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t=e.languages.javascript,n=/\{(?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})+\}/.source,r="(@(?:arg|argument|param|property)\\s+(?:"+n+"\\s+)?)";e.languages.jsdoc=e.languages.extend("javadoclike",{parameter:{pattern:RegExp(r+/(?:(?!\s)[$\w\xA0-\uFFFF.])+(?=\s|$)/.source),lookbehind:!0,inside:{punctuation:/\./}}}),e.languages.insertBefore("jsdoc","keyword",{"optional-parameter":{pattern:RegExp(r+/\[(?:(?!\s)[$\w\xA0-\uFFFF.])+(?:=[^[\]]+)?\](?=\s|$)/.source),lookbehind:!0,inside:{parameter:{pattern:/(^\[)[$\w\xA0-\uFFFF\.]+/,lookbehind:!0,inside:{punctuation:/\./}},code:{pattern:/(=)[\s\S]*(?=\]$)/,lookbehind:!0,inside:t,alias:"language-javascript"},punctuation:/[=[\]]/}},"class-name":[{pattern:RegExp(/(@(?:augments|class|extends|interface|memberof!?|template|this|typedef)\s+(?:<TYPE>\s+)?)[A-Z]\w*(?:\.[A-Z]\w*)*/.source.replace(/<TYPE>/g,function(){return 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e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(C),C.languages.c=C.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),C.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),C.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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C.languages.c.boolean,C.languages.objectivec=C.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete C.languages.objectivec["class-name"],C.languages.objc=C.languages.objectivec,C.languages.reason=C.languages.extend("clike",{string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^\\\r\n"])*"/,greedy:!0},"class-name":/\b[A-Z]\w*/,keyword:/\b(?:and|as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|method|module|mutable|new|nonrec|object|of|open|or|private|rec|sig|struct|switch|then|to|try|type|val|virtual|when|while|with)\b/,operator:/\.{3}|:[:=]|\|>|->|=(?:==?|>)?|<=?|>=?|[|^?'#!~`]|[+\-*\/]\.?|\b(?:asr|land|lor|lsl|lsr|lxor|mod)\b/}),C.languages.insertBefore("reason","class-name",{char:{pattern:/'(?:\\x[\da-f]{2}|\\o[0-3][0-7][0-7]|\\\d{3}|\\.|[^'\\\r\n])'/,greedy:!0},constructor:/\b[A-Z]\w*\b(?!\s*\.)/,label:{pattern:/\b[a-z]\w*(?=::)/,alias:"symbol"}}),delete C.languages.reason.function,function(e){for(var t=/\/\*(?:[^*/]|\*(?!\/)|\/(?!\*)|<self>)*\*\//.source,n=0;n<2;n++)t=t.replace(/<self>/g,function(){return 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C.languages.go["class-name"],function(e){var t=/\b(?:alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|char8_t|class|co_await|co_return|co_yield|compl|concept|const|const_cast|consteval|constexpr|constinit|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|final|float|for|friend|goto|if|import|inline|int|int16_t|int32_t|int64_t|int8_t|long|module|mutable|namespace|new|noexcept|nullptr|operator|override|private|protected|public|register|reinterpret_cast|requires|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|uint16_t|uint32_t|uint64_t|uint8_t|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,n=/\b(?!<keyword>)\w+(?:\s*\.\s*\w+)*\b/.source.replace(/<keyword>/g,function(){return 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256&t.flags?(t.flags&=-257,$s(e,t,s,r=ds(Error(o(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(i=r.fallback,a=t.mode,r=Mc({mode:"visible",children:r.children},a,0,null),(i=Fc(i,a,s,null)).flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,1&t.mode&&Qo(t,e.child,null,s),t.child.memoizedState=Ms(s),t.memoizedState=Fs,i);if(!(1&t.mode))return $s(e,t,s,null);if("$!"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,$s(e,t,s,r=ds(i=Error(o(419)),r,void 0))}if(l=0!==(s&e.childLanes),ws||l){if(null!==(r=Pl)){switch(s&-s){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 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diff --git a/assets/js/runtime~main.0edf3581.js b/assets/js/runtime~main.de98056e.js
similarity index 97%
rename from assets/js/runtime~main.0edf3581.js
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index cb3f39e..1964538 100644
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index 190a1bd..8579969 100644
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2024</time> · <!-- -->6 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/1241122?v=4" alt="Mirko Kämpf" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Mirko Kämpf</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>In the second part of the article series we describe the implementation of the Kafka Source and Kafka Sink component for Apache Wayang.
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class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/1241122?v=4" alt="Mirko Kämpf" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Mirko Kämpf</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>The third part of this article series is an activity log.
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margin-vert--md"><time datetime="2024-04-17T00:00:00.000Z" itemprop="datePublished">April 17, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/glauesppen" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/glauesppen" alt="Gláucia Esppenchutz" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/glauesppen" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Gláucia Esppenchutz</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>AI systems and applications are widely used nowadays, from assisting grammar spellings to
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class="container_mt6G margin-vert--md"><time datetime="2024-04-17T00:00:00.000Z" itemprop="datePublished">April 17, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/glauesppen" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/glauesppen" alt="Gláucia Esppenchutz" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/glauesppen" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Gláucia Esppenchutz</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>AI systems and applications are widely used nowadays, from assisting grammar spellings to
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datetime="2024-03-10T00:00:00.000Z" itemprop="datePublished">March 10, 2024</time> · <!-- -->6 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/1241122?v=4" alt="Mirko Kämpf" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Mirko Kämpf</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>The third part of this article series is an activity log.
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class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/kafka-meets-wayang-2">Apache Kafka meets Wayang - Part 2</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/kafka-meets-wayang-1">Apache Kafka meets Wayang - Part 1</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/website_update">Website updated</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><header class="margin-bottom--xl"><h1>One post tagged with "ML"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl" itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="We are happy to announce that we have extended Wayang to be able to utilize any ML platform and any ML operators."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/blog/wayang-tensorflow">Integrating ML platforms in Wayang</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-05-07T00:00:00.000Z" itemprop="datePublished">May 7, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/zkaoudi" alt="Zoi Kaoudi" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Zoi Kaoudi</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>We are happy to announce that we have extended Wayang to be able to utilize any ML platform and any ML operators.
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margin-vert--md"><time datetime="2024-04-09T00:00:00.000Z" itemprop="datePublished">April 9, 2024</time> · <!-- -->4 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/juripetersen" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/43411515?v=4" alt="Juri Petersen" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/juripetersen" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Juri Petersen</span></a></div><small class="avatar__subtitle" itemprop="description">Apache Committer</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>In the vast landscape of data processing, efficiency and flexibility 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datetime="2024-03-10T00:00:00.000Z" itemprop="datePublished">March 10, 2024</time> · <!-- -->6 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/1241122?v=4" alt="Mirko Kämpf" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/kamir" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Mirko Kämpf</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>The third part of this article series is an activity log.
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Wayang</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-05-07T00:00:00.000Z" itemprop="datePublished">May 7, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/zkaoudi" alt="Zoi Kaoudi" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Zoi Kaoudi</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>We are happy to announce that we have extended Wayang to be able to utilize any ML platform and any ML operators.
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In this blog post, we will clarify the main differences and how they impact various applications and use cases."><header><h2 class="title_f1Hy" itemprop="headline"><a itemprop="url" href="/blog/wayang-vs-trino">Apache Wayang vs. Presto/Trino</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-03-08T00:00:00.000Z" itemprop="datePublished">March 8, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/zkaoudi" alt="Zoi Kaoudi" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Zoi Kaoudi</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div class="markdown" itemprop="articleBody"><p>We have been asked several times about the difference between Apache Wayang and Presto/Trino. In this blog post, we will clarify the main differences and how they impact various applications and use cases.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/wayang">wayang</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/presto">presto</a></li><li class="tag_QGVx"><a class="tag_zVej tagRegular_sFm0" href="/blog/tags/trino">trino</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Apache Wayang vs. Presto/Trino" href="/blog/wayang-vs-trino"><b>Read More</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"></nav></main></div></div></div><footer class="footer footer--dark"><div class="container container-fluid"><div class="row footer__links"><div class="col footer__col"><div class="footer__title">Community</div><ul 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class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/juripetersen" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/u/43411515?v=4" alt="Juri Petersen" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/juripetersen" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Juri Petersen</span></a></div><small class="avatar__subtitle" itemprop="description">Apache Committer</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>In the vast landscape of data processing, efficiency and flexibility are
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2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/zkaoudi" alt="Zoi Kaoudi" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Zoi Kaoudi</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>We are happy to announce that we have extended Wayang to be able to utilize any ML platform and any ML operators.
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href="/blog/wayang-vs-trino">Apache Wayang vs. Presto/Trino</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/kafka-meets-wayang-2">Apache Kafka meets Wayang - Part 2</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/kafka-meets-wayang-1">Apache Kafka meets Wayang - Part 1</a></li><li class="sidebarItem__DBe"><a class="sidebarItemLink_mo7H" href="/blog/website_update">Website updated</a></li></ul></nav></aside><main class="col col--7" itemscope="" itemtype="https://schema.org/Blog"><article itemprop="blogPost" itemscope="" itemtype="https://schema.org/BlogPosting"><meta itemprop="description" content="We have been asked several times about the difference between Apache Wayang and Presto/Trino. In this blog post, we will clarify the main differences and how they impact various applications and use cases."><header><h1 class="title_f1Hy" itemprop="headline">Apache Wayang vs. Presto/Trino</h1><div class="container_mt6G margin-vert--md"><time datetime="2024-03-08T00:00:00.000Z" itemprop="datePublished">March 8, 2024</time> · <!-- -->3 min read</div><div class="margin-top--md margin-bottom--sm row"><div class="col col--6 authorCol_Hf19"><div class="avatar margin-bottom--sm"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" class="avatar__photo-link"><img class="avatar__photo" src="https://avatars.githubusercontent.com/zkaoudi" alt="Zoi Kaoudi" itemprop="image"></a><div class="avatar__intro" itemprop="author" itemscope="" itemtype="https://schema.org/Person"><div class="avatar__name"><a href="https://github.com/zkaoudi" target="_blank" rel="noopener noreferrer" itemprop="url"><span itemprop="name">Zoi Kaoudi</span></a></div><small class="avatar__subtitle" itemprop="description">(P)PMC Apache Wayang</small></div></div></div></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>We have been asked several times about the difference between Apache Wayang and Presto/Trino. In this blog post, we will clarify the main differences and how they impact various applications and use cases.</p>
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Model</h1></header><table><thead><tr><th>ID</th><th>Description</th><th>Status</th><th></th></tr></thead><tbody><tr><td>Code</td><td></td><td></td><td></td></tr><tr><td>CD10</td><td>The project produces Open Source software for distribution to the public, at no charge.</td><td>Yes. The project source code is licensed under the Apache License 2.0.</td><td></td></tr><tr><td>CD20</td><td>Anyone can easily discover and access the project's code.</td><td>Yes. The official website includes GitHub link which can access the project's repository on GitHub directly.</td><td></td></tr><tr><td>CD30</td><td>Anyone using standard, widely-available tools, can build the code in a reproducible way.</td><td>Yes. Our webpage and github repo provide "Getting Started" and several examples to allow users build our system.</td><td></td></tr><tr><td>CD40</td><td>The full history of the project's code is available via a source code control system, in a way that allows anyone to recreate any released version.</td><td>Yes. We use git and anyone can view the full history of the project via commit logs and tags of the releases.</td><td></td></tr><tr><td>CD50</td><td>The source code control system establishes the provenance of each line of code in a reliable way, based on strong authentication of the committer. When third parties contribute code, commit messages provide reliable information about the code provenance.</td><td>Yes. The project uses GitHub and managed by Apache Infra and ensures provenance of each line of code to a committer. Third-party contributions are accepted in accordance with the contributing guides.</td><td></td></tr><tr><td>Licenses and Copyright</td><td></td><td></td><td></td></tr><tr><td>LC10</td><td>The Apache License, version 2.0, covers the released code.</td><td>Yes. All source files are licensed with Apache License v2.0. LICENSE can be found in the GitHub repository.</td><td></td></tr><tr><td>LC20</td><td>Libraries that are mandatory dependencies of the project's code do not create more restrictions than the Apache License does.</td><td>Yes. None of the dependencies create more restrictions.</td><td></td></tr><tr><td>LC30</td><td>The libraries mentioned in LC20 are available as Open Source software.</td><td>Yes. All libraries are open source.</td><td></td></tr><tr><td>LC40</td><td>Committers are bound by an Individual Contributor Agreement (the "Apache iCLA") that defines which code they may commit and how they need to identify code that is not their own.</td><td>Yes. All committers have iCLAs.</td><td></td></tr><tr><td>LC50</td><td>The project clearly defines and documents the copyright ownership of everything that the project produces.</td><td>Yes. All source files are with APLv2 header</td><td></td></tr><tr><td>Releases</td><td></td><td></td><td></td></tr><tr><td>RE10</td><td>Releases consist of source code, distributed using standard and open archive formats that are expected to stay readable in the long term.</td><td>Yes. Source release is distributed via dist.apache.org and linked from download page.</td><td></td></tr><tr><td>RE20</td><td>The project's PPMC (Project Management Committee, see CS10) approves each software release in order to make the release an act of the Foundation.</td><td>Yes. All releases have been voted at <a href="mailto:dev@wayang.apache.org" target="_blank" rel="noopener noreferrer">dev@wayang.apache.org</a> and <a href="mailto:general@incubator.apache.org" target="_blank" rel="noopener noreferrer">general@incubator.apache.org</a>, and have at least 3 PPMC member's votes.</td><td></td></tr><tr><td>RE30</td><td>Releases are signed and/or distributed along with digests that anyone can reliably use to validate the downloaded archives.</td><td>Yes. All releases are signed, and the KEYS are available.</td><td></td></tr><tr><td>RE40</td><td>The project can distribute convenience binaries alongside source code, but they are not Apache Releases, they are provided with no guarantee.</td><td>Yes. Users can easily build binaries from source code. We do not provide binaries as Apache Releases.</td><td></td></tr><tr><td>RE50</td><td>The project documents a repeatable release process so that someone new to the project can independently generate the complete set of artifacts required for a release.</td><td>Yes. Newcomers can follow our very detailed Release guide to make a new Apache Wayang release, and so far we had 3 different release managers.</td><td></td></tr><tr><td>Quality</td><td></td><td></td><td></td></tr><tr><td>QU10</td><td>The project is open and honest about the quality of its code. Various levels of quality and maturity for various modules are natural and acceptable as long as they are clearly communicated.</td><td>Yes. The project records all bugs using Github Issues.</td><td></td></tr><tr><td>QU20</td><td>The project puts a very high priority on producing secure software.</td><td>Yes. All security issues will be addressed within 3 days.</td><td></td></tr><tr><td>QU30</td><td>The project provides a well-documented, secure and private channel to report security issues, along with a documented way of responding to them.</td><td>Yes. <a href="https://wayang.apache.org/docs/community/security" target="_blank" rel="noopener noreferrer">https://wayang.apache.org/docs/community/security</a></td><td></td></tr><tr><td>QU40</td><td>The project puts a high priority on backwards compatibility and aims to document any incompatible changes and provide tools and documentation to help users transition to new features.</td><td>Yes. Each release contains a “New features” on the website containing the release notes. The project aims to make no backward incompatible changes within a given major version.</td><td></td></tr><tr><td>QU50</td><td>The project strives to respond to documented bug reports in a timely manner.</td><td>Yes. The community has very small response times to issues and pull requests</td><td></td></tr><tr><td>Community</td><td></td><td></td><td></td></tr><tr><td>CO10</td><td>The project has a well-known homepage that points to all the information required to operate according to this maturity model.</td><td>Yes. The official website includes all information users need to run Apache Wayang.</td><td></td></tr><tr><td>CO20</td><td>The community welcomes contributions from anyone who acts in good faith and in a respectful manner, and who adds value to the project.</td><td>Yes. The contributing guide provides information for anyone to contribute.</td><td></td></tr><tr><td>CO30</td><td>Contributions include source code, documentation, constructive bug reports, constructive discussions, marketing and generally anything that adds value to the project.</td><td>Yes. All good contributions including code and non-code are welcomed.</td><td></td></tr><tr><td>CO40</td><td>The community strives to be meritocratic and gives more rights and responsibilities to contributors who, over time, add value to the project.</td><td>Yes. The community has elected 3 new PPMC members/Committers the last year.</td><td></td></tr><tr><td>CO50</td><td>The project documents how contributors can earn more rights such as commit access or decision power, and applies these principles consistently.</td><td>Yes. The website has a clear guide on how to become a committer <a href="https://wayang.apache.org/docs/community/committer" target="_blank" rel="noopener noreferrer">https://wayang.apache.org/docs/community/committer</a></td><td></td></tr><tr><td>CO60</td><td>The community operates based on consensus of its members (see CS10) who have decision power. Dictators, benevolent or not, are not welcome in Apache projects.</td><td>Yes. All decisions are made after voting by community members.</td><td></td></tr><tr><td>CO70</td><td>The project strives to answer user questions in a timely manner.</td><td>Yes. Our community answers user questions in a timely manner.</td><td></td></tr><tr><td>Consensus</td><td></td><td></td><td></td></tr><tr><td>CS10</td><td>The project maintains a public list of its contributors who have decision power. The project's PPMC (Project Management Committee) consists of those contributors.</td><td>Yes. See Team with all PPMC members/committers and contributors.</td><td></td></tr><tr><td>CS20</td><td>Decisions require a consensus among PPMC members and are documented on the project's main communications channel. The PPMC takes community opinions into account, but the PPMC has the final word.</td><td>Yes. All decisions are made by votes on <a href="mailto:dev@wayang.apache.org" target="_blank" rel="noopener noreferrer">dev@wayang.apache.org</a>, and with at least 3 +1 votes from PPMC.</td><td></td></tr><tr><td>CS30</td><td>The project uses documented voting rules to build consensus when discussion is not sufficient.</td><td>Yes. The project uses the standard ASF voting rules.</td><td></td></tr><tr><td>CS40</td><td>In Apache projects, vetoes are only valid for code commits. The person exercising the veto must justify it with a technical explanation, as per the Apache voting rules defined in CS30.</td><td>Yes. Apache Wayang community has not used the veto power yet except for code commits.</td><td></td></tr><tr><td>CS50</td><td>All "important" discussions happen asynchronously in written form on the project's main communications channel. Offline, face-to-face or private discussions that affect the project are also documented on that channel.</td><td>Yes. All important discussions and conclusions are sent to the dev list</td><td></td></tr><tr><td>Independence</td><td></td><td></td><td></td></tr><tr><td>IN10</td><td>The project is independent from any corporate or organizational influence.</td><td>Yes. The PPMC members of Apache Wayang are from several organizations.</td><td></td></tr><tr><td>IN20</td><td>Contributors act as themselves, not as representatives of a corporation or organization.</td><td>Yes. The contributors act on their own initiative without representing a corporation or organization.</td><td></td></tr><tr><td>Trademark and Branding</td><td></td><td></td><td></td></tr><tr><td>TB10</td><td>The project uses "Apache Foo™" as the project and software product name consistently, with appropriate trademark attributions</td><td>Yes. The project uses appropriately the trademark symbol.</td><td></td></tr><tr><td>TB20</td><td>The project's primary homepage is at projectname.apache.org.</td><td>Yes. The project's homepage is at wayang.apache.org</td><td></td></tr><tr><td>TB30</td><td>The ASF has trademark rights, including any registrations, to the project name, logo, and any other major branding elements.</td><td>Yes. ASF has the trademark rights</td><td></td></tr><tr><td>TB40</td><td>The project monitors for any major misuses of their project's brand by others.</td><td>Yes. 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class="hero__subtitle">Unifying Data Processing Engines for Cross-Platform Analytics</p><div class="buttons_AeoN"><a href="https://github.com/apache/incubator-wayang" target="_blank" rel="noopener noreferrer" class="button button--secondary button--lg">GitHub</a><a class="button button--secondary button--lg" href="/docs/guide/getting-started">Get Started</a></div></div></header><main><section class="features_t9lD"><div class="container"><div class="row"><div class="col col--4"><div class="text--center"><svg width="800" height="800" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" class="featureSvg_GfXr" role="img"><title>data-share</title><path d="M5 25v-9.172l-3.586 3.586L0 18l6-6 6 6-1.414 1.414L7 15.828V25h12v2H7a2.002 2.002 0 0 1-2-2ZM24 22h4a2.002 2.002 0 0 1 2 2v4a2.002 2.002 0 0 1-2 2h-4a2.002 2.002 0 0 1-2-2v-4a2.002 2.002 0 0 1 2-2Zm4 6v-4h-4.002L24 28ZM27 6v9.172l3.586-3.586L32 13l-6 6-6-6 1.414-1.414L25 15.172V6H13V4h12a2.002 2.002 0 0 1 2 2ZM2 6h6v2H2zM2 2h8v2H2z"></path><path data-name="<Transparent Rectangle>" style="fill:none" d="M0 0h32v32H0z"></path></svg></div><div class="text--center padding-horiz--md"><h3>Platform independence </h3><p>Change one line of code and move applications across different data processing engines</p></div></div><div class="col col--4"><div class="text--center"><svg width="800" height="800" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" class="featureSvg_GfXr" role="img"><title>api</title><path d="M26 22a3.86 3.86 0 0 0-2 .57l-3.09-3.1a6 6 0 0 0 0-6.94L24 9.43a3.86 3.86 0 0 0 2 .57 4 4 0 1 0-4-4 3.86 3.86 0 0 0 .57 2l-3.1 3.09a6 6 0 0 0-6.94 0L9.43 8A3.86 3.86 0 0 0 10 6a4 4 0 1 0-4 4 3.86 3.86 0 0 0 2-.57l3.09 3.1a6 6 0 0 0 0 6.94L8 22.57A3.86 3.86 0 0 0 6 22a4 4 0 1 0 4 4 3.86 3.86 0 0 0-.57-2l3.1-3.09a6 6 0 0 0 6.94 0l3.1 3.09a3.86 3.86 0 0 0-.57 2 4 4 0 1 0 4-4Zm0-18a2 2 0 1 1-2 2 2 2 0 0 1 2-2ZM4 6a2 2 0 1 1 2 2 2 2 0 0 1-2-2Zm2 22a2 2 0 1 1 2-2 2 2 0 0 1-2 2Zm10-8a4 4 0 1 1 4-4 4 4 0 0 1-4 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3.535-3.535c-47.86-47.861-47.86-125.38 0-173.24 23.93-23.93 55.267-35.881 86.621-35.881a2.5 2.5 0 1 0 .07-5 .817.817 0 0 0-.07 0zm79.903 44.607a5.004 5.004 0 0 0-3.438 1.514l-62.701 62.701a24.804 24.804 0 0 0-13.764-4.166c-13.748 0-25 11.252-25 25s11.252 25 25 25 25-11.252 25-25c0-5.076-1.539-9.809-4.166-13.764l62.701-62.701c3.271-3.179.927-8.718-3.632-8.584zm-79.903 70.049c8.343 0 15 6.657 15 15s-6.657 15-15 15-15-6.657-15-15 6.657-15 15-15zm0 2.5c-6.874 0-12.5 5.626-12.5 12.5a2.5 2.5 0 1 0 5 .07v-.071c0-4.172 3.328-7.5 7.5-7.5a2.5 2.5 0 1 0 .07-5l-.07.001z"></path></svg></div><div class="text--center padding-horiz--md"><h3>Automated platform selection</h3><p>Let the optimizer choose the best platform combination for each task based on cost models to improve performance</p></div></div><div class="col col--4"><div class="text--center"><svg width="800" height="800" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" stroke="#e59536" class="featureSvg_GfXr" 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class="hero__subtitle">Unifying Data Processing Engines for Cross-Platform Analytics</p><div class="buttons_AeoN"><a href="https://github.com/apache/incubator-wayang" target="_blank" rel="noopener noreferrer" class="button button--secondary button--lg">GitHub</a><a class="button button--secondary button--lg" href="/docs/guide/getting-started">Get Started</a></div></div></header><main><section class="features_t9lD"><div class="container"><div class="row"><div class="col col--3"><div class="text--center"><svg width="800" height="800" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" class="featureSvg_GfXr" role="img"><title>data-share</title><path d="M5 25v-9.172l-3.586 3.586L0 18l6-6 6 6-1.414 1.414L7 15.828V25h12v2H7a2.002 2.002 0 0 1-2-2ZM24 22h4a2.002 2.002 0 0 1 2 2v4a2.002 2.002 0 0 1-2 2h-4a2.002 2.002 0 0 1-2-2v-4a2.002 2.002 0 0 1 2-2Zm4 6v-4h-4.002L24 28ZM27 6v9.172l3.586-3.586L32 13l-6 6-6-6 1.414-1.414L25 15.172V6H13V4h12a2.002 2.002 0 0 1 2 2ZM2 6h6v2H2zM2 2h8v2H2z"></path><path data-name="<Transparent Rectangle>" style="fill:none" d="M0 0h32v32H0z"></path></svg></div><div class="text--center padding-horiz--md"><h3>Platform independence </h3><p>Change one line of code and move applications across different data processing engines</p></div></div><div class="col col--3"><div class="text--center"><svg width="800" height="800" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" class="featureSvg_GfXr" role="img"><title>api</title><path d="M26 22a3.86 3.86 0 0 0-2 .57l-3.09-3.1a6 6 0 0 0 0-6.94L24 9.43a3.86 3.86 0 0 0 2 .57 4 4 0 1 0-4-4 3.86 3.86 0 0 0 .57 2l-3.1 3.09a6 6 0 0 0-6.94 0L9.43 8A3.86 3.86 0 0 0 10 6a4 4 0 1 0-4 4 3.86 3.86 0 0 0 2-.57l3.09 3.1a6 6 0 0 0 0 6.94L8 22.57A3.86 3.86 0 0 0 6 22a4 4 0 1 0 4 4 3.86 3.86 0 0 0-.57-2l3.1-3.09a6 6 0 0 0 6.94 0l3.1 3.09a3.86 3.86 0 0 0-.57 2 4 4 0 1 0 4-4Zm0-18a2 2 0 1 1-2 2 2 2 0 0 1 2-2ZM4 6a2 2 0 1 1 2 2 2 2 0 0 1-2-2Zm2 22a2 2 0 1 1 2-2 2 2 0 0 1-2 2Zm10-8a4 4 0 1 1 4-4 4 4 0 0 1-4 4Zm10 8a2 2 0 1 1 2-2 2 2 0 0 1-2 2Z"></path><path style="fill:none" d="M0 0h32v32H0z"></path></svg></div><div class="text--center padding-horiz--md"><h3>Cross-platform task execution</h3><p>Mix Apache Spark, PostgreSQL, Apache Flink, Java Streams, JDBC and more in one pipeline</p></div></div><div class="col col--3"><div class="text--center"><svg height="800" width="800" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 289.939 289.939" xml:space="preserve" class="featureSvg_GfXr" role="img"><path d="M144.969 20.485c-37.119 0-74.239 14.145-102.531 42.438-56.584 56.584-56.584 148.478 0 205.063a5 5 0 1 0 7.078-7.066l-.006-.006c-52.763-52.763-52.763-138.155 0-190.918 52.763-52.763 138.155-52.763 190.918 0 52.763 52.763 52.763 138.155 0 190.918a5 5 0 1 0 7.064 7.08l.008-.008c56.584-56.584 56.584-148.478 0-205.063-28.291-28.293-65.411-42.438-102.531-42.438zm0 15.313c-32.626 0-65.271 12.458-90.156 37.344-49.771 49.771-49.771 130.541 0 180.312a2.5 2.5 0 1 0 3.535-3.535c-47.86-47.861-47.86-125.38 0-173.24 23.93-23.93 55.267-35.881 86.621-35.881a2.5 2.5 0 1 0 .07-5 .817.817 0 0 0-.07 0zm79.903 44.607a5.004 5.004 0 0 0-3.438 1.514l-62.701 62.701a24.804 24.804 0 0 0-13.764-4.166c-13.748 0-25 11.252-25 25s11.252 25 25 25 25-11.252 25-25c0-5.076-1.539-9.809-4.166-13.764l62.701-62.701c3.271-3.179.927-8.718-3.632-8.584zm-79.903 70.049c8.343 0 15 6.657 15 15s-6.657 15-15 15-15-6.657-15-15 6.657-15 15-15zm0 2.5c-6.874 0-12.5 5.626-12.5 12.5a2.5 2.5 0 1 0 5 .07v-.071c0-4.172 3.328-7.5 7.5-7.5a2.5 2.5 0 1 0 .07-5l-.07.001z"></path></svg></div><div class="text--center padding-horiz--md"><h3>Automated platform selection</h3><p>Let the optimizer choose the best platform combination for each task based on cost models to improve performance</p></div></div><div class="col col--3"><div class="text--center"><svg width="800" height="800" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" stroke="#e59536" class="featureSvg_GfXr" 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+++ /dev/null
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This would also allow us to do more local data processing, and businesses can stop moving data around the world, but rather care about data locality while they expose and share specific information to others by using data federation. This reduces complexity of data management and cost dramatically. For this purpose, we illustrate a cross organizational data sharing scenario from the finance sector soon. This analysis pattern will also be relevant in the context of data analysis along supply chains, another typical example where data from many stakeholder together is needed but never managed in one place, for good reasons. Data federation can help us to unlock the hidden value of all those isolated data lakes. ","version":null,"tagName":"h2"},{"title":"A cross organizational data sharing scenario","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#a-cross-organizational-data-sharing-scenario","content":" Our goal is the implementation of a cross organization decentralized data processing scenario, in which protected local data should be processed in combination with public data from public sources in a collaborative manner. Instead of copying all data into a central data lake or a central data platform we decided to use federated analytics. Apache Wayang is the tool we work with. In our case, the public data is hosted on publicly available websites or data pods. A client can use the HTTP(S) protocol to read the data which is given in a well defined format. For simplicity we decided to use CSV format. When we look into the data of each participant we have a different perspective. Our processing procedure should calculate a particular metric on the local data of each participant. An example of such a metric is the average spending of all users on a particular product category per month. This can vary from partner to partner, hence, we want to be able to calculate a peer-group comparison so that each partner can see its own metric compared with a global average calculated from contributions by all partners. Such a process requires global averaging and local averaging. And due to governance constraints, we can’t bring all raw data together in one place. Instead, we want to use Apache Wayang for this purpose. We simplify the procedure and split it into two phases. Phase one is the process, which allows each participant to calculate the local metrics. This requires only local data. The second phase requires data from all collaborating partners. The monthly sum and counter values per partner and category are needed in one place by all other parties. Hence, the algorithm of the first phase stores the local results locally, and the contributions to the global results in an externally accessible Kafka topic. We assume this is done by each of the partners. Now we have a scenario, in which an Apache Wayang process must be able to read data from multiple Apache Kafka topics from multiple Apache Kafka clusters but finally writes into a single Kafka topic, which then can be accessed by all the participating clients. The illustration shows the data flows in such a scenario. Jobs with red border are executed by the participants in isolation within their own data processing environments. But they share some of the data, using publicly accessible Kafka topics, marked by A. Job 4 is the Apache Wayang job in our focus: here we intent to read data from 3 different source systems, and write results into a fourth system (marked as B), which can be accesses by all participants again. With this in mind we want to implement an Apache Wayang application which implements the illustrated Job 4. Since as of today, there is now KafkaSource and KafkaSink available in Apache Wayang, an implementation of both will be our first step. Our assumption is, that in the beginning, there won’t be much data. Apache Spark is not required to cope with the load, but we expect, that in the future, a single Java application would not be able to handle our workload. Hence, we want to utilize the Apache Wayang abstraction over multiple processing platforms, starting with Java. Later, we want to switch to Apache Spark. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 2","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-2","content":"","keywords":"","version":null},{"title":"Apache Wayang’s Read & Write Path for Kafka topics","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#apache-wayangs-read--write-path-for-kafka-topics","content":" To describe the read and write paths for data in the context of the created Apache Wayang code snippet, the primary classes and interfaces we need to understand are as follows: WayangContext: This class is essential for initializing the Wayang processing environment. It allows you to configure the execution environment and register plugins that define which platforms Wayang can use for data processing tasks, such as Java.basicPlugin() for local Java execution. JavaPlanBuilder: This class is used to build and define the data processing pipeline (or plan) in Wayang. It provides a fluent API to specify the operations to be performed on the data, from reading the input to processing it and writing the output. ","version":null,"tagName":"h2"},{"title":"Read Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#read-path","content":" The read path describes how data is ingested from a source into the Wayang processing pipeline: Reading from Kafka Topic: The method readKafkaTopic(topicName) is used to ingest data from a specified Kafka topic. This is the starting point of the data processing pipeline, where topicName represents the name of the Kafka topic from which data is read. Data Tokenization and Preparation: Once the data is read from Kafka, it undergoes several transformations such as Splitting, Filtering, and Mapping. What follows are the procedures known as Reducing, Grouping, Co-Grouping, and Counting. ","version":null,"tagName":"h3"},{"title":"Write Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#write-path","content":" Writing to Kafka Topic: The final step in the pipeline involves writing the processed data back to a Kafka topic using .writeKafkaTopic(...). This method takes parameters that specify the target Kafka topic, a serialization function to format the data as strings, and additional configuration for load profile estimation, which optimizes the writing process. This read-write path provides a comprehensive flow of data from ingestion from Kafka, through various processing steps, and finally back to Kafka, showcasing a full cycle of data processing within Apache Wayang's abstracted environment and is implemented in our example program shown in listing 1. ","version":null,"tagName":"h3"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#implementation-of-input--and-output-operators","content":" The next section shows how a new pair of operators can be implemented to extend Apache Wayang’s capabilities on the input and output side. We created the Kafka Source and Kafka Sink components so that our cross organizational data collaboration scenario can be implemented using data streaming infrastructure. Level 1 – Wayang execution plan with abstract operators The implementation of our Kafka Source and Kafka Sink components for Apache Wayang requires new methods and classes on three layers. First of all in the API package. Here we use the JavaPlanBuilder to expose the function for selecting a Kafka topic as the source to be used by client. The class JavaPlanBuilder in package org.apache.wayang.api in the project wayang-api/wayang-api-scala-java exposes our new functionality to our external client. An instance of the JavaPlanBuilder is used to define the data processing pipeline. We use its readKafkaTopic() which specifies the source Kafka topic to read from, and for the write path we use the writeKafkaTopic() method. Both Methods do only trigger activities in the background. For the output side, we use the DataQuantaBuilder class, which offers an implementation of the writeKafkaTopic function. This function is designed to send processed data, referred to as DataQuanta, to a specified Kafka topic. Essentially, it marks the final step in a data processing sequence constructed using the Apache Wayang framework. In the DataQuanta class we implemented the methods writeKafkaTopic and writeKafkaTopicJava which use the KafkaTopicSink class. In this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation The second layer builds the bridge between the WayangContext and PlanBuilders which work together with DataQuanta and the DataQuantaBuilder. Also, the mapping between the abstract components and the specific implementations are defined in this layer. Therefore, the mappings package has a class Mappings in which all relevant input and output operators are listed. We use it to register the KafkaSourceMapping and a KafkaSinkMapping for the particular platform, Java in our case. These classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In our case this leads to a Java program running on a JVM which is set up by the Apache Wayang framework using the logical components of the execution plan. Those mappings link the real implementation of our operators the ones used in an execution plan. The JavaKafkaTopicSource and the JavaKafkaTopicSink extend the KafkaTopicSource and KafkaTopicSink so that the lower level implementation of those classes become available within Wayang’s Java Platform context. In this layer, the KafkaConsumer class and the KafkaProducer class are used, but both are configured and instantiated in the next layer underneath. All this is done in the project wayang-plarforms/wayang-java. Layer 3 – Input/Output Connector Layer The KafkaTopicSource and KafkaTopicSink classes build the third layer of our implementation. Both are implemented in Java programming language. In this layer, the real Kafka-Client logic is defined. Details about consumer and producers, client configuration, and schema handling have to be handled here. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#summary","content":" Both classes in the third layer implement the Kafka client logic which is needed by the Wayang-execution plan when external data flows should be established. The layer above handles the mapping of the components at startup time. All this wiring is needed to keep Wayang open and flexible so that multiple external systems can be used in a variety of combinations and using multiple target platforms in combinations. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#outlook","content":" The next part of the article series will cover the creation of an Kafka Source and Sink component for the Apache Spark platform, which allows our use case to scale. Finally, in part four we bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 3","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-3","content":"","keywords":"","version":null},{"title":"The goal of this implementation","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#the-goal-of-this-implementation","content":" We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. In our example scenario, data is available in multiple different clusters, in different regions and owned by different organizations. Each organization uses the "stream sharing" feature provided by Confluent cloud. This way, the operator of our central processing job has been granted appropriate permissions. The plaftorm provided the necessary configuration properties, including access coordinates and credentials in the name of the topic owner to us. The following illustration has already been introduced in part one of the blog series, but for clarity we repeat it here. Today, we focus on Job 4 in the image. We implement a program which uses data federation based on multiple sources. Each source allows us to read the data from that particular topic so that we can process it in a different governance context. In this example it is a public processing context, in which data from multiple private processing contexts are used together. This use case is already prepared for high processing loads We can utilize the scalability capabilities of Apache Spark or simply use a Java program for initial tests of the solution. Switching between both is done in one line of code in Apache Wayang. Again, we start with a WayangContext, as shown by examples in the Wayang code repository. WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin()); We simply switched the backend system towards Apache Spark by using the WayangContext with Spark.basicPlugin(). The JavaPlanBuilder and all other logic of our example job won't be touched. In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module. ","version":null,"tagName":"h2"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#implementation-of-input--and-output-operators","content":" We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. Hence we work with Wayang's Java API. Level 1 – Wayang execution plan with abstract operators Since the JavaPlanBuilder already exposes the function for selecting a Kafka topic as source and the DataQuantaBuilder class exposes the writeKafkaTopic function we can move on quickly. Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder. We must provide the mapping between the abstract components and the specific implementations in this layer. Therefore, the mappings package in project wayang-platforms/wayang-spark has a class Mappings in which our KafkaTopicSinkMapping and KafkaTopicSourceMapping will be registered. Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime. A mapping links an operator implementation to the abstraction used in an execution plan. We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round. For the Spark platform we simply replace the occurences of JavaPlatform with SparkPlatform. Furthermore, we create an implementation of the SparkKafkaTopicSource and SparkKafkaTopicSink. Layer 3 – Input/Output Connector Layer Let's quickly recap, how does Apache Spark interacts with Apache Kafka? There is already an integration which gives us a DataSet using the Spark SQL framework. For Spark Streaming, there is also a Kafka integration using the SparkSession's readStream() function. Kafka client properties are provided as key value pairs k and v by using the option( k, v ) function. For writing into a topic, we can use the writeStream() function. But from a first look, it seems to be not the best fit. Another approach is possible. We can use simple RDDs to process data previously consumed from Apache Kafka. This is a more low-level approach compared to using Datasets with Spark Structured Streaming, and it typically involves using the Kafka RDD API provided by Spark. This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. However, we might need that approach for the integration with Apache Wayang. For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then we parallelize the records in an RDD. This allows us to reuse KafkaTopicSource and KafkaTopicSink classes we built last time. Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer. The selected approach does not yet fully take advantage from Spark's parallelism at load time. For higher loads and especially for streaming processing we would have to investigate another approache, using a SparkStreamingContext, but this is out of scope for now. Since we can't reuse the JavaKafkaTopicSource and JavaKafkaTopicSink we rather implement SparkKafkaTopicSource and SparkKafkaTopicSink based on given SparkTextFileSource and SparkTextFileSink which both cary all needed RDD specific logic. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#summary","content":" As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. We could easily follow the pattern we have worked out in the previous exercise. But a bunch of much more interesting work will follow next. More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well. The code has been submitted to the Apache Wayang repository. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#outlook","content":" The next part of the article series will cover the real world example as described in image 1. We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Wayang and the Federated AI","type":0,"sectionRef":"#","url":"/blog/wayang-federated-ai","content":"","keywords":"","version":null},{"title":"Apache Wayang in the Federated AI world","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#apache-wayang-in-the-federated-ai-world","content":" Apache Wayang (Wayang, for short), a project in an incubation phase at Apache Software Foundation (ASF), integrates big data platforms and tools by removing the complexity of worrying about low-level details. Interestingly, even if it was not designed for, Wayang could also serve as a scalable platform for federated learning: the Wayang community is starting to work on integrating federated learning capabilities. In a federated learning approach, Wayang would allow different local models to be built and exchange its model results across other data centers to combine them into a single enhanced model. ","version":null,"tagName":"h2"},{"title":"A real-world example","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#a-real-world-example","content":" Let's consider a real-world scenario. Hospitals and health organizations have increased their investments in machine/deep learning initiatives to learn more and predict diagnostics. However, due to legal frameworks, sharing patients' information or diagnostics is impossible, and the solution would be to apply federated learning. To solve this problem, we could use Wayang to help to train the models. See the diagram 1 below: As a first step, the data scientists would send an ML task to Wayang, which will work as an abstraction layer to connect to different data processing platforms, sparing the time to build integration code for each. Then, the data platforms process and generate the results that will be sent back to Wayang. Wayang aggregates the results into one "global result" and sends it back to the requestor as a next step. The process repeats until the desired results are achieved. Although it is very much like a Federated learning pipeline, Wayang removes a considerable layer of complexity from the developers by integrating with diverse types of data platforms. It also brings fast development and reduces the need for a deep understanding of data infrastructure or integrations. Developers can focus on the logic and how to execute tasks instead of details about data processors. ","version":null,"tagName":"h2"},{"title":"Follow Wayang","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#follow-wayang","content":" Apache Wayang is in an incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. Thank you! I (Gláucia) want to thank professor Jorge Quiané for the guidance to write this blog post. Thanks for incentivate me to join the project and for the knowledge shared. I will always remember you. ","version":null,"tagName":"h3"},{"title":"Pywayang - Apache Wayang's Python API","type":0,"sectionRef":"#","url":"/blog/wayang-python-api","content":"","keywords":"","version":null},{"title":"Expanding Apache Wayang's APIs","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#expanding-apache-wayangs-apis","content":" Apache Wayang's architecture decouples the process of planning from the resulting execution, allowing users to specify platform agnostic plans through the provided APIs. Python's popularity and convenience for data processing workloads makes it an obvious candidate for a desired API. Previous APIs, such as the Scala API wayang-api-scala-java benefited from the interoperability of Java and Scala that allows to reuse objects from other languages to provide new interfaces. Accessing JVM objects in Python is possible through several libraries, but in doing so, future APIs in other programming languages would need similar libraries and implementations in order to exist. As a contrast to that, providing an API within Apache Wayang that receives input plans from any source and executes them within allows to create plans and submit them in any programming language. The following figure shows the architecture of pywayang: The Python API allows users to specify WayangPlans with UDFs in Python.pywayang then serializes the UDFs and constructs the WayangPlan in JSON format, preparing it to be sent to Apache Wayang's JSON API. When receiving a valid JSON plan, the JSON API uses the optimizer to construct an execution plan. However, since UDFs are defined in Python and thus need to be executed in Python as well, an operators function needs to be wrapped into a WrappedPythonFunction: val mapOperator = new MapPartitionsOperator[Input, Output]( new MapPartitionsDescriptor[Input, Output]( new WrappedPythonFunction[Input, Output]( ByteString.copyFromUtf8(udf) ), classOf[Input], classOf[Output], ) ) This wrapped functional descriptor allows to handle execution of UDFs in Python through a socket connection with the pywayang worker. Input data is sourced from the platform chosen by the optimizer and Apache Wayang handles routing the output data to the next operator. A new API in any programming languages would have to specify two things: A way to create plans that conform to a JSON format specified in the Wayang JSON API.A worker that handles encoding and decoding of user defined functions (UDFs), as they need to be executed on iterables in their respective language. After that, the API can be added as a module in Wayang, so that operators will be wrapped and UDFs can be executed in the desired programming language. ","version":null,"tagName":"h2"},{"title":"Defining WayangPlans in Python","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#defining-wayangplans-in-python","content":" As the "Hello World!" of data processing systems, wordcount will pose as our primary example to display how users can interact with Apache Wayang through the python package pywayang. from pywy.dataquanta import WayangContext from pywy.platforms.java import JavaPlugin from pywy.platforms.spark import SparkPlugin def wordcount(): ctx = WayangContext() \\ .register({JavaPlugin, SparkPlugin}) \\ .textfile("file://README.md") \\ .flatmap(lambda w: w.split()) \\ .filter(lambda w: w.strip() != "") \\ .map(lambda w: (w.lower(), 1)) \\ .reduce_by_key(lambda t: t[0], lambda t1, t2: (t1[0], int(t1[1]) + int(t2[1]))) \\ .store_textfile("file:///wordcount-out-python.txt") if __name__ == "__main__": wordcount() The example displays a mode of operation that resembles the ScalaPlanBuilder and the JavaPlanBuilder. Plans are specified in a functional way, chaining operations until a terminal operation results in execution of the plan. ","version":null,"tagName":"h2"},{"title":"Wayang-API-JSON","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wayang-api-json","content":" The wayang-api-json module provides an executable that starts a REST server. This server accepts a WayangPlan in JSON format. Starting the REST API as a background process can be done by executing the following: mvn clean package -pl :wayang-assembly -Pdistribution cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.api.json.Main & ","version":null,"tagName":"h2"},{"title":"Wrapping pipelines in MapPartition operators","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wrapping-pipelines-in-mappartition-operators","content":" With this architecture, the execution of an operator comes with an additional overhead, because the UDFs will have to be executed in python. Python operators receive iterators through a socket and also return their result to Wayang through that connection. To minimize the overhead, unary operators that return unary results will be grouped in pipelines. One pipeline of operators will be submitted to the Wayang JSON API as a single MapPartition operator. This means that the UDFs specified in this pipeline can be chained and only on call from Wayang to the Python worker will have to be made for a given pipeline. ","version":null,"tagName":"h2"},{"title":"Coming soon","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#coming-soon","content":" As the Python API is currently in development and we are applying finishing touches, this article serves as an outlook for what users can expect to see soon. Author: juripetersen ","version":null,"tagName":"h2"},{"title":"Integrating ML platforms in Wayang","type":0,"sectionRef":"#","url":"/blog/wayang-tensorflow","content":"","keywords":"","version":null},{"title":"Step 1: Introducing a Model","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-1-introducing-a-model","content":" With respect to the model, we followed Wayang’s abstraction philosophy: We created a Model interface to be used as input or output by Wayang operators and then extended it for the platform-specific operators. Different model interfaces can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model A platform-specific model needs to be instantiated to be used as the output of a training operator and as input for an inference operator. You can see an example of the SparkMLModel here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/model/SparkMLModel.java ","version":null,"tagName":"h2"},{"title":"Step 2: Introducing Training Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-2-introducing-training-operators","content":" We added the desired Wayang (platform-agnostic) training operators which are binary to unary operators, taking as input the X and y values and outputting a Model. You can find an example of a LinearRegressionOperator here: https://github.com/apache/incubator-wayang/blob/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/LinearRegressionOperator.java Platform-specific execution operators, such as SparkLinearRegressionOperator, can be easily added as any other execution operator: extending the corresponding Wayang operator and providing the mappings from the Wayang to the execution operator. See, for example, the SparkLinearRegressionOperator: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkLinearRegressionOperator.java ","version":null,"tagName":"h2"},{"title":"Step 3: Introducing Prediction Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-3-introducing-prediction-operators","content":" Additionally, we created a PredictOperator, a BinaryToUnary Wayang (platform-agnostic) operator which takes as input the data quanta and a model and outputs the data quanta with the predictions output by the model. https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/PredictOperator.java Then, a concrete platform-specific operator extends from the abstract one. See the SparkPredictOperator for an example: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkPredictOperator.java ","version":null,"tagName":"h2"},{"title":"Deep Learning Models","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#deep-learning-models","content":" Unlike traditional machine learning models, the definition of deep learning models is more flexible. Users can combine different blocks (e.g., fully connected blocks, convolutional blocks) to build their desired models. The whole model can be represented as a graph on which the vertices represent blocks and the edges represent connections between blocks. In this case, we built a DLModel class that implements the Model interface, which contains a user-defined, platform-agnostic graph of the model: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model/DLModel.java For training, we implemented the platform-agnostic DLModelTrainingOperator Wayang operator: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/DLTrainingOperator.java ","version":null,"tagName":"h2"},{"title":"New ML platform -- Tensorflow Integration","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#new-ml-platform----tensorflow-integration","content":" We have added Tensorflow as a new platform by creating a new module (wayang-tensorflow) inside the wayang-platforms parent module and implementing a Tensorflow driver. The TensorflowExecutor driver is responsible for creating and destroying Tensorflow resources, such as a model graph and a model parameter context. When a training task scheduled on Tensorflow, it will be mapped to TensorflowDLModelTrainingOperator. In this process, the DLModel will be converted to TensorflowModel, which means that the user-defined model graph will be converted to a Tensorflow model graph. Likewise, for inference, the PredictOperator will be mapped to TensorflowPredictOperator. All the code for the tensorflow platform can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-tensorflow/src/main/java/org/apache/wayang/tensorflow ","version":null,"tagName":"h2"},{"title":"Acknowledgement","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#acknowledgement","content":" The source code for the support of ML operators and the Tensorflow integration has been contributed by Mingxi Liu. ","version":null,"tagName":"h3"},{"title":"Follow Wayang","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#follow-wayang","content":" Apache Wayang is in incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. ","version":null,"tagName":"h3"},{"title":"Apache Wayang vs. Presto/Trino","type":0,"sectionRef":"#","url":"/blog/wayang-vs-trino","content":"","keywords":"","version":null},{"title":"Key Distinctions","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#key-distinctions","content":" Trino/Presto is a query engine for distributed SQL query processing. It is composed of a coordinator and multiple workers. The coordinator consists of a query optimizer and a scheduler, while the workers are responsible for performing the necessary query processing. Data is fetched from external systems via a Connector API, i.e., Trino/Presto supports multiple data sources. Notably,query processing is is conducted exclusively by Trino/Presto workers, not the external systems. In contrast, Wayang is a middleware for integrating diverse data platforms, including but not limited to query engines. This means that Wayang leverages the processing capabilities of the underlying data platforms to complete a given job, with no actual query processing taking place within Wayang itself. Below you can graphically see the difference between the two systems. Note that not all available data sources or data platforms are illustrated for simplicity reasons. Below you can see how Wayang integrates data platforms and utilizes them for any data processing required. Below you can see how Trino unifies different data sources and then performs data processing in a distributed manner. I hope this makes it clear now. In fact, Trino can be easily plugged to Wayang as a platform and be seamlessly integrated with other data platforms, as shown below. ","version":null,"tagName":"h2"},{"title":"What are the advantages of using Wayang?","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#what-are-the-advantages-of-using-wayang","content":" Wayang brings several benefits thanks to its integration layer: Seamless integration of SQL query engines with ML and other data analysis systems within a single job, eliminating the need to materialize intermediate results. Users are freed from the task of specifying the query engines for an application if they desire. By submitting their Wayang job, the cross-platform optimizer can automatically determine the best data platform to use for improved performance or cost savings. Wayang facilitates cross-platform data processing by utilizing multiple data platforms to execute a query for a single job, optimizing performance and cost efficiency. Data does not have to be transferred outside their original location. ","version":null,"tagName":"h2"},{"title":"Conclusion","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#conclusion","content":" Trino is a distributed SQL query engine which performs all the query processing of an input SQL query in a distributed manner. Wayang, on the other hand, is a data platform integrator which can automatically determine which data platform(s) is best suited for an application. Author: zkaoudi ","version":null,"tagName":"h2"},{"title":"Website updated","type":0,"sectionRef":"#","url":"/blog/website_update","content":"","keywords":"","version":null},{"title":"Cheatsheet","type":1,"pageTitle":"Website updated","url":"/blog/website_update#cheatsheet","content":" List: Line one Line one.oneLine one.two Line two Line two.oneLine two.two Line three ...... Another style for a list: Line oneLine twoLine three ","version":null,"tagName":"h2"},{"title":"Becoming a committer","type":0,"sectionRef":"#","url":"/docs/community/committer","content":"","keywords":"","version":"Next"},{"title":"Sustained contributions to Wayang:","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#sustained-contributions-to-wayang","content":" Committers should have a history of major contributions to Wayang. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within Wayang's engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of Spark. Note that contributions to critical parts of Spark, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement. The type and level of contributions considered may vary by project area – for example, we greatly encourage contributors who want to work on mainly the documentation, or mainly on platform support for specific OSes, storage systems, etc. The (P)PMC also adds new (P)PMC members. (P)PMC members are expected to carry out (P)PMC responsibilities as described in Apache Guidance, including helping vote on releases, enforce Apache project trademarks, take responsibility for legal and license issues, and ensure the project follows Apache project mechanics. The PMC periodically adds committers to the PMC who have shown they understand and can help with these activities. ","version":"Next","tagName":"h3"},{"title":"Review process","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#review-process","content":" All contributions should be reviewed before merging as described in Contributing to Wayang. In particular, if you are working on an area of the codebase you are unfamiliar with, look at the Git history for that code to see who reviewed patches before. You can do this using git log --format=full <filename>, by examining the “Commit” field to see who committed each patch. ","version":"Next","tagName":"h3"},{"title":"When to commit/merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#when-to-commitmerge-a-pull-request","content":" PRs shall not be merged during active, on-topic discussion unless they address issues such as critical security fixes of a public vulnerability. Under extenuating circumstances, PRs may be merged during active, off-topic discussion and the discussion directed to a more appropriate venue. Time should be given prior to merging for those involved with the conversation to explain if they believe they are on-topic. Lazy consensus requires giving time for discussion to settle while understanding that people may not be working on Wayang as their full-time job and may take holidays. It is believed that by doing this, we can limit how often people feel the need to exercise their veto. All -1s with justification merit discussion. A -1 from a non-committer can be overridden only with input from multiple committers, and suitable time must be offered for any committer to raise concerns. A -1 from a committer who cannot be reached requires a consensus vote of the (P)PMC under ASF voting rules to determine the next steps within the ASF guidelines for code vetoes. The Wayang project typically uses a 72h time window to conclude votes. These policies serve to reiterate the core principle that code must not be merged with a pending veto or before a consensus has been reached (lazy or otherwise). It is the (P)PMC’s hope that vetoes continue to be infrequent, and when they occur, that all parties will take the time to build consensus prior to additional feature work. Being a committer means exercising your judgement while working in a community of people with diverse views. There is nothing wrong in getting a second (or third or fourth) opinion when you are uncertain. Thank you for your dedication to the Wayang project; it is appreciated by the developers and users of Wayang. It is hoped that these guidelines do not slow down development; rather, by removing some of the uncertainty, the goal is to make it easier for us to reach consensus. If you have ideas on how to improve these guidelines or other Wayang project operating procedures, you should reach out on the dev@ list to start the discussion. ","version":"Next","tagName":"h2"},{"title":"How we merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#how-we-merge-a-pull-request","content":" Changes pushed to the master branch on Apache cannot be removed; that is, we can’t force-push to it. So please don’t add any test commits or anything like that, only real patches. We typically enforce a review from minimum two committers, who are ideally owners of submodules, like ML4ALL or JDBC. They investigate the PR, add remarks and request changes. Please be not insultet in any way, the team needs to make sure that all code committed to main has a igh quality and does not break current functionality. Ask dev@ if you have trouble with these steps, or want help doing your first merge. Once a PR is merged please leave a comment on the PR stating which branch(es) it has been merged with. ","version":"Next","tagName":"h2"},{"title":"Policy on backporting bug fixes","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#policy-on-backporting-bug-fixes","content":" We go in line with pwendell (Apache Spark PMC): The trade off when backporting is you get to deliver the fix to people running older versions (great!), but you risk introducing new or even worse bugs in maintenance releases (bad!). The decision point is when you have a bug fix and it’s not clear whether it is worth backporting. I think the following facets are important to consider: Backports are an extremely valuable service to the community and should be considered for any bug fix.Introducing a new bug in a maintenance release must be avoided at all costs. It over time would erode confidence in our release process.Distributions or advanced users can always backport risky patches on their own, if they see fit.For me, the consequence of these is that we should backport in the following situations: Both the bug and the fix are well understood and isolated. Code being modified is well tested.The bug being addressed is high priority to the community.The backported fix does not vary widely from the master branch fix.We tend to avoid backports in the converse situations: The bug or fix are not well understood. For instance, it relates to interactions between complex components or third party libraries (e.g. Hadoop libraries). The code is not well tested outside of the immediate bug being fixed. The bug is not clearly a high priority for the community. The backported fix is widely different from the master branch fix. ","version":"Next","tagName":"h3"},{"title":"Mailing List","type":0,"sectionRef":"#","url":"/docs/community/mailinglist","content":"","keywords":"","version":"Next"},{"title":"How to subscribe to a mailing list","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#how-to-subscribe-to-a-mailing-list","content":" Send an email without any contents or subject to LISTNAME-subscribe@wayang.apache.org. (replace LISTNAME with dev, commits, ..) Wait till you receive an email with the subject “confirm subscribe to LISTNAME@wayang.apache.org”. Reply to that email, without editing the subject or including any contents Wait till you receive an email with the subject “WELCOME to LISTNAME@wayang.apache.org”. If you send us an email with a code snippet, make sure that: you do not link to files in external services as such files can change, get deleted, or the link might break and thus make an archived email thread uselessyou paste text instead of screenshots of textyou keep formatting when pasting code in order to keep the code readablethere are enough import statements to avoid ambiguitiesif you are using some platform, please specify the version ","version":"Next","tagName":"h2"},{"title":"Watch the email without subscribe","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#watch-the-email-without-subscribe","content":" These two channels are public mailing list, and you can get access to them on Website without subscribing. Emails of commits@wayang.apache.orgEmails of dev@wayang.apache.org ","version":"Next","tagName":"h2"},{"title":"How to contribute","type":0,"sectionRef":"#","url":"/docs/community/contribute","content":"","keywords":"","version":"Next"},{"title":"Contributing by helping other users","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-helping-other-users","content":" A great way to contribute to Wayang is to help answer user questions on the user@ mailing list or on StackOverflow. There are always many new Wayang users; taking a few minutes to help answer a question is a very valuable community service. Contributors should subscribe to this list and follow it in order to keep up to date on what’s happening in Wayang. Answering questions is an excellent and visible way to help the community, which also demonstrates your expertise. See the Mailing Lists guide for guidelines about how to effectively participate in discussions on the mailing list, as well as forums like StackOverflow. ","version":"Next","tagName":"h3"},{"title":"Contributing by testing releases","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-testing-releases","content":" Wayang’s release process is community-oriented, and members of the community can vote on new releases on the dev@ mailing list. Wayang users are invited to subscribe to this list to receive announcements, and test their workloads on newer release and provide feedback on any performance or correctness issues found in the newer release. ","version":"Next","tagName":"h3"},{"title":"Contributing by reviewing changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-reviewing-changes","content":" Changes to Wayang source code are proposed, reviewed and committed via GitHub pull requests (described later). Anyone can view and comment on active changes here. Reviewing others’ changes is a good way to learn how the change process works and gain exposure to activity in various parts of the code. You can help by reviewing the changes and asking questions or pointing out issues – as simple as typos or small issues of style. ","version":"Next","tagName":"h2"},{"title":"Contributing documentation changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-documentation-changes","content":" To propose a change to release documentation (that is, docs that appear under Developer section), fork the website repo and edit the Markdown source files in Wayang’s docs/ directory, the README file shows how to build the documentation locally to test your changes. The process to propose a doc change is otherwise the same as the process for proposing code changes below. To propose a change to the rest of the documentation (that is, docs that do not appear under Developer section), similarly, edit the Markdown in the wayang-website repository and open a pull request. ","version":"Next","tagName":"h2"},{"title":"Contributing user libraries to Wayang","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-user-libraries-to-wayang","content":" Just as Java and Scala applications can access a huge selection of libraries and utilities, none of which are part of Java or Scala themselves, Wayang aims to support a rich ecosystem of libraries. Many new useful utilities or features belong outside of Spark rather than in the core. For example: query optimizer code and language support probably has to be a part of core Wayang, but, useful machine learning algorithms can happily exist outside of Wayang. To that end, large and independent new functionality is often rejected for inclusion in Wayang itself, but, can and should be hosted as a separate project and repository, and included in the wayang-packages.org collection. ","version":"Next","tagName":"h2"},{"title":"Contributing bug reports","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-bug-reports","content":" Ideally, bug reports are accompanied by a proposed code change to fix the bug. This isn’t always possible, as those who discover a bug may not have the experience to fix it. A bug may be reported by creating a JIRA or a GitHub Issue but without creating a pull request (see below). Bug reports are only useful however if they include enough information to understand, isolate and ideally reproduce the bug. Simply encountering an error does not mean a bug should be reported; as below, search JIRA and search and inquire on the Wayang user / dev mailing lists first. Unreproducible bugs, or simple error reports, may be closed. It’s very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. Note that, data correctness/data loss bugs are very serious. Make sure the corresponding bug report JIRA ticket is labeled as correctness or data-loss. If the bug report doesn’t get enough attention, please send an email to dev@, to draw more attentions. It is possible to propose new features as well. These are generally not helpful unless accompanied by detail, such as a design document and/or code change. Large new contributions should consider wayang-packages.org first (see above), or be discussed on the mailing list first. Feature requests may be rejected, or closed after a long period of inactivity. ","version":"Next","tagName":"h2"},{"title":"Contributing to JIRA maintenance","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-to-jira-maintenance","content":" Given the volume of issues raised in the Apache Wayang JIRA, inevitably some issues are duplicates, or become obsolete and eventually fixed otherwise, or can’t be reproduced, or could benefit from more detail, and so on. It’s useful to help identify these issues and resolve them, either by advancing the discussion or even resolving the JIRA. Most contributors are able to directly resolve JIRAs. Use judgment in determining whether you are quite confident the issue should be resolved, although changes can be easily undone. If in doubt, just leave a comment on the JIRA. When resolving JIRAs, observe a few useful conventions: Resolve as Fixed if there’s a change you can point to that resolved the issue Set Fix Version(s), if and only if the resolution is FixedSet Assignee to the person who most contributed to the resolution, which is usually the person who opened the PR that resolved the issue.In case several people contributed, prefer to assign to the more ‘junior’, non-committer contributor For issues that can’t be reproduced against master as reported, resolve as Cannot Reproduce Fixed is reasonable too, if it’s clear what other previous pull request resolved it. Link to it.If the issue is the same as or a subset of another issue, resolved as DuplicateMake sure to link to the JIRA it duplicatesPrefer to resolve the issue that has less activity or discussion as the duplicate If the issue seems clearly obsolete and applies to issues or components that have changed radically since it was opened, resolve as Not a Problem If the issue doesn’t make sense – not actionable, for example, a non-Spark issue, resolve as Invalid If it’s a coherent issue, but there is a clear indication that there is not support or interest in acting on it, then resolve as Won’t Fix Sometimes, a contributor will already have a particular new change or bug in mind. If seeking ideas, consult the list of starter tasks in JIRA, or ask the user@ mailing list. Before proceeding, contributors should evaluate if the proposed change is likely to be relevant, new and actionable: Is it clear that code must change? Proposing a JIRA and pull request is appropriate only when a clear problem or change has been identified. If simply having trouble using Spark, use the mailing lists first, rather than consider filing a JIRA or proposing a change. When in doubt, email user@ first about the possible changeSearch the user@ and dev@ mailing list archives for related discussions. Often, the problem has been discussed before, with a resolution that doesn’t require a code change, or recording what kinds of changes will not be accepted as a resolution.Search JIRA for existing issues: https://issues.apache.org/jira/browse/WAYANGIs the scope of the change matched to the contributor’s level of experience? Anyone is qualified to suggest a typo fix, but refactoring core scheduling logic requires much more understanding of Wayang. Some changes require building up experience first (see above).It’s worth reemphasizing that changes to the core of Wayang, or to highly complex and important modules like SQL and Catalyst, are more difficult to make correctly. They will be subjected to more scrutiny, and held to a higher standard of review than changes to less critical code. ","version":"Next","tagName":"h3"},{"title":"ML4ALL and optimizer-specific contribution guidelines","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#ml4all-and-optimizer-specific-contribution-guidelines","content":" While a rich set of algorithms is an important goal for Wayang, scaling the project requires that maintainability, consistency, and code quality come first. New algorithms should: Be used and accepted (academic citations and concrete use cases can help justify this)Be well documentedHave APIs consistent with other algorithms in WayangCome with a reasonable expectation of developer supportError message guidelinesExceptions thrown in Wayang should be associated with standardized and actionable error messages Error messages should answer the following questions: What was the problem?Why did the problem happen?How can the problem be solved? When writing error messages, you should: Use active voiceAvoid time-based statements, such as promises of future supportUse the present tense to describe the error and provide suggestionsProvide concrete examples if the resolution is unclearAvoid sounding accusatory, judgmental, or insultingBe directDo not use programming jargon in user-facing errors 8 See the error message guidelines for more details. ","version":"Next","tagName":"h3"},{"title":"Code review criteria","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-review-criteria","content":" Before considering how to contribute code, it’s useful to understand how code is reviewed, and why changes may be rejected. See the detailed guide for code reviewers from Google’s Engineering Practices documentation. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review. Positives Fixes the root cause of a bug in existing functionalityAdds functionality or fixes a problem needed by a large number of usersSimple, targetedMaintains or improves consistency across Python, Java, ScalaEasily tested; has testsReduces complexity and lines of codeChange has already been discussed and is known to committers Negatives, risks Band-aids a symptom of a bug onlyIntroduces complex new functionality, especially an API that needs to be supportedAdds complexity that only helps a niche use caseAdds user-space functionality that does not need to be maintained in Wayang, but could be hosted externally and indexed by wayang-packages.orgChanges a public API or semantics (rarely allowed)Adds large dependenciesChanges versions of existing dependenciesAdds a large amount of codeMakes lots of modifications in one “big bang” change ","version":"Next","tagName":"h3"},{"title":"Contributing code changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-code-changes","content":" Please review the preceding section before proposing a code change. This section documents how to do so. When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so. ","version":"Next","tagName":"h3"},{"title":"Cloning the Apache Wayang™ source code","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#cloning-the-apache-wayang-source-code","content":" If you are interested in working with the newest under-development code or contributing to Apache Spark development, you can check out the master branch from Git: # Master development branch git clone git://github.com/apache/incubator-wayang.git Once you’ve downloaded Wayang, you can find instructions for installing and building it on the Compiling Apache Wayang page. ","version":"Next","tagName":"h3"},{"title":"JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#jira","content":" Generally, Wayang uses JIRA to track logical issues, including bugs and improvements, and uses GitHub pull requests to manage the review and merge of specific code changes. That is, JIRAs are used to describe what should be fixed or changed, and high-level approaches, and pull requests describe how to implement that change in the project’s source code. For example, major design decisions are discussed in JIRA. Find the existing Spark JIRA that the change pertains to. Do not create a new JIRA if creating a change to address an existing issue in JIRA; add to the existing discussion and work insteadLook for existing pull requests that are linked from the JIRA, to understand if someone is already working on the JIRA If the change is new, then it usually needs a new JIRA. However, trivial changes, where the what should change is virtually the same as the how it should change do not require a JIRA. Example: Fix typos in Foo wayang docIf required, create a new JIRA: Provide a descriptive Title. “Update web UI” or “Problem in scheduler” is not sufficient. “Kafka Streaming support fails to handle empty queue in YARN cluster mode” is good.Write a detailed description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document.Set required fields: Issue Type. Generally, Bug, Improvement and New Feature are the only types used in Spark.Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. JIRA tends to unfortunately conflate “size” and “importance” in its Priority field values. Their meaning is roughly: Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers for their target versions.Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficultMajor: a small minority of users are missing important functionality without this, and there is a workaroundMinor: a niche use case is missing some support, but it does not affect usage or is easily worked aroundTrivial: a nice-to-have change but unlikely to be any problem in practice otherwise ComponentAffects Version. For Bugs, assign at least one version that is known to exhibit the problem or need the changeLabel. Not widely used, except for the following: correctness: a correctness issuedata-loss: a data loss issuerelease-notes: the change’s effects need mention in release notes. The JIRA or pull request should include detail suitable for inclusion in release notes – see “Docs Text” below.starter: small, simple change suitable for new contributors Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include in Release Notes. This should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the JIRA is opened, but will likely need to be updated with final details when the issue is resolved. Do not set the following fields: Fix Version This is assigned by committers only when resolved.Target Version This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. Do not include a patch file; pull requests are used to propose the actual change. If the change is a large change, consider inviting discussion on the issue at dev@ first before proceeding to implement the change. ","version":"Next","tagName":"h3"},{"title":"Pull request","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#pull-request","content":" Before creating a pull request in Apache Wayang, it is important to check if tests can pass on your branch because our GitHub Actions workflows automatically run tests for your pull request/following commits and every run burdens the limited resources of GitHub Actions in Apache Wayang repository. Below steps will take your through the process. Fork the GitHub repository at https://github.com/apache/incubator-wayang if you haven’t alreadyGo to “Actions” tab on your forked repository and enable “Build and test” and “Report test results” workflowsClone your fork and create a new branchConsider whether documentation or tests need to be added or updated as part of the change, and add them as needed. When you add tests, make sure the tests are self-descriptive.Also, you should consider writing a JIRA ID in the tests when your pull request targets to fix a specific issue. In practice, usually it is added when a JIRA type is a bug or a PR adds a couple of tests to an existing test class. See the examples below: @Test public void testCase() { // WAYANG-12345: a short description of the test Consider whether benchmark results should be added or updated as part of the change, and add them as needed by Running benchmarks in your forked repository to generate benchmark results.Run all tests with in your build to verify that the code still compiles, passes tests, and passes style checks. If style checks fail, review the Code Style Guide below.Push commits to your branch. This will trigger “Build and test” and “Report test results” workflows on your forked repository and start testing and validating your changes.Open a pull request against the master branch of apache/incubator-wayang. (Only in special cases would the PR be opened against other branches). This will trigger workflows “On pull request*” (on the Wayang repo) that will look/watch for successful workflow runs on “your” forked repository (it will wait if one is running). The PR title should be of the form [WAYANG-xxxx][COMPONENT] Title, where WAYANG-xxxx is the relevant JIRA number, COMPONENT is one of the PR categories and Title may be the JIRA’s title or a more specific title describing the PR itself.If the pull request is still a work in progress, and so is not ready to be merged, but needs to be pushed to GitHub to facilitate review, then add [WIP] after the component.Consider identifying committers or other contributors who have worked on the code being changed. Find the file(s) in GitHub and click “Blame” to see a line-by-line annotation of who changed the code last. You can add @username in the PR description to ping them immediately.Please state that the contribution is your original work and that you license the work to the project under the project’s open source license. The related JIRA, if any, will be marked as “In Progress” and your pull request will automatically be linked to it. There is no need to be the Assignee of the JIRA to work on it, though you are welcome to comment that you have begun work. ","version":"Next","tagName":"h3"},{"title":"The review process","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#the-review-process","content":" Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch.Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change.Keep in mind that changes to more critical parts of Spark, like its core and SQL components, will be subjected to more review, and may require more testing and proof of its correctness than other changes.Reviewers can indicate that a change looks suitable for merging with a comment such as: “I think this patch looks good”. Wayang uses the LGTM convention for indicating the strongest level of technical sign-off on a patch: simply comment with the word “LGTM”. It specifically means: “I’ve looked at this thoroughly and take as much ownership as if I wrote the patch myself”. If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community.Sometimes, other changes will be merged which conflict with your pull request’s changes. The PR can’t be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by git remote add upstream https://github.com/apache/incubator-wayang.git, running git fetch upstream followed by git rebase upstream/master and resolving the conflicts by hand, then pushing the result to your branch.Try to be responsive to the discussion rather than let days pass between replies ","version":"Next","tagName":"h3"},{"title":"Closing your pull request / JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#closing-your-pull-request--jira","content":" If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated JIRA if any Note that in the rare case you are asked to open a pull request against a branch besides master, that you will actually have to close the pull request manuallyThe JIRA will be Assigned to the primary contributor to the change as a way of giving credit. If the JIRA isn’t closed and/or Assigned promptly, comment on the JIRA. If your pull request is ultimately rejected, please close it promptly … because committers can’t close PRs directlyPull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like “mind closing this PR?” This means that the committer is specifically requesting that it be closed. If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that’s easier to include, like a smaller and/or less invasive change.If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a “soft no”. It is helpful to withdraw and close the PR in this case.If a pull request is closed because it is deemed not the right approach to resolve a JIRA, then leave the JIRA open. However if the review makes it clear that the issue identified in the JIRA is not going to be resolved by any pull request (not a problem, won’t fix) then also resolve the JIRA. ","version":"Next","tagName":"h3"},{"title":"Code style guide","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-style-guide","content":" Please follow the style of the existing codebase. For Python code, Apache Wayang follows PEP 8 with one exception: lines can be up to 100 characters in length, not 79.For R code, Apache Wayang follows Google’s R Style Guide with three exceptions: lines can be up to 100 characters in length, not 80, there is no limit on function name but it has a initial lower case latter and S4 objects/methods are allowed.For Java code, Apache Wayang follows Oracle’s Java code conventions and Scala guidelines below. The latter is preferred.For Scala code, Apache Wayang follows the official Scala style guide. ","version":"Next","tagName":"h2"},{"title":"If in doubt","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#if-in-doubt","content":" If you’re not sure about the right style for something, try to follow the style of the existing codebase. Look at whether there are other examples in the code that use your feature. Feel free to ask on the dev@ list as well and/or ask committers. ","version":"Next","tagName":"h3"},{"title":"Code of conduct","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-of-conduct","content":" The Apache Wayang project follows the Apache Software Foundation Code of Conduct. The code of conduct applies to all spaces managed by the Apache Software Foundation, including IRC, all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our communities. A code of conduct which is specific to in-person events (ie., conferences) is codified in the published ASF anti-harassment policy. We expect this code of conduct to be honored by everyone who participates in the Apache community formally or informally, or claims any affiliation with the Foundation, in any Foundation-related activities and especially when representing the ASF, in any role. This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. We expect it to be followed in spirit as much as in the letter, so that it can enrich all of us and the technical communities in which we participate. For more information and specific guidelines, refer to the Apache Software Foundation Code of Conduct. This guide was originally released by Apache Spark, the Apache Wayang project adapted the guide. ","version":"Next","tagName":"h2"},{"title":"How to make a release","type":0,"sectionRef":"#","url":"/docs/community/release","content":"","keywords":"","version":"Next"},{"title":"First time release","type":1,"pageTitle":"How to make a release","url":"/docs/community/release#first-time-release","content":" ============================================= Add your Apache credentials in ~.m2/settings.xml If you don’t have this directory or file, you should create a .m2 directory in your user home and inside that create a settings.xml file with at least this content: <?xml version="1.0" encoding="UTF-8"?> <settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.1.0 http://maven.apache.org/xsd/settings-1.1.0.xsd" xmlns="http://maven.apache.org/SETTINGS/1.1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <servers> <!-- Apache Repo Settings --> <server> <id>apache.snapshots.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> <server> <id>apache.releases.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> </servers> </settings> The following is about key management. Details are described here You need to generate a key with GnuPG Install GnuPG (e.g., brew install gnupg)Run: gpg --full-generate-keyExport the public key: gpg --export --armor mygpgpublickey Install pinentry (e.g., brew install pinentry-mac) so that you can be promted to enter your credentials via command line Configuration A. Make Apache the default key:APACHEID=your_asf_id KEY_ID=$(gpg --list-keys --with-colons $APACHEID@apache.org | egrep "^pub" | awk -F: '{print $5}') echo "default-key $KEY_ID" >> ~/.gnupg/gpg.conf B. export GPG_TTY=$(tty) echo $GPG_TTY C. Configure gnupg to use standard DNS resolution # resolves common "gpg: keyserver receive failed: Network is unreachable" and # "gpg: keyserver receive failed: No keyserver available" errors echo "standard-resolver" > ~/.gnupg/dirmngr.conf sudo pkill dirmngr D. Set configuration to use SHA512 keys by default: cat <<EOL >> ~/.gnupg/gpg.conf personal-digest-preferences SHA512 cert-digest-algo SHA512 default-preference-list SHA512 SHA384 SHA256 SHA224 AES256 AES192 AES CAST5 ZLIB BZIP2 ZIP Uncompressed EOL Upload the key to one of the key servers gpg --keyserver keyserver.ubuntu.com --send-key $KEY_ID gpg --keyserver keys.openpgp.org --send-key $KEY_ID gpg --keyserver pgp.mit.edu --send-key $KEY_ID Make sure your public key is appended in the KEYS file found in the release server here ============================================= Run mvn release:prepare (you can skip tests with -Darguments='-DskipTests=True, if you have done this before) Run mvn clean release:perform (This step will ask for your apache credentials, if everything related to the keys is configured properly.) Go to: https://repository.apache.org, login, go to Staging Repositories and find the orgapachewayang-{somenumber} and close it with description "Apache Wayang (incubating) 1.0.0-rc5" Make a directory structure as described below and copy the corresponding source files from the directory target/checkout/target. The structure should be: ./KEYS ./1.0.0/ ./1.0.0/rc5 ./1.0.0/rc5/README ./1.0.0/rc5/RELEASE_NOTES ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.asc ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.sha12 Make sure the KEYS file contains your public key. The KEYS file can be found in https://dist.apache.org/repos/dist/release/incubator/wayang/ cd 1.0.0 svn import rc5 https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 -m "Staging of rc5 of Wayang 1.0.0" Send the voting email to the dev list. If the voting passes, send the voting email to the general list. Once the voting in the general list passes, the staged artifacts can be released. This is done by moving them inside the Apache SVN: svn move -m "Release of Apache Wayang (incubating) 1.0.0" https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 https://dist.apache.org/repos/dist/release/incubator/wayang/1.0.0 Then release the maven artifacts: In order to do this, the release manager logs into Nexus at https://repository.apache.org/, selects the staging repository and clicks on the Release button. Bring all changes of the release branch to the main. You can also remove 1.0.0 from the dev directory of the svn now that the release is out. Create and upload the javadocs: Go to the source code directory and run mvn compile javadoc:javadoc javadoc:aggregate The javadocs can then be found in the target directory Upload the javadocs in the website: https://github.com/apache/incubator-wayang-website/tree/main/static/docs/api/javadocs ","version":"Next","tagName":"h3"},{"title":"Maturity Model","type":0,"sectionRef":"#","url":"/docs/community/maturity","content":"Maturity Model ID\tDescription\tStatus\tCode CD10\tThe project produces Open Source software for distribution to the public, at no charge.\tYes. The project source code is licensed under the Apache License 2.0. CD20\tAnyone can easily discover and access the project's code.\tYes. The official website includes GitHub link which can access the project's repository on GitHub directly. CD30\tAnyone using standard, widely-available tools, can build the code in a reproducible way.\tYes. Our webpage and github repo provide "Getting Started" and several examples to allow users build our system. CD40\tThe full history of the project's code is available via a source code control system, in a way that allows anyone to recreate any released version.\tYes. We use git and anyone can view the full history of the project via commit logs and tags of the releases. CD50\tThe source code control system establishes the provenance of each line of code in a reliable way, based on strong authentication of the committer. When third parties contribute code, commit messages provide reliable information about the code provenance.\tYes. The project uses GitHub and managed by Apache Infra and ensures provenance of each line of code to a committer. Third-party contributions are accepted in accordance with the contributing guides. Licenses and Copyright LC10\tThe Apache License, version 2.0, covers the released code.\tYes. All source files are licensed with Apache License v2.0. LICENSE can be found in the GitHub repository. LC20\tLibraries that are mandatory dependencies of the project's code do not create more restrictions than the Apache License does.\tYes. None of the dependencies create more restrictions. LC30\tThe libraries mentioned in LC20 are available as Open Source software.\tYes. All libraries are open source. LC40\tCommitters are bound by an Individual Contributor Agreement (the "Apache iCLA") that defines which code they may commit and how they need to identify code that is not their own.\tYes. All committers have iCLAs. LC50\tThe project clearly defines and documents the copyright ownership of everything that the project produces.\tYes. All source files are with APLv2 header Releases RE10\tReleases consist of source code, distributed using standard and open archive formats that are expected to stay readable in the long term.\tYes. Source release is distributed via dist.apache.org and linked from download page. RE20\tThe project's PPMC (Project Management Committee, see CS10) approves each software release in order to make the release an act of the Foundation.\tYes. All releases have been voted at dev@wayang.apache.org and general@incubator.apache.org, and have at least 3 PPMC member's votes. RE30\tReleases are signed and/or distributed along with digests that anyone can reliably use to validate the downloaded archives.\tYes. All releases are signed, and the KEYS are available. RE40\tThe project can distribute convenience binaries alongside source code, but they are not Apache Releases, they are provided with no guarantee.\tYes. Users can easily build binaries from source code. We do not provide binaries as Apache Releases. RE50\tThe project documents a repeatable release process so that someone new to the project can independently generate the complete set of artifacts required for a release.\tYes. Newcomers can follow our very detailed Release guide to make a new Apache Wayang release, and so far we had 3 different release managers. Quality QU10\tThe project is open and honest about the quality of its code. Various levels of quality and maturity for various modules are natural and acceptable as long as they are clearly communicated.\tYes. The project records all bugs using Github Issues. QU20\tThe project puts a very high priority on producing secure software.\tYes. All security issues will be addressed within 3 days. QU30\tThe project provides a well-documented, secure and private channel to report security issues, along with a documented way of responding to them.\tYes. https://wayang.apache.org/docs/community/security QU40\tThe project puts a high priority on backwards compatibility and aims to document any incompatible changes and provide tools and documentation to help users transition to new features.\tYes. Each release contains a “New features” on the website containing the release notes. The project aims to make no backward incompatible changes within a given major version. QU50\tThe project strives to respond to documented bug reports in a timely manner.\tYes. The community has very small response times to issues and pull requests Community CO10\tThe project has a well-known homepage that points to all the information required to operate according to this maturity model.\tYes. The official website includes all information users need to run Apache Wayang. CO20\tThe community welcomes contributions from anyone who acts in good faith and in a respectful manner, and who adds value to the project.\tYes. The contributing guide provides information for anyone to contribute. CO30\tContributions include source code, documentation, constructive bug reports, constructive discussions, marketing and generally anything that adds value to the project.\tYes. All good contributions including code and non-code are welcomed. CO40\tThe community strives to be meritocratic and gives more rights and responsibilities to contributors who, over time, add value to the project.\tYes. The community has elected 3 new PPMC members/Committers the last year. CO50\tThe project documents how contributors can earn more rights such as commit access or decision power, and applies these principles consistently.\tYes. The website has a clear guide on how to become a committer https://wayang.apache.org/docs/community/committer CO60\tThe community operates based on consensus of its members (see CS10) who have decision power. Dictators, benevolent or not, are not welcome in Apache projects.\tYes. All decisions are made after voting by community members. CO70\tThe project strives to answer user questions in a timely manner.\tYes. Our community answers user questions in a timely manner. Consensus CS10\tThe project maintains a public list of its contributors who have decision power. The project's PPMC (Project Management Committee) consists of those contributors.\tYes. See Team with all PPMC members/committers and contributors. CS20\tDecisions require a consensus among PPMC members and are documented on the project's main communications channel. The PPMC takes community opinions into account, but the PPMC has the final word.\tYes. All decisions are made by votes on dev@wayang.apache.org, and with at least 3 +1 votes from PPMC. CS30\tThe project uses documented voting rules to build consensus when discussion is not sufficient.\tYes. The project uses the standard ASF voting rules. CS40\tIn Apache projects, vetoes are only valid for code commits. The person exercising the veto must justify it with a technical explanation, as per the Apache voting rules defined in CS30.\tYes. Apache Wayang community has not used the veto power yet except for code commits. CS50\tAll "important" discussions happen asynchronously in written form on the project's main communications channel. Offline, face-to-face or private discussions that affect the project are also documented on that channel.\tYes. All important discussions and conclusions are sent to the dev list Independence IN10\tThe project is independent from any corporate or organizational influence.\tYes. The PPMC members of Apache Wayang are from several organizations. IN20\tContributors act as themselves, not as representatives of a corporation or organization.\tYes. The contributors act on their own initiative without representing a corporation or organization. Trademark and Branding TB10\tThe project uses "Apache Foo™" as the project and software product name consistently, with appropriate trademark attributions\tYes. The project uses appropriately the trademark symbol. TB20\tThe project's primary homepage is at projectname.apache.org.\tYes. The project's homepage is at wayang.apache.org TB30\tThe ASF has trademark rights, including any registrations, to the project name, logo, and any other major branding elements.\tYes. ASF has the trademark rights TB40\tThe project monitors for any major misuses of their project's brand by others.\tYes. The PPMC members have already addressed misuses.\t","keywords":"","version":"Next"},{"title":"Wayang Security","type":0,"sectionRef":"#","url":"/docs/community/security","content":"","keywords":"","version":"Next"},{"title":"Advisories for Dependencies","type":1,"pageTitle":"Wayang Security","url":"/docs/community/security#advisories-for-dependencies","content":" Many organizations employ security scanning tools to identify components with known security advisories. Although we strongly recommend these tools as they can alert users to potential risks, they often generate false positives. This occurs because a vulnerable dependency may not necessarily impact Apache Wayang if used in a non-exploitable manner. Therefore, advisories regarding Apache Wayang's dependencies are not automatically considered critical. However, if additional analysis indicates that Apache Wayang might be affected by a dependency's vulnerability, please report your findings privately to security@wayang.apache.org. If a dependency advisory is identified, please: Verify if our DependencyCheck suppressions contain relevant details.Check our issue tracker for discussions regarding this advisory.Conduct your own analysis to determine whether Apache Wayang is affected. If affected, report your findings privately through security@wayang.apache.org.If not affected, please contribute by updating the DependencyCheck suppression list, clearly documenting why Apache Wayang is not impacted. ","version":"Next","tagName":"h2"},{"title":"Community Hubs","type":0,"sectionRef":"#","url":"/docs/community/repositories","content":"","keywords":"","version":"Next"},{"title":"Source Code","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#source-code","content":" Apache Wayang ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang.git GitHub mirror : https://github.com/apache/incubator-wayang.git Wayang Web Page Repository ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang-website.git GitHub mirror : https://github.com/apache/incubator-wayang-website.git ","version":"Next","tagName":"h3"},{"title":"Issues, Bugs and Features","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#issues-bugs-and-features","content":" If you have a specific bug to report or feature request, we suggest opening an issue in Jira. ","version":"Next","tagName":"h3"},{"title":"Team","type":0,"sectionRef":"#","url":"/docs/community/team","content":"","keywords":"","version":"Next"},{"title":"Committers and Recent Contributors","type":1,"pageTitle":"Team","url":"/docs/community/team#committers-and-recent-contributors","content":" Name\tRole\tApache ID\tOrganizationAlexander Alten\tPMC, Committer\taloalt\tScalytics Calvin Kirs\tPMC, Committer\tkirs\tSelectDB, ASF Zoi Kaoudi\tPMC, Committer\tzkaoudi\tITU Copenhagen, Scalytics Kaustubh Beedkar\tPMC, Committer\tkbeedkar\tScalytics, IIT Dehli Mirko Kaempf\tPMC, Committer\tkamir\tEcolytiq Juri Petersen\tPMC, Committer\tjuri\tITU Copenhagen Mads Sejer Pedersen\tPMC, Committer\tsejer\tITU Copenhagen Jean-Baptiste Onofré\tPMC, Committer\tjbonofre\tDremio, ASF Yasser Idris\tPMC, Committer\tyidris\tExpensePoint, Alexandria University Anis Troudi\tCommitter\tatroudi Christofer Dutz\tCommitter\tchrisdutz\tASF Mingxi Liu\tContributor East China Normal University Michalis Vargiamis\tContributor Scalytics Jeppe Halvorsen\tContributor ITU Copenhagen, Falck Christoffer Kristensen\tContributor ITU Copenhagen, Stretch Qonnect Aurélien Bertrand\tContributor Maastricht University, TecAlliance Aditya Goel\tContributor IIT Delhi Dudipala Vigneshwar Reddy\tContributor IIT Delhi Akshit Goel\tContributor IIT Delhi Vedanta Neogi\tContributor IIT Delhi Christos-Evangelos Lampropoulos\tContributor University of Patras Bertty Contreras\tContributor\tbertty Jorge Quiané\tContributor\tquiaru Rodrigo Pardo Meza\tContributor\trpardomeza Glaucia Esppenchutz\tContributor\tglauesppen Gábor Gévay\tContributor\tggevay\t You can reach committers directly at <apache-id>@apache.org ","version":"Next","tagName":"h2"},{"title":"Mentors during the incubation process","type":1,"pageTitle":"Team","url":"/docs/community/team#mentors-during-the-incubation-process","content":" The following people were very kind to mentor the project while in incubation. Name\tApache IDBernd Fondermann\tberndf Christofer Dutz\tcdutz Jean-Baptiste Onofré\tjbonofre Lars George\tlarsgeorge ","version":"Next","tagName":"h2"},{"title":"Donations to Wayang","type":1,"pageTitle":"Team","url":"/docs/community/team#donations-to-wayang","content":" The Logo was donated by Brian Vera ","version":"Next","tagName":"h2"},{"title":"API Documentation","type":0,"sectionRef":"#","url":"/docs/guide/api-documentation","content":"API Documentation The latest Javadocs can be found here.","keywords":"","version":"Next"},{"title":"Adding new operators in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/adding-operators","content":"","keywords":"","version":"Next"},{"title":"Step 1: Add a Wayang operator","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-1-add-a-wayang-operator","content":" Wayang operators are located under the wayang-basic in the org.apache.wayang.basic.operators package. An operator needs to extend from one of the following abstract classes: UnaryToUnaryOperator, BinaryToUnaryOperator, UnarySource, UnarySink. For a unary to unary operator, see for example here. For enhanced performance in Wayang, consider adding a cardinality estimator by overriding the createCardinalityEstimator() function as here. ","version":"Next","tagName":"h2"},{"title":"Step 2: Add the (platform-specific) execution operators","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-2-add-the-platform-specific-execution-operators","content":" Execution operators are located under the corresponding module of wayang-platforms. For instance, Java execution operators are located in the org.apache.wayang.java.operators package of the wayang-java module. An execution operator needs to extend from its corresponding Wayang operator and implement the corresponding platform operator interface. For the above MapOperator, the following is the corresponding JavaMapOperator. For enhanced performance in Wayang, consider adding a load function as well: For this you need to overwrite the getLoadProfileEstimatorConfigurationKey() function and provide the right key that will then be read from a properties file. For the JavaMapOperator it's: wayang.java.map.load. Then add in the corresponding properties file (e.g., this is for the java executor) the template which is the mathematical formula that represents the cost of this operator and an instantiation of it. See here for the example of the map operator. ","version":"Next","tagName":"h2"},{"title":"Step 3: Add mappings","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-3-add-mappings","content":" Create mappings from the Wayang operator to the platform-specific execution operators. The mappings are located in the corresponding execution module in the org.apache.wayang.java.operators package. For the above MapOperator and JavaMapOperator, see here. After that you need to declare this mapping in Wayang in the corresponding Mappings class. ","version":"Next","tagName":"h2"},{"title":"Step 4: Expand the Java scala-like API","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-4-expand-the-java-scala-like-api","content":" Once you created a new operator you need to expose it to the API so that users can use it as a function in the dataflow job they create. For this, you need to go to the module wayang-api/wayang-api-scala-java and expand the JavaPlanBuilder.scala file to include a new source operator or the DataQuantaBuilder for non-source operators. ","version":"Next","tagName":"h2"},{"title":"Developing in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/developing-in-wayang","content":"","keywords":"","version":"Next"},{"title":"Compile the module you modified","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#compile-the-module-you-modified","content":" Within the root directory of Wayang, compile only the module you modified for faster compilation: mvn clean install -DskipTests -pl <modified_module> or change the directory to your module and compile there: cd <modified_module> && mvn clean install Important: before making a Pull Request make sure all modules compile and all tests are passing: mvn clean install ","version":"Next","tagName":"h2"},{"title":"Package the project","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#package-the-project","content":" mvn clean package -pl :wayang-assembly -Pdistribution ","version":"Next","tagName":"h2"},{"title":"Execute your code","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#execute-your-code","content":" Before executing your code, make sure the required environment variables are set correctly. cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Apache Wayang Release Odysse","type":0,"sectionRef":"#","url":"/blog/wayang-release-odysse","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#intro","content":" The ASF provides a robust infrastructure for open communities of software developers. We can share ideas, combine forces, contribute code, docs, review-energy, art work, and from time to time we can nail it down. A release defines an intermediate result of the continuous community work. How we do such a release in the Apache Wayang team is an essential aspect towards graduation. First of all, there are some references to take into account, such as: https://www.apache.org/legal/release-policy.htmlhttps://maven.apache.org/maven-release/maven-release-plugin/ Assuming you are (P)PMC, and assuming that you have the right permissions for such a release, you can follow the path as described in this guide: https://plc4x.apache.org/developers/release/release.html I tried to follow exactly this procedure, several times. I failed. Here I share the current status of my release attempts. I plan a longer tour, and do not want to block the project for a long time. Hence I create this draft, and I hope we can unblock this project as soon as possible. ","version":null,"tagName":"h2"},{"title":"Status:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#status","content":" I am not able to conduct the mvn release:perform step. Anything before worked, sometimes only after some digging, but it worked. We assume, that due to my membership in two ASF incubator projects I am not able to upload the artefacts to the Nexus repository (H1). It can be, that I have not the correct user and password in my settings.xml file (H2). But I tested a manual login to the nexus server https://repository.apache.org/service/local/staging/deploy/maven2 with success. And beyond that I have no idea how I can verify this detail alone. ","version":null,"tagName":"h2"},{"title":"Idea / Proposal","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#idea--proposal","content":" (1) It would be great, if someone - who has done a release in any other ASF project or in Apache Wayang - could follow the steps I share, so that we can check where the problem hides itself. (2) As a follow-up task, I suggest to add a Release Guide to the Apache Wayang project, including release manager onboarding steps, and checklists for the particular project, derived from the referenced sources which are listed above. But for now it is all about sharing the status (as I did serveral times on multiple chanels, including JIRA, Slack, Mailing lists) and finding a solution for Apache Wayang release 1.0. ","version":null,"tagName":"h2"},{"title":"Latest Error:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#latest-error","content":" mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h2"},{"title":"Activity Log","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#activity-log","content":" mvn release:clean mvn versions:set -DnewVersion=1.0.0-RC2 mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: You don't have a SNAPSHOT project in the reactor projects list. -> [Help 1] mvn versions:commit mvn versions:set -DnewVersion=1.0.0-RC2-SNAPSHOT mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Cannot prepare the release because you have local modifications : git status git add . git commit -m "prepare for release 1.0.0-RC2-SNAPSHOT" git push mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -DdryRun=true mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -XXX Caused by: org.eclipse.aether.transfer.NoRepositoryConnectorException: Blocked mirror for repositories: [repository.jboss.org (http://repository.jboss.org/nexus/content/groups/public/, default, releases)] ","version":null,"tagName":"h2"},{"title":"Dependency on JDK-11 during release","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#dependency-on-jdk-11-during-release","content":" FIXED with local JDK11 Setup brew install openjdk@11 curl -s "https://get.sdkman.io" | bash source "$HOME/.sdkman/bin/sdkman-init.sh" which java which java /Users/kamir/.sdkman/candidates/java/current/bin/java ➜ GITHUB.active export JAVA_HOME=/Users/kamir/.sdkman/candidates/java/current ➜ GITHUB.active mvn clean -XXX export JAVA_HOME= sdk install java 11.0.24-amzn sdk home java 11.0.24-amzn /usr/libexec/java_home -v 11 jenv add /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home jenv global 11.0 jenv shell 11.0 jenv local 11.0 java -version ","version":null,"tagName":"h3"},{"title":"Manual update of release-version","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#manual-update-of-release-version","content":" During the release procedure, do I have to set the version here in this configuration section manually? <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-release-plugin</artifactId> <version>3.0.1</version> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>0.7.1</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>wayang-0.7.1</tag> <scmReleaseCommitComment>@{prefix} prepare release 0.7.1</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> </plugin> It seems that these properties must be updated manually. ","version":null,"tagName":"h3"},{"title":"Warning regarding \"illegal reflective access operation\"","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#warning-regarding-illegal-reflective-access-operation","content":" [ERROR] WARNING: An illegal reflective access operation has occurred [ERROR] WARNING: Illegal reflective access by org.codehaus.groovy.reflection.CachedClass (file:/Users/mkaempf/.m2/repository/org/codehaus/groovy/groovy-all/2.4.9/groovy-all-2.4.9.jar) to method java.lang.Object.finalize() [ERROR] WARNING: Please consider reporting this to the maintainers of org.codehaus.groovy.reflection.CachedClass [ERROR] WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations [ERROR] WARNING: All illegal access operations will be denied in a future release This is still an OPEN ISSUE ! ","version":null,"tagName":"h3"},{"title":"RAT Check fails","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#rat-check-fails","content":" [INFO] [ERROR] Failed to execute goal org.apache.rat:apache-rat-plugin:0.13:check (license-check) on project wayang: Too many files with unapproved license: 1 See RAT report in: /Users/mkaempf/GITHUB.private/incubator-wayang/target/rat.txt -> [Help 1] ***************************************************** Printing headers for text files without a valid license header... ===================================================== == File: .java-version ===================================================== 11.0 FIXED by adding .java-versions to .gitignore ","version":null,"tagName":"h3"},{"title":"Tag could not be created in SCM.","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#tag-could-not-be-created-in-scm","content":" [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Unable to tag SCM [ERROR] Provider message: [ERROR] The git-tag command failed. [ERROR] Command output: [ERROR] fatal: tag 'wayang-0.7.1' already exists FIXED by manual changes in pom.xml. <scm> <connection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</connection> <developerConnection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</developerConnection> <url>https://github.com/apache/incubator-wayang</url> <tag>1.0.0-RC2-SNAPSHOT</tag> </scm> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>1.0.0-RC2-SNAPSHOT</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>1.0.0-RC2-SNAPSHOT</tag> <scmReleaseCommitComment>@{prefix} prepare release 1.0.0-RC2-SNAPSHOT</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> mvn release:prepare -Darguments='-DskipTests=True -Dresume=True' mvn clean package mvn release:perform -X -DskipTests - you need your password for the keystore to sign the build artefacts. So far so good. But now the sun went down. [INFO] [ERROR] Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException -> [Help 1] [INFO] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.plugin.MojoExecutionException: ArtifactDeployerException [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:201) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.shared.transfer.artifact.deploy.ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:126) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:278) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.transfer.ArtifactTransferException: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.connector.basic.ArtifactTransportListener.transferFailed (ArtifactTransportListener.java:44) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:417) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: java.lang.NullPointerException [INFO] at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) [INFO] at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) [INFO] at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) [INFO] at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) [INFO] at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) [INFO] at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) [INFO] at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) [INFO] at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) [INFO] at org.apache.http.impl.execchain.RetryExec.execute (RetryExec.java:89) [INFO] at org.apache.http.impl.execchain.ServiceUnavailableRetryExec.execute (ServiceUnavailableRetryExec.java:85) [INFO] at org.apache.http.impl.execchain.RedirectExec.execute (RedirectExec.java:110) [INFO] at org.apache.http.impl.client.InternalHttpClient.doExecute (InternalHttpClient.java:185) [INFO] at org.apache.http.impl.client.CloseableHttpClient.execute (CloseableHttpClient.java:72) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.execute (HttpTransporter.java:485) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.implPut (HttpTransporter.java:469) [INFO] at org.eclipse.aether.spi.connector.transport.AbstractTransporter.put (AbstractTransporter.java:107) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$PutTaskRunner.runTask (BasicRepositoryConnector.java:564) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:414) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] [ERROR] [INFO] [ERROR] [INFO] [ERROR] For more information about the errors and possible solutions, please read the following articles: [INFO] [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [INFO] [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [INFO] [DEBUG] Shutting down 'file-lock' factory [INFO] [DEBUG] Shutting down 'rwlock-local' factory [INFO] [DEBUG] Shutting down 'semaphore-local' factory [INFO] [DEBUG] Shutting down 'noop' factory [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary for Apache Wayang (incubating) 1.0.0-RC3-SNAPSHOT: [INFO] [INFO] Apache Wayang (incubating) ......................... FAILURE [ 23.626 s] [INFO] Wayang Commons ..................................... SKIPPED [INFO] wayang-utils-profile-db ............................ SKIPPED [INFO] Wayang Core ........................................ SKIPPED [INFO] Wayang Basic ....................................... SKIPPED [INFO] Wayang Platform .................................... SKIPPED [INFO] Wayang Platform Java ............................... SKIPPED [INFO] Wayang Platform Spark .............................. SKIPPED [INFO] Wayang Platform JDBC Template ...................... SKIPPED [INFO] Wayang Platform Postgres ........................... SKIPPED [INFO] Wayang Platform SQLite3 ............................ SKIPPED [INFO] Wayang Platform Giraph ............................. SKIPPED [INFO] Wayang Platform Apache Flink ....................... SKIPPED [INFO] Wayang Platform Generic Jdbc ....................... SKIPPED [INFO] Wayang API ......................................... SKIPPED [INFO] Wayang API Scala-Java .............................. SKIPPED [INFO] Wayang Integration Test ............................ SKIPPED [INFO] Wayang API Python .................................. SKIPPED [INFO] wayang-api-sql ..................................... SKIPPED [INFO] Wayang Profiler .................................... SKIPPED [INFO] Wayang Extensions .................................. SKIPPED [INFO] wayang-iejoin ...................................... SKIPPED [INFO] Wayang - Common resources .......................... SKIPPED [INFO] wayang-benchmark ................................... SKIPPED [INFO] Wayang ML4all ...................................... SKIPPED [INFO] Wayang Project Assembly ............................ SKIPPED [INFO] ------------------------------------------------------------------------ [INFO] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Total time: 24.093 s [INFO] Finished at: 2024-06-25T10:53:32+02:00 [INFO] ------------------------------------------------------------------------ [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 -> [Help 1] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.plugin.MojoExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:198) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.ReleaseExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:115) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.exec.MavenExecutorException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.exec.InvokerMavenExecutor.executeGoals (InvokerMavenExecutor.java:129) at org.apache.maven.shared.release.exec.AbstractMavenExecutor.executeGoals (AbstractMavenExecutor.java:70) at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:105) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [ERROR] [ERROR] [ERROR] For more information about the errors and possible solutions, please read the following articles: [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [DEBUG] Shutting down 'file-lock' factory [DEBUG] Shutting down 'rwlock-local' factory [DEBUG] Shutting down 'semaphore-local' factory [DEBUG] Shutting down 'noop' factory The null pointer exception indicates problems in settings.xml. This could be fixed. But now, the error changes with and I am still not able to stage the build results. mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h3"},{"title":"Checklist for next iteration:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#checklist-for-next-iteration","content":" The error you're encountering indicates that there is a problem with deploying the artifacts using the maven-deploy-plugin. The root cause of the error is a NullPointerException during the deployment process, specifically related to the org.eclipse.aether.transfer.ArtifactTransferException. Here's a step-by-step guide to troubleshoot and resolve this issue: ","version":null,"tagName":"h2"},{"title":"1. Check Maven Settings","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#1-check-maven-settings","content":" Ensure that your Maven settings (settings.xml) are correctly configured for deployment. Verify that the repository settings and credentials are correctly specified. [DONE] ","version":null,"tagName":"h3"},{"title":"2. Verify Repository URL","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#2-verify-repository-url","content":" Make sure the repository URL in your pom.xml or settings.xml is correct and reachable. The URL should point to the correct staging repository for deployment. [OPEN] - I did not touch it, so I guess it is correct in the pom.xml. ","version":null,"tagName":"h3"},{"title":"3. Maven Version Compatibility","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#3-maven-version-compatibility","content":" Verify that you are using a compatible version of Maven. Sometimes, upgrading or downgrading Maven can resolve such issues. Apache Maven 3.9.6 (bc0240f3c744dd6b6ec2920b3cd08dcc295161ae) Maven home: /usr/local/Cellar/maven/3.9.6/libexec Java version: 11.0.15.1, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home Default locale: en_DE, platform encoding: US-ASCII OS name: "mac os x", version: "13.2.1", arch: "x86_64", family: "mac" [DONE] ","version":null,"tagName":"h3"},{"title":"4. Check for Network Issues","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#4-check-for-network-issues","content":" Ensure there are no network issues that might be causing problems in connecting to the repository. Sometimes, network configurations, firewalls, or proxy settings can interfere with the deployment process. [DONE] ","version":null,"tagName":"h3"},{"title":"5. Update Maven Plugins","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#5-update-maven-plugins","content":" Ensure that you are using the latest versions of the Maven plugins. Sometimes, bugs in older versions can cause unexpected issues. [OPEN] ","version":null,"tagName":"h3"},{"title":"6. Configure the maven-deploy-plugin in pom.xml","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#6-configure-the-maven-deploy-plugin-in-pomxml","content":" Make sure the maven-deploy-plugin is correctly configured in your pom.xml. Here's an example configuration: <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>3.0.0-M1</version> <configuration> <repositoryId>apache.releases.https</repositoryId> <url>https://repository.apache.org/service/local/staging/deploy/maven2</url> </configuration> </plugin> ","version":null,"tagName":"h3"},{"title":"7. Increase Verbose Logging","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#7-increase-verbose-logging","content":" Enable verbose logging to get more details about the error. You can do this by adding the -X flag when running the Maven command: mvn clean deploy -X [DONE] ","version":null,"tagName":"h3"},{"title":"8. Retry with a Clean Local Repository","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#8-retry-with-a-clean-local-repository","content":" Sometimes, a corrupt local repository can cause issues. Try cleaning your local Maven repository and re-running the deployment: mvn clean install -U mvn deploy [OPEN] - Does not match to an authentication issue. ","version":null,"tagName":"h3"},{"title":"9. Check for Missing Credentials","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#9-check-for-missing-credentials","content":" Ensure that the credentials for the repository are correctly set up in your settings.xml: <servers> <server> <id>apache.releases.https</id> <username>your-username</username> <password>your-password</password> </server> </servers> [DONE] ","version":null,"tagName":"h3"},{"title":"10. Review the Full Stack Trace","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#10-review-the-full-stack-trace","content":" The full stack trace indicates a NullPointerException: Caused by: java.lang.NullPointerException at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) ... This suggests that there might be an issue with how credentials are being handled. Double-check that the credentials are being correctly passed and processed. [DONE] ","version":null,"tagName":"h3"},{"title":"Installing and building Apache Wayang","type":0,"sectionRef":"#","url":"/docs/guide/installation","content":"","keywords":"","version":"Next"},{"title":"Clone repository","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#clone-repository","content":" git clone https://github.com/apache/incubator-wayang.git ","version":"Next","tagName":"h2"},{"title":"Create binaries","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#create-binaries","content":" Running following commands to build Wayang and generate the tar.gz cd incubator-wayang ./mvnw clean install -DskipTests ./mvnw clean package -pl :wayang-assembly -Pdistribution Then you can find the wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz under wayang-assembly/target directory. ","version":"Next","tagName":"h2"},{"title":"Prepare the environment","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#prepare-the-environment","content":" ","version":"Next","tagName":"h2"},{"title":"Wayang","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#wayang","content":" tar -xvf wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz cd wayang-0.7.1-SNAPSHOT In linux echo "export WAYANG_HOME=$(pwd)" >> ~/.bashrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.bashrc source ~/.bashrc In MacOS echo "export WAYANG_HOME=$(pwd)" >> ~/.zshrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.zshrc source ~/.zshrc ","version":"Next","tagName":"h3"},{"title":"Others","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#others","content":" You need to install Apache Spark version 3 or higher. Don’t forget to set the SPARK_HOME environment variable.You need to install Apache Hadoop version 3 or higher. Don’t forget to set the HADOOP_HOME environment variable. ","version":"Next","tagName":"h3"},{"title":"Run the program","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#run-the-program","content":" To execute the WordCount example with Apache Wayang, you need to execute your program with the 'wayang-submit' command: cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.apps.wordcount.Main java file://$(pwd)/README.md Then you should be able to see the output of the Wordcount example. Compiling Apache Wayang Apache Wayang (incubating) has different dependencies, for compiling, it needs to add some profile in the compilation to enable maven works properly. mvn clean compile The line before is because the plugin the Antlr is not needed in all the modules, as well it has happened with Scala language. When maven compiles one or more modules using those plugins in the compilation time, it needs to add. The modules are: wayang-api-scala-javawayang-core (Antlr)wayang-iejoinwayang-sparkwayang-profilerwayang-tests-integration Executing Coverage mvn clean verify jacoco:report the final report is placed on ./target/aggregate.exec/aggregate.exec ","version":"Next","tagName":"h2"},{"title":"How the optimizer works (WIP)","type":0,"sectionRef":"#","url":"/docs/guide/optimizer","content":"","keywords":"","version":"Next"},{"title":"Configuration Files","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#configuration-files","content":" Description of configuration files and how to edit them ","version":"Next","tagName":"h2"},{"title":"Key Configuration Parameters","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#key-configuration-parameters","content":" List of key parameters and how they affect performance ","version":"Next","tagName":"h2"},{"title":"Tuning Tips","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#tuning-tips","content":" Tips on tuning Apache Wayang for different scenarios ","version":"Next","tagName":"h2"},{"title":"ML4all: scalable ML system for everyone","type":0,"sectionRef":"#","url":"/docs/guide/ml4all","content":"","keywords":"","version":"Next"},{"title":"Abstraction","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#abstraction","content":" ML4all abstracts most ML algorithms with seven operators: (1) Transform receives a data point to transform (e.g., normalize it) and outputs a new data point. (2) Stage initializes all the required global param- eters (e.g., centroids for the k-means algorithm). (3) Compute performs user-defined computations on the input data point and returns a new data point. For example, it can compute the nearest cen- troid for each input data point. (4) Update updates the global parameters based on a user-defined formula. For example, it can update the new centroids based on the output computed by the Compute operator. (5) Sample takes as input the size of the desired sample and the data points to sample from and re- turns a reduced set of sampled data points. (6) Converge specifies a function that outputs a convergence dataset required for determining whether the iterations should continue or stop. (7) Loop specifies the stopping condition on the convergence dataset. Similar to MapReduce, where users need to implement a map and reduce function, users of ML4all wishing to develop their own algorithm should implement the above interfaces. The interfaces can be found in org.apache.wayang.ml4all.abstraction.api. Examples for KMeans clustering and stochastic gradient descent can be found in org.apache.wayang.ml4all.algorithms. ","version":"Next","tagName":"h2"},{"title":"Example runs","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#example-runs","content":" Kmeans: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunKMeans java,spark <url_path_to_file>/USCensus1990-sample.input 3 68 0 1 SGD: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunSGD spark <url_path_to_file>/adult.zeros.input 100827 123 10 0.001 ","version":"Next","tagName":"h2"},{"title":"What is Apache Wayang™?","type":0,"sectionRef":"#","url":"/docs/introduction/about","content":"","keywords":"","version":"Next"},{"title":"Architecture and Software stack","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#architecture-and-software-stack","content":" Apache Wayang's unique architecture, unlike traditional DBMSs, decouples the physical planning and execution layers, empowering developers to express their data processing logic in a platform-agnostic fashion. This separation of concerns allows developers to focus on the algorithmic aspects of their applications without being constrained by the intricacies of specific processing platforms. At the bottom layers of the software stack, there are the different data storage mediums and the supported data processing platforms. On top of these, Wayang’s core consists of the following main components: the optimizer, the executor, the monitor, and platform-specific drivers. Wayang currently supports two main APIs: the Java one and the Scala one. A Python API is also supported with limited operator coverage for the moment. Besides using any of the supported languages, users can directly input SQL queries via the SQL library, which transforms them into a Wayang plan. Wayang also comes with an ML library for running ML tasks. Users can directly utilize the provided algorithms or can implement their own algorithm using a simple ML abstraction. To enable support for more programming languages in an efficient way, Wayang will soon come with a Polyglot library. Below you can see on the left, a Wayang plan representing the stochastic gradient descent algorithm, which used in most deep learning tasks. On the right, you can see how the optimizer decided to execute it. Orange nodes are the operators that ran on Spark and green the operators executed as a single Java process. ","version":"Next","tagName":"h3"},{"title":"Query Optimizer","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#query-optimizer","content":" Wayang's query optimizer is the principal component of the core. It receives as input a Wayang plan and outputs an execution (platform-specific) plan (see example plan above) with the goal of minimizing the total cost. The metric for the cost can be anything, from execution runtime to monetary cost or energy consumption. To achieve this, the optimizer first “inflates” the plan: For each node that corresponds to a Wayang operator, it adds all the corresponding execution (platform-specific) operators. Once the inflated plan is created, the optimizer attaches not only the operator’s costs but also the costs for moving intermediate data from one platform to another. By default, Wayang uses linear cost formulas to estimate these costs but one can plug their own optimizer, e.g., an ML-based one. At the last step of query optimization, our enumeration algorithm considers available options to output the optimal execution plan w.r.t. a defined cost. Note that users can control the optimizer by specifying in their code where an operator has to be executed via the withTargetPlatform(plat) call on the desired operator. Then, the optimizer takes into consideration the decisions of the user and outputs an execution plan by navigating a reduced search space during the plan enumeration. ","version":"Next","tagName":"h3"},{"title":"History","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#history","content":" Wayang (formely called Rheem) is the product of many years of top quality research. Below you can find the main publications: Apache Wayang: A Unified Data Analytics Framework. SIGMOD Rec. 52(3): 30-35 (2023) pdf Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework. SIGMOD Conference Companion 2025: 35-38 pdf ML-based Cross-Platform Query Optimization. ICDE 2020: 1489-1500 pdf RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems. VLDB J. 29(6): 1287-1310 (2020) pdf RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -. Proc. VLDB Endow. 11(11): 1414-1427 (2018) pdf ","version":"Next","tagName":"h3"},{"title":"Using Wayang's docker image","type":0,"sectionRef":"#","url":"/docs/guide/wayang-docker","content":"","keywords":"","version":"Next"},{"title":"Step 1: Creating a docker-compose file","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-1-creating-a-docker-compose-file","content":" This guide assumes knowledge aboutdocker anddocker compose. We provide a pre-built docker imagethat contains the necessary tooling in order to run or develop Apache Wayang. The tools necessary for this are: Java 11Apache SparkHadoopMaven A docker-compose.yml containing the following services will suffice to run TPC-H benchmarks: name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always Placing this file in the root directory of Wayang's source will mount volumes containing the application and its dependencies after installation into the app container. ","version":"Next","tagName":"h2"},{"title":"Step 2: Connecting to the app container","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-2-connecting-to-the-app-container","content":" In order to get a interactive bash session that allows running commands inside of the app container, run the following: docker exec -it apache-wayang-app bash ","version":"Next","tagName":"h2"},{"title":"Step 3: Compiling Wayang and running benchmarks","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-3-compiling-wayang-and-running-benchmarks","content":" Within the root directory of Wayang (/var/www/html in our container), run the following command to install all packages in Wayang: mvn clean install -DskipTests Packaging the project to build the executable: mvn clean package -pl :wayang-assembly -Pdistribution Execute your code: cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-incubating-dist.tar.gz cd wayang-0.7.1 ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Optional: Add clusters for additional platforms to your docker setup","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#optional-add-clusters-for-additional-platforms-to-your-docker-setup","content":" name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always spark-master: image: cluster-apache-spark:3.0.2 spark-worker-a: image: cluster-apache-spark:3.0.2 depends_on: - spark-master environment: - SPARK_MASTER=spark://spark-master:7077 - SPARK_WORKER_CORES=1 - SPARK_WORKER_MEMORY=1G - SPARK_DRIVER_MEMORY=1G - SPARK_EXECUTOR_MEMORY=1G - SPARK_WORKLOAD=worker - SPARK_LOCAL_IP=spark-worker-a ","version":"Next","tagName":"h2"},{"title":"Guide to Development and Usage with Apache Wayang (incubating)","type":0,"sectionRef":"#","url":"/docs/guide/examples","content":"","keywords":"","version":"Next"},{"title":"Example 1: Machine Learning for query optimization in Apache Wayang","type":1,"pageTitle":"Guide to Development and Usage with Apache Wayang (incubating)","url":"/docs/guide/examples#example-1-machine-learning-for-query-optimization-in-apache-wayang","content":" Apache Wayang can be customized with concrete implementations of the EstimatableCost interface in order to optimize for a desired metric. The implementation can be enabled by providing it to a Configuration. public class CustomEstimatableCost implements EstimatableCost { /* Provide concrete implementations to match desired cost function(s) * by implementing the interface in this class. */ } public class WordCount { public static void main(String[] args) { /* Create a Wayang context and specify the platforms Wayang will consider */ Configuration config = new Configuration(); /* Provision of a EstimatableCost that implements the interface.*/ config.setCostModel(new CustomEstimatableCost()); WayangContext wayangContext = new WayangContext(config) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); /*... omitted */ } } In combination with an encoding scheme and a third party package to load ML models, the following example shows how to predict runtimes of query execution plans runtimes in Apache Wayang (incubating): public class MLCost implements EstimatableCost { public EstimatableCostFactory getFactory() { return new Factory(); } public static class Factory implements EstimatableCostFactory { @Override public EstimatableCost makeCost() { return new MLCost(); } } @Override public ProbabilisticDoubleInterval getEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } @Override public ProbabilisticDoubleInterval getParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } /** Returns a squashed cost estimate. */ @Override public double getSquashedEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public double getSquashedParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public Tuple<List<ProbabilisticDoubleInterval>, List<Double>> getParallelOperatorJunctionAllCostEstimate(PlanImplementation plan, Operator operator) { List<ProbabilisticDoubleInterval> intervalList = new ArrayList<ProbabilisticDoubleInterval>(); List<Double> doubleList = new ArrayList<Double>(); intervalList.add(this.getEstimate(plan, true)); doubleList.add(this.getSquashedEstimate(plan, true)); return new Tuple<>(intervalList, doubleList); } public PlanImplementation pickBestExecutionPlan( Collection<PlanImplementation> executionPlans, ExecutionPlan existingPlan, Set<Channel> openChannels, Set<ExecutionStage> executedStages) { final PlanImplementation bestPlanImplementation = executionPlans.stream() .reduce((p1, p2) -> { final double t1 = p1.getSquashedCostEstimate(); final double t2 = p2.getSquashedCostEstimate(); return t1 < t2 ? p1 : p2; }) .orElseThrow(() -> new WayangException("Could not find an execution plan.")); return bestPlanImplementation; } } Third-party packages such as OnnxRuntime can be used to load pre-trained .onnx files that contain desired ML models. public class OrtMLModel { private static OrtMLModel INSTANCE; private OrtSession session; private OrtEnvironment env; private final Map<String, OnnxTensor> inputMap = new HashMap<>(); private final Set<String> requestedOutputs = new HashSet<>(); public static OrtMLModel getInstance(Configuration configuration) throws OrtException { if (INSTANCE == null) { INSTANCE = new OrtMLModel(configuration); } return INSTANCE; } private OrtMLModel(Configuration configuration) throws OrtException { this.loadModel(configuration.getStringProperty("wayang.ml.model.file")); } public void loadModel(String filePath) throws OrtException { if (this.env == null) { this.env = OrtEnvironment.getEnvironment(); } if (this.session == null) { this.session = env.createSession(filePath, new OrtSession.SessionOptions()); } } public void closeSession() throws OrtException { this.session.close(); this.env.close(); } /** * @param encodedVector * @return NaN on error, and a predicted cost on any other value. * @throws OrtException */ public double runModel(Vector<Long> encodedVector) throws OrtException { double costPrediction; OnnxTensor tensor = OnnxTensor.createTensor(env, encodedVector); this.inputMap.put("input", tensor); this.requestedOutputs.add("output"); BiFunction<Result, String, Double> unwrapFunc = (r, s) -> { try { return ((double[]) r.get(s).get().getValue())[0]; } catch (OrtException e) { return Double.NaN; } }; try (Result r = session.run(inputMap, requestedOutputs)) { costPrediction = unwrapFunc.apply(r, "output"); } return costPrediction; } } ","version":"Next","tagName":"h2"},{"title":"Benchmarking Wayang","type":0,"sectionRef":"#","url":"/docs/introduction/benchmark","content":"","keywords":"","version":"Next"},{"title":"Running a Relational Query Across Multiple Independent Databases (Federation)","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#running-a-relational-query-across-multiple-independent-databases-federation","content":" In this use case, we showcase how Apache Wayang addresses the common challenge of analyzing data scattered across diverse systems, a prevalent issue in many data-driven projects. Through Wayang, developers can seamlessly perform analytics on relational data residing in multiple systems. The in-situ data processing approach employed by Wayang introduces data privacy-preserving mechanisms into distributed data analytics tasks. Datasets: To test the capabilities of Wayang, we utilized the TPC-H benchmark, which comprises five datasets/relations. The lineitem and orders relations are stored in HDFS, while customer, supplier, and region are in Postgres, and nation is in S3 or the local file system. Our testing covered a range of dataset sizes, from 1GB to 100GB, to ensure our product's scalability and robustness. Query/task: We evaluate the performance of the complex SQL TPC-H query 5: SELECT N_NAME, SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) AS REVENUE FROM CUSTOMER, ORDERS, LINEITEM, SUPPLIER, NATION, REGION WHERE C_CUSTKEY = O_CUSTKEY AND L_ORDERKEY = O_ORDERKEY AND L_SUPPKEY = S_SUPPKEY AND C_NATIONKEY = S_NATIONKEY AND S_NATIONKEY = N_NATIONKEY AND N_REGIONKEY = R_REGIONKEY AND R_NAME = 'ASIA' AND O_ORDERDATE >= '1994-01-01' AND O_ORDERDATE < DATEADD(YY, 1, cast('1994-01-01' as date)) GROUP BY N_NAME ORDER BY REVENUE DESC Baselines: We compared Wayang with two widely-used systems for storing and querying relational data: Spark and Postgres. To conduct a fair comparison, we first moved all datasets to the system being tested (Spark or Postgres). Results: The results of our tests, presented in Figure 1, show the execution time in seconds of the SQL query 5. Note that the time required to transfer data to Spark or Postgres was excluded from the results. As seen in Figure 1, Wayang significantly outperformed Postgres while retaining a runtime very close to Spark. It's important to note that for Spark to function, we needed twice the time to extract datasets from Postgres and transfer them to Spark. Wayang achieved such impressive performance by seamlessly combining Postgres with Spark: Wayang's query optimizer chose to perform all selections and projections on the data stored in Postgres before extracting data to join with the relations in HDFS. Additionally, the optimizer determined that executing the join between lineitem and supplier in Spark would be beneficial, as it could distribute the computational load of joining to multiple workers. All of this was done without the need for the user to specify where each operation should be executed. ","version":"Next","tagName":"h3"},{"title":"Reducing Execution Costs for Machine Learning Tasks Using Multiple Systems","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#reducing-execution-costs-for-machine-learning-tasks-using-multiple-systems","content":" To evaluate Apache Wayang's ability to accelerate machine learning tasks while optimizing costs by leveraging multiple systems, we conducted a comprehensive benchmark. The benchmark involved storing all data within a single repository, a scenario where selectively offloading data to more powerful engines can significantly enhance performance. To assess this feature, we employed a widely used machine learning algorithm, stochastic gradient descent, to perform classification tasks. Datasets: We use two real-world datasets that we downloaded from the UCI machine learning repository, namely higgs and rcv1. higgs consists of ~11 million data points with 28 features each and rcv1 contains ~677 thousand data points with ~47 thousand features each. In addition, we construct a synthetic dataset so that we can stress our product with even larger datasets, specifically with 88 million data points of 100 features each. Query/task: We test the performance of training classification models for higgs and the synthetic dataset and a regression model rcv1. All three training models use stochastic gradient descent but with a different loss function. We use Hinge loss to simulate support vector machines for the classification tasks and the logistic loss for the regression task. Baselines: We compare Wayang against MLlib from Apache Spark and SystemML from IBM, two very popular machine learning libraries. Results: Our results, shown in Figure 2, demonstrate that Wayang outperforms both baselines by more than an order of magnitude in runtime performance for large datasets. Apache Wayang's intelligent optimizer dynamically utilizes a hybrid approach, seamlessly blending Spark and local Java execution, to significantly improve the performance of the stochastic gradient descent algorithm. This optimization strategy involves leveraging Spark for efficient preprocessing and data preparation, while transitioning to local Java execution for the subsequent gradient computation phase. During this phase, the dataset shrinks considerably, making it more efficient to process using a single machine. This optimization technique, while not readily apparent without specialized expertise, is seamlessly implemented by Apache Wayang, requiring no additional user intervention. ","version":"Next","tagName":"h3"},{"title":"Optimizing Big Data Analytics by Adapting Platforms to Data and Task Characteristics","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#optimizing-big-data-analytics-by-adapting-platforms-to-data-and-task-characteristics","content":" We demonstrate the platform adaptation capabilities of Apache Wayang, showcasing how it optimizes analytical tasks by tailoring execution to the specific data and task characteristics. Our approach involves dynamically selecting the optimal processing engine based on the nature of the data and the complexity of the analytical operation. This flexibility allows Wayang to deliver enhanced performance and efficiency, particularly for complex analytical workflows that demand the strengths of multiple processing engines. Datasets: We use Wikipedia abstracts and store them in HDFS. We vary the dataset size from 1GB to 800GB. Query/task: We test our product with a widely popular big data analytical task, namely WordCount. It counts the number of distinct words in a text corpus. Different variations of wordcount are useful in various text mining applications, such as term frequency, word clouds, etc. Baselines: We use three different platforms where Wordcount can be executed: Apache Spark, Apache Flink, and a single node Java program. We then set Wayang to be able to automatically choose which of the three platforms to use for each dataset. Results: The benchmark results, presented in Figure 3, provide evidence of Apache Wayang's platform adaptation prowess, consistently selecting the fastest available platform for various dataset sizes in our WordCount example. By incorporating execution cost modeling into the query optimizer, Wayang eliminates the need for manual platform selection or migration, allowing users to solely focus on their analytical tasks. Our platform adaptation feature seamlessly identifies the optimal platform for each query or task based on data and query characteristics, ensuring faster execution and enhanced efficiency in big data analytics. ","version":"Next","tagName":"h3"},{"title":"Features","type":0,"sectionRef":"#","url":"/docs/introduction/features","content":"","keywords":"","version":"Next"},{"title":"Cross platform enablement","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cross-platform-enablement","content":" Apache Wayang's cross-platform optimizer stands out as its most distinguishing feature. It not only selects the most suitable processing engine for each task, but also empowers users to execute a single task across multiple platforms. This capability is achieved through an extensible set of graph transformations applied to the Apache Wayang plan, which identifies alternative execution plans that potentially offer better performance. These alternative plans are then evaluated using platform-specific cost models, which can either be user-defined or learned from historical data. These cost models are parameterized based on the underlying hardware configuration, such as the number of computing nodes for distributed operations. ","version":"Next","tagName":"h3"},{"title":"Scalable ML (ML4All)","type":1,"pageTitle":"Features","url":"/docs/introduction/features#scalable-ml-ml4all","content":" ML4all is part of Wayang, enhancing machine learning workflows. ML4all alleviates users from the complexities of algorithm selection and low-level implementation details, enabling them to concentrate on the core aspects of their machine learning applications. By employing an innovative abstraction, ML4all addresses a broad spectrum of machine learning tasks and utilizes a cost-based optimizer to dynamically identify the optimal gradient descent algorithm for each scenario. Notably, ML4all demonstrates performance improvements of two orders of magnitude over existing systems, enabling the processing of large datasets that were previously impractical. ","version":"Next","tagName":"h3"},{"title":"High Efficiency","type":1,"pageTitle":"Features","url":"/docs/introduction/features#high-efficiency","content":" Apache Wayang's suite of optimized operators and sophisticated query optimization techniques enables it to effectively process large-scale and small-scale datasets. By employing a data processing abstraction based on UDFs, Wayang allows applications to convey semantic information about their functions, optimization hints (such as iteration counts), constraints (e.g., physical placement of operators), and alternative execution plans. The optimizer incorporates these insights to refine the execution plan, aiming to achieve optimal performance in a comprehensive manner. ","version":"Next","tagName":"h3"},{"title":"Flexibility","type":1,"pageTitle":"Features","url":"/docs/introduction/features#flexibility","content":" Apache Wayang offers a collection of operators that applications utilize to define their tasks. A crucial aspect of Apache Wayang is its flexible operator mapping structure, which enables developers to effortlessly add, modify, or remove mappings between Wayang and execution operators. This flexibility empowers developers to seamlessly integrate additional Wayang and execution operators into their applications. ","version":"Next","tagName":"h3"},{"title":"Cost Saving","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cost-saving","content":" Developers can focus on building their applications without the need to understand the complexities of underlying platforms. This simplifies the development process and removes the requirement for developers to be experts in big data infrastructures. Apache Wayang automatically determines the most suitable data processing platforms for specific tasks and deploys applications accordingly. ","version":"Next","tagName":"h3"},{"title":"Additional Features","type":1,"pageTitle":"Features","url":"/docs/introduction/features#additional-features","content":" Zero-copy: cross-platform in-situ data processingHigh performance: A highly-extensible API framework to generate DAG based federated execution plane at runtime to speed up data processing, providing 50-180x speed up by: reduce data movement to single platformsreduce ETL overhead to perform large-scale analyticsreduce data duplication and storageexecute data processing on the best available technology, including local JVM stream processing Data application agnosticity run data tasks on multiple platforms without re-platforming the code (move jobs from Hadoop to Spark without changing the code at large)implement a sustainable AI strategy by using data pools in-situ Enterprise ready federated learning (in development) ","version":"Next","tagName":"h3"},{"title":"Releases","type":0,"sectionRef":"#","url":"/docs/start/download","content":"","keywords":"","version":"Next"},{"title":"1.1.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#110-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"1.0.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#100-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"0.7.1 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#071-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"Frameworks supported","type":1,"pageTitle":"Releases","url":"/docs/start/download#frameworks-supported","content":" Apache Flink v1.7.1Apache Giraph v1.2.0-hadoop2GraphChi v0.2.2 (only available with scala 11.x)Java Streams (version depends on the java version)JDBC-TemplatePostgres v9.4.1208 (Implementation JDBC-Template)Apache Spark v3.1.2 (scala 12.x) and v2.4.8 (scala 11.x)SQLite3 v3.8.11.2 (implementation JDBC-Template) NOTE: The supported platforms for Scala may vary depending on the Scala version.\\ ","version":"Next","tagName":"h3"},{"title":"Previous releases","type":1,"pageTitle":"Releases","url":"/docs/start/download#previous-releases","content":" Older releases can be found here ","version":"Next","tagName":"h3"},{"title":"Getting started","type":0,"sectionRef":"#","url":"/docs/guide/getting-started","content":"","keywords":"","version":"Next"},{"title":"Requirements","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#requirements","content":" Apache Wayang (incubating) is built upon the foundations of Java 11 and Scala 2.12, providing a robust and versatile platform for data processing applications. If you intend to build Wayang from source, you will also need to have Apache Maven, the popular build automation tool, installed on your system. Additionally, be mindful that some of the processing platforms supported by Wayang may have their own specific installation requirements. ","version":"Next","tagName":"h2"},{"title":"Get Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#get-wayang","content":" Apache Wayang is readily available through Maven Central, facilitating seamless integration into your development workflow. For instance, to utilize Wayang in your Maven-based project, simply add the following dependency to your project's POM file: <dependency> <groupId>org.apache.wayang</groupId> <artifactId>wayang-***</artifactId> <version>0.7.1</version> </dependency> Note the ***: Wayang ships with multiple modules that can be included in your app, depending on how you want to use it: wayang-core: provides core data structures and the optimizer (required)wayang-basic: provides common operators and data types for your apps (recommended)wayang-api: provides an easy-to-use Scala and Java API to assemble Wayang plans (recommended)wayang-java, wayang-spark, wayang-graphchi, wayang-sqlite3, wayang-postgres: adapters for the various supported processing platformswayang-profiler: provides functionality to learn operator and UDF cost functions from historical execution data For the sake of version flexibility, you still have to include your Hadoop (hadoop-hdfs and hadoop-common) and Spark (spark-core and spark-graphx) version of choice. In addition, you can obtain the most recent snapshot version of Wayang via Sonatype's snapshot repository. Just included <repositories> <repository> <id>sonatype-snapshots</id> <name>Sonatype Snapshot Repository</name> <url>https://oss.sonatype.org/content/repositories/snapshots</url> </repository> <repositories> If you need to rebuild Wayang, e.g., to use a different Scala version, you can simply do so via Maven: Adapt the version variables (e.g., spark.version) in the main pom.xml file.Build Wayang with the adapted versions. $ mvn clean install Note the standalone profile to fix Hadoop and Spark versions, so that Wayang apps do not explicitly need to declare the corresponding dependencies. Also, note the distro profile, which assembles a binary Wayang distribution. To activate these profiles, you need to specify them when running maven, i.e., mvn clean install -P<profile name> ","version":"Next","tagName":"h3"},{"title":"Configure Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#configure-wayang","content":" To enable Apache Wayang's smooth operation, you need to equip it with details about your processing platforms' capabilities and how to interact with them. A default configuration is available for initial testing, but creating a properties file is generally preferable for fine-tuning the configuration to suit your specific requirements. To harness this personalized configuration effortlessly, launch your application via $ java -Dwayang.configuration=url://to/my/wayang.properties ... Essential configuration settings: General settings wayang.core.log.enabled (= true): whether to log execution statistics to allow learning better cardinality and cost estimators for the optimizerwayang.core.log.executions (= ~/.wayang/executions.json) where to log execution times of operator groupswayang.core.log.cardinalities (= ~/.wayang/cardinalities.json) where to log cardinality measurementswayang.core.optimizer.instrumentation (= org.apache.wayang.core.profiling.OutboundInstrumentationStrategy): where to measure cardinalities in Wayang plans; other options are org.apache.wayang.core.profiling.NoInstrumentationStrategy and org.apache.wayang.core.profiling.FullInstrumentationStrategywayang.core.optimizer.reoptimize (= false): whether to progressively optimize Wayang planswayang.basic.tempdir (= file:///tmp): where to store temporary files, in particular for inter-platform communication Java Streams wayang.java.cpu.mhz (= 2700): clock frequency of processor the JVM runs on in MHzwayang.java.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to JVM in ms/MB Apache Spark spark.master (= local): Spark master various other Spark settings are supported, e.g., spark.executor.memory, spark.serializer, ... wayang.spark.cpu.mhz (= 2700): clock frequency of processor the Spark workers run on in MHzwayang.spark.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to the Spark workers in ms/MBwayang.spark.network.ms-per-mb (= 8.6): average network throughput of the Spark workers in ms/MBwayang.spark.init.ms (= 4500): time it takes Spark to initialize in ms GraphChi wayang.graphchi.cpu.mhz (= 2700): clock frequency of processor GraphChi runs on in MHzwayang.graphchi.cpu.cores (= 2): number of cores GraphChi runs onwayang.graphchi.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to GraphChi in ms/MB SQLite wayang.sqlite3.jdbc.url: JDBC URL to use SQLitewayang.sqlite3.jdbc.user: optional user namewayang.sqlite3.jdbc.password: optional passwordwayang.sqlite3.cpu.mhz (= 2700): clock frequency of processor SQLite runs on in MHzwayang.sqlite3.cpu.cores (= 2): number of cores SQLite runs on PostgreSQL wayang.postgres.jdbc.url: JDBC URL to use PostgreSQLwayang.postgres.jdbc.user: optional user namewayang.postgres.jdbc.password: optional passwordwayang.postgres.cpu.mhz (= 2700): clock frequency of processor PostgreSQL runs on in MHzwayang.postgres.cpu.cores (= 2): number of cores PostgreSQL runs on To effectively define your applications with Apache Wayang, utilize its Scala or Java API, conveniently found within the wayang-api module. For clear illustrations, refer to the provided examples below. ","version":"Next","tagName":"h3"},{"title":"Cost Functions","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#cost-functions","content":" Wayang provides a utility to learn cost functions from historical execution data. Specifically, Wayang can learn configurations for load profile estimators (that estimate CPU load, disk load etc.) for both operators and UDFs, as long as the configuration provides a template for those estimators. As an example, the JavaMapOperator draws its load profile estimator configuration via the configuration key wayang.java.map.load. Now, it is possible to specify a load profile estimator template in the configuration under the key <original key>.template, e.g.: wayang.java.map.load.template = {\\ "in":1, "out":1,\\ "cpu":"?*in0"\\ } This template encapsulates a load profile estimator that requires at minimum one input cardinality and one output cardinality. Furthermore, it simulates CPU load by assuming a direct relationship with the input cardinality. However, more complex functions are possible. In particular, you can use the variables in0, in1, ... and out0, out1, ... to incorporate the input and output cardinalities, respectively;operator properties, such as numIterations for the PageRankOperator implementations;the operators +, -, *, /, %, ^, and parantheses;the functions min(x0, x1, ...)), max(x0, x1, ...), abs(x), log(x, base), ln(x), ld(x);and the constants e and pi. While Apache Wayang provides templates for all execution operators, you will need to explicitly define your user-defined functions (UDFs) by specifying their cost functions, which are based on configuration parameters. This involves creating an initial specification and template for each UDF. As soon as execution data has been collected, you can initiate: java ... org.apache.wayang.profiler.ga.GeneticOptimizerApp [configuration URL [execution log]] This tool will attempt to determine suitable values for the question marks (?) within the load profile estimator templates, aligning them with the collected execution data and pre-defined configuration entries for the load profile estimators. These optimized values can then be directly incorporated into your configuration. ","version":"Next","tagName":"h2"},{"title":"Examples","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#examples","content":" For some executable examples, have a look at this repository. ","version":"Next","tagName":"h2"},{"title":"WordCount","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#wordcount","content":" Java API import org.apache.wayang.api.JavaPlanBuilder; import org.apache.wayang.basic.data.Tuple2; import org.apache.wayang.core.api.Configuration; import org.apache.wayang.core.api.WayangContext; import org.apache.wayang.core.optimizer.cardinality.DefaultCardinalityEstimator; import org.apache.wayang.java.Java; import org.apache.wayang.spark.Spark; import java.util.Collection; import java.util.Arrays; public class WordcountJava { public static void main(String[] args){ // Settings String inputUrl = "file:/tmp.txt"; // Get a plan builder. WayangContext wayangContext = new WayangContext(new Configuration()) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); JavaPlanBuilder planBuilder = new JavaPlanBuilder(wayangContext) .withJobName(String.format("WordCount (%s)", inputUrl)) .withUdfJarOf(WordcountJava.class); // Start building the WayangPlan. Collection<Tuple2<String, Integer>> wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(line -> Arrays.asList(line.split("\\\\W+"))) .withSelectivity(10, 100, 0.9) .withName("Split words") // Filter empty tokens. .filter(token -> !token.isEmpty()) .withSelectivity(0.99, 0.99, 0.99) .withName("Filter empty words") // Attach counter to each word. .map(word -> new Tuple2<>(word.toLowerCase(), 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey( Tuple2::getField0, (t1, t2) -> new Tuple2<>(t1.getField0(), t1.getField1() + t2.getField1()) ) .withCardinalityEstimator(new DefaultCardinalityEstimator(0.9, 1, false, in -> Math.round(0.01 * in[0]))) .withName("Add counters") // Execute the plan and collect the results. .collect(); System.out.println(wordcounts); } } Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark object WordcountScala { def main(args: Array[String]) { // Settings val inputUrl = "file:/tmp.txt" // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"WordCount ($inputUrl)") .withUdfJarsOf(this.getClass) val wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(_.split("\\\\W+"), selectivity = 10).withName("Split words") // Filter empty tokens. .filter(_.nonEmpty, selectivity = 0.99).withName("Filter empty words") // Attach counter to each word. .map(word => (word.toLowerCase, 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey(_._1, (c1, c2) => (c1._1, c1._2 + c2._2)).withName("Add counters") .withCardinalityEstimator((in: Long) => math.round(in * 0.01)) // Execute the plan and collect the results. .collect() println(wordcounts) } } ","version":"Next","tagName":"h3"},{"title":"k-means","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#k-means","content":" Wayang is also capable of iterative processing, which is, e.g., very important for machine learning algorithms, such as k-means. Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.core.function.FunctionDescriptor.ExtendedSerializableFunction import org.apache.wayang.core.function.ExecutionContext import org.apache.wayang.core.optimizer.costs.LoadProfileEstimators import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark import scala.util.Random import scala.collection.JavaConversions._ object kmeans { def main(args: Array[String]) { // Settings val inputUrl = "file:/kmeans.txt" val k = 5 val iterations = 100 val configuration = new Configuration // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"k-means ($inputUrl, k=$k, $iterations iterations)") .withUdfJarsOf(this.getClass) case class Point(x: Double, y: Double) case class TaggedPoint(x: Double, y: Double, cluster: Int) case class TaggedPointCounter(x: Double, y: Double, cluster: Int, count: Long) { def add_points(that: TaggedPointCounter) = TaggedPointCounter(this.x + that.x, this.y + that.y, this.cluster, this.count + that.count) def average = TaggedPointCounter(x / count, y / count, cluster, 0) } // Read and parse the input file(s). val points = planBuilder .readTextFile(inputUrl).withName("Read file") .map { line => val fields = line.split(",") Point(fields(0).toDouble, fields(1).toDouble) }.withName("Create points") // Create initial centroids. val random = new Random val initialCentroids = planBuilder .loadCollection(for (i <- 1 to k) yield TaggedPointCounter(random.nextGaussian(), random.nextGaussian(), i, 0)).withName("Load random centroids") // Declare UDF to select centroid for each data point. class SelectNearestCentroid extends ExtendedSerializableFunction[Point, TaggedPointCounter] { /** Keeps the broadcasted centroids. */ var centroids: Iterable[TaggedPointCounter] = _ override def open(executionCtx: ExecutionContext) = { centroids = executionCtx.getBroadcast[TaggedPointCounter]("centroids") } override def apply(point: Point): TaggedPointCounter = { var minDistance = Double.PositiveInfinity var nearestCentroidId = -1 for (centroid <- centroids) { val distance = Math.pow(Math.pow(point.x - centroid.x, 2) + Math.pow(point.y - centroid.y, 2), 0.5) if (distance < minDistance) { minDistance = distance nearestCentroidId = centroid.cluster } } new TaggedPointCounter(point.x, point.y, nearestCentroidId, 1) } } // Do the k-means loop. val finalCentroids = initialCentroids.repeat(iterations, { currentCentroids => points .mapJava(new SelectNearestCentroid, udfLoad = LoadProfileEstimators.createFromSpecification( "my.udf.costfunction.key", configuration )) .withBroadcast(currentCentroids, "centroids").withName("Find nearest centroid") .reduceByKey(_.cluster, _.add_points(_)).withName("Add up points") .withCardinalityEstimator(k) .map(_.average).withName("Average points") }).withName("Loop") // Collect the results. .collect() println(finalCentroids) } } ","version":"Next","tagName":"h3"}],"options":{"id":"default"}}
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+{"searchDocs":[{"title":"Apache Kafka meets Wayang - Part 1","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-1","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#intro","content":" This article is the first of a four part series about federated data analysis using Apache Wayang. The first article starts with an introduction of a typical data colaboration scenario which will emerge in our digital future. In part two and three we will share a summary of our Apache Kafka client implementation for Apache Wayang. We started with the Java Platform (part 2) and the Apache Spark implementation follows (W.I.P.) in part three. The use case behind this work is an imaginary data collaboration scenario. We see this example and the demand for a solution already in many places. For us this is motivation enough to propose a solution. This would also allow us to do more local data processing, and businesses can stop moving data around the world, but rather care about data locality while they expose and share specific information to others by using data federation. This reduces complexity of data management and cost dramatically. For this purpose, we illustrate a cross organizational data sharing scenario from the finance sector soon. This analysis pattern will also be relevant in the context of data analysis along supply chains, another typical example where data from many stakeholder together is needed but never managed in one place, for good reasons. Data federation can help us to unlock the hidden value of all those isolated data lakes. ","version":null,"tagName":"h2"},{"title":"A cross organizational data sharing scenario","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#a-cross-organizational-data-sharing-scenario","content":" Our goal is the implementation of a cross organization decentralized data processing scenario, in which protected local data should be processed in combination with public data from public sources in a collaborative manner. Instead of copying all data into a central data lake or a central data platform we decided to use federated analytics. Apache Wayang is the tool we work with. In our case, the public data is hosted on publicly available websites or data pods. A client can use the HTTP(S) protocol to read the data which is given in a well defined format. For simplicity we decided to use CSV format. When we look into the data of each participant we have a different perspective. Our processing procedure should calculate a particular metric on the local data of each participant. An example of such a metric is the average spending of all users on a particular product category per month. This can vary from partner to partner, hence, we want to be able to calculate a peer-group comparison so that each partner can see its own metric compared with a global average calculated from contributions by all partners. Such a process requires global averaging and local averaging. And due to governance constraints, we can’t bring all raw data together in one place. Instead, we want to use Apache Wayang for this purpose. We simplify the procedure and split it into two phases. Phase one is the process, which allows each participant to calculate the local metrics. This requires only local data. The second phase requires data from all collaborating partners. The monthly sum and counter values per partner and category are needed in one place by all other parties. Hence, the algorithm of the first phase stores the local results locally, and the contributions to the global results in an externally accessible Kafka topic. We assume this is done by each of the partners. Now we have a scenario, in which an Apache Wayang process must be able to read data from multiple Apache Kafka topics from multiple Apache Kafka clusters but finally writes into a single Kafka topic, which then can be accessed by all the participating clients. The illustration shows the data flows in such a scenario. Jobs with red border are executed by the participants in isolation within their own data processing environments. But they share some of the data, using publicly accessible Kafka topics, marked by A. Job 4 is the Apache Wayang job in our focus: here we intent to read data from 3 different source systems, and write results into a fourth system (marked as B), which can be accesses by all participants again. With this in mind we want to implement an Apache Wayang application which implements the illustrated Job 4. Since as of today, there is now KafkaSource and KafkaSink available in Apache Wayang, an implementation of both will be our first step. Our assumption is, that in the beginning, there won’t be much data. Apache Spark is not required to cope with the load, but we expect, that in the future, a single Java application would not be able to handle our workload. Hence, we want to utilize the Apache Wayang abstraction over multiple processing platforms, starting with Java. Later, we want to switch to Apache Spark. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 2","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-2","content":"","keywords":"","version":null},{"title":"Apache Wayang’s Read & Write Path for Kafka topics","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#apache-wayangs-read--write-path-for-kafka-topics","content":" To describe the read and write paths for data in the context of the created Apache Wayang code snippet, the primary classes and interfaces we need to understand are as follows: WayangContext: This class is essential for initializing the Wayang processing environment. It allows you to configure the execution environment and register plugins that define which platforms Wayang can use for data processing tasks, such as Java.basicPlugin() for local Java execution. JavaPlanBuilder: This class is used to build and define the data processing pipeline (or plan) in Wayang. It provides a fluent API to specify the operations to be performed on the data, from reading the input to processing it and writing the output. ","version":null,"tagName":"h2"},{"title":"Read Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#read-path","content":" The read path describes how data is ingested from a source into the Wayang processing pipeline: Reading from Kafka Topic: The method readKafkaTopic(topicName) is used to ingest data from a specified Kafka topic. This is the starting point of the data processing pipeline, where topicName represents the name of the Kafka topic from which data is read. Data Tokenization and Preparation: Once the data is read from Kafka, it undergoes several transformations such as Splitting, Filtering, and Mapping. What follows are the procedures known as Reducing, Grouping, Co-Grouping, and Counting. ","version":null,"tagName":"h3"},{"title":"Write Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#write-path","content":" Writing to Kafka Topic: The final step in the pipeline involves writing the processed data back to a Kafka topic using .writeKafkaTopic(...). This method takes parameters that specify the target Kafka topic, a serialization function to format the data as strings, and additional configuration for load profile estimation, which optimizes the writing process. This read-write path provides a comprehensive flow of data from ingestion from Kafka, through various processing steps, and finally back to Kafka, showcasing a full cycle of data processing within Apache Wayang's abstracted environment and is implemented in our example program shown in listing 1. ","version":null,"tagName":"h3"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#implementation-of-input--and-output-operators","content":" The next section shows how a new pair of operators can be implemented to extend Apache Wayang’s capabilities on the input and output side. We created the Kafka Source and Kafka Sink components so that our cross organizational data collaboration scenario can be implemented using data streaming infrastructure. Level 1 – Wayang execution plan with abstract operators The implementation of our Kafka Source and Kafka Sink components for Apache Wayang requires new methods and classes on three layers. First of all in the API package. Here we use the JavaPlanBuilder to expose the function for selecting a Kafka topic as the source to be used by client. The class JavaPlanBuilder in package org.apache.wayang.api in the project wayang-api/wayang-api-scala-java exposes our new functionality to our external client. An instance of the JavaPlanBuilder is used to define the data processing pipeline. We use its readKafkaTopic() which specifies the source Kafka topic to read from, and for the write path we use the writeKafkaTopic() method. Both Methods do only trigger activities in the background. For the output side, we use the DataQuantaBuilder class, which offers an implementation of the writeKafkaTopic function. This function is designed to send processed data, referred to as DataQuanta, to a specified Kafka topic. Essentially, it marks the final step in a data processing sequence constructed using the Apache Wayang framework. In the DataQuanta class we implemented the methods writeKafkaTopic and writeKafkaTopicJava which use the KafkaTopicSink class. In this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation The second layer builds the bridge between the WayangContext and PlanBuilders which work together with DataQuanta and the DataQuantaBuilder. Also, the mapping between the abstract components and the specific implementations are defined in this layer. Therefore, the mappings package has a class Mappings in which all relevant input and output operators are listed. We use it to register the KafkaSourceMapping and a KafkaSinkMapping for the particular platform, Java in our case. These classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In our case this leads to a Java program running on a JVM which is set up by the Apache Wayang framework using the logical components of the execution plan. Those mappings link the real implementation of our operators the ones used in an execution plan. The JavaKafkaTopicSource and the JavaKafkaTopicSink extend the KafkaTopicSource and KafkaTopicSink so that the lower level implementation of those classes become available within Wayang’s Java Platform context. In this layer, the KafkaConsumer class and the KafkaProducer class are used, but both are configured and instantiated in the next layer underneath. All this is done in the project wayang-plarforms/wayang-java. Layer 3 – Input/Output Connector Layer The KafkaTopicSource and KafkaTopicSink classes build the third layer of our implementation. Both are implemented in Java programming language. In this layer, the real Kafka-Client logic is defined. Details about consumer and producers, client configuration, and schema handling have to be handled here. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#summary","content":" Both classes in the third layer implement the Kafka client logic which is needed by the Wayang-execution plan when external data flows should be established. The layer above handles the mapping of the components at startup time. All this wiring is needed to keep Wayang open and flexible so that multiple external systems can be used in a variety of combinations and using multiple target platforms in combinations. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#outlook","content":" The next part of the article series will cover the creation of an Kafka Source and Sink component for the Apache Spark platform, which allows our use case to scale. Finally, in part four we bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 3","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-3","content":"","keywords":"","version":null},{"title":"The goal of this implementation","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#the-goal-of-this-implementation","content":" We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. In our example scenario, data is available in multiple different clusters, in different regions and owned by different organizations. Each organization uses the "stream sharing" feature provided by Confluent cloud. This way, the operator of our central processing job has been granted appropriate permissions. The plaftorm provided the necessary configuration properties, including access coordinates and credentials in the name of the topic owner to us. The following illustration has already been introduced in part one of the blog series, but for clarity we repeat it here. Today, we focus on Job 4 in the image. We implement a program which uses data federation based on multiple sources. Each source allows us to read the data from that particular topic so that we can process it in a different governance context. In this example it is a public processing context, in which data from multiple private processing contexts are used together. This use case is already prepared for high processing loads We can utilize the scalability capabilities of Apache Spark or simply use a Java program for initial tests of the solution. Switching between both is done in one line of code in Apache Wayang. Again, we start with a WayangContext, as shown by examples in the Wayang code repository. WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin()); We simply switched the backend system towards Apache Spark by using the WayangContext with Spark.basicPlugin(). The JavaPlanBuilder and all other logic of our example job won't be touched. In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module. ","version":null,"tagName":"h2"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#implementation-of-input--and-output-operators","content":" We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. Hence we work with Wayang's Java API. Level 1 – Wayang execution plan with abstract operators Since the JavaPlanBuilder already exposes the function for selecting a Kafka topic as source and the DataQuantaBuilder class exposes the writeKafkaTopic function we can move on quickly. Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder. We must provide the mapping between the abstract components and the specific implementations in this layer. Therefore, the mappings package in project wayang-platforms/wayang-spark has a class Mappings in which our KafkaTopicSinkMapping and KafkaTopicSourceMapping will be registered. Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime. A mapping links an operator implementation to the abstraction used in an execution plan. We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round. For the Spark platform we simply replace the occurences of JavaPlatform with SparkPlatform. Furthermore, we create an implementation of the SparkKafkaTopicSource and SparkKafkaTopicSink. Layer 3 – Input/Output Connector Layer Let's quickly recap, how does Apache Spark interacts with Apache Kafka? There is already an integration which gives us a DataSet using the Spark SQL framework. For Spark Streaming, there is also a Kafka integration using the SparkSession's readStream() function. Kafka client properties are provided as key value pairs k and v by using the option( k, v ) function. For writing into a topic, we can use the writeStream() function. But from a first look, it seems to be not the best fit. Another approach is possible. We can use simple RDDs to process data previously consumed from Apache Kafka. This is a more low-level approach compared to using Datasets with Spark Structured Streaming, and it typically involves using the Kafka RDD API provided by Spark. This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. However, we might need that approach for the integration with Apache Wayang. For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then we parallelize the records in an RDD. This allows us to reuse KafkaTopicSource and KafkaTopicSink classes we built last time. Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer. The selected approach does not yet fully take advantage from Spark's parallelism at load time. For higher loads and especially for streaming processing we would have to investigate another approache, using a SparkStreamingContext, but this is out of scope for now. Since we can't reuse the JavaKafkaTopicSource and JavaKafkaTopicSink we rather implement SparkKafkaTopicSource and SparkKafkaTopicSink based on given SparkTextFileSource and SparkTextFileSink which both cary all needed RDD specific logic. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#summary","content":" As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. We could easily follow the pattern we have worked out in the previous exercise. But a bunch of much more interesting work will follow next. More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well. The code has been submitted to the Apache Wayang repository. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#outlook","content":" The next part of the article series will cover the real world example as described in image 1. We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Wayang and the Federated AI","type":0,"sectionRef":"#","url":"/blog/wayang-federated-ai","content":"","keywords":"","version":null},{"title":"Apache Wayang in the Federated AI world","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#apache-wayang-in-the-federated-ai-world","content":" Apache Wayang (Wayang, for short), a project in an incubation phase at Apache Software Foundation (ASF), integrates big data platforms and tools by removing the complexity of worrying about low-level details. Interestingly, even if it was not designed for, Wayang could also serve as a scalable platform for federated learning: the Wayang community is starting to work on integrating federated learning capabilities. In a federated learning approach, Wayang would allow different local models to be built and exchange its model results across other data centers to combine them into a single enhanced model. ","version":null,"tagName":"h2"},{"title":"A real-world example","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#a-real-world-example","content":" Let's consider a real-world scenario. Hospitals and health organizations have increased their investments in machine/deep learning initiatives to learn more and predict diagnostics. However, due to legal frameworks, sharing patients' information or diagnostics is impossible, and the solution would be to apply federated learning. To solve this problem, we could use Wayang to help to train the models. See the diagram 1 below: As a first step, the data scientists would send an ML task to Wayang, which will work as an abstraction layer to connect to different data processing platforms, sparing the time to build integration code for each. Then, the data platforms process and generate the results that will be sent back to Wayang. Wayang aggregates the results into one "global result" and sends it back to the requestor as a next step. The process repeats until the desired results are achieved. Although it is very much like a Federated learning pipeline, Wayang removes a considerable layer of complexity from the developers by integrating with diverse types of data platforms. It also brings fast development and reduces the need for a deep understanding of data infrastructure or integrations. Developers can focus on the logic and how to execute tasks instead of details about data processors. ","version":null,"tagName":"h2"},{"title":"Follow Wayang","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#follow-wayang","content":" Apache Wayang is in an incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. Thank you! I (Gláucia) want to thank professor Jorge Quiané for the guidance to write this blog post. Thanks for incentivate me to join the project and for the knowledge shared. I will always remember you. ","version":null,"tagName":"h3"},{"title":"Pywayang - Apache Wayang's Python API","type":0,"sectionRef":"#","url":"/blog/wayang-python-api","content":"","keywords":"","version":null},{"title":"Expanding Apache Wayang's APIs","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#expanding-apache-wayangs-apis","content":" Apache Wayang's architecture decouples the process of planning from the resulting execution, allowing users to specify platform agnostic plans through the provided APIs. Python's popularity and convenience for data processing workloads makes it an obvious candidate for a desired API. Previous APIs, such as the Scala API wayang-api-scala-java benefited from the interoperability of Java and Scala that allows to reuse objects from other languages to provide new interfaces. Accessing JVM objects in Python is possible through several libraries, but in doing so, future APIs in other programming languages would need similar libraries and implementations in order to exist. As a contrast to that, providing an API within Apache Wayang that receives input plans from any source and executes them within allows to create plans and submit them in any programming language. The following figure shows the architecture of pywayang: The Python API allows users to specify WayangPlans with UDFs in Python.pywayang then serializes the UDFs and constructs the WayangPlan in JSON format, preparing it to be sent to Apache Wayang's JSON API. When receiving a valid JSON plan, the JSON API uses the optimizer to construct an execution plan. However, since UDFs are defined in Python and thus need to be executed in Python as well, an operators function needs to be wrapped into a WrappedPythonFunction: val mapOperator = new MapPartitionsOperator[Input, Output]( new MapPartitionsDescriptor[Input, Output]( new WrappedPythonFunction[Input, Output]( ByteString.copyFromUtf8(udf) ), classOf[Input], classOf[Output], ) ) This wrapped functional descriptor allows to handle execution of UDFs in Python through a socket connection with the pywayang worker. Input data is sourced from the platform chosen by the optimizer and Apache Wayang handles routing the output data to the next operator. A new API in any programming languages would have to specify two things: A way to create plans that conform to a JSON format specified in the Wayang JSON API.A worker that handles encoding and decoding of user defined functions (UDFs), as they need to be executed on iterables in their respective language. After that, the API can be added as a module in Wayang, so that operators will be wrapped and UDFs can be executed in the desired programming language. ","version":null,"tagName":"h2"},{"title":"Defining WayangPlans in Python","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#defining-wayangplans-in-python","content":" As the "Hello World!" of data processing systems, wordcount will pose as our primary example to display how users can interact with Apache Wayang through the python package pywayang. from pywy.dataquanta import WayangContext from pywy.platforms.java import JavaPlugin from pywy.platforms.spark import SparkPlugin def wordcount(): ctx = WayangContext() \\ .register({JavaPlugin, SparkPlugin}) \\ .textfile("file://README.md") \\ .flatmap(lambda w: w.split()) \\ .filter(lambda w: w.strip() != "") \\ .map(lambda w: (w.lower(), 1)) \\ .reduce_by_key(lambda t: t[0], lambda t1, t2: (t1[0], int(t1[1]) + int(t2[1]))) \\ .store_textfile("file:///wordcount-out-python.txt") if __name__ == "__main__": wordcount() The example displays a mode of operation that resembles the ScalaPlanBuilder and the JavaPlanBuilder. Plans are specified in a functional way, chaining operations until a terminal operation results in execution of the plan. ","version":null,"tagName":"h2"},{"title":"Wayang-API-JSON","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wayang-api-json","content":" The wayang-api-json module provides an executable that starts a REST server. This server accepts a WayangPlan in JSON format. Starting the REST API as a background process can be done by executing the following: mvn clean package -pl :wayang-assembly -Pdistribution cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.api.json.Main & ","version":null,"tagName":"h2"},{"title":"Wrapping pipelines in MapPartition operators","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wrapping-pipelines-in-mappartition-operators","content":" With this architecture, the execution of an operator comes with an additional overhead, because the UDFs will have to be executed in python. Python operators receive iterators through a socket and also return their result to Wayang through that connection. To minimize the overhead, unary operators that return unary results will be grouped in pipelines. One pipeline of operators will be submitted to the Wayang JSON API as a single MapPartition operator. This means that the UDFs specified in this pipeline can be chained and only on call from Wayang to the Python worker will have to be made for a given pipeline. ","version":null,"tagName":"h2"},{"title":"Coming soon","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#coming-soon","content":" As the Python API is currently in development and we are applying finishing touches, this article serves as an outlook for what users can expect to see soon. Author: juripetersen ","version":null,"tagName":"h2"},{"title":"Integrating ML platforms in Wayang","type":0,"sectionRef":"#","url":"/blog/wayang-tensorflow","content":"","keywords":"","version":null},{"title":"Step 1: Introducing a Model","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-1-introducing-a-model","content":" With respect to the model, we followed Wayang’s abstraction philosophy: We created a Model interface to be used as input or output by Wayang operators and then extended it for the platform-specific operators. Different model interfaces can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model A platform-specific model needs to be instantiated to be used as the output of a training operator and as input for an inference operator. You can see an example of the SparkMLModel here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/model/SparkMLModel.java ","version":null,"tagName":"h2"},{"title":"Step 2: Introducing Training Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-2-introducing-training-operators","content":" We added the desired Wayang (platform-agnostic) training operators which are binary to unary operators, taking as input the X and y values and outputting a Model. You can find an example of a LinearRegressionOperator here: https://github.com/apache/incubator-wayang/blob/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/LinearRegressionOperator.java Platform-specific execution operators, such as SparkLinearRegressionOperator, can be easily added as any other execution operator: extending the corresponding Wayang operator and providing the mappings from the Wayang to the execution operator. See, for example, the SparkLinearRegressionOperator: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkLinearRegressionOperator.java ","version":null,"tagName":"h2"},{"title":"Step 3: Introducing Prediction Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-3-introducing-prediction-operators","content":" Additionally, we created a PredictOperator, a BinaryToUnary Wayang (platform-agnostic) operator which takes as input the data quanta and a model and outputs the data quanta with the predictions output by the model. https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/PredictOperator.java Then, a concrete platform-specific operator extends from the abstract one. See the SparkPredictOperator for an example: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkPredictOperator.java ","version":null,"tagName":"h2"},{"title":"Deep Learning Models","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#deep-learning-models","content":" Unlike traditional machine learning models, the definition of deep learning models is more flexible. Users can combine different blocks (e.g., fully connected blocks, convolutional blocks) to build their desired models. The whole model can be represented as a graph on which the vertices represent blocks and the edges represent connections between blocks. In this case, we built a DLModel class that implements the Model interface, which contains a user-defined, platform-agnostic graph of the model: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model/DLModel.java For training, we implemented the platform-agnostic DLModelTrainingOperator Wayang operator: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/DLTrainingOperator.java ","version":null,"tagName":"h2"},{"title":"New ML platform -- Tensorflow Integration","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#new-ml-platform----tensorflow-integration","content":" We have added Tensorflow as a new platform by creating a new module (wayang-tensorflow) inside the wayang-platforms parent module and implementing a Tensorflow driver. The TensorflowExecutor driver is responsible for creating and destroying Tensorflow resources, such as a model graph and a model parameter context. When a training task scheduled on Tensorflow, it will be mapped to TensorflowDLModelTrainingOperator. In this process, the DLModel will be converted to TensorflowModel, which means that the user-defined model graph will be converted to a Tensorflow model graph. Likewise, for inference, the PredictOperator will be mapped to TensorflowPredictOperator. All the code for the tensorflow platform can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-tensorflow/src/main/java/org/apache/wayang/tensorflow ","version":null,"tagName":"h2"},{"title":"Acknowledgement","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#acknowledgement","content":" The source code for the support of ML operators and the Tensorflow integration has been contributed by Mingxi Liu. ","version":null,"tagName":"h3"},{"title":"Follow Wayang","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#follow-wayang","content":" Apache Wayang is in incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. ","version":null,"tagName":"h3"},{"title":"Apache Wayang vs. Presto/Trino","type":0,"sectionRef":"#","url":"/blog/wayang-vs-trino","content":"","keywords":"","version":null},{"title":"Key Distinctions","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#key-distinctions","content":" Trino/Presto is a query engine for distributed SQL query processing. It is composed of a coordinator and multiple workers. The coordinator consists of a query optimizer and a scheduler, while the workers are responsible for performing the necessary query processing. Data is fetched from external systems via a Connector API, i.e., Trino/Presto supports multiple data sources. Notably,query processing is is conducted exclusively by Trino/Presto workers, not the external systems. In contrast, Wayang is a middleware for integrating diverse data platforms, including but not limited to query engines. This means that Wayang leverages the processing capabilities of the underlying data platforms to complete a given job, with no actual query processing taking place within Wayang itself. Below you can graphically see the difference between the two systems. Note that not all available data sources or data platforms are illustrated for simplicity reasons. Below you can see how Wayang integrates data platforms and utilizes them for any data processing required. Below you can see how Trino unifies different data sources and then performs data processing in a distributed manner. I hope this makes it clear now. In fact, Trino can be easily plugged to Wayang as a platform and be seamlessly integrated with other data platforms, as shown below. ","version":null,"tagName":"h2"},{"title":"What are the advantages of using Wayang?","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#what-are-the-advantages-of-using-wayang","content":" Wayang brings several benefits thanks to its integration layer: Seamless integration of SQL query engines with ML and other data analysis systems within a single job, eliminating the need to materialize intermediate results. Users are freed from the task of specifying the query engines for an application if they desire. By submitting their Wayang job, the cross-platform optimizer can automatically determine the best data platform to use for improved performance or cost savings. Wayang facilitates cross-platform data processing by utilizing multiple data platforms to execute a query for a single job, optimizing performance and cost efficiency. Data does not have to be transferred outside their original location. ","version":null,"tagName":"h2"},{"title":"Conclusion","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#conclusion","content":" Trino is a distributed SQL query engine which performs all the query processing of an input SQL query in a distributed manner. Wayang, on the other hand, is a data platform integrator which can automatically determine which data platform(s) is best suited for an application. Author: zkaoudi ","version":null,"tagName":"h2"},{"title":"Website updated","type":0,"sectionRef":"#","url":"/blog/website_update","content":"","keywords":"","version":null},{"title":"Cheatsheet","type":1,"pageTitle":"Website updated","url":"/blog/website_update#cheatsheet","content":" List: Line one Line one.oneLine one.two Line two Line two.oneLine two.two Line three ...... Another style for a list: Line oneLine twoLine three ","version":null,"tagName":"h2"},{"title":"Becoming a committer","type":0,"sectionRef":"#","url":"/docs/community/committer","content":"","keywords":"","version":"Next"},{"title":"Sustained contributions to Wayang:","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#sustained-contributions-to-wayang","content":" Committers should have a history of major contributions to Wayang. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within Wayang's engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of Spark. Note that contributions to critical parts of Spark, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement. The type and level of contributions considered may vary by project area – for example, we greatly encourage contributors who want to work on mainly the documentation, or mainly on platform support for specific OSes, storage systems, etc. The (P)PMC also adds new (P)PMC members. (P)PMC members are expected to carry out (P)PMC responsibilities as described in Apache Guidance, including helping vote on releases, enforce Apache project trademarks, take responsibility for legal and license issues, and ensure the project follows Apache project mechanics. The PMC periodically adds committers to the PMC who have shown they understand and can help with these activities. ","version":"Next","tagName":"h3"},{"title":"Review process","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#review-process","content":" All contributions should be reviewed before merging as described in Contributing to Wayang. In particular, if you are working on an area of the codebase you are unfamiliar with, look at the Git history for that code to see who reviewed patches before. You can do this using git log --format=full <filename>, by examining the “Commit” field to see who committed each patch. ","version":"Next","tagName":"h3"},{"title":"When to commit/merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#when-to-commitmerge-a-pull-request","content":" PRs shall not be merged during active, on-topic discussion unless they address issues such as critical security fixes of a public vulnerability. Under extenuating circumstances, PRs may be merged during active, off-topic discussion and the discussion directed to a more appropriate venue. Time should be given prior to merging for those involved with the conversation to explain if they believe they are on-topic. Lazy consensus requires giving time for discussion to settle while understanding that people may not be working on Wayang as their full-time job and may take holidays. It is believed that by doing this, we can limit how often people feel the need to exercise their veto. All -1s with justification merit discussion. A -1 from a non-committer can be overridden only with input from multiple committers, and suitable time must be offered for any committer to raise concerns. A -1 from a committer who cannot be reached requires a consensus vote of the (P)PMC under ASF voting rules to determine the next steps within the ASF guidelines for code vetoes. The Wayang project typically uses a 72h time window to conclude votes. These policies serve to reiterate the core principle that code must not be merged with a pending veto or before a consensus has been reached (lazy or otherwise). It is the (P)PMC’s hope that vetoes continue to be infrequent, and when they occur, that all parties will take the time to build consensus prior to additional feature work. Being a committer means exercising your judgement while working in a community of people with diverse views. There is nothing wrong in getting a second (or third or fourth) opinion when you are uncertain. Thank you for your dedication to the Wayang project; it is appreciated by the developers and users of Wayang. It is hoped that these guidelines do not slow down development; rather, by removing some of the uncertainty, the goal is to make it easier for us to reach consensus. If you have ideas on how to improve these guidelines or other Wayang project operating procedures, you should reach out on the dev@ list to start the discussion. ","version":"Next","tagName":"h2"},{"title":"How we merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#how-we-merge-a-pull-request","content":" Changes pushed to the master branch on Apache cannot be removed; that is, we can’t force-push to it. So please don’t add any test commits or anything like that, only real patches. We typically enforce a review from minimum two committers, who are ideally owners of submodules, like ML4ALL or JDBC. They investigate the PR, add remarks and request changes. Please be not insultet in any way, the team needs to make sure that all code committed to main has a igh quality and does not break current functionality. Ask dev@ if you have trouble with these steps, or want help doing your first merge. Once a PR is merged please leave a comment on the PR stating which branch(es) it has been merged with. ","version":"Next","tagName":"h2"},{"title":"Policy on backporting bug fixes","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#policy-on-backporting-bug-fixes","content":" We go in line with pwendell (Apache Spark PMC): The trade off when backporting is you get to deliver the fix to people running older versions (great!), but you risk introducing new or even worse bugs in maintenance releases (bad!). The decision point is when you have a bug fix and it’s not clear whether it is worth backporting. I think the following facets are important to consider: Backports are an extremely valuable service to the community and should be considered for any bug fix.Introducing a new bug in a maintenance release must be avoided at all costs. It over time would erode confidence in our release process.Distributions or advanced users can always backport risky patches on their own, if they see fit.For me, the consequence of these is that we should backport in the following situations: Both the bug and the fix are well understood and isolated. Code being modified is well tested.The bug being addressed is high priority to the community.The backported fix does not vary widely from the master branch fix.We tend to avoid backports in the converse situations: The bug or fix are not well understood. For instance, it relates to interactions between complex components or third party libraries (e.g. Hadoop libraries). The code is not well tested outside of the immediate bug being fixed. The bug is not clearly a high priority for the community. The backported fix is widely different from the master branch fix. ","version":"Next","tagName":"h3"},{"title":"Mailing List","type":0,"sectionRef":"#","url":"/docs/community/mailinglist","content":"","keywords":"","version":"Next"},{"title":"How to subscribe to a mailing list","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#how-to-subscribe-to-a-mailing-list","content":" Send an email without any contents or subject to LISTNAME-subscribe@wayang.apache.org. (replace LISTNAME with dev, commits, ..) Wait till you receive an email with the subject “confirm subscribe to LISTNAME@wayang.apache.org”. Reply to that email, without editing the subject or including any contents Wait till you receive an email with the subject “WELCOME to LISTNAME@wayang.apache.org”. If you send us an email with a code snippet, make sure that: you do not link to files in external services as such files can change, get deleted, or the link might break and thus make an archived email thread uselessyou paste text instead of screenshots of textyou keep formatting when pasting code in order to keep the code readablethere are enough import statements to avoid ambiguitiesif you are using some platform, please specify the version ","version":"Next","tagName":"h2"},{"title":"Watch the email without subscribe","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#watch-the-email-without-subscribe","content":" These two channels are public mailing list, and you can get access to them on Website without subscribing. Emails of commits@wayang.apache.orgEmails of dev@wayang.apache.org ","version":"Next","tagName":"h2"},{"title":"Apache Wayang Release Odysse","type":0,"sectionRef":"#","url":"/blog/wayang-release-odysse","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#intro","content":" The ASF provides a robust infrastructure for open communities of software developers. We can share ideas, combine forces, contribute code, docs, review-energy, art work, and from time to time we can nail it down. A release defines an intermediate result of the continuous community work. How we do such a release in the Apache Wayang team is an essential aspect towards graduation. First of all, there are some references to take into account, such as: https://www.apache.org/legal/release-policy.htmlhttps://maven.apache.org/maven-release/maven-release-plugin/ Assuming you are (P)PMC, and assuming that you have the right permissions for such a release, you can follow the path as described in this guide: https://plc4x.apache.org/developers/release/release.html I tried to follow exactly this procedure, several times. I failed. Here I share the current status of my release attempts. I plan a longer tour, and do not want to block the project for a long time. Hence I create this draft, and I hope we can unblock this project as soon as possible. ","version":null,"tagName":"h2"},{"title":"Status:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#status","content":" I am not able to conduct the mvn release:perform step. Anything before worked, sometimes only after some digging, but it worked. We assume, that due to my membership in two ASF incubator projects I am not able to upload the artefacts to the Nexus repository (H1). It can be, that I have not the correct user and password in my settings.xml file (H2). But I tested a manual login to the nexus server https://repository.apache.org/service/local/staging/deploy/maven2 with success. And beyond that I have no idea how I can verify this detail alone. ","version":null,"tagName":"h2"},{"title":"Idea / Proposal","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#idea--proposal","content":" (1) It would be great, if someone - who has done a release in any other ASF project or in Apache Wayang - could follow the steps I share, so that we can check where the problem hides itself. (2) As a follow-up task, I suggest to add a Release Guide to the Apache Wayang project, including release manager onboarding steps, and checklists for the particular project, derived from the referenced sources which are listed above. But for now it is all about sharing the status (as I did serveral times on multiple chanels, including JIRA, Slack, Mailing lists) and finding a solution for Apache Wayang release 1.0. ","version":null,"tagName":"h2"},{"title":"Latest Error:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#latest-error","content":" mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h2"},{"title":"Activity Log","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#activity-log","content":" mvn release:clean mvn versions:set -DnewVersion=1.0.0-RC2 mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: You don't have a SNAPSHOT project in the reactor projects list. -> [Help 1] mvn versions:commit mvn versions:set -DnewVersion=1.0.0-RC2-SNAPSHOT mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Cannot prepare the release because you have local modifications : git status git add . git commit -m "prepare for release 1.0.0-RC2-SNAPSHOT" git push mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -DdryRun=true mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -XXX Caused by: org.eclipse.aether.transfer.NoRepositoryConnectorException: Blocked mirror for repositories: [repository.jboss.org (http://repository.jboss.org/nexus/content/groups/public/, default, releases)] ","version":null,"tagName":"h2"},{"title":"Dependency on JDK-11 during release","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#dependency-on-jdk-11-during-release","content":" FIXED with local JDK11 Setup brew install openjdk@11 curl -s "https://get.sdkman.io" | bash source "$HOME/.sdkman/bin/sdkman-init.sh" which java which java /Users/kamir/.sdkman/candidates/java/current/bin/java ➜ GITHUB.active export JAVA_HOME=/Users/kamir/.sdkman/candidates/java/current ➜ GITHUB.active mvn clean -XXX export JAVA_HOME= sdk install java 11.0.24-amzn sdk home java 11.0.24-amzn /usr/libexec/java_home -v 11 jenv add /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home jenv global 11.0 jenv shell 11.0 jenv local 11.0 java -version ","version":null,"tagName":"h3"},{"title":"Manual update of release-version","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#manual-update-of-release-version","content":" During the release procedure, do I have to set the version here in this configuration section manually? <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-release-plugin</artifactId> <version>3.0.1</version> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>0.7.1</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>wayang-0.7.1</tag> <scmReleaseCommitComment>@{prefix} prepare release 0.7.1</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> </plugin> It seems that these properties must be updated manually. ","version":null,"tagName":"h3"},{"title":"Warning regarding \"illegal reflective access operation\"","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#warning-regarding-illegal-reflective-access-operation","content":" [ERROR] WARNING: An illegal reflective access operation has occurred [ERROR] WARNING: Illegal reflective access by org.codehaus.groovy.reflection.CachedClass (file:/Users/mkaempf/.m2/repository/org/codehaus/groovy/groovy-all/2.4.9/groovy-all-2.4.9.jar) to method java.lang.Object.finalize() [ERROR] WARNING: Please consider reporting this to the maintainers of org.codehaus.groovy.reflection.CachedClass [ERROR] WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations [ERROR] WARNING: All illegal access operations will be denied in a future release This is still an OPEN ISSUE ! ","version":null,"tagName":"h3"},{"title":"RAT Check fails","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#rat-check-fails","content":" [INFO] [ERROR] Failed to execute goal org.apache.rat:apache-rat-plugin:0.13:check (license-check) on project wayang: Too many files with unapproved license: 1 See RAT report in: /Users/mkaempf/GITHUB.private/incubator-wayang/target/rat.txt -> [Help 1] ***************************************************** Printing headers for text files without a valid license header... ===================================================== == File: .java-version ===================================================== 11.0 FIXED by adding .java-versions to .gitignore ","version":null,"tagName":"h3"},{"title":"Tag could not be created in SCM.","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#tag-could-not-be-created-in-scm","content":" [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Unable to tag SCM [ERROR] Provider message: [ERROR] The git-tag command failed. [ERROR] Command output: [ERROR] fatal: tag 'wayang-0.7.1' already exists FIXED by manual changes in pom.xml. <scm> <connection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</connection> <developerConnection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</developerConnection> <url>https://github.com/apache/incubator-wayang</url> <tag>1.0.0-RC2-SNAPSHOT</tag> </scm> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>1.0.0-RC2-SNAPSHOT</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>1.0.0-RC2-SNAPSHOT</tag> <scmReleaseCommitComment>@{prefix} prepare release 1.0.0-RC2-SNAPSHOT</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> mvn release:prepare -Darguments='-DskipTests=True -Dresume=True' mvn clean package mvn release:perform -X -DskipTests - you need your password for the keystore to sign the build artefacts. So far so good. But now the sun went down. [INFO] [ERROR] Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException -> [Help 1] [INFO] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.plugin.MojoExecutionException: ArtifactDeployerException [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:201) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.shared.transfer.artifact.deploy.ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:126) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:278) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.transfer.ArtifactTransferException: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.connector.basic.ArtifactTransportListener.transferFailed (ArtifactTransportListener.java:44) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:417) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: java.lang.NullPointerException [INFO] at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) [INFO] at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) [INFO] at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) [INFO] at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) [INFO] at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) [INFO] at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) [INFO] at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) [INFO] at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) [INFO] at org.apache.http.impl.execchain.RetryExec.execute (RetryExec.java:89) [INFO] at org.apache.http.impl.execchain.ServiceUnavailableRetryExec.execute (ServiceUnavailableRetryExec.java:85) [INFO] at org.apache.http.impl.execchain.RedirectExec.execute (RedirectExec.java:110) [INFO] at org.apache.http.impl.client.InternalHttpClient.doExecute (InternalHttpClient.java:185) [INFO] at org.apache.http.impl.client.CloseableHttpClient.execute (CloseableHttpClient.java:72) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.execute (HttpTransporter.java:485) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.implPut (HttpTransporter.java:469) [INFO] at org.eclipse.aether.spi.connector.transport.AbstractTransporter.put (AbstractTransporter.java:107) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$PutTaskRunner.runTask (BasicRepositoryConnector.java:564) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:414) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] [ERROR] [INFO] [ERROR] [INFO] [ERROR] For more information about the errors and possible solutions, please read the following articles: [INFO] [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [INFO] [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [INFO] [DEBUG] Shutting down 'file-lock' factory [INFO] [DEBUG] Shutting down 'rwlock-local' factory [INFO] [DEBUG] Shutting down 'semaphore-local' factory [INFO] [DEBUG] Shutting down 'noop' factory [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary for Apache Wayang (incubating) 1.0.0-RC3-SNAPSHOT: [INFO] [INFO] Apache Wayang (incubating) ......................... FAILURE [ 23.626 s] [INFO] Wayang Commons ..................................... SKIPPED [INFO] wayang-utils-profile-db ............................ SKIPPED [INFO] Wayang Core ........................................ SKIPPED [INFO] Wayang Basic ....................................... SKIPPED [INFO] Wayang Platform .................................... SKIPPED [INFO] Wayang Platform Java ............................... SKIPPED [INFO] Wayang Platform Spark .............................. SKIPPED [INFO] Wayang Platform JDBC Template ...................... SKIPPED [INFO] Wayang Platform Postgres ........................... SKIPPED [INFO] Wayang Platform SQLite3 ............................ SKIPPED [INFO] Wayang Platform Giraph ............................. SKIPPED [INFO] Wayang Platform Apache Flink ....................... SKIPPED [INFO] Wayang Platform Generic Jdbc ....................... SKIPPED [INFO] Wayang API ......................................... SKIPPED [INFO] Wayang API Scala-Java .............................. SKIPPED [INFO] Wayang Integration Test ............................ SKIPPED [INFO] Wayang API Python .................................. SKIPPED [INFO] wayang-api-sql ..................................... SKIPPED [INFO] Wayang Profiler .................................... SKIPPED [INFO] Wayang Extensions .................................. SKIPPED [INFO] wayang-iejoin ...................................... SKIPPED [INFO] Wayang - Common resources .......................... SKIPPED [INFO] wayang-benchmark ................................... SKIPPED [INFO] Wayang ML4all ...................................... SKIPPED [INFO] Wayang Project Assembly ............................ SKIPPED [INFO] ------------------------------------------------------------------------ [INFO] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Total time: 24.093 s [INFO] Finished at: 2024-06-25T10:53:32+02:00 [INFO] ------------------------------------------------------------------------ [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 -> [Help 1] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.plugin.MojoExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:198) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.ReleaseExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:115) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.exec.MavenExecutorException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.exec.InvokerMavenExecutor.executeGoals (InvokerMavenExecutor.java:129) at org.apache.maven.shared.release.exec.AbstractMavenExecutor.executeGoals (AbstractMavenExecutor.java:70) at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:105) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [ERROR] [ERROR] [ERROR] For more information about the errors and possible solutions, please read the following articles: [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [DEBUG] Shutting down 'file-lock' factory [DEBUG] Shutting down 'rwlock-local' factory [DEBUG] Shutting down 'semaphore-local' factory [DEBUG] Shutting down 'noop' factory The null pointer exception indicates problems in settings.xml. This could be fixed. But now, the error changes with and I am still not able to stage the build results. mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h3"},{"title":"Checklist for next iteration:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#checklist-for-next-iteration","content":" The error you're encountering indicates that there is a problem with deploying the artifacts using the maven-deploy-plugin. The root cause of the error is a NullPointerException during the deployment process, specifically related to the org.eclipse.aether.transfer.ArtifactTransferException. Here's a step-by-step guide to troubleshoot and resolve this issue: ","version":null,"tagName":"h2"},{"title":"1. Check Maven Settings","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#1-check-maven-settings","content":" Ensure that your Maven settings (settings.xml) are correctly configured for deployment. Verify that the repository settings and credentials are correctly specified. [DONE] ","version":null,"tagName":"h3"},{"title":"2. Verify Repository URL","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#2-verify-repository-url","content":" Make sure the repository URL in your pom.xml or settings.xml is correct and reachable. The URL should point to the correct staging repository for deployment. [OPEN] - I did not touch it, so I guess it is correct in the pom.xml. ","version":null,"tagName":"h3"},{"title":"3. Maven Version Compatibility","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#3-maven-version-compatibility","content":" Verify that you are using a compatible version of Maven. Sometimes, upgrading or downgrading Maven can resolve such issues. Apache Maven 3.9.6 (bc0240f3c744dd6b6ec2920b3cd08dcc295161ae) Maven home: /usr/local/Cellar/maven/3.9.6/libexec Java version: 11.0.15.1, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home Default locale: en_DE, platform encoding: US-ASCII OS name: "mac os x", version: "13.2.1", arch: "x86_64", family: "mac" [DONE] ","version":null,"tagName":"h3"},{"title":"4. Check for Network Issues","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#4-check-for-network-issues","content":" Ensure there are no network issues that might be causing problems in connecting to the repository. Sometimes, network configurations, firewalls, or proxy settings can interfere with the deployment process. [DONE] ","version":null,"tagName":"h3"},{"title":"5. Update Maven Plugins","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#5-update-maven-plugins","content":" Ensure that you are using the latest versions of the Maven plugins. Sometimes, bugs in older versions can cause unexpected issues. [OPEN] ","version":null,"tagName":"h3"},{"title":"6. Configure the maven-deploy-plugin in pom.xml","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#6-configure-the-maven-deploy-plugin-in-pomxml","content":" Make sure the maven-deploy-plugin is correctly configured in your pom.xml. Here's an example configuration: <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>3.0.0-M1</version> <configuration> <repositoryId>apache.releases.https</repositoryId> <url>https://repository.apache.org/service/local/staging/deploy/maven2</url> </configuration> </plugin> ","version":null,"tagName":"h3"},{"title":"7. Increase Verbose Logging","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#7-increase-verbose-logging","content":" Enable verbose logging to get more details about the error. You can do this by adding the -X flag when running the Maven command: mvn clean deploy -X [DONE] ","version":null,"tagName":"h3"},{"title":"8. Retry with a Clean Local Repository","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#8-retry-with-a-clean-local-repository","content":" Sometimes, a corrupt local repository can cause issues. Try cleaning your local Maven repository and re-running the deployment: mvn clean install -U mvn deploy [OPEN] - Does not match to an authentication issue. ","version":null,"tagName":"h3"},{"title":"9. Check for Missing Credentials","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#9-check-for-missing-credentials","content":" Ensure that the credentials for the repository are correctly set up in your settings.xml: <servers> <server> <id>apache.releases.https</id> <username>your-username</username> <password>your-password</password> </server> </servers> [DONE] ","version":null,"tagName":"h3"},{"title":"10. Review the Full Stack Trace","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#10-review-the-full-stack-trace","content":" The full stack trace indicates a NullPointerException: Caused by: java.lang.NullPointerException at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) ... This suggests that there might be an issue with how credentials are being handled. Double-check that the credentials are being correctly passed and processed. [DONE] ","version":null,"tagName":"h3"},{"title":"How to contribute","type":0,"sectionRef":"#","url":"/docs/community/contribute","content":"","keywords":"","version":"Next"},{"title":"Contributing by helping other users","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-helping-other-users","content":" A great way to contribute to Wayang is to help answer user questions on the user@ mailing list or on StackOverflow. There are always many new Wayang users; taking a few minutes to help answer a question is a very valuable community service. Contributors should subscribe to this list and follow it in order to keep up to date on what’s happening in Wayang. Answering questions is an excellent and visible way to help the community, which also demonstrates your expertise. See the Mailing Lists guide for guidelines about how to effectively participate in discussions on the mailing list, as well as forums like StackOverflow. ","version":"Next","tagName":"h3"},{"title":"Contributing by testing releases","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-testing-releases","content":" Wayang’s release process is community-oriented, and members of the community can vote on new releases on the dev@ mailing list. Wayang users are invited to subscribe to this list to receive announcements, and test their workloads on newer release and provide feedback on any performance or correctness issues found in the newer release. ","version":"Next","tagName":"h3"},{"title":"Contributing by reviewing changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-reviewing-changes","content":" Changes to Wayang source code are proposed, reviewed and committed via GitHub pull requests (described later). Anyone can view and comment on active changes here. Reviewing others’ changes is a good way to learn how the change process works and gain exposure to activity in various parts of the code. You can help by reviewing the changes and asking questions or pointing out issues – as simple as typos or small issues of style. ","version":"Next","tagName":"h2"},{"title":"Contributing documentation changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-documentation-changes","content":" To propose a change to release documentation (that is, docs that appear under Developer section), fork the website repo and edit the Markdown source files in Wayang’s docs/ directory, the README file shows how to build the documentation locally to test your changes. The process to propose a doc change is otherwise the same as the process for proposing code changes below. To propose a change to the rest of the documentation (that is, docs that do not appear under Developer section), similarly, edit the Markdown in the wayang-website repository and open a pull request. ","version":"Next","tagName":"h2"},{"title":"Contributing user libraries to Wayang","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-user-libraries-to-wayang","content":" Just as Java and Scala applications can access a huge selection of libraries and utilities, none of which are part of Java or Scala themselves, Wayang aims to support a rich ecosystem of libraries. Many new useful utilities or features belong outside of Spark rather than in the core. For example: query optimizer code and language support probably has to be a part of core Wayang, but, useful machine learning algorithms can happily exist outside of Wayang. To that end, large and independent new functionality is often rejected for inclusion in Wayang itself, but, can and should be hosted as a separate project and repository, and included in the wayang-packages.org collection. ","version":"Next","tagName":"h2"},{"title":"Contributing bug reports","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-bug-reports","content":" Ideally, bug reports are accompanied by a proposed code change to fix the bug. This isn’t always possible, as those who discover a bug may not have the experience to fix it. A bug may be reported by creating a JIRA or a GitHub Issue but without creating a pull request (see below). Bug reports are only useful however if they include enough information to understand, isolate and ideally reproduce the bug. Simply encountering an error does not mean a bug should be reported; as below, search JIRA and search and inquire on the Wayang user / dev mailing lists first. Unreproducible bugs, or simple error reports, may be closed. It’s very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. Note that, data correctness/data loss bugs are very serious. Make sure the corresponding bug report JIRA ticket is labeled as correctness or data-loss. If the bug report doesn’t get enough attention, please send an email to dev@, to draw more attentions. It is possible to propose new features as well. These are generally not helpful unless accompanied by detail, such as a design document and/or code change. Large new contributions should consider wayang-packages.org first (see above), or be discussed on the mailing list first. Feature requests may be rejected, or closed after a long period of inactivity. ","version":"Next","tagName":"h2"},{"title":"Contributing to JIRA maintenance","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-to-jira-maintenance","content":" Given the volume of issues raised in the Apache Wayang JIRA, inevitably some issues are duplicates, or become obsolete and eventually fixed otherwise, or can’t be reproduced, or could benefit from more detail, and so on. It’s useful to help identify these issues and resolve them, either by advancing the discussion or even resolving the JIRA. Most contributors are able to directly resolve JIRAs. Use judgment in determining whether you are quite confident the issue should be resolved, although changes can be easily undone. If in doubt, just leave a comment on the JIRA. When resolving JIRAs, observe a few useful conventions: Resolve as Fixed if there’s a change you can point to that resolved the issue Set Fix Version(s), if and only if the resolution is FixedSet Assignee to the person who most contributed to the resolution, which is usually the person who opened the PR that resolved the issue.In case several people contributed, prefer to assign to the more ‘junior’, non-committer contributor For issues that can’t be reproduced against master as reported, resolve as Cannot Reproduce Fixed is reasonable too, if it’s clear what other previous pull request resolved it. Link to it.If the issue is the same as or a subset of another issue, resolved as DuplicateMake sure to link to the JIRA it duplicatesPrefer to resolve the issue that has less activity or discussion as the duplicate If the issue seems clearly obsolete and applies to issues or components that have changed radically since it was opened, resolve as Not a Problem If the issue doesn’t make sense – not actionable, for example, a non-Spark issue, resolve as Invalid If it’s a coherent issue, but there is a clear indication that there is not support or interest in acting on it, then resolve as Won’t Fix Sometimes, a contributor will already have a particular new change or bug in mind. If seeking ideas, consult the list of starter tasks in JIRA, or ask the user@ mailing list. Before proceeding, contributors should evaluate if the proposed change is likely to be relevant, new and actionable: Is it clear that code must change? Proposing a JIRA and pull request is appropriate only when a clear problem or change has been identified. If simply having trouble using Spark, use the mailing lists first, rather than consider filing a JIRA or proposing a change. When in doubt, email user@ first about the possible changeSearch the user@ and dev@ mailing list archives for related discussions. Often, the problem has been discussed before, with a resolution that doesn’t require a code change, or recording what kinds of changes will not be accepted as a resolution.Search JIRA for existing issues: https://issues.apache.org/jira/browse/WAYANGIs the scope of the change matched to the contributor’s level of experience? Anyone is qualified to suggest a typo fix, but refactoring core scheduling logic requires much more understanding of Wayang. Some changes require building up experience first (see above).It’s worth reemphasizing that changes to the core of Wayang, or to highly complex and important modules like SQL and Catalyst, are more difficult to make correctly. They will be subjected to more scrutiny, and held to a higher standard of review than changes to less critical code. ","version":"Next","tagName":"h3"},{"title":"ML4ALL and optimizer-specific contribution guidelines","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#ml4all-and-optimizer-specific-contribution-guidelines","content":" While a rich set of algorithms is an important goal for Wayang, scaling the project requires that maintainability, consistency, and code quality come first. New algorithms should: Be used and accepted (academic citations and concrete use cases can help justify this)Be well documentedHave APIs consistent with other algorithms in WayangCome with a reasonable expectation of developer supportError message guidelinesExceptions thrown in Wayang should be associated with standardized and actionable error messages Error messages should answer the following questions: What was the problem?Why did the problem happen?How can the problem be solved? When writing error messages, you should: Use active voiceAvoid time-based statements, such as promises of future supportUse the present tense to describe the error and provide suggestionsProvide concrete examples if the resolution is unclearAvoid sounding accusatory, judgmental, or insultingBe directDo not use programming jargon in user-facing errors 8 See the error message guidelines for more details. ","version":"Next","tagName":"h3"},{"title":"Code review criteria","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-review-criteria","content":" Before considering how to contribute code, it’s useful to understand how code is reviewed, and why changes may be rejected. See the detailed guide for code reviewers from Google’s Engineering Practices documentation. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review. Positives Fixes the root cause of a bug in existing functionalityAdds functionality or fixes a problem needed by a large number of usersSimple, targetedMaintains or improves consistency across Python, Java, ScalaEasily tested; has testsReduces complexity and lines of codeChange has already been discussed and is known to committers Negatives, risks Band-aids a symptom of a bug onlyIntroduces complex new functionality, especially an API that needs to be supportedAdds complexity that only helps a niche use caseAdds user-space functionality that does not need to be maintained in Wayang, but could be hosted externally and indexed by wayang-packages.orgChanges a public API or semantics (rarely allowed)Adds large dependenciesChanges versions of existing dependenciesAdds a large amount of codeMakes lots of modifications in one “big bang” change ","version":"Next","tagName":"h3"},{"title":"Contributing code changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-code-changes","content":" Please review the preceding section before proposing a code change. This section documents how to do so. When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so. ","version":"Next","tagName":"h3"},{"title":"Cloning the Apache Wayang™ source code","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#cloning-the-apache-wayang-source-code","content":" If you are interested in working with the newest under-development code or contributing to Apache Spark development, you can check out the master branch from Git: # Master development branch git clone git://github.com/apache/incubator-wayang.git Once you’ve downloaded Wayang, you can find instructions for installing and building it on the Compiling Apache Wayang page. ","version":"Next","tagName":"h3"},{"title":"JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#jira","content":" Generally, Wayang uses JIRA to track logical issues, including bugs and improvements, and uses GitHub pull requests to manage the review and merge of specific code changes. That is, JIRAs are used to describe what should be fixed or changed, and high-level approaches, and pull requests describe how to implement that change in the project’s source code. For example, major design decisions are discussed in JIRA. Find the existing Spark JIRA that the change pertains to. Do not create a new JIRA if creating a change to address an existing issue in JIRA; add to the existing discussion and work insteadLook for existing pull requests that are linked from the JIRA, to understand if someone is already working on the JIRA If the change is new, then it usually needs a new JIRA. However, trivial changes, where the what should change is virtually the same as the how it should change do not require a JIRA. Example: Fix typos in Foo wayang docIf required, create a new JIRA: Provide a descriptive Title. “Update web UI” or “Problem in scheduler” is not sufficient. “Kafka Streaming support fails to handle empty queue in YARN cluster mode” is good.Write a detailed description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document.Set required fields: Issue Type. Generally, Bug, Improvement and New Feature are the only types used in Spark.Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. JIRA tends to unfortunately conflate “size” and “importance” in its Priority field values. Their meaning is roughly: Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers for their target versions.Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficultMajor: a small minority of users are missing important functionality without this, and there is a workaroundMinor: a niche use case is missing some support, but it does not affect usage or is easily worked aroundTrivial: a nice-to-have change but unlikely to be any problem in practice otherwise ComponentAffects Version. For Bugs, assign at least one version that is known to exhibit the problem or need the changeLabel. Not widely used, except for the following: correctness: a correctness issuedata-loss: a data loss issuerelease-notes: the change’s effects need mention in release notes. The JIRA or pull request should include detail suitable for inclusion in release notes – see “Docs Text” below.starter: small, simple change suitable for new contributors Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include in Release Notes. This should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the JIRA is opened, but will likely need to be updated with final details when the issue is resolved. Do not set the following fields: Fix Version This is assigned by committers only when resolved.Target Version This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. Do not include a patch file; pull requests are used to propose the actual change. If the change is a large change, consider inviting discussion on the issue at dev@ first before proceeding to implement the change. ","version":"Next","tagName":"h3"},{"title":"Pull request","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#pull-request","content":" Before creating a pull request in Apache Wayang, it is important to check if tests can pass on your branch because our GitHub Actions workflows automatically run tests for your pull request/following commits and every run burdens the limited resources of GitHub Actions in Apache Wayang repository. Below steps will take your through the process. Fork the GitHub repository at https://github.com/apache/incubator-wayang if you haven’t alreadyGo to “Actions” tab on your forked repository and enable “Build and test” and “Report test results” workflowsClone your fork and create a new branchConsider whether documentation or tests need to be added or updated as part of the change, and add them as needed. When you add tests, make sure the tests are self-descriptive.Also, you should consider writing a JIRA ID in the tests when your pull request targets to fix a specific issue. In practice, usually it is added when a JIRA type is a bug or a PR adds a couple of tests to an existing test class. See the examples below: @Test public void testCase() { // WAYANG-12345: a short description of the test Consider whether benchmark results should be added or updated as part of the change, and add them as needed by Running benchmarks in your forked repository to generate benchmark results.Run all tests with in your build to verify that the code still compiles, passes tests, and passes style checks. If style checks fail, review the Code Style Guide below.Push commits to your branch. This will trigger “Build and test” and “Report test results” workflows on your forked repository and start testing and validating your changes.Open a pull request against the master branch of apache/incubator-wayang. (Only in special cases would the PR be opened against other branches). This will trigger workflows “On pull request*” (on the Wayang repo) that will look/watch for successful workflow runs on “your” forked repository (it will wait if one is running). The PR title should be of the form [WAYANG-xxxx][COMPONENT] Title, where WAYANG-xxxx is the relevant JIRA number, COMPONENT is one of the PR categories and Title may be the JIRA’s title or a more specific title describing the PR itself.If the pull request is still a work in progress, and so is not ready to be merged, but needs to be pushed to GitHub to facilitate review, then add [WIP] after the component.Consider identifying committers or other contributors who have worked on the code being changed. Find the file(s) in GitHub and click “Blame” to see a line-by-line annotation of who changed the code last. You can add @username in the PR description to ping them immediately.Please state that the contribution is your original work and that you license the work to the project under the project’s open source license. The related JIRA, if any, will be marked as “In Progress” and your pull request will automatically be linked to it. There is no need to be the Assignee of the JIRA to work on it, though you are welcome to comment that you have begun work. ","version":"Next","tagName":"h3"},{"title":"The review process","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#the-review-process","content":" Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch.Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change.Keep in mind that changes to more critical parts of Spark, like its core and SQL components, will be subjected to more review, and may require more testing and proof of its correctness than other changes.Reviewers can indicate that a change looks suitable for merging with a comment such as: “I think this patch looks good”. Wayang uses the LGTM convention for indicating the strongest level of technical sign-off on a patch: simply comment with the word “LGTM”. It specifically means: “I’ve looked at this thoroughly and take as much ownership as if I wrote the patch myself”. If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community.Sometimes, other changes will be merged which conflict with your pull request’s changes. The PR can’t be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by git remote add upstream https://github.com/apache/incubator-wayang.git, running git fetch upstream followed by git rebase upstream/master and resolving the conflicts by hand, then pushing the result to your branch.Try to be responsive to the discussion rather than let days pass between replies ","version":"Next","tagName":"h3"},{"title":"Closing your pull request / JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#closing-your-pull-request--jira","content":" If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated JIRA if any Note that in the rare case you are asked to open a pull request against a branch besides master, that you will actually have to close the pull request manuallyThe JIRA will be Assigned to the primary contributor to the change as a way of giving credit. If the JIRA isn’t closed and/or Assigned promptly, comment on the JIRA. If your pull request is ultimately rejected, please close it promptly … because committers can’t close PRs directlyPull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like “mind closing this PR?” This means that the committer is specifically requesting that it be closed. If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that’s easier to include, like a smaller and/or less invasive change.If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a “soft no”. It is helpful to withdraw and close the PR in this case.If a pull request is closed because it is deemed not the right approach to resolve a JIRA, then leave the JIRA open. However if the review makes it clear that the issue identified in the JIRA is not going to be resolved by any pull request (not a problem, won’t fix) then also resolve the JIRA. ","version":"Next","tagName":"h3"},{"title":"Code style guide","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-style-guide","content":" Please follow the style of the existing codebase. For Python code, Apache Wayang follows PEP 8 with one exception: lines can be up to 100 characters in length, not 79.For R code, Apache Wayang follows Google’s R Style Guide with three exceptions: lines can be up to 100 characters in length, not 80, there is no limit on function name but it has a initial lower case latter and S4 objects/methods are allowed.For Java code, Apache Wayang follows Oracle’s Java code conventions and Scala guidelines below. The latter is preferred.For Scala code, Apache Wayang follows the official Scala style guide. ","version":"Next","tagName":"h2"},{"title":"If in doubt","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#if-in-doubt","content":" If you’re not sure about the right style for something, try to follow the style of the existing codebase. Look at whether there are other examples in the code that use your feature. Feel free to ask on the dev@ list as well and/or ask committers. ","version":"Next","tagName":"h3"},{"title":"Code of conduct","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-of-conduct","content":" The Apache Wayang project follows the Apache Software Foundation Code of Conduct. The code of conduct applies to all spaces managed by the Apache Software Foundation, including IRC, all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our communities. A code of conduct which is specific to in-person events (ie., conferences) is codified in the published ASF anti-harassment policy. We expect this code of conduct to be honored by everyone who participates in the Apache community formally or informally, or claims any affiliation with the Foundation, in any Foundation-related activities and especially when representing the ASF, in any role. This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. We expect it to be followed in spirit as much as in the letter, so that it can enrich all of us and the technical communities in which we participate. For more information and specific guidelines, refer to the Apache Software Foundation Code of Conduct. This guide was originally released by Apache Spark, the Apache Wayang project adapted the guide. ","version":"Next","tagName":"h2"},{"title":"Maturity Model","type":0,"sectionRef":"#","url":"/docs/community/maturity","content":"Maturity Model ID\tDescription\tStatus\tCode CD10\tThe project produces Open Source software for distribution to the public, at no charge.\tYes. The project source code is licensed under the Apache License 2.0. CD20\tAnyone can easily discover and access the project's code.\tYes. The official website includes GitHub link which can access the project's repository on GitHub directly. CD30\tAnyone using standard, widely-available tools, can build the code in a reproducible way.\tYes. Our webpage and github repo provide "Getting Started" and several examples to allow users build our system. CD40\tThe full history of the project's code is available via a source code control system, in a way that allows anyone to recreate any released version.\tYes. We use git and anyone can view the full history of the project via commit logs and tags of the releases. CD50\tThe source code control system establishes the provenance of each line of code in a reliable way, based on strong authentication of the committer. When third parties contribute code, commit messages provide reliable information about the code provenance.\tYes. The project uses GitHub and managed by Apache Infra and ensures provenance of each line of code to a committer. Third-party contributions are accepted in accordance with the contributing guides. Licenses and Copyright LC10\tThe Apache License, version 2.0, covers the released code.\tYes. All source files are licensed with Apache License v2.0. LICENSE can be found in the GitHub repository. LC20\tLibraries that are mandatory dependencies of the project's code do not create more restrictions than the Apache License does.\tYes. None of the dependencies create more restrictions. LC30\tThe libraries mentioned in LC20 are available as Open Source software.\tYes. All libraries are open source. LC40\tCommitters are bound by an Individual Contributor Agreement (the "Apache iCLA") that defines which code they may commit and how they need to identify code that is not their own.\tYes. All committers have iCLAs. LC50\tThe project clearly defines and documents the copyright ownership of everything that the project produces.\tYes. All source files are with APLv2 header Releases RE10\tReleases consist of source code, distributed using standard and open archive formats that are expected to stay readable in the long term.\tYes. Source release is distributed via dist.apache.org and linked from download page. RE20\tThe project's PPMC (Project Management Committee, see CS10) approves each software release in order to make the release an act of the Foundation.\tYes. All releases have been voted at dev@wayang.apache.org and general@incubator.apache.org, and have at least 3 PPMC member's votes. RE30\tReleases are signed and/or distributed along with digests that anyone can reliably use to validate the downloaded archives.\tYes. All releases are signed, and the KEYS are available. RE40\tThe project can distribute convenience binaries alongside source code, but they are not Apache Releases, they are provided with no guarantee.\tYes. Users can easily build binaries from source code. We do not provide binaries as Apache Releases. RE50\tThe project documents a repeatable release process so that someone new to the project can independently generate the complete set of artifacts required for a release.\tYes. Newcomers can follow our very detailed Release guide to make a new Apache Wayang release, and so far we had 3 different release managers. Quality QU10\tThe project is open and honest about the quality of its code. Various levels of quality and maturity for various modules are natural and acceptable as long as they are clearly communicated.\tYes. The project records all bugs using Github Issues. QU20\tThe project puts a very high priority on producing secure software.\tYes. All security issues will be addressed within 3 days. QU30\tThe project provides a well-documented, secure and private channel to report security issues, along with a documented way of responding to them.\tYes. https://wayang.apache.org/docs/community/security QU40\tThe project puts a high priority on backwards compatibility and aims to document any incompatible changes and provide tools and documentation to help users transition to new features.\tYes. Each release contains a “New features” on the website containing the release notes. The project aims to make no backward incompatible changes within a given major version. QU50\tThe project strives to respond to documented bug reports in a timely manner.\tYes. The community has very small response times to issues and pull requests Community CO10\tThe project has a well-known homepage that points to all the information required to operate according to this maturity model.\tYes. The official website includes all information users need to run Apache Wayang. CO20\tThe community welcomes contributions from anyone who acts in good faith and in a respectful manner, and who adds value to the project.\tYes. The contributing guide provides information for anyone to contribute. CO30\tContributions include source code, documentation, constructive bug reports, constructive discussions, marketing and generally anything that adds value to the project.\tYes. All good contributions including code and non-code are welcomed. CO40\tThe community strives to be meritocratic and gives more rights and responsibilities to contributors who, over time, add value to the project.\tYes. The community has elected 3 new PPMC members/Committers the last year. CO50\tThe project documents how contributors can earn more rights such as commit access or decision power, and applies these principles consistently.\tYes. The website has a clear guide on how to become a committer https://wayang.apache.org/docs/community/committer CO60\tThe community operates based on consensus of its members (see CS10) who have decision power. Dictators, benevolent or not, are not welcome in Apache projects.\tYes. All decisions are made after voting by community members. CO70\tThe project strives to answer user questions in a timely manner.\tYes. Our community answers user questions in a timely manner. Consensus CS10\tThe project maintains a public list of its contributors who have decision power. The project's PPMC (Project Management Committee) consists of those contributors.\tYes. See Team with all PPMC members/committers and contributors. CS20\tDecisions require a consensus among PPMC members and are documented on the project's main communications channel. The PPMC takes community opinions into account, but the PPMC has the final word.\tYes. All decisions are made by votes on dev@wayang.apache.org, and with at least 3 +1 votes from PPMC. CS30\tThe project uses documented voting rules to build consensus when discussion is not sufficient.\tYes. The project uses the standard ASF voting rules. CS40\tIn Apache projects, vetoes are only valid for code commits. The person exercising the veto must justify it with a technical explanation, as per the Apache voting rules defined in CS30.\tYes. Apache Wayang community has not used the veto power yet except for code commits. CS50\tAll "important" discussions happen asynchronously in written form on the project's main communications channel. Offline, face-to-face or private discussions that affect the project are also documented on that channel.\tYes. All important discussions and conclusions are sent to the dev list Independence IN10\tThe project is independent from any corporate or organizational influence.\tYes. The PPMC members of Apache Wayang are from several organizations. IN20\tContributors act as themselves, not as representatives of a corporation or organization.\tYes. The contributors act on their own initiative without representing a corporation or organization. Trademark and Branding TB10\tThe project uses "Apache Foo™" as the project and software product name consistently, with appropriate trademark attributions\tYes. The project uses appropriately the trademark symbol. TB20\tThe project's primary homepage is at projectname.apache.org.\tYes. The project's homepage is at wayang.apache.org TB30\tThe ASF has trademark rights, including any registrations, to the project name, logo, and any other major branding elements.\tYes. ASF has the trademark rights TB40\tThe project monitors for any major misuses of their project's brand by others.\tYes. The PPMC members have already addressed misuses.\t","keywords":"","version":"Next"},{"title":"How to make a release","type":0,"sectionRef":"#","url":"/docs/community/release","content":"","keywords":"","version":"Next"},{"title":"First time release","type":1,"pageTitle":"How to make a release","url":"/docs/community/release#first-time-release","content":" ============================================= Add your Apache credentials in ~.m2/settings.xml If you don’t have this directory or file, you should create a .m2 directory in your user home and inside that create a settings.xml file with at least this content: <?xml version="1.0" encoding="UTF-8"?> <settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.1.0 http://maven.apache.org/xsd/settings-1.1.0.xsd" xmlns="http://maven.apache.org/SETTINGS/1.1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <servers> <!-- Apache Repo Settings --> <server> <id>apache.snapshots.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> <server> <id>apache.releases.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> </servers> </settings> The following is about key management. Details are described here You need to generate a key with GnuPG Install GnuPG (e.g., brew install gnupg)Run: gpg --full-generate-keyExport the public key: gpg --export --armor mygpgpublickey Install pinentry (e.g., brew install pinentry-mac) so that you can be promted to enter your credentials via command line Configuration A. Make Apache the default key:APACHEID=your_asf_id KEY_ID=$(gpg --list-keys --with-colons $APACHEID@apache.org | egrep "^pub" | awk -F: '{print $5}') echo "default-key $KEY_ID" >> ~/.gnupg/gpg.conf B. export GPG_TTY=$(tty) echo $GPG_TTY C. Configure gnupg to use standard DNS resolution # resolves common "gpg: keyserver receive failed: Network is unreachable" and # "gpg: keyserver receive failed: No keyserver available" errors echo "standard-resolver" > ~/.gnupg/dirmngr.conf sudo pkill dirmngr D. Set configuration to use SHA512 keys by default: cat <<EOL >> ~/.gnupg/gpg.conf personal-digest-preferences SHA512 cert-digest-algo SHA512 default-preference-list SHA512 SHA384 SHA256 SHA224 AES256 AES192 AES CAST5 ZLIB BZIP2 ZIP Uncompressed EOL Upload the key to one of the key servers gpg --keyserver keyserver.ubuntu.com --send-key $KEY_ID gpg --keyserver keys.openpgp.org --send-key $KEY_ID gpg --keyserver pgp.mit.edu --send-key $KEY_ID Make sure your public key is appended in the KEYS file found in the release server here ============================================= Run mvn release:prepare (you can skip tests with -Darguments='-DskipTests=True, if you have done this before) Run mvn clean release:perform (This step will ask for your apache credentials, if everything related to the keys is configured properly.) Go to: https://repository.apache.org, login, go to Staging Repositories and find the orgapachewayang-{somenumber} and close it with description "Apache Wayang (incubating) 1.0.0-rc5" Make a directory structure as described below and copy the corresponding source files from the directory target/checkout/target. The structure should be: ./KEYS ./1.0.0/ ./1.0.0/rc5 ./1.0.0/rc5/README ./1.0.0/rc5/RELEASE_NOTES ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.asc ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.sha12 Make sure the KEYS file contains your public key. The KEYS file can be found in https://dist.apache.org/repos/dist/release/incubator/wayang/ cd 1.0.0 svn import rc5 https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 -m "Staging of rc5 of Wayang 1.0.0" Send the voting email to the dev list. If the voting passes, send the voting email to the general list. Once the voting in the general list passes, the staged artifacts can be released. This is done by moving them inside the Apache SVN: svn move -m "Release of Apache Wayang (incubating) 1.0.0" https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 https://dist.apache.org/repos/dist/release/incubator/wayang/1.0.0 Then release the maven artifacts: In order to do this, the release manager logs into Nexus at https://repository.apache.org/, selects the staging repository and clicks on the Release button. Bring all changes of the release branch to the main. You can also remove 1.0.0 from the dev directory of the svn now that the release is out. Create and upload the javadocs: Go to the source code directory and run mvn compile javadoc:javadoc javadoc:aggregate The javadocs can then be found in the target directory Upload the javadocs in the website: https://github.com/apache/incubator-wayang-website/tree/main/static/docs/api/javadocs ","version":"Next","tagName":"h3"},{"title":"Community Hubs","type":0,"sectionRef":"#","url":"/docs/community/repositories","content":"","keywords":"","version":"Next"},{"title":"Source Code","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#source-code","content":" Apache Wayang ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang.git GitHub mirror : https://github.com/apache/incubator-wayang.git Wayang Web Page Repository ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang-website.git GitHub mirror : https://github.com/apache/incubator-wayang-website.git ","version":"Next","tagName":"h3"},{"title":"Issues, Bugs and Features","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#issues-bugs-and-features","content":" If you have a specific bug to report or feature request, we suggest opening an issue in Jira. ","version":"Next","tagName":"h3"},{"title":"Wayang Security","type":0,"sectionRef":"#","url":"/docs/community/security","content":"","keywords":"","version":"Next"},{"title":"Advisories for Dependencies","type":1,"pageTitle":"Wayang Security","url":"/docs/community/security#advisories-for-dependencies","content":" Many organizations employ security scanning tools to identify components with known security advisories. Although we strongly recommend these tools as they can alert users to potential risks, they often generate false positives. This occurs because a vulnerable dependency may not necessarily impact Apache Wayang if used in a non-exploitable manner. Therefore, advisories regarding Apache Wayang's dependencies are not automatically considered critical. However, if additional analysis indicates that Apache Wayang might be affected by a dependency's vulnerability, please report your findings privately to security@wayang.apache.org. If a dependency advisory is identified, please: Verify if our DependencyCheck suppressions contain relevant details.Check our issue tracker for discussions regarding this advisory.Conduct your own analysis to determine whether Apache Wayang is affected. If affected, report your findings privately through security@wayang.apache.org.If not affected, please contribute by updating the DependencyCheck suppression list, clearly documenting why Apache Wayang is not impacted. ","version":"Next","tagName":"h2"},{"title":"API Documentation","type":0,"sectionRef":"#","url":"/docs/guide/api-documentation","content":"API Documentation The latest Javadocs can be found here.","keywords":"","version":"Next"},{"title":"Developing in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/developing-in-wayang","content":"","keywords":"","version":"Next"},{"title":"Compile the module you modified","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#compile-the-module-you-modified","content":" Within the root directory of Wayang, compile only the module you modified for faster compilation: mvn clean install -DskipTests -pl <modified_module> or change the directory to your module and compile there: cd <modified_module> && mvn clean install Important: before making a Pull Request make sure all modules compile and all tests are passing: mvn clean install ","version":"Next","tagName":"h2"},{"title":"Package the project","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#package-the-project","content":" mvn clean package -pl :wayang-assembly -Pdistribution ","version":"Next","tagName":"h2"},{"title":"Execute your code","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#execute-your-code","content":" Before executing your code, make sure the required environment variables are set correctly. cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Adding new operators in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/adding-operators","content":"","keywords":"","version":"Next"},{"title":"Step 1: Add a Wayang operator","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-1-add-a-wayang-operator","content":" Wayang operators are located under the wayang-basic in the org.apache.wayang.basic.operators package. An operator needs to extend from one of the following abstract classes: UnaryToUnaryOperator, BinaryToUnaryOperator, UnarySource, UnarySink. For a unary to unary operator, see for example here. For enhanced performance in Wayang, consider adding a cardinality estimator by overriding the createCardinalityEstimator() function as here. ","version":"Next","tagName":"h2"},{"title":"Step 2: Add the (platform-specific) execution operators","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-2-add-the-platform-specific-execution-operators","content":" Execution operators are located under the corresponding module of wayang-platforms. For instance, Java execution operators are located in the org.apache.wayang.java.operators package of the wayang-java module. An execution operator needs to extend from its corresponding Wayang operator and implement the corresponding platform operator interface. For the above MapOperator, the following is the corresponding JavaMapOperator. For enhanced performance in Wayang, consider adding a load function as well: For this you need to overwrite the getLoadProfileEstimatorConfigurationKey() function and provide the right key that will then be read from a properties file. For the JavaMapOperator it's: wayang.java.map.load. Then add in the corresponding properties file (e.g., this is for the java executor) the template which is the mathematical formula that represents the cost of this operator and an instantiation of it. See here for the example of the map operator. ","version":"Next","tagName":"h2"},{"title":"Step 3: Add mappings","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-3-add-mappings","content":" Create mappings from the Wayang operator to the platform-specific execution operators. The mappings are located in the corresponding execution module in the org.apache.wayang.java.operators package. For the above MapOperator and JavaMapOperator, see here. After that you need to declare this mapping in Wayang in the corresponding Mappings class. ","version":"Next","tagName":"h2"},{"title":"Step 4: Expand the Java scala-like API","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-4-expand-the-java-scala-like-api","content":" Once you created a new operator you need to expose it to the API so that users can use it as a function in the dataflow job they create. For this, you need to go to the module wayang-api/wayang-api-scala-java and expand the JavaPlanBuilder.scala file to include a new source operator or the DataQuantaBuilder for non-source operators. ","version":"Next","tagName":"h2"},{"title":"Team","type":0,"sectionRef":"#","url":"/docs/community/team","content":"","keywords":"","version":"Next"},{"title":"Committers and Recent Contributors","type":1,"pageTitle":"Team","url":"/docs/community/team#committers-and-recent-contributors","content":" Name\tRole\tApache ID\tOrganizationAlexander Alten\tPMC, Committer\taloalt\tScalytics Calvin Kirs\tPMC, Committer\tkirs\tSelectDB, ASF Zoi Kaoudi\tPMC, Committer\tzkaoudi\tITU Copenhagen, Scalytics Kaustubh Beedkar\tPMC, Committer\tkbeedkar\tScalytics, IIT Dehli Mirko Kaempf\tPMC, Committer\tkamir\tEcolytiq Juri Petersen\tPMC, Committer\tjuri\tITU Copenhagen Mads Sejer Pedersen\tPMC, Committer\tsejer\tITU Copenhagen Jean-Baptiste Onofré\tPMC, Committer\tjbonofre\tDremio, ASF Yasser Idris\tPMC, Committer\tyidris\tExpensePoint, Alexandria University Anis Troudi\tCommitter\tatroudi Christofer Dutz\tCommitter\tchrisdutz\tASF Mingxi Liu\tContributor East China Normal University Michalis Vargiamis\tContributor Scalytics Jeppe Halvorsen\tContributor ITU Copenhagen, Falck Christoffer Kristensen\tContributor ITU Copenhagen, Stretch Qonnect Aurélien Bertrand\tContributor Maastricht University, TecAlliance Aditya Goel\tContributor IIT Delhi Dudipala Vigneshwar Reddy\tContributor IIT Delhi Akshit Goel\tContributor IIT Delhi Vedanta Neogi\tContributor IIT Delhi Christos-Evangelos Lampropoulos\tContributor University of Patras Bertty Contreras\tContributor\tbertty Jorge Quiané\tContributor\tquiaru Rodrigo Pardo Meza\tContributor\trpardomeza Glaucia Esppenchutz\tContributor\tglauesppen Gábor Gévay\tContributor\tggevay\t You can reach committers directly at <apache-id>@apache.org ","version":"Next","tagName":"h2"},{"title":"Mentors during the incubation process","type":1,"pageTitle":"Team","url":"/docs/community/team#mentors-during-the-incubation-process","content":" The following people were very kind to mentor the project while in incubation. Name\tApache IDBernd Fondermann\tberndf Christofer Dutz\tcdutz Jean-Baptiste Onofré\tjbonofre Lars George\tlarsgeorge ","version":"Next","tagName":"h2"},{"title":"Donations to Wayang","type":1,"pageTitle":"Team","url":"/docs/community/team#donations-to-wayang","content":" The Logo was donated by Brian Vera ","version":"Next","tagName":"h2"},{"title":"Installing and building Apache Wayang","type":0,"sectionRef":"#","url":"/docs/guide/installation","content":"","keywords":"","version":"Next"},{"title":"Clone repository","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#clone-repository","content":" git clone https://github.com/apache/incubator-wayang.git ","version":"Next","tagName":"h2"},{"title":"Create binaries","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#create-binaries","content":" Running following commands to build Wayang and generate the tar.gz cd incubator-wayang ./mvnw clean install -DskipTests ./mvnw clean package -pl :wayang-assembly -Pdistribution Then you can find the wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz under wayang-assembly/target directory. ","version":"Next","tagName":"h2"},{"title":"Prepare the environment","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#prepare-the-environment","content":" ","version":"Next","tagName":"h2"},{"title":"Wayang","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#wayang","content":" tar -xvf wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz cd wayang-0.7.1-SNAPSHOT In linux echo "export WAYANG_HOME=$(pwd)" >> ~/.bashrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.bashrc source ~/.bashrc In MacOS echo "export WAYANG_HOME=$(pwd)" >> ~/.zshrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.zshrc source ~/.zshrc ","version":"Next","tagName":"h3"},{"title":"Others","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#others","content":" You need to install Apache Spark version 3 or higher. Don’t forget to set the SPARK_HOME environment variable.You need to install Apache Hadoop version 3 or higher. Don’t forget to set the HADOOP_HOME environment variable. ","version":"Next","tagName":"h3"},{"title":"Run the program","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#run-the-program","content":" To execute the WordCount example with Apache Wayang, you need to execute your program with the 'wayang-submit' command: cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.apps.wordcount.Main java file://$(pwd)/README.md Then you should be able to see the output of the Wordcount example. Compiling Apache Wayang Apache Wayang (incubating) has different dependencies, for compiling, it needs to add some profile in the compilation to enable maven works properly. mvn clean compile The line before is because the plugin the Antlr is not needed in all the modules, as well it has happened with Scala language. When maven compiles one or more modules using those plugins in the compilation time, it needs to add. The modules are: wayang-api-scala-javawayang-core (Antlr)wayang-iejoinwayang-sparkwayang-profilerwayang-tests-integration Executing Coverage mvn clean verify jacoco:report the final report is placed on ./target/aggregate.exec/aggregate.exec ","version":"Next","tagName":"h2"},{"title":"How the optimizer works (WIP)","type":0,"sectionRef":"#","url":"/docs/guide/optimizer","content":"","keywords":"","version":"Next"},{"title":"Configuration Files","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#configuration-files","content":" Description of configuration files and how to edit them ","version":"Next","tagName":"h2"},{"title":"Key Configuration Parameters","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#key-configuration-parameters","content":" List of key parameters and how they affect performance ","version":"Next","tagName":"h2"},{"title":"Tuning Tips","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#tuning-tips","content":" Tips on tuning Apache Wayang for different scenarios ","version":"Next","tagName":"h2"},{"title":"ML4all: scalable ML system for everyone","type":0,"sectionRef":"#","url":"/docs/guide/ml4all","content":"","keywords":"","version":"Next"},{"title":"Abstraction","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#abstraction","content":" ML4all abstracts most ML algorithms with seven operators: (1) Transform receives a data point to transform (e.g., normalize it) and outputs a new data point. (2) Stage initializes all the required global param- eters (e.g., centroids for the k-means algorithm). (3) Compute performs user-defined computations on the input data point and returns a new data point. For example, it can compute the nearest cen- troid for each input data point. (4) Update updates the global parameters based on a user-defined formula. For example, it can update the new centroids based on the output computed by the Compute operator. (5) Sample takes as input the size of the desired sample and the data points to sample from and re- turns a reduced set of sampled data points. (6) Converge specifies a function that outputs a convergence dataset required for determining whether the iterations should continue or stop. (7) Loop specifies the stopping condition on the convergence dataset. Similar to MapReduce, where users need to implement a map and reduce function, users of ML4all wishing to develop their own algorithm should implement the above interfaces. The interfaces can be found in org.apache.wayang.ml4all.abstraction.api. Examples for KMeans clustering and stochastic gradient descent can be found in org.apache.wayang.ml4all.algorithms. ","version":"Next","tagName":"h2"},{"title":"Example runs","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#example-runs","content":" Kmeans: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunKMeans java,spark <url_path_to_file>/USCensus1990-sample.input 3 68 0 1 SGD: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunSGD spark <url_path_to_file>/adult.zeros.input 100827 123 10 0.001 ","version":"Next","tagName":"h2"},{"title":"Using Wayang's docker image","type":0,"sectionRef":"#","url":"/docs/guide/wayang-docker","content":"","keywords":"","version":"Next"},{"title":"Step 1: Creating a docker-compose file","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-1-creating-a-docker-compose-file","content":" This guide assumes knowledge aboutdocker anddocker compose. We provide a pre-built docker imagethat contains the necessary tooling in order to run or develop Apache Wayang. The tools necessary for this are: Java 11Apache SparkHadoopMaven A docker-compose.yml containing the following services will suffice to run TPC-H benchmarks: name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always Placing this file in the root directory of Wayang's source will mount volumes containing the application and its dependencies after installation into the app container. ","version":"Next","tagName":"h2"},{"title":"Step 2: Connecting to the app container","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-2-connecting-to-the-app-container","content":" In order to get a interactive bash session that allows running commands inside of the app container, run the following: docker exec -it apache-wayang-app bash ","version":"Next","tagName":"h2"},{"title":"Step 3: Compiling Wayang and running benchmarks","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-3-compiling-wayang-and-running-benchmarks","content":" Within the root directory of Wayang (/var/www/html in our container), run the following command to install all packages in Wayang: mvn clean install -DskipTests Packaging the project to build the executable: mvn clean package -pl :wayang-assembly -Pdistribution Execute your code: cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-incubating-dist.tar.gz cd wayang-0.7.1 ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Optional: Add clusters for additional platforms to your docker setup","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#optional-add-clusters-for-additional-platforms-to-your-docker-setup","content":" name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always spark-master: image: cluster-apache-spark:3.0.2 spark-worker-a: image: cluster-apache-spark:3.0.2 depends_on: - spark-master environment: - SPARK_MASTER=spark://spark-master:7077 - SPARK_WORKER_CORES=1 - SPARK_WORKER_MEMORY=1G - SPARK_DRIVER_MEMORY=1G - SPARK_EXECUTOR_MEMORY=1G - SPARK_WORKLOAD=worker - SPARK_LOCAL_IP=spark-worker-a ","version":"Next","tagName":"h2"},{"title":"What is Apache Wayang™?","type":0,"sectionRef":"#","url":"/docs/introduction/about","content":"","keywords":"","version":"Next"},{"title":"Architecture and Software stack","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#architecture-and-software-stack","content":" Apache Wayang's unique architecture, unlike traditional DBMSs, decouples the physical planning and execution layers, empowering developers to express their data processing logic in a platform-agnostic fashion. This separation of concerns allows developers to focus on the algorithmic aspects of their applications without being constrained by the intricacies of specific processing platforms. At the bottom layers of the software stack, there are the different data storage mediums and the supported data processing platforms. On top of these, Wayang’s core consists of the following main components: the optimizer, the executor, the monitor, and platform-specific drivers. Wayang currently supports two main APIs: the Java one and the Scala one. A Python API is also supported with limited operator coverage for the moment. Besides using any of the supported languages, users can directly input SQL queries via the SQL library, which transforms them into a Wayang plan. Wayang also comes with an ML library for running ML tasks. Users can directly utilize the provided algorithms or can implement their own algorithm using a simple ML abstraction. To enable support for more programming languages in an efficient way, Wayang will soon come with a Polyglot library. Below you can see on the left, a Wayang plan representing the stochastic gradient descent algorithm, which used in most deep learning tasks. On the right, you can see how the optimizer decided to execute it. Orange nodes are the operators that ran on Spark and green the operators executed as a single Java process. ","version":"Next","tagName":"h3"},{"title":"Query Optimizer","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#query-optimizer","content":" Wayang's query optimizer is the principal component of the core. It receives as input a Wayang plan and outputs an execution (platform-specific) plan (see example plan above) with the goal of minimizing the total cost. The metric for the cost can be anything, from execution runtime to monetary cost or energy consumption. To achieve this, the optimizer first “inflates” the plan: For each node that corresponds to a Wayang operator, it adds all the corresponding execution (platform-specific) operators. Once the inflated plan is created, the optimizer attaches not only the operator’s costs but also the costs for moving intermediate data from one platform to another. By default, Wayang uses linear cost formulas to estimate these costs but one can plug their own optimizer, e.g., an ML-based one. At the last step of query optimization, our enumeration algorithm considers available options to output the optimal execution plan w.r.t. a defined cost. Note that users can control the optimizer by specifying in their code where an operator has to be executed via the withTargetPlatform(plat) call on the desired operator. Then, the optimizer takes into consideration the decisions of the user and outputs an execution plan by navigating a reduced search space during the plan enumeration. ","version":"Next","tagName":"h3"},{"title":"History","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#history","content":" Wayang (formely called Rheem) is the product of many years of top quality research. Below you can find the main publications: Apache Wayang: A Unified Data Analytics Framework. SIGMOD Rec. 52(3): 30-35 (2023) pdf Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework. SIGMOD Conference Companion 2025: 35-38 pdf ML-based Cross-Platform Query Optimization. ICDE 2020: 1489-1500 pdf RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems. VLDB J. 29(6): 1287-1310 (2020) pdf RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -. Proc. VLDB Endow. 11(11): 1414-1427 (2018) pdf ","version":"Next","tagName":"h3"},{"title":"Benchmarking Wayang","type":0,"sectionRef":"#","url":"/docs/introduction/benchmark","content":"","keywords":"","version":"Next"},{"title":"Running a Relational Query Across Multiple Independent Databases (Federation)","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#running-a-relational-query-across-multiple-independent-databases-federation","content":" In this use case, we showcase how Apache Wayang addresses the common challenge of analyzing data scattered across diverse systems, a prevalent issue in many data-driven projects. Through Wayang, developers can seamlessly perform analytics on relational data residing in multiple systems. The in-situ data processing approach employed by Wayang introduces data privacy-preserving mechanisms into distributed data analytics tasks. Datasets: To test the capabilities of Wayang, we utilized the TPC-H benchmark, which comprises five datasets/relations. The lineitem and orders relations are stored in HDFS, while customer, supplier, and region are in Postgres, and nation is in S3 or the local file system. Our testing covered a range of dataset sizes, from 1GB to 100GB, to ensure our product's scalability and robustness. Query/task: We evaluate the performance of the complex SQL TPC-H query 5: SELECT N_NAME, SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) AS REVENUE FROM CUSTOMER, ORDERS, LINEITEM, SUPPLIER, NATION, REGION WHERE C_CUSTKEY = O_CUSTKEY AND L_ORDERKEY = O_ORDERKEY AND L_SUPPKEY = S_SUPPKEY AND C_NATIONKEY = S_NATIONKEY AND S_NATIONKEY = N_NATIONKEY AND N_REGIONKEY = R_REGIONKEY AND R_NAME = 'ASIA' AND O_ORDERDATE >= '1994-01-01' AND O_ORDERDATE < DATEADD(YY, 1, cast('1994-01-01' as date)) GROUP BY N_NAME ORDER BY REVENUE DESC Baselines: We compared Wayang with two widely-used systems for storing and querying relational data: Spark and Postgres. To conduct a fair comparison, we first moved all datasets to the system being tested (Spark or Postgres). Results: The results of our tests, presented in Figure 1, show the execution time in seconds of the SQL query 5. Note that the time required to transfer data to Spark or Postgres was excluded from the results. As seen in Figure 1, Wayang significantly outperformed Postgres while retaining a runtime very close to Spark. It's important to note that for Spark to function, we needed twice the time to extract datasets from Postgres and transfer them to Spark. Wayang achieved such impressive performance by seamlessly combining Postgres with Spark: Wayang's query optimizer chose to perform all selections and projections on the data stored in Postgres before extracting data to join with the relations in HDFS. Additionally, the optimizer determined that executing the join between lineitem and supplier in Spark would be beneficial, as it could distribute the computational load of joining to multiple workers. All of this was done without the need for the user to specify where each operation should be executed. ","version":"Next","tagName":"h3"},{"title":"Reducing Execution Costs for Machine Learning Tasks Using Multiple Systems","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#reducing-execution-costs-for-machine-learning-tasks-using-multiple-systems","content":" To evaluate Apache Wayang's ability to accelerate machine learning tasks while optimizing costs by leveraging multiple systems, we conducted a comprehensive benchmark. The benchmark involved storing all data within a single repository, a scenario where selectively offloading data to more powerful engines can significantly enhance performance. To assess this feature, we employed a widely used machine learning algorithm, stochastic gradient descent, to perform classification tasks. Datasets: We use two real-world datasets that we downloaded from the UCI machine learning repository, namely higgs and rcv1. higgs consists of ~11 million data points with 28 features each and rcv1 contains ~677 thousand data points with ~47 thousand features each. In addition, we construct a synthetic dataset so that we can stress our product with even larger datasets, specifically with 88 million data points of 100 features each. Query/task: We test the performance of training classification models for higgs and the synthetic dataset and a regression model rcv1. All three training models use stochastic gradient descent but with a different loss function. We use Hinge loss to simulate support vector machines for the classification tasks and the logistic loss for the regression task. Baselines: We compare Wayang against MLlib from Apache Spark and SystemML from IBM, two very popular machine learning libraries. Results: Our results, shown in Figure 2, demonstrate that Wayang outperforms both baselines by more than an order of magnitude in runtime performance for large datasets. Apache Wayang's intelligent optimizer dynamically utilizes a hybrid approach, seamlessly blending Spark and local Java execution, to significantly improve the performance of the stochastic gradient descent algorithm. This optimization strategy involves leveraging Spark for efficient preprocessing and data preparation, while transitioning to local Java execution for the subsequent gradient computation phase. During this phase, the dataset shrinks considerably, making it more efficient to process using a single machine. This optimization technique, while not readily apparent without specialized expertise, is seamlessly implemented by Apache Wayang, requiring no additional user intervention. ","version":"Next","tagName":"h3"},{"title":"Optimizing Big Data Analytics by Adapting Platforms to Data and Task Characteristics","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#optimizing-big-data-analytics-by-adapting-platforms-to-data-and-task-characteristics","content":" We demonstrate the platform adaptation capabilities of Apache Wayang, showcasing how it optimizes analytical tasks by tailoring execution to the specific data and task characteristics. Our approach involves dynamically selecting the optimal processing engine based on the nature of the data and the complexity of the analytical operation. This flexibility allows Wayang to deliver enhanced performance and efficiency, particularly for complex analytical workflows that demand the strengths of multiple processing engines. Datasets: We use Wikipedia abstracts and store them in HDFS. We vary the dataset size from 1GB to 800GB. Query/task: We test our product with a widely popular big data analytical task, namely WordCount. It counts the number of distinct words in a text corpus. Different variations of wordcount are useful in various text mining applications, such as term frequency, word clouds, etc. Baselines: We use three different platforms where Wordcount can be executed: Apache Spark, Apache Flink, and a single node Java program. We then set Wayang to be able to automatically choose which of the three platforms to use for each dataset. Results: The benchmark results, presented in Figure 3, provide evidence of Apache Wayang's platform adaptation prowess, consistently selecting the fastest available platform for various dataset sizes in our WordCount example. By incorporating execution cost modeling into the query optimizer, Wayang eliminates the need for manual platform selection or migration, allowing users to solely focus on their analytical tasks. Our platform adaptation feature seamlessly identifies the optimal platform for each query or task based on data and query characteristics, ensuring faster execution and enhanced efficiency in big data analytics. ","version":"Next","tagName":"h3"},{"title":"Features","type":0,"sectionRef":"#","url":"/docs/introduction/features","content":"","keywords":"","version":"Next"},{"title":"Cross platform enablement","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cross-platform-enablement","content":" Apache Wayang's cross-platform optimizer stands out as its most distinguishing feature. It not only selects the most suitable processing engine for each task, but also empowers users to execute a single task across multiple platforms. This capability is achieved through an extensible set of graph transformations applied to the Apache Wayang plan, which identifies alternative execution plans that potentially offer better performance. These alternative plans are then evaluated using platform-specific cost models, which can either be user-defined or learned from historical data. These cost models are parameterized based on the underlying hardware configuration, such as the number of computing nodes for distributed operations. ","version":"Next","tagName":"h3"},{"title":"Scalable ML (ML4All)","type":1,"pageTitle":"Features","url":"/docs/introduction/features#scalable-ml-ml4all","content":" ML4all is part of Wayang, enhancing machine learning workflows. ML4all alleviates users from the complexities of algorithm selection and low-level implementation details, enabling them to concentrate on the core aspects of their machine learning applications. By employing an innovative abstraction, ML4all addresses a broad spectrum of machine learning tasks and utilizes a cost-based optimizer to dynamically identify the optimal gradient descent algorithm for each scenario. Notably, ML4all demonstrates performance improvements of two orders of magnitude over existing systems, enabling the processing of large datasets that were previously impractical. ","version":"Next","tagName":"h3"},{"title":"High Efficiency","type":1,"pageTitle":"Features","url":"/docs/introduction/features#high-efficiency","content":" Apache Wayang's suite of optimized operators and sophisticated query optimization techniques enables it to effectively process large-scale and small-scale datasets. By employing a data processing abstraction based on UDFs, Wayang allows applications to convey semantic information about their functions, optimization hints (such as iteration counts), constraints (e.g., physical placement of operators), and alternative execution plans. The optimizer incorporates these insights to refine the execution plan, aiming to achieve optimal performance in a comprehensive manner. ","version":"Next","tagName":"h3"},{"title":"Flexibility","type":1,"pageTitle":"Features","url":"/docs/introduction/features#flexibility","content":" Apache Wayang offers a collection of operators that applications utilize to define their tasks. A crucial aspect of Apache Wayang is its flexible operator mapping structure, which enables developers to effortlessly add, modify, or remove mappings between Wayang and execution operators. This flexibility empowers developers to seamlessly integrate additional Wayang and execution operators into their applications. ","version":"Next","tagName":"h3"},{"title":"Cost Saving","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cost-saving","content":" Developers can focus on building their applications without the need to understand the complexities of underlying platforms. This simplifies the development process and removes the requirement for developers to be experts in big data infrastructures. Apache Wayang automatically determines the most suitable data processing platforms for specific tasks and deploys applications accordingly. ","version":"Next","tagName":"h3"},{"title":"Additional Features","type":1,"pageTitle":"Features","url":"/docs/introduction/features#additional-features","content":" Zero-copy: cross-platform in-situ data processingHigh performance: A highly-extensible API framework to generate DAG based federated execution plane at runtime to speed up data processing, providing 50-180x speed up by: reduce data movement to single platformsreduce ETL overhead to perform large-scale analyticsreduce data duplication and storageexecute data processing on the best available technology, including local JVM stream processing Data application agnosticity run data tasks on multiple platforms without re-platforming the code (move jobs from Hadoop to Spark without changing the code at large)implement a sustainable AI strategy by using data pools in-situ Enterprise ready federated learning (in development) ","version":"Next","tagName":"h3"},{"title":"Releases","type":0,"sectionRef":"#","url":"/docs/start/download","content":"","keywords":"","version":"Next"},{"title":"1.1.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#110-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"1.0.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#100-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"0.7.1 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#071-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"Frameworks supported","type":1,"pageTitle":"Releases","url":"/docs/start/download#frameworks-supported","content":" Apache Flink v1.7.1Apache Giraph v1.2.0-hadoop2GraphChi v0.2.2 (only available with scala 11.x)Java Streams (version depends on the java version)JDBC-TemplatePostgres v9.4.1208 (Implementation JDBC-Template)Apache Spark v3.1.2 (scala 12.x) and v2.4.8 (scala 11.x)SQLite3 v3.8.11.2 (implementation JDBC-Template) NOTE: The supported platforms for Scala may vary depending on the Scala version.\\ ","version":"Next","tagName":"h3"},{"title":"Previous releases","type":1,"pageTitle":"Releases","url":"/docs/start/download#previous-releases","content":" Older releases can be found here ","version":"Next","tagName":"h3"},{"title":"Getting started","type":0,"sectionRef":"#","url":"/docs/guide/getting-started","content":"","keywords":"","version":"Next"},{"title":"Requirements","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#requirements","content":" Apache Wayang (incubating) is built upon the foundations of Java 11 and Scala 2.12, providing a robust and versatile platform for data processing applications. If you intend to build Wayang from source, you will also need to have Apache Maven, the popular build automation tool, installed on your system. Additionally, be mindful that some of the processing platforms supported by Wayang may have their own specific installation requirements. ","version":"Next","tagName":"h2"},{"title":"Get Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#get-wayang","content":" Apache Wayang is readily available through Maven Central, facilitating seamless integration into your development workflow. For instance, to utilize Wayang in your Maven-based project, simply add the following dependency to your project's POM file: <dependency> <groupId>org.apache.wayang</groupId> <artifactId>wayang-***</artifactId> <version>0.7.1</version> </dependency> Note the ***: Wayang ships with multiple modules that can be included in your app, depending on how you want to use it: wayang-core: provides core data structures and the optimizer (required)wayang-basic: provides common operators and data types for your apps (recommended)wayang-api: provides an easy-to-use Scala and Java API to assemble Wayang plans (recommended)wayang-java, wayang-spark, wayang-graphchi, wayang-sqlite3, wayang-postgres: adapters for the various supported processing platformswayang-profiler: provides functionality to learn operator and UDF cost functions from historical execution data For the sake of version flexibility, you still have to include your Hadoop (hadoop-hdfs and hadoop-common) and Spark (spark-core and spark-graphx) version of choice. In addition, you can obtain the most recent snapshot version of Wayang via Sonatype's snapshot repository. Just included <repositories> <repository> <id>sonatype-snapshots</id> <name>Sonatype Snapshot Repository</name> <url>https://oss.sonatype.org/content/repositories/snapshots</url> </repository> <repositories> If you need to rebuild Wayang, e.g., to use a different Scala version, you can simply do so via Maven: Adapt the version variables (e.g., spark.version) in the main pom.xml file.Build Wayang with the adapted versions. $ mvn clean install Note the standalone profile to fix Hadoop and Spark versions, so that Wayang apps do not explicitly need to declare the corresponding dependencies. Also, note the distro profile, which assembles a binary Wayang distribution. To activate these profiles, you need to specify them when running maven, i.e., mvn clean install -P<profile name> ","version":"Next","tagName":"h3"},{"title":"Configure Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#configure-wayang","content":" To enable Apache Wayang's smooth operation, you need to equip it with details about your processing platforms' capabilities and how to interact with them. A default configuration is available for initial testing, but creating a properties file is generally preferable for fine-tuning the configuration to suit your specific requirements. To harness this personalized configuration effortlessly, launch your application via $ java -Dwayang.configuration=url://to/my/wayang.properties ... Essential configuration settings: General settings wayang.core.log.enabled (= true): whether to log execution statistics to allow learning better cardinality and cost estimators for the optimizerwayang.core.log.executions (= ~/.wayang/executions.json) where to log execution times of operator groupswayang.core.log.cardinalities (= ~/.wayang/cardinalities.json) where to log cardinality measurementswayang.core.optimizer.instrumentation (= org.apache.wayang.core.profiling.OutboundInstrumentationStrategy): where to measure cardinalities in Wayang plans; other options are org.apache.wayang.core.profiling.NoInstrumentationStrategy and org.apache.wayang.core.profiling.FullInstrumentationStrategywayang.core.optimizer.reoptimize (= false): whether to progressively optimize Wayang planswayang.basic.tempdir (= file:///tmp): where to store temporary files, in particular for inter-platform communication Java Streams wayang.java.cpu.mhz (= 2700): clock frequency of processor the JVM runs on in MHzwayang.java.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to JVM in ms/MB Apache Spark spark.master (= local): Spark master various other Spark settings are supported, e.g., spark.executor.memory, spark.serializer, ... wayang.spark.cpu.mhz (= 2700): clock frequency of processor the Spark workers run on in MHzwayang.spark.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to the Spark workers in ms/MBwayang.spark.network.ms-per-mb (= 8.6): average network throughput of the Spark workers in ms/MBwayang.spark.init.ms (= 4500): time it takes Spark to initialize in ms GraphChi wayang.graphchi.cpu.mhz (= 2700): clock frequency of processor GraphChi runs on in MHzwayang.graphchi.cpu.cores (= 2): number of cores GraphChi runs onwayang.graphchi.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to GraphChi in ms/MB SQLite wayang.sqlite3.jdbc.url: JDBC URL to use SQLitewayang.sqlite3.jdbc.user: optional user namewayang.sqlite3.jdbc.password: optional passwordwayang.sqlite3.cpu.mhz (= 2700): clock frequency of processor SQLite runs on in MHzwayang.sqlite3.cpu.cores (= 2): number of cores SQLite runs on PostgreSQL wayang.postgres.jdbc.url: JDBC URL to use PostgreSQLwayang.postgres.jdbc.user: optional user namewayang.postgres.jdbc.password: optional passwordwayang.postgres.cpu.mhz (= 2700): clock frequency of processor PostgreSQL runs on in MHzwayang.postgres.cpu.cores (= 2): number of cores PostgreSQL runs on To effectively define your applications with Apache Wayang, utilize its Scala or Java API, conveniently found within the wayang-api module. For clear illustrations, refer to the provided examples below. ","version":"Next","tagName":"h3"},{"title":"Cost Functions","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#cost-functions","content":" Wayang provides a utility to learn cost functions from historical execution data. Specifically, Wayang can learn configurations for load profile estimators (that estimate CPU load, disk load etc.) for both operators and UDFs, as long as the configuration provides a template for those estimators. As an example, the JavaMapOperator draws its load profile estimator configuration via the configuration key wayang.java.map.load. Now, it is possible to specify a load profile estimator template in the configuration under the key <original key>.template, e.g.: wayang.java.map.load.template = {\\ "in":1, "out":1,\\ "cpu":"?*in0"\\ } This template encapsulates a load profile estimator that requires at minimum one input cardinality and one output cardinality. Furthermore, it simulates CPU load by assuming a direct relationship with the input cardinality. However, more complex functions are possible. In particular, you can use the variables in0, in1, ... and out0, out1, ... to incorporate the input and output cardinalities, respectively;operator properties, such as numIterations for the PageRankOperator implementations;the operators +, -, *, /, %, ^, and parantheses;the functions min(x0, x1, ...)), max(x0, x1, ...), abs(x), log(x, base), ln(x), ld(x);and the constants e and pi. While Apache Wayang provides templates for all execution operators, you will need to explicitly define your user-defined functions (UDFs) by specifying their cost functions, which are based on configuration parameters. This involves creating an initial specification and template for each UDF. As soon as execution data has been collected, you can initiate: java ... org.apache.wayang.profiler.ga.GeneticOptimizerApp [configuration URL [execution log]] This tool will attempt to determine suitable values for the question marks (?) within the load profile estimator templates, aligning them with the collected execution data and pre-defined configuration entries for the load profile estimators. These optimized values can then be directly incorporated into your configuration. ","version":"Next","tagName":"h2"},{"title":"Examples","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#examples","content":" For some executable examples, have a look at this repository. ","version":"Next","tagName":"h2"},{"title":"WordCount","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#wordcount","content":" Java API import org.apache.wayang.api.JavaPlanBuilder; import org.apache.wayang.basic.data.Tuple2; import org.apache.wayang.core.api.Configuration; import org.apache.wayang.core.api.WayangContext; import org.apache.wayang.core.optimizer.cardinality.DefaultCardinalityEstimator; import org.apache.wayang.java.Java; import org.apache.wayang.spark.Spark; import java.util.Collection; import java.util.Arrays; public class WordcountJava { public static void main(String[] args){ // Settings String inputUrl = "file:/tmp.txt"; // Get a plan builder. WayangContext wayangContext = new WayangContext(new Configuration()) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); JavaPlanBuilder planBuilder = new JavaPlanBuilder(wayangContext) .withJobName(String.format("WordCount (%s)", inputUrl)) .withUdfJarOf(WordcountJava.class); // Start building the WayangPlan. Collection<Tuple2<String, Integer>> wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(line -> Arrays.asList(line.split("\\\\W+"))) .withSelectivity(10, 100, 0.9) .withName("Split words") // Filter empty tokens. .filter(token -> !token.isEmpty()) .withSelectivity(0.99, 0.99, 0.99) .withName("Filter empty words") // Attach counter to each word. .map(word -> new Tuple2<>(word.toLowerCase(), 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey( Tuple2::getField0, (t1, t2) -> new Tuple2<>(t1.getField0(), t1.getField1() + t2.getField1()) ) .withCardinalityEstimator(new DefaultCardinalityEstimator(0.9, 1, false, in -> Math.round(0.01 * in[0]))) .withName("Add counters") // Execute the plan and collect the results. .collect(); System.out.println(wordcounts); } } Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark object WordcountScala { def main(args: Array[String]) { // Settings val inputUrl = "file:/tmp.txt" // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"WordCount ($inputUrl)") .withUdfJarsOf(this.getClass) val wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(_.split("\\\\W+"), selectivity = 10).withName("Split words") // Filter empty tokens. .filter(_.nonEmpty, selectivity = 0.99).withName("Filter empty words") // Attach counter to each word. .map(word => (word.toLowerCase, 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey(_._1, (c1, c2) => (c1._1, c1._2 + c2._2)).withName("Add counters") .withCardinalityEstimator((in: Long) => math.round(in * 0.01)) // Execute the plan and collect the results. .collect() println(wordcounts) } } ","version":"Next","tagName":"h3"},{"title":"k-means","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#k-means","content":" Wayang is also capable of iterative processing, which is, e.g., very important for machine learning algorithms, such as k-means. Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.core.function.FunctionDescriptor.ExtendedSerializableFunction import org.apache.wayang.core.function.ExecutionContext import org.apache.wayang.core.optimizer.costs.LoadProfileEstimators import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark import scala.util.Random import scala.collection.JavaConversions._ object kmeans { def main(args: Array[String]) { // Settings val inputUrl = "file:/kmeans.txt" val k = 5 val iterations = 100 val configuration = new Configuration // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"k-means ($inputUrl, k=$k, $iterations iterations)") .withUdfJarsOf(this.getClass) case class Point(x: Double, y: Double) case class TaggedPoint(x: Double, y: Double, cluster: Int) case class TaggedPointCounter(x: Double, y: Double, cluster: Int, count: Long) { def add_points(that: TaggedPointCounter) = TaggedPointCounter(this.x + that.x, this.y + that.y, this.cluster, this.count + that.count) def average = TaggedPointCounter(x / count, y / count, cluster, 0) } // Read and parse the input file(s). val points = planBuilder .readTextFile(inputUrl).withName("Read file") .map { line => val fields = line.split(",") Point(fields(0).toDouble, fields(1).toDouble) }.withName("Create points") // Create initial centroids. val random = new Random val initialCentroids = planBuilder .loadCollection(for (i <- 1 to k) yield TaggedPointCounter(random.nextGaussian(), random.nextGaussian(), i, 0)).withName("Load random centroids") // Declare UDF to select centroid for each data point. class SelectNearestCentroid extends ExtendedSerializableFunction[Point, TaggedPointCounter] { /** Keeps the broadcasted centroids. */ var centroids: Iterable[TaggedPointCounter] = _ override def open(executionCtx: ExecutionContext) = { centroids = executionCtx.getBroadcast[TaggedPointCounter]("centroids") } override def apply(point: Point): TaggedPointCounter = { var minDistance = Double.PositiveInfinity var nearestCentroidId = -1 for (centroid <- centroids) { val distance = Math.pow(Math.pow(point.x - centroid.x, 2) + Math.pow(point.y - centroid.y, 2), 0.5) if (distance < minDistance) { minDistance = distance nearestCentroidId = centroid.cluster } } new TaggedPointCounter(point.x, point.y, nearestCentroidId, 1) } } // Do the k-means loop. val finalCentroids = initialCentroids.repeat(iterations, { currentCentroids => points .mapJava(new SelectNearestCentroid, udfLoad = LoadProfileEstimators.createFromSpecification( "my.udf.costfunction.key", configuration )) .withBroadcast(currentCentroids, "centroids").withName("Find nearest centroid") .reduceByKey(_.cluster, _.add_points(_)).withName("Add up points") .withCardinalityEstimator(k) .map(_.average).withName("Average points") }).withName("Loop") // Collect the results. .collect() println(finalCentroids) } } ","version":"Next","tagName":"h3"},{"title":"Guide to Development and Usage with Apache Wayang (incubating)","type":0,"sectionRef":"#","url":"/docs/guide/examples","content":"","keywords":"","version":"Next"},{"title":"Example 1: Machine Learning for query optimization in Apache Wayang","type":1,"pageTitle":"Guide to Development and Usage with Apache Wayang (incubating)","url":"/docs/guide/examples#example-1-machine-learning-for-query-optimization-in-apache-wayang","content":" Apache Wayang can be customized with concrete implementations of the EstimatableCost interface in order to optimize for a desired metric. The implementation can be enabled by providing it to a Configuration. public class CustomEstimatableCost implements EstimatableCost { /* Provide concrete implementations to match desired cost function(s) * by implementing the interface in this class. */ } public class WordCount { public static void main(String[] args) { /* Create a Wayang context and specify the platforms Wayang will consider */ Configuration config = new Configuration(); /* Provision of a EstimatableCost that implements the interface.*/ config.setCostModel(new CustomEstimatableCost()); WayangContext wayangContext = new WayangContext(config) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); /*... omitted */ } } In combination with an encoding scheme and a third party package to load ML models, the following example shows how to predict runtimes of query execution plans runtimes in Apache Wayang (incubating): public class MLCost implements EstimatableCost { public EstimatableCostFactory getFactory() { return new Factory(); } public static class Factory implements EstimatableCostFactory { @Override public EstimatableCost makeCost() { return new MLCost(); } } @Override public ProbabilisticDoubleInterval getEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } @Override public ProbabilisticDoubleInterval getParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } /** Returns a squashed cost estimate. */ @Override public double getSquashedEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public double getSquashedParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public Tuple<List<ProbabilisticDoubleInterval>, List<Double>> getParallelOperatorJunctionAllCostEstimate(PlanImplementation plan, Operator operator) { List<ProbabilisticDoubleInterval> intervalList = new ArrayList<ProbabilisticDoubleInterval>(); List<Double> doubleList = new ArrayList<Double>(); intervalList.add(this.getEstimate(plan, true)); doubleList.add(this.getSquashedEstimate(plan, true)); return new Tuple<>(intervalList, doubleList); } public PlanImplementation pickBestExecutionPlan( Collection<PlanImplementation> executionPlans, ExecutionPlan existingPlan, Set<Channel> openChannels, Set<ExecutionStage> executedStages) { final PlanImplementation bestPlanImplementation = executionPlans.stream() .reduce((p1, p2) -> { final double t1 = p1.getSquashedCostEstimate(); final double t2 = p2.getSquashedCostEstimate(); return t1 < t2 ? p1 : p2; }) .orElseThrow(() -> new WayangException("Could not find an execution plan.")); return bestPlanImplementation; } } Third-party packages such as OnnxRuntime can be used to load pre-trained .onnx files that contain desired ML models. public class OrtMLModel { private static OrtMLModel INSTANCE; private OrtSession session; private OrtEnvironment env; private final Map<String, OnnxTensor> inputMap = new HashMap<>(); private final Set<String> requestedOutputs = new HashSet<>(); public static OrtMLModel getInstance(Configuration configuration) throws OrtException { if (INSTANCE == null) { INSTANCE = new OrtMLModel(configuration); } return INSTANCE; } private OrtMLModel(Configuration configuration) throws OrtException { this.loadModel(configuration.getStringProperty("wayang.ml.model.file")); } public void loadModel(String filePath) throws OrtException { if (this.env == null) { this.env = OrtEnvironment.getEnvironment(); } if (this.session == null) { this.session = env.createSession(filePath, new OrtSession.SessionOptions()); } } public void closeSession() throws OrtException { this.session.close(); this.env.close(); } /** * @param encodedVector * @return NaN on error, and a predicted cost on any other value. * @throws OrtException */ public double runModel(Vector<Long> encodedVector) throws OrtException { double costPrediction; OnnxTensor tensor = OnnxTensor.createTensor(env, encodedVector); this.inputMap.put("input", tensor); this.requestedOutputs.add("output"); BiFunction<Result, String, Double> unwrapFunc = (r, s) -> { try { return ((double[]) r.get(s).get().getValue())[0]; } catch (OrtException e) { return Double.NaN; } }; try (Result r = session.run(inputMap, requestedOutputs)) { costPrediction = unwrapFunc.apply(r, "output"); } return costPrediction; } } ","version":"Next","tagName":"h2"}],"options":{"id":"default"}}
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-{"searchDocs":[{"title":"Apache Kafka meets Wayang - Part 1","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-1","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#intro","content":" This article is the first of a four part series about federated data analysis using Apache Wayang. The first article starts with an introduction of a typical data colaboration scenario which will emerge in our digital future. In part two and three we will share a summary of our Apache Kafka client implementation for Apache Wayang. We started with the Java Platform (part 2) and the Apache Spark implementation follows (W.I.P.) in part three. The use case behind this work is an imaginary data collaboration scenario. We see this example and the demand for a solution already in many places. For us this is motivation enough to propose a solution. This would also allow us to do more local data processing, and businesses can stop moving data around the world, but rather care about data locality while they expose and share specific information to others by using data federation. This reduces complexity of data management and cost dramatically. For this purpose, we illustrate a cross organizational data sharing scenario from the finance sector soon. This analysis pattern will also be relevant in the context of data analysis along supply chains, another typical example where data from many stakeholder together is needed but never managed in one place, for good reasons. Data federation can help us to unlock the hidden value of all those isolated data lakes. ","version":null,"tagName":"h2"},{"title":"A cross organizational data sharing scenario","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#a-cross-organizational-data-sharing-scenario","content":" Our goal is the implementation of a cross organization decentralized data processing scenario, in which protected local data should be processed in combination with public data from public sources in a collaborative manner. Instead of copying all data into a central data lake or a central data platform we decided to use federated analytics. Apache Wayang is the tool we work with. In our case, the public data is hosted on publicly available websites or data pods. A client can use the HTTP(S) protocol to read the data which is given in a well defined format. For simplicity we decided to use CSV format. When we look into the data of each participant we have a different perspective. Our processing procedure should calculate a particular metric on the local data of each participant. An example of such a metric is the average spending of all users on a particular product category per month. This can vary from partner to partner, hence, we want to be able to calculate a peer-group comparison so that each partner can see its own metric compared with a global average calculated from contributions by all partners. Such a process requires global averaging and local averaging. And due to governance constraints, we can’t bring all raw data together in one place. Instead, we want to use Apache Wayang for this purpose. We simplify the procedure and split it into two phases. Phase one is the process, which allows each participant to calculate the local metrics. This requires only local data. The second phase requires data from all collaborating partners. The monthly sum and counter values per partner and category are needed in one place by all other parties. Hence, the algorithm of the first phase stores the local results locally, and the contributions to the global results in an externally accessible Kafka topic. We assume this is done by each of the partners. Now we have a scenario, in which an Apache Wayang process must be able to read data from multiple Apache Kafka topics from multiple Apache Kafka clusters but finally writes into a single Kafka topic, which then can be accessed by all the participating clients. The illustration shows the data flows in such a scenario. Jobs with red border are executed by the participants in isolation within their own data processing environments. But they share some of the data, using publicly accessible Kafka topics, marked by A. Job 4 is the Apache Wayang job in our focus: here we intent to read data from 3 different source systems, and write results into a fourth system (marked as B), which can be accesses by all participants again. With this in mind we want to implement an Apache Wayang application which implements the illustrated Job 4. Since as of today, there is now KafkaSource and KafkaSink available in Apache Wayang, an implementation of both will be our first step. Our assumption is, that in the beginning, there won’t be much data. Apache Spark is not required to cope with the load, but we expect, that in the future, a single Java application would not be able to handle our workload. Hence, we want to utilize the Apache Wayang abstraction over multiple processing platforms, starting with Java. Later, we want to switch to Apache Spark. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 2","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-2","content":"","keywords":"","version":null},{"title":"Apache Wayang’s Read & Write Path for Kafka topics","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#apache-wayangs-read--write-path-for-kafka-topics","content":" To describe the read and write paths for data in the context of the created Apache Wayang code snippet, the primary classes and interfaces we need to understand are as follows: WayangContext: This class is essential for initializing the Wayang processing environment. It allows you to configure the execution environment and register plugins that define which platforms Wayang can use for data processing tasks, such as Java.basicPlugin() for local Java execution. JavaPlanBuilder: This class is used to build and define the data processing pipeline (or plan) in Wayang. It provides a fluent API to specify the operations to be performed on the data, from reading the input to processing it and writing the output. ","version":null,"tagName":"h2"},{"title":"Read Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#read-path","content":" The read path describes how data is ingested from a source into the Wayang processing pipeline: Reading from Kafka Topic: The method readKafkaTopic(topicName) is used to ingest data from a specified Kafka topic. This is the starting point of the data processing pipeline, where topicName represents the name of the Kafka topic from which data is read. Data Tokenization and Preparation: Once the data is read from Kafka, it undergoes several transformations such as Splitting, Filtering, and Mapping. What follows are the procedures known as Reducing, Grouping, Co-Grouping, and Counting. ","version":null,"tagName":"h3"},{"title":"Write Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#write-path","content":" Writing to Kafka Topic: The final step in the pipeline involves writing the processed data back to a Kafka topic using .writeKafkaTopic(...). This method takes parameters that specify the target Kafka topic, a serialization function to format the data as strings, and additional configuration for load profile estimation, which optimizes the writing process. This read-write path provides a comprehensive flow of data from ingestion from Kafka, through various processing steps, and finally back to Kafka, showcasing a full cycle of data processing within Apache Wayang's abstracted environment and is implemented in our example program shown in listing 1. ","version":null,"tagName":"h3"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#implementation-of-input--and-output-operators","content":" The next section shows how a new pair of operators can be implemented to extend Apache Wayang’s capabilities on the input and output side. We created the Kafka Source and Kafka Sink components so that our cross organizational data collaboration scenario can be implemented using data streaming infrastructure. Level 1 – Wayang execution plan with abstract operators The implementation of our Kafka Source and Kafka Sink components for Apache Wayang requires new methods and classes on three layers. First of all in the API package. Here we use the JavaPlanBuilder to expose the function for selecting a Kafka topic as the source to be used by client. The class JavaPlanBuilder in package org.apache.wayang.api in the project wayang-api/wayang-api-scala-java exposes our new functionality to our external client. An instance of the JavaPlanBuilder is used to define the data processing pipeline. We use its readKafkaTopic() which specifies the source Kafka topic to read from, and for the write path we use the writeKafkaTopic() method. Both Methods do only trigger activities in the background. For the output side, we use the DataQuantaBuilder class, which offers an implementation of the writeKafkaTopic function. This function is designed to send processed data, referred to as DataQuanta, to a specified Kafka topic. Essentially, it marks the final step in a data processing sequence constructed using the Apache Wayang framework. In the DataQuanta class we implemented the methods writeKafkaTopic and writeKafkaTopicJava which use the KafkaTopicSink class. In this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation The second layer builds the bridge between the WayangContext and PlanBuilders which work together with DataQuanta and the DataQuantaBuilder. Also, the mapping between the abstract components and the specific implementations are defined in this layer. Therefore, the mappings package has a class Mappings in which all relevant input and output operators are listed. We use it to register the KafkaSourceMapping and a KafkaSinkMapping for the particular platform, Java in our case. These classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In our case this leads to a Java program running on a JVM which is set up by the Apache Wayang framework using the logical components of the execution plan. Those mappings link the real implementation of our operators the ones used in an execution plan. The JavaKafkaTopicSource and the JavaKafkaTopicSink extend the KafkaTopicSource and KafkaTopicSink so that the lower level implementation of those classes become available within Wayang’s Java Platform context. In this layer, the KafkaConsumer class and the KafkaProducer class are used, but both are configured and instantiated in the next layer underneath. All this is done in the project wayang-plarforms/wayang-java. Layer 3 – Input/Output Connector Layer The KafkaTopicSource and KafkaTopicSink classes build the third layer of our implementation. Both are implemented in Java programming language. In this layer, the real Kafka-Client logic is defined. Details about consumer and producers, client configuration, and schema handling have to be handled here. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#summary","content":" Both classes in the third layer implement the Kafka client logic which is needed by the Wayang-execution plan when external data flows should be established. The layer above handles the mapping of the components at startup time. All this wiring is needed to keep Wayang open and flexible so that multiple external systems can be used in a variety of combinations and using multiple target platforms in combinations. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#outlook","content":" The next part of the article series will cover the creation of an Kafka Source and Sink component for the Apache Spark platform, which allows our use case to scale. Finally, in part four we bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 3","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-3","content":"","keywords":"","version":null},{"title":"The goal of this implementation","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#the-goal-of-this-implementation","content":" We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. In our example scenario, data is available in multiple different clusters, in different regions and owned by different organizations. Each organization uses the "stream sharing" feature provided by Confluent cloud. This way, the operator of our central processing job has been granted appropriate permissions. The plaftorm provided the necessary configuration properties, including access coordinates and credentials in the name of the topic owner to us. The following illustration has already been introduced in part one of the blog series, but for clarity we repeat it here. Today, we focus on Job 4 in the image. We implement a program which uses data federation based on multiple sources. Each source allows us to read the data from that particular topic so that we can process it in a different governance context. In this example it is a public processing context, in which data from multiple private processing contexts are used together. This use case is already prepared for high processing loads We can utilize the scalability capabilities of Apache Spark or simply use a Java program for initial tests of the solution. Switching between both is done in one line of code in Apache Wayang. Again, we start with a WayangContext, as shown by examples in the Wayang code repository. WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin()); We simply switched the backend system towards Apache Spark by using the WayangContext with Spark.basicPlugin(). The JavaPlanBuilder and all other logic of our example job won't be touched. In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module. ","version":null,"tagName":"h2"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#implementation-of-input--and-output-operators","content":" We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. Hence we work with Wayang's Java API. Level 1 – Wayang execution plan with abstract operators Since the JavaPlanBuilder already exposes the function for selecting a Kafka topic as source and the DataQuantaBuilder class exposes the writeKafkaTopic function we can move on quickly. Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder. We must provide the mapping between the abstract components and the specific implementations in this layer. Therefore, the mappings package in project wayang-platforms/wayang-spark has a class Mappings in which our KafkaTopicSinkMapping and KafkaTopicSourceMapping will be registered. Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime. A mapping links an operator implementation to the abstraction used in an execution plan. We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round. For the Spark platform we simply replace the occurences of JavaPlatform with SparkPlatform. Furthermore, we create an implementation of the SparkKafkaTopicSource and SparkKafkaTopicSink. Layer 3 – Input/Output Connector Layer Let's quickly recap, how does Apache Spark interacts with Apache Kafka? There is already an integration which gives us a DataSet using the Spark SQL framework. For Spark Streaming, there is also a Kafka integration using the SparkSession's readStream() function. Kafka client properties are provided as key value pairs k and v by using the option( k, v ) function. For writing into a topic, we can use the writeStream() function. But from a first look, it seems to be not the best fit. Another approach is possible. We can use simple RDDs to process data previously consumed from Apache Kafka. This is a more low-level approach compared to using Datasets with Spark Structured Streaming, and it typically involves using the Kafka RDD API provided by Spark. This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. However, we might need that approach for the integration with Apache Wayang. For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then we parallelize the records in an RDD. This allows us to reuse KafkaTopicSource and KafkaTopicSink classes we built last time. Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer. The selected approach does not yet fully take advantage from Spark's parallelism at load time. For higher loads and especially for streaming processing we would have to investigate another approache, using a SparkStreamingContext, but this is out of scope for now. Since we can't reuse the JavaKafkaTopicSource and JavaKafkaTopicSink we rather implement SparkKafkaTopicSource and SparkKafkaTopicSink based on given SparkTextFileSource and SparkTextFileSink which both cary all needed RDD specific logic. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#summary","content":" As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. We could easily follow the pattern we have worked out in the previous exercise. But a bunch of much more interesting work will follow next. More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well. The code has been submitted to the Apache Wayang repository. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#outlook","content":" The next part of the article series will cover the real world example as described in image 1. We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Wayang and the Federated AI","type":0,"sectionRef":"#","url":"/blog/wayang-federated-ai","content":"","keywords":"","version":null},{"title":"Apache Wayang in the Federated AI world","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#apache-wayang-in-the-federated-ai-world","content":" Apache Wayang (Wayang, for short), a project in an incubation phase at Apache Software Foundation (ASF), integrates big data platforms and tools by removing the complexity of worrying about low-level details. Interestingly, even if it was not designed for, Wayang could also serve as a scalable platform for federated learning: the Wayang community is starting to work on integrating federated learning capabilities. In a federated learning approach, Wayang would allow different local models to be built and exchange its model results across other data centers to combine them into a single enhanced model. ","version":null,"tagName":"h2"},{"title":"A real-world example","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#a-real-world-example","content":" Let's consider a real-world scenario. Hospitals and health organizations have increased their investments in machine/deep learning initiatives to learn more and predict diagnostics. However, due to legal frameworks, sharing patients' information or diagnostics is impossible, and the solution would be to apply federated learning. To solve this problem, we could use Wayang to help to train the models. See the diagram 1 below: As a first step, the data scientists would send an ML task to Wayang, which will work as an abstraction layer to connect to different data processing platforms, sparing the time to build integration code for each. Then, the data platforms process and generate the results that will be sent back to Wayang. Wayang aggregates the results into one "global result" and sends it back to the requestor as a next step. The process repeats until the desired results are achieved. Although it is very much like a Federated learning pipeline, Wayang removes a considerable layer of complexity from the developers by integrating with diverse types of data platforms. It also brings fast development and reduces the need for a deep understanding of data infrastructure or integrations. Developers can focus on the logic and how to execute tasks instead of details about data processors. ","version":null,"tagName":"h2"},{"title":"Follow Wayang","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#follow-wayang","content":" Apache Wayang is in an incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. Thank you! I (Gláucia) want to thank professor Jorge Quiané for the guidance to write this blog post. Thanks for incentivate me to join the project and for the knowledge shared. I will always remember you. ","version":null,"tagName":"h3"},{"title":"Pywayang - Apache Wayang's Python API","type":0,"sectionRef":"#","url":"/blog/wayang-python-api","content":"","keywords":"","version":null},{"title":"Expanding Apache Wayang's APIs","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#expanding-apache-wayangs-apis","content":" Apache Wayang's architecture decouples the process of planning from the resulting execution, allowing users to specify platform agnostic plans through the provided APIs. Python's popularity and convenience for data processing workloads makes it an obvious candidate for a desired API. Previous APIs, such as the Scala API wayang-api-scala-java benefited from the interoperability of Java and Scala that allows to reuse objects from other languages to provide new interfaces. Accessing JVM objects in Python is possible through several libraries, but in doing so, future APIs in other programming languages would need similar libraries and implementations in order to exist. As a contrast to that, providing an API within Apache Wayang that receives input plans from any source and executes them within allows to create plans and submit them in any programming language. The following figure shows the architecture of pywayang: The Python API allows users to specify WayangPlans with UDFs in Python.pywayang then serializes the UDFs and constructs the WayangPlan in JSON format, preparing it to be sent to Apache Wayang's JSON API. When receiving a valid JSON plan, the JSON API uses the optimizer to construct an execution plan. However, since UDFs are defined in Python and thus need to be executed in Python as well, an operators function needs to be wrapped into a WrappedPythonFunction: val mapOperator = new MapPartitionsOperator[Input, Output]( new MapPartitionsDescriptor[Input, Output]( new WrappedPythonFunction[Input, Output]( ByteString.copyFromUtf8(udf) ), classOf[Input], classOf[Output], ) ) This wrapped functional descriptor allows to handle execution of UDFs in Python through a socket connection with the pywayang worker. Input data is sourced from the platform chosen by the optimizer and Apache Wayang handles routing the output data to the next operator. A new API in any programming languages would have to specify two things: A way to create plans that conform to a JSON format specified in the Wayang JSON API.A worker that handles encoding and decoding of user defined functions (UDFs), as they need to be executed on iterables in their respective language. After that, the API can be added as a module in Wayang, so that operators will be wrapped and UDFs can be executed in the desired programming language. ","version":null,"tagName":"h2"},{"title":"Defining WayangPlans in Python","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#defining-wayangplans-in-python","content":" As the "Hello World!" of data processing systems, wordcount will pose as our primary example to display how users can interact with Apache Wayang through the python package pywayang. from pywy.dataquanta import WayangContext from pywy.platforms.java import JavaPlugin from pywy.platforms.spark import SparkPlugin def wordcount(): ctx = WayangContext() \\ .register({JavaPlugin, SparkPlugin}) \\ .textfile("file://README.md") \\ .flatmap(lambda w: w.split()) \\ .filter(lambda w: w.strip() != "") \\ .map(lambda w: (w.lower(), 1)) \\ .reduce_by_key(lambda t: t[0], lambda t1, t2: (t1[0], int(t1[1]) + int(t2[1]))) \\ .store_textfile("file:///wordcount-out-python.txt") if __name__ == "__main__": wordcount() The example displays a mode of operation that resembles the ScalaPlanBuilder and the JavaPlanBuilder. Plans are specified in a functional way, chaining operations until a terminal operation results in execution of the plan. ","version":null,"tagName":"h2"},{"title":"Wayang-API-JSON","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wayang-api-json","content":" The wayang-api-json module provides an executable that starts a REST server. This server accepts a WayangPlan in JSON format. Starting the REST API as a background process can be done by executing the following: mvn clean package -pl :wayang-assembly -Pdistribution cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.api.json.Main & ","version":null,"tagName":"h2"},{"title":"Wrapping pipelines in MapPartition operators","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wrapping-pipelines-in-mappartition-operators","content":" With this architecture, the execution of an operator comes with an additional overhead, because the UDFs will have to be executed in python. Python operators receive iterators through a socket and also return their result to Wayang through that connection. To minimize the overhead, unary operators that return unary results will be grouped in pipelines. One pipeline of operators will be submitted to the Wayang JSON API as a single MapPartition operator. This means that the UDFs specified in this pipeline can be chained and only on call from Wayang to the Python worker will have to be made for a given pipeline. ","version":null,"tagName":"h2"},{"title":"Coming soon","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#coming-soon","content":" As the Python API is currently in development and we are applying finishing touches, this article serves as an outlook for what users can expect to see soon. Author: juripetersen ","version":null,"tagName":"h2"},{"title":"Integrating ML platforms in Wayang","type":0,"sectionRef":"#","url":"/blog/wayang-tensorflow","content":"","keywords":"","version":null},{"title":"Step 1: Introducing a Model","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-1-introducing-a-model","content":" With respect to the model, we followed Wayang’s abstraction philosophy: We created a Model interface to be used as input or output by Wayang operators and then extended it for the platform-specific operators. Different model interfaces can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model A platform-specific model needs to be instantiated to be used as the output of a training operator and as input for an inference operator. You can see an example of the SparkMLModel here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/model/SparkMLModel.java ","version":null,"tagName":"h2"},{"title":"Step 2: Introducing Training Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-2-introducing-training-operators","content":" We added the desired Wayang (platform-agnostic) training operators which are binary to unary operators, taking as input the X and y values and outputting a Model. You can find an example of a LinearRegressionOperator here: https://github.com/apache/incubator-wayang/blob/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/LinearRegressionOperator.java Platform-specific execution operators, such as SparkLinearRegressionOperator, can be easily added as any other execution operator: extending the corresponding Wayang operator and providing the mappings from the Wayang to the execution operator. See, for example, the SparkLinearRegressionOperator: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkLinearRegressionOperator.java ","version":null,"tagName":"h2"},{"title":"Step 3: Introducing Prediction Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-3-introducing-prediction-operators","content":" Additionally, we created a PredictOperator, a BinaryToUnary Wayang (platform-agnostic) operator which takes as input the data quanta and a model and outputs the data quanta with the predictions output by the model. https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/PredictOperator.java Then, a concrete platform-specific operator extends from the abstract one. See the SparkPredictOperator for an example: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkPredictOperator.java ","version":null,"tagName":"h2"},{"title":"Deep Learning Models","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#deep-learning-models","content":" Unlike traditional machine learning models, the definition of deep learning models is more flexible. Users can combine different blocks (e.g., fully connected blocks, convolutional blocks) to build their desired models. The whole model can be represented as a graph on which the vertices represent blocks and the edges represent connections between blocks. In this case, we built a DLModel class that implements the Model interface, which contains a user-defined, platform-agnostic graph of the model: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model/DLModel.java For training, we implemented the platform-agnostic DLModelTrainingOperator Wayang operator: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/DLTrainingOperator.java ","version":null,"tagName":"h2"},{"title":"New ML platform -- Tensorflow Integration","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#new-ml-platform----tensorflow-integration","content":" We have added Tensorflow as a new platform by creating a new module (wayang-tensorflow) inside the wayang-platforms parent module and implementing a Tensorflow driver. The TensorflowExecutor driver is responsible for creating and destroying Tensorflow resources, such as a model graph and a model parameter context. When a training task scheduled on Tensorflow, it will be mapped to TensorflowDLModelTrainingOperator. In this process, the DLModel will be converted to TensorflowModel, which means that the user-defined model graph will be converted to a Tensorflow model graph. Likewise, for inference, the PredictOperator will be mapped to TensorflowPredictOperator. All the code for the tensorflow platform can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-tensorflow/src/main/java/org/apache/wayang/tensorflow ","version":null,"tagName":"h2"},{"title":"Acknowledgement","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#acknowledgement","content":" The source code for the support of ML operators and the Tensorflow integration has been contributed by Mingxi Liu. ","version":null,"tagName":"h3"},{"title":"Follow Wayang","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#follow-wayang","content":" Apache Wayang is in incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. ","version":null,"tagName":"h3"},{"title":"Apache Wayang vs. Presto/Trino","type":0,"sectionRef":"#","url":"/blog/wayang-vs-trino","content":"","keywords":"","version":null},{"title":"Key Distinctions","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#key-distinctions","content":" Trino/Presto is a query engine for distributed SQL query processing. It is composed of a coordinator and multiple workers. The coordinator consists of a query optimizer and a scheduler, while the workers are responsible for performing the necessary query processing. Data is fetched from external systems via a Connector API, i.e., Trino/Presto supports multiple data sources. Notably,query processing is is conducted exclusively by Trino/Presto workers, not the external systems. In contrast, Wayang is a middleware for integrating diverse data platforms, including but not limited to query engines. This means that Wayang leverages the processing capabilities of the underlying data platforms to complete a given job, with no actual query processing taking place within Wayang itself. Below you can graphically see the difference between the two systems. Note that not all available data sources or data platforms are illustrated for simplicity reasons. Below you can see how Wayang integrates data platforms and utilizes them for any data processing required. Below you can see how Trino unifies different data sources and then performs data processing in a distributed manner. I hope this makes it clear now. In fact, Trino can be easily plugged to Wayang as a platform and be seamlessly integrated with other data platforms, as shown below. ","version":null,"tagName":"h2"},{"title":"What are the advantages of using Wayang?","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#what-are-the-advantages-of-using-wayang","content":" Wayang brings several benefits thanks to its integration layer: Seamless integration of SQL query engines with ML and other data analysis systems within a single job, eliminating the need to materialize intermediate results. Users are freed from the task of specifying the query engines for an application if they desire. By submitting their Wayang job, the cross-platform optimizer can automatically determine the best data platform to use for improved performance or cost savings. Wayang facilitates cross-platform data processing by utilizing multiple data platforms to execute a query for a single job, optimizing performance and cost efficiency. Data does not have to be transferred outside their original location. ","version":null,"tagName":"h2"},{"title":"Conclusion","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#conclusion","content":" Trino is a distributed SQL query engine which performs all the query processing of an input SQL query in a distributed manner. Wayang, on the other hand, is a data platform integrator which can automatically determine which data platform(s) is best suited for an application. Author: zkaoudi ","version":null,"tagName":"h2"},{"title":"Website updated","type":0,"sectionRef":"#","url":"/blog/website_update","content":"","keywords":"","version":null},{"title":"Cheatsheet","type":1,"pageTitle":"Website updated","url":"/blog/website_update#cheatsheet","content":" List: Line one Line one.oneLine one.two Line two Line two.oneLine two.two Line three ...... Another style for a list: Line oneLine twoLine three ","version":null,"tagName":"h2"},{"title":"Becoming a committer","type":0,"sectionRef":"#","url":"/docs/community/committer","content":"","keywords":"","version":"Next"},{"title":"Sustained contributions to Wayang:","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#sustained-contributions-to-wayang","content":" Committers should have a history of major contributions to Wayang. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within Wayang's engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of Spark. Note that contributions to critical parts of Spark, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement. The type and level of contributions considered may vary by project area – for example, we greatly encourage contributors who want to work on mainly the documentation, or mainly on platform support for specific OSes, storage systems, etc. The (P)PMC also adds new (P)PMC members. (P)PMC members are expected to carry out (P)PMC responsibilities as described in Apache Guidance, including helping vote on releases, enforce Apache project trademarks, take responsibility for legal and license issues, and ensure the project follows Apache project mechanics. The PMC periodically adds committers to the PMC who have shown they understand and can help with these activities. ","version":"Next","tagName":"h3"},{"title":"Review process","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#review-process","content":" All contributions should be reviewed before merging as described in Contributing to Wayang. In particular, if you are working on an area of the codebase you are unfamiliar with, look at the Git history for that code to see who reviewed patches before. You can do this using git log --format=full <filename>, by examining the “Commit” field to see who committed each patch. ","version":"Next","tagName":"h3"},{"title":"When to commit/merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#when-to-commitmerge-a-pull-request","content":" PRs shall not be merged during active, on-topic discussion unless they address issues such as critical security fixes of a public vulnerability. Under extenuating circumstances, PRs may be merged during active, off-topic discussion and the discussion directed to a more appropriate venue. Time should be given prior to merging for those involved with the conversation to explain if they believe they are on-topic. Lazy consensus requires giving time for discussion to settle while understanding that people may not be working on Wayang as their full-time job and may take holidays. It is believed that by doing this, we can limit how often people feel the need to exercise their veto. All -1s with justification merit discussion. A -1 from a non-committer can be overridden only with input from multiple committers, and suitable time must be offered for any committer to raise concerns. A -1 from a committer who cannot be reached requires a consensus vote of the (P)PMC under ASF voting rules to determine the next steps within the ASF guidelines for code vetoes. The Wayang project typically uses a 72h time window to conclude votes. These policies serve to reiterate the core principle that code must not be merged with a pending veto or before a consensus has been reached (lazy or otherwise). It is the (P)PMC’s hope that vetoes continue to be infrequent, and when they occur, that all parties will take the time to build consensus prior to additional feature work. Being a committer means exercising your judgement while working in a community of people with diverse views. There is nothing wrong in getting a second (or third or fourth) opinion when you are uncertain. Thank you for your dedication to the Wayang project; it is appreciated by the developers and users of Wayang. It is hoped that these guidelines do not slow down development; rather, by removing some of the uncertainty, the goal is to make it easier for us to reach consensus. If you have ideas on how to improve these guidelines or other Wayang project operating procedures, you should reach out on the dev@ list to start the discussion. ","version":"Next","tagName":"h2"},{"title":"How we merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#how-we-merge-a-pull-request","content":" Changes pushed to the master branch on Apache cannot be removed; that is, we can’t force-push to it. So please don’t add any test commits or anything like that, only real patches. We typically enforce a review from minimum two committers, who are ideally owners of submodules, like ML4ALL or JDBC. They investigate the PR, add remarks and request changes. Please be not insultet in any way, the team needs to make sure that all code committed to main has a igh quality and does not break current functionality. Ask dev@ if you have trouble with these steps, or want help doing your first merge. Once a PR is merged please leave a comment on the PR stating which branch(es) it has been merged with. ","version":"Next","tagName":"h2"},{"title":"Policy on backporting bug fixes","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#policy-on-backporting-bug-fixes","content":" We go in line with pwendell (Apache Spark PMC): The trade off when backporting is you get to deliver the fix to people running older versions (great!), but you risk introducing new or even worse bugs in maintenance releases (bad!). The decision point is when you have a bug fix and it’s not clear whether it is worth backporting. I think the following facets are important to consider: Backports are an extremely valuable service to the community and should be considered for any bug fix.Introducing a new bug in a maintenance release must be avoided at all costs. It over time would erode confidence in our release process.Distributions or advanced users can always backport risky patches on their own, if they see fit.For me, the consequence of these is that we should backport in the following situations: Both the bug and the fix are well understood and isolated. Code being modified is well tested.The bug being addressed is high priority to the community.The backported fix does not vary widely from the master branch fix.We tend to avoid backports in the converse situations: The bug or fix are not well understood. For instance, it relates to interactions between complex components or third party libraries (e.g. Hadoop libraries). The code is not well tested outside of the immediate bug being fixed. The bug is not clearly a high priority for the community. The backported fix is widely different from the master branch fix. ","version":"Next","tagName":"h3"},{"title":"Mailing List","type":0,"sectionRef":"#","url":"/docs/community/mailinglist","content":"","keywords":"","version":"Next"},{"title":"How to subscribe to a mailing list","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#how-to-subscribe-to-a-mailing-list","content":" Send an email without any contents or subject to LISTNAME-subscribe@wayang.apache.org. (replace LISTNAME with dev, commits, ..) Wait till you receive an email with the subject “confirm subscribe to LISTNAME@wayang.apache.org”. Reply to that email, without editing the subject or including any contents Wait till you receive an email with the subject “WELCOME to LISTNAME@wayang.apache.org”. If you send us an email with a code snippet, make sure that: you do not link to files in external services as such files can change, get deleted, or the link might break and thus make an archived email thread uselessyou paste text instead of screenshots of textyou keep formatting when pasting code in order to keep the code readablethere are enough import statements to avoid ambiguitiesif you are using some platform, please specify the version ","version":"Next","tagName":"h2"},{"title":"Watch the email without subscribe","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#watch-the-email-without-subscribe","content":" These two channels are public mailing list, and you can get access to them on Website without subscribing. Emails of commits@wayang.apache.orgEmails of dev@wayang.apache.org ","version":"Next","tagName":"h2"},{"title":"How to contribute","type":0,"sectionRef":"#","url":"/docs/community/contribute","content":"","keywords":"","version":"Next"},{"title":"Contributing by helping other users","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-helping-other-users","content":" A great way to contribute to Wayang is to help answer user questions on the user@ mailing list or on StackOverflow. There are always many new Wayang users; taking a few minutes to help answer a question is a very valuable community service. Contributors should subscribe to this list and follow it in order to keep up to date on what’s happening in Wayang. Answering questions is an excellent and visible way to help the community, which also demonstrates your expertise. See the Mailing Lists guide for guidelines about how to effectively participate in discussions on the mailing list, as well as forums like StackOverflow. ","version":"Next","tagName":"h3"},{"title":"Contributing by testing releases","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-testing-releases","content":" Wayang’s release process is community-oriented, and members of the community can vote on new releases on the dev@ mailing list. Wayang users are invited to subscribe to this list to receive announcements, and test their workloads on newer release and provide feedback on any performance or correctness issues found in the newer release. ","version":"Next","tagName":"h3"},{"title":"Contributing by reviewing changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-reviewing-changes","content":" Changes to Wayang source code are proposed, reviewed and committed via GitHub pull requests (described later). Anyone can view and comment on active changes here. Reviewing others’ changes is a good way to learn how the change process works and gain exposure to activity in various parts of the code. You can help by reviewing the changes and asking questions or pointing out issues – as simple as typos or small issues of style. ","version":"Next","tagName":"h2"},{"title":"Contributing documentation changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-documentation-changes","content":" To propose a change to release documentation (that is, docs that appear under Developer section), fork the website repo and edit the Markdown source files in Wayang’s docs/ directory, the README file shows how to build the documentation locally to test your changes. The process to propose a doc change is otherwise the same as the process for proposing code changes below. To propose a change to the rest of the documentation (that is, docs that do not appear under Developer section), similarly, edit the Markdown in the wayang-website repository and open a pull request. ","version":"Next","tagName":"h2"},{"title":"Contributing user libraries to Wayang","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-user-libraries-to-wayang","content":" Just as Java and Scala applications can access a huge selection of libraries and utilities, none of which are part of Java or Scala themselves, Wayang aims to support a rich ecosystem of libraries. Many new useful utilities or features belong outside of Spark rather than in the core. For example: query optimizer code and language support probably has to be a part of core Wayang, but, useful machine learning algorithms can happily exist outside of Wayang. To that end, large and independent new functionality is often rejected for inclusion in Wayang itself, but, can and should be hosted as a separate project and repository, and included in the wayang-packages.org collection. ","version":"Next","tagName":"h2"},{"title":"Contributing bug reports","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-bug-reports","content":" Ideally, bug reports are accompanied by a proposed code change to fix the bug. This isn’t always possible, as those who discover a bug may not have the experience to fix it. A bug may be reported by creating a JIRA or a GitHub Issue but without creating a pull request (see below). Bug reports are only useful however if they include enough information to understand, isolate and ideally reproduce the bug. Simply encountering an error does not mean a bug should be reported; as below, search JIRA and search and inquire on the Wayang user / dev mailing lists first. Unreproducible bugs, or simple error reports, may be closed. It’s very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. Note that, data correctness/data loss bugs are very serious. Make sure the corresponding bug report JIRA ticket is labeled as correctness or data-loss. If the bug report doesn’t get enough attention, please send an email to dev@, to draw more attentions. It is possible to propose new features as well. These are generally not helpful unless accompanied by detail, such as a design document and/or code change. Large new contributions should consider wayang-packages.org first (see above), or be discussed on the mailing list first. Feature requests may be rejected, or closed after a long period of inactivity. ","version":"Next","tagName":"h2"},{"title":"Contributing to JIRA maintenance","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-to-jira-maintenance","content":" Given the volume of issues raised in the Apache Wayang JIRA, inevitably some issues are duplicates, or become obsolete and eventually fixed otherwise, or can’t be reproduced, or could benefit from more detail, and so on. It’s useful to help identify these issues and resolve them, either by advancing the discussion or even resolving the JIRA. Most contributors are able to directly resolve JIRAs. Use judgment in determining whether you are quite confident the issue should be resolved, although changes can be easily undone. If in doubt, just leave a comment on the JIRA. When resolving JIRAs, observe a few useful conventions: Resolve as Fixed if there’s a change you can point to that resolved the issue Set Fix Version(s), if and only if the resolution is FixedSet Assignee to the person who most contributed to the resolution, which is usually the person who opened the PR that resolved the issue.In case several people contributed, prefer to assign to the more ‘junior’, non-committer contributor For issues that can’t be reproduced against master as reported, resolve as Cannot Reproduce Fixed is reasonable too, if it’s clear what other previous pull request resolved it. Link to it.If the issue is the same as or a subset of another issue, resolved as DuplicateMake sure to link to the JIRA it duplicatesPrefer to resolve the issue that has less activity or discussion as the duplicate If the issue seems clearly obsolete and applies to issues or components that have changed radically since it was opened, resolve as Not a Problem If the issue doesn’t make sense – not actionable, for example, a non-Spark issue, resolve as Invalid If it’s a coherent issue, but there is a clear indication that there is not support or interest in acting on it, then resolve as Won’t Fix Sometimes, a contributor will already have a particular new change or bug in mind. If seeking ideas, consult the list of starter tasks in JIRA, or ask the user@ mailing list. Before proceeding, contributors should evaluate if the proposed change is likely to be relevant, new and actionable: Is it clear that code must change? Proposing a JIRA and pull request is appropriate only when a clear problem or change has been identified. If simply having trouble using Spark, use the mailing lists first, rather than consider filing a JIRA or proposing a change. When in doubt, email user@ first about the possible changeSearch the user@ and dev@ mailing list archives for related discussions. Often, the problem has been discussed before, with a resolution that doesn’t require a code change, or recording what kinds of changes will not be accepted as a resolution.Search JIRA for existing issues: https://issues.apache.org/jira/browse/WAYANGIs the scope of the change matched to the contributor’s level of experience? Anyone is qualified to suggest a typo fix, but refactoring core scheduling logic requires much more understanding of Wayang. Some changes require building up experience first (see above).It’s worth reemphasizing that changes to the core of Wayang, or to highly complex and important modules like SQL and Catalyst, are more difficult to make correctly. They will be subjected to more scrutiny, and held to a higher standard of review than changes to less critical code. ","version":"Next","tagName":"h3"},{"title":"ML4ALL and optimizer-specific contribution guidelines","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#ml4all-and-optimizer-specific-contribution-guidelines","content":" While a rich set of algorithms is an important goal for Wayang, scaling the project requires that maintainability, consistency, and code quality come first. New algorithms should: Be used and accepted (academic citations and concrete use cases can help justify this)Be well documentedHave APIs consistent with other algorithms in WayangCome with a reasonable expectation of developer supportError message guidelinesExceptions thrown in Wayang should be associated with standardized and actionable error messages Error messages should answer the following questions: What was the problem?Why did the problem happen?How can the problem be solved? When writing error messages, you should: Use active voiceAvoid time-based statements, such as promises of future supportUse the present tense to describe the error and provide suggestionsProvide concrete examples if the resolution is unclearAvoid sounding accusatory, judgmental, or insultingBe directDo not use programming jargon in user-facing errors 8 See the error message guidelines for more details. ","version":"Next","tagName":"h3"},{"title":"Code review criteria","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-review-criteria","content":" Before considering how to contribute code, it’s useful to understand how code is reviewed, and why changes may be rejected. See the detailed guide for code reviewers from Google’s Engineering Practices documentation. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review. Positives Fixes the root cause of a bug in existing functionalityAdds functionality or fixes a problem needed by a large number of usersSimple, targetedMaintains or improves consistency across Python, Java, ScalaEasily tested; has testsReduces complexity and lines of codeChange has already been discussed and is known to committers Negatives, risks Band-aids a symptom of a bug onlyIntroduces complex new functionality, especially an API that needs to be supportedAdds complexity that only helps a niche use caseAdds user-space functionality that does not need to be maintained in Wayang, but could be hosted externally and indexed by wayang-packages.orgChanges a public API or semantics (rarely allowed)Adds large dependenciesChanges versions of existing dependenciesAdds a large amount of codeMakes lots of modifications in one “big bang” change ","version":"Next","tagName":"h3"},{"title":"Contributing code changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-code-changes","content":" Please review the preceding section before proposing a code change. This section documents how to do so. When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so. ","version":"Next","tagName":"h3"},{"title":"Cloning the Apache Wayang™ source code","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#cloning-the-apache-wayang-source-code","content":" If you are interested in working with the newest under-development code or contributing to Apache Spark development, you can check out the master branch from Git: # Master development branch git clone git://github.com/apache/incubator-wayang.git Once you’ve downloaded Wayang, you can find instructions for installing and building it on the Compiling Apache Wayang page. ","version":"Next","tagName":"h3"},{"title":"JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#jira","content":" Generally, Wayang uses JIRA to track logical issues, including bugs and improvements, and uses GitHub pull requests to manage the review and merge of specific code changes. That is, JIRAs are used to describe what should be fixed or changed, and high-level approaches, and pull requests describe how to implement that change in the project’s source code. For example, major design decisions are discussed in JIRA. Find the existing Spark JIRA that the change pertains to. Do not create a new JIRA if creating a change to address an existing issue in JIRA; add to the existing discussion and work insteadLook for existing pull requests that are linked from the JIRA, to understand if someone is already working on the JIRA If the change is new, then it usually needs a new JIRA. However, trivial changes, where the what should change is virtually the same as the how it should change do not require a JIRA. Example: Fix typos in Foo wayang docIf required, create a new JIRA: Provide a descriptive Title. “Update web UI” or “Problem in scheduler” is not sufficient. “Kafka Streaming support fails to handle empty queue in YARN cluster mode” is good.Write a detailed description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document.Set required fields: Issue Type. Generally, Bug, Improvement and New Feature are the only types used in Spark.Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. JIRA tends to unfortunately conflate “size” and “importance” in its Priority field values. Their meaning is roughly: Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers for their target versions.Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficultMajor: a small minority of users are missing important functionality without this, and there is a workaroundMinor: a niche use case is missing some support, but it does not affect usage or is easily worked aroundTrivial: a nice-to-have change but unlikely to be any problem in practice otherwise ComponentAffects Version. For Bugs, assign at least one version that is known to exhibit the problem or need the changeLabel. Not widely used, except for the following: correctness: a correctness issuedata-loss: a data loss issuerelease-notes: the change’s effects need mention in release notes. The JIRA or pull request should include detail suitable for inclusion in release notes – see “Docs Text” below.starter: small, simple change suitable for new contributors Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include in Release Notes. This should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the JIRA is opened, but will likely need to be updated with final details when the issue is resolved. Do not set the following fields: Fix Version This is assigned by committers only when resolved.Target Version This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. Do not include a patch file; pull requests are used to propose the actual change. If the change is a large change, consider inviting discussion on the issue at dev@ first before proceeding to implement the change. ","version":"Next","tagName":"h3"},{"title":"Pull request","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#pull-request","content":" Before creating a pull request in Apache Wayang, it is important to check if tests can pass on your branch because our GitHub Actions workflows automatically run tests for your pull request/following commits and every run burdens the limited resources of GitHub Actions in Apache Wayang repository. Below steps will take your through the process. Fork the GitHub repository at https://github.com/apache/incubator-wayang if you haven’t alreadyGo to “Actions” tab on your forked repository and enable “Build and test” and “Report test results” workflowsClone your fork and create a new branchConsider whether documentation or tests need to be added or updated as part of the change, and add them as needed. When you add tests, make sure the tests are self-descriptive.Also, you should consider writing a JIRA ID in the tests when your pull request targets to fix a specific issue. In practice, usually it is added when a JIRA type is a bug or a PR adds a couple of tests to an existing test class. See the examples below: @Test public void testCase() { // WAYANG-12345: a short description of the test Consider whether benchmark results should be added or updated as part of the change, and add them as needed by Running benchmarks in your forked repository to generate benchmark results.Run all tests with in your build to verify that the code still compiles, passes tests, and passes style checks. If style checks fail, review the Code Style Guide below.Push commits to your branch. This will trigger “Build and test” and “Report test results” workflows on your forked repository and start testing and validating your changes.Open a pull request against the master branch of apache/incubator-wayang. (Only in special cases would the PR be opened against other branches). This will trigger workflows “On pull request*” (on the Wayang repo) that will look/watch for successful workflow runs on “your” forked repository (it will wait if one is running). The PR title should be of the form [WAYANG-xxxx][COMPONENT] Title, where WAYANG-xxxx is the relevant JIRA number, COMPONENT is one of the PR categories and Title may be the JIRA’s title or a more specific title describing the PR itself.If the pull request is still a work in progress, and so is not ready to be merged, but needs to be pushed to GitHub to facilitate review, then add [WIP] after the component.Consider identifying committers or other contributors who have worked on the code being changed. Find the file(s) in GitHub and click “Blame” to see a line-by-line annotation of who changed the code last. You can add @username in the PR description to ping them immediately.Please state that the contribution is your original work and that you license the work to the project under the project’s open source license. The related JIRA, if any, will be marked as “In Progress” and your pull request will automatically be linked to it. There is no need to be the Assignee of the JIRA to work on it, though you are welcome to comment that you have begun work. ","version":"Next","tagName":"h3"},{"title":"The review process","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#the-review-process","content":" Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch.Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change.Keep in mind that changes to more critical parts of Spark, like its core and SQL components, will be subjected to more review, and may require more testing and proof of its correctness than other changes.Reviewers can indicate that a change looks suitable for merging with a comment such as: “I think this patch looks good”. Wayang uses the LGTM convention for indicating the strongest level of technical sign-off on a patch: simply comment with the word “LGTM”. It specifically means: “I’ve looked at this thoroughly and take as much ownership as if I wrote the patch myself”. If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community.Sometimes, other changes will be merged which conflict with your pull request’s changes. The PR can’t be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by git remote add upstream https://github.com/apache/incubator-wayang.git, running git fetch upstream followed by git rebase upstream/master and resolving the conflicts by hand, then pushing the result to your branch.Try to be responsive to the discussion rather than let days pass between replies ","version":"Next","tagName":"h3"},{"title":"Closing your pull request / JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#closing-your-pull-request--jira","content":" If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated JIRA if any Note that in the rare case you are asked to open a pull request against a branch besides master, that you will actually have to close the pull request manuallyThe JIRA will be Assigned to the primary contributor to the change as a way of giving credit. If the JIRA isn’t closed and/or Assigned promptly, comment on the JIRA. If your pull request is ultimately rejected, please close it promptly … because committers can’t close PRs directlyPull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like “mind closing this PR?” This means that the committer is specifically requesting that it be closed. If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that’s easier to include, like a smaller and/or less invasive change.If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a “soft no”. It is helpful to withdraw and close the PR in this case.If a pull request is closed because it is deemed not the right approach to resolve a JIRA, then leave the JIRA open. However if the review makes it clear that the issue identified in the JIRA is not going to be resolved by any pull request (not a problem, won’t fix) then also resolve the JIRA. ","version":"Next","tagName":"h3"},{"title":"Code style guide","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-style-guide","content":" Please follow the style of the existing codebase. For Python code, Apache Wayang follows PEP 8 with one exception: lines can be up to 100 characters in length, not 79.For R code, Apache Wayang follows Google’s R Style Guide with three exceptions: lines can be up to 100 characters in length, not 80, there is no limit on function name but it has a initial lower case latter and S4 objects/methods are allowed.For Java code, Apache Wayang follows Oracle’s Java code conventions and Scala guidelines below. The latter is preferred.For Scala code, Apache Wayang follows the official Scala style guide. ","version":"Next","tagName":"h2"},{"title":"If in doubt","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#if-in-doubt","content":" If you’re not sure about the right style for something, try to follow the style of the existing codebase. Look at whether there are other examples in the code that use your feature. Feel free to ask on the dev@ list as well and/or ask committers. ","version":"Next","tagName":"h3"},{"title":"Code of conduct","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-of-conduct","content":" The Apache Wayang project follows the Apache Software Foundation Code of Conduct. The code of conduct applies to all spaces managed by the Apache Software Foundation, including IRC, all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our communities. A code of conduct which is specific to in-person events (ie., conferences) is codified in the published ASF anti-harassment policy. We expect this code of conduct to be honored by everyone who participates in the Apache community formally or informally, or claims any affiliation with the Foundation, in any Foundation-related activities and especially when representing the ASF, in any role. This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. We expect it to be followed in spirit as much as in the letter, so that it can enrich all of us and the technical communities in which we participate. For more information and specific guidelines, refer to the Apache Software Foundation Code of Conduct. This guide was originally released by Apache Spark, the Apache Wayang project adapted the guide. ","version":"Next","tagName":"h2"},{"title":"How to make a release","type":0,"sectionRef":"#","url":"/docs/community/release","content":"","keywords":"","version":"Next"},{"title":"First time release","type":1,"pageTitle":"How to make a release","url":"/docs/community/release#first-time-release","content":" ============================================= Add your Apache credentials in ~.m2/settings.xml If you don’t have this directory or file, you should create a .m2 directory in your user home and inside that create a settings.xml file with at least this content: <?xml version="1.0" encoding="UTF-8"?> <settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.1.0 http://maven.apache.org/xsd/settings-1.1.0.xsd" xmlns="http://maven.apache.org/SETTINGS/1.1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <servers> <!-- Apache Repo Settings --> <server> <id>apache.snapshots.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> <server> <id>apache.releases.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> </servers> </settings> The following is about key management. Details are described here You need to generate a key with GnuPG Install GnuPG (e.g., brew install gnupg)Run: gpg --full-generate-keyExport the public key: gpg --export --armor mygpgpublickey Install pinentry (e.g., brew install pinentry-mac) so that you can be promted to enter your credentials via command line Configuration A. Make Apache the default key:APACHEID=your_asf_id KEY_ID=$(gpg --list-keys --with-colons $APACHEID@apache.org | egrep "^pub" | awk -F: '{print $5}') echo "default-key $KEY_ID" >> ~/.gnupg/gpg.conf B. export GPG_TTY=$(tty) echo $GPG_TTY C. Configure gnupg to use standard DNS resolution # resolves common "gpg: keyserver receive failed: Network is unreachable" and # "gpg: keyserver receive failed: No keyserver available" errors echo "standard-resolver" > ~/.gnupg/dirmngr.conf sudo pkill dirmngr D. Set configuration to use SHA512 keys by default: cat <<EOL >> ~/.gnupg/gpg.conf personal-digest-preferences SHA512 cert-digest-algo SHA512 default-preference-list SHA512 SHA384 SHA256 SHA224 AES256 AES192 AES CAST5 ZLIB BZIP2 ZIP Uncompressed EOL Upload the key to one of the key servers gpg --keyserver keyserver.ubuntu.com --send-key $KEY_ID gpg --keyserver keys.openpgp.org --send-key $KEY_ID gpg --keyserver pgp.mit.edu --send-key $KEY_ID Make sure your public key is appended in the KEYS file found in the release server here ============================================= Run mvn release:prepare (you can skip tests with -Darguments='-DskipTests=True, if you have done this before) Run mvn clean release:perform (This step will ask for your apache credentials, if everything related to the keys is configured properly.) Go to: https://repository.apache.org, login, go to Staging Repositories and find the orgapachewayang-{somenumber} and close it with description "Apache Wayang (incubating) 1.0.0-rc5" Make a directory structure as described below and copy the corresponding source files from the directory target/checkout/target. The structure should be: ./KEYS ./1.0.0/ ./1.0.0/rc5 ./1.0.0/rc5/README ./1.0.0/rc5/RELEASE_NOTES ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.asc ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.sha12 Make sure the KEYS file contains your public key. The KEYS file can be found in https://dist.apache.org/repos/dist/release/incubator/wayang/ cd 1.0.0 svn import rc5 https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 -m "Staging of rc5 of Wayang 1.0.0" Send the voting email to the dev list. If the voting passes, send the voting email to the general list. Once the voting in the general list passes, the staged artifacts can be released. This is done by moving them inside the Apache SVN: svn move -m "Release of Apache Wayang (incubating) 1.0.0" https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 https://dist.apache.org/repos/dist/release/incubator/wayang/1.0.0 Then release the maven artifacts: In order to do this, the release manager logs into Nexus at https://repository.apache.org/, selects the staging repository and clicks on the Release button. Bring all changes of the release branch to the main. You can also remove 1.0.0 from the dev directory of the svn now that the release is out. Create and upload the javadocs: Go to the source code directory and run mvn compile javadoc:javadoc javadoc:aggregate The javadocs can then be found in the target directory Upload the javadocs in the website: https://github.com/apache/incubator-wayang-website/tree/main/static/docs/api/javadocs ","version":"Next","tagName":"h3"},{"title":"Maturity Model","type":0,"sectionRef":"#","url":"/docs/community/maturity","content":"Maturity Model ID\tDescription\tStatus\tCode CD10\tThe project produces Open Source software for distribution to the public, at no charge.\tYes. The project source code is licensed under the Apache License 2.0. CD20\tAnyone can easily discover and access the project's code.\tYes. The official website includes GitHub link which can access the project's repository on GitHub directly. CD30\tAnyone using standard, widely-available tools, can build the code in a reproducible way.\tYes. Our webpage and github repo provide "Getting Started" and several examples to allow users build our system. CD40\tThe full history of the project's code is available via a source code control system, in a way that allows anyone to recreate any released version.\tYes. We use git and anyone can view the full history of the project via commit logs and tags of the releases. CD50\tThe source code control system establishes the provenance of each line of code in a reliable way, based on strong authentication of the committer. When third parties contribute code, commit messages provide reliable information about the code provenance.\tYes. The project uses GitHub and managed by Apache Infra and ensures provenance of each line of code to a committer. Third-party contributions are accepted in accordance with the contributing guides. Licenses and Copyright LC10\tThe Apache License, version 2.0, covers the released code.\tYes. All source files are licensed with Apache License v2.0. LICENSE can be found in the GitHub repository. LC20\tLibraries that are mandatory dependencies of the project's code do not create more restrictions than the Apache License does.\tYes. None of the dependencies create more restrictions. LC30\tThe libraries mentioned in LC20 are available as Open Source software.\tYes. All libraries are open source. LC40\tCommitters are bound by an Individual Contributor Agreement (the "Apache iCLA") that defines which code they may commit and how they need to identify code that is not their own.\tYes. All committers have iCLAs. LC50\tThe project clearly defines and documents the copyright ownership of everything that the project produces.\tYes. All source files are with APLv2 header Releases RE10\tReleases consist of source code, distributed using standard and open archive formats that are expected to stay readable in the long term.\tYes. Source release is distributed via dist.apache.org and linked from download page. RE20\tThe project's PPMC (Project Management Committee, see CS10) approves each software release in order to make the release an act of the Foundation.\tYes. All releases have been voted at dev@wayang.apache.org and general@incubator.apache.org, and have at least 3 PPMC member's votes. RE30\tReleases are signed and/or distributed along with digests that anyone can reliably use to validate the downloaded archives.\tYes. All releases are signed, and the KEYS are available. RE40\tThe project can distribute convenience binaries alongside source code, but they are not Apache Releases, they are provided with no guarantee.\tYes. Users can easily build binaries from source code. We do not provide binaries as Apache Releases. RE50\tThe project documents a repeatable release process so that someone new to the project can independently generate the complete set of artifacts required for a release.\tYes. Newcomers can follow our very detailed Release guide to make a new Apache Wayang release, and so far we had 3 different release managers. Quality QU10\tThe project is open and honest about the quality of its code. Various levels of quality and maturity for various modules are natural and acceptable as long as they are clearly communicated.\tYes. The project records all bugs using Github Issues. QU20\tThe project puts a very high priority on producing secure software.\tYes. All security issues will be addressed within 3 days. QU30\tThe project provides a well-documented, secure and private channel to report security issues, along with a documented way of responding to them.\tYes. https://wayang.apache.org/docs/community/security QU40\tThe project puts a high priority on backwards compatibility and aims to document any incompatible changes and provide tools and documentation to help users transition to new features.\tYes. Each release contains a “New features” on the website containing the release notes. The project aims to make no backward incompatible changes within a given major version. QU50\tThe project strives to respond to documented bug reports in a timely manner.\tYes. The community has very small response times to issues and pull requests Community CO10\tThe project has a well-known homepage that points to all the information required to operate according to this maturity model.\tYes. The official website includes all information users need to run Apache Wayang. CO20\tThe community welcomes contributions from anyone who acts in good faith and in a respectful manner, and who adds value to the project.\tYes. The contributing guide provides information for anyone to contribute. CO30\tContributions include source code, documentation, constructive bug reports, constructive discussions, marketing and generally anything that adds value to the project.\tYes. All good contributions including code and non-code are welcomed. CO40\tThe community strives to be meritocratic and gives more rights and responsibilities to contributors who, over time, add value to the project.\tYes. The community has elected 3 new PPMC members/Committers the last year. CO50\tThe project documents how contributors can earn more rights such as commit access or decision power, and applies these principles consistently.\tYes. The website has a clear guide on how to become a committer https://wayang.apache.org/docs/community/committer CO60\tThe community operates based on consensus of its members (see CS10) who have decision power. Dictators, benevolent or not, are not welcome in Apache projects.\tYes. All decisions are made after voting by community members. CO70\tThe project strives to answer user questions in a timely manner.\tYes. Our community answers user questions in a timely manner. Consensus CS10\tThe project maintains a public list of its contributors who have decision power. The project's PPMC (Project Management Committee) consists of those contributors.\tYes. See Team with all PPMC members/committers and contributors. CS20\tDecisions require a consensus among PPMC members and are documented on the project's main communications channel. The PPMC takes community opinions into account, but the PPMC has the final word.\tYes. All decisions are made by votes on dev@wayang.apache.org, and with at least 3 +1 votes from PPMC. CS30\tThe project uses documented voting rules to build consensus when discussion is not sufficient.\tYes. The project uses the standard ASF voting rules. CS40\tIn Apache projects, vetoes are only valid for code commits. The person exercising the veto must justify it with a technical explanation, as per the Apache voting rules defined in CS30.\tYes. Apache Wayang community has not used the veto power yet except for code commits. CS50\tAll "important" discussions happen asynchronously in written form on the project's main communications channel. Offline, face-to-face or private discussions that affect the project are also documented on that channel.\tYes. All important discussions and conclusions are sent to the dev list Independence IN10\tThe project is independent from any corporate or organizational influence.\tYes. The PPMC members of Apache Wayang are from several organizations. IN20\tContributors act as themselves, not as representatives of a corporation or organization.\tYes. The contributors act on their own initiative without representing a corporation or organization. Trademark and Branding TB10\tThe project uses "Apache Foo™" as the project and software product name consistently, with appropriate trademark attributions\tYes. The project uses appropriately the trademark symbol. TB20\tThe project's primary homepage is at projectname.apache.org.\tYes. The project's homepage is at wayang.apache.org TB30\tThe ASF has trademark rights, including any registrations, to the project name, logo, and any other major branding elements.\tYes. ASF has the trademark rights TB40\tThe project monitors for any major misuses of their project's brand by others.\tYes. The PPMC members have already addressed misuses.\t","keywords":"","version":"Next"},{"title":"Wayang Security","type":0,"sectionRef":"#","url":"/docs/community/security","content":"","keywords":"","version":"Next"},{"title":"Advisories for Dependencies","type":1,"pageTitle":"Wayang Security","url":"/docs/community/security#advisories-for-dependencies","content":" Many organizations employ security scanning tools to identify components with known security advisories. Although we strongly recommend these tools as they can alert users to potential risks, they often generate false positives. This occurs because a vulnerable dependency may not necessarily impact Apache Wayang if used in a non-exploitable manner. Therefore, advisories regarding Apache Wayang's dependencies are not automatically considered critical. However, if additional analysis indicates that Apache Wayang might be affected by a dependency's vulnerability, please report your findings privately to security@wayang.apache.org. If a dependency advisory is identified, please: Verify if our DependencyCheck suppressions contain relevant details.Check our issue tracker for discussions regarding this advisory.Conduct your own analysis to determine whether Apache Wayang is affected. If affected, report your findings privately through security@wayang.apache.org.If not affected, please contribute by updating the DependencyCheck suppression list, clearly documenting why Apache Wayang is not impacted. ","version":"Next","tagName":"h2"},{"title":"Community Hubs","type":0,"sectionRef":"#","url":"/docs/community/repositories","content":"","keywords":"","version":"Next"},{"title":"Source Code","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#source-code","content":" Apache Wayang ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang.git GitHub mirror : https://github.com/apache/incubator-wayang.git Wayang Web Page Repository ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang-website.git GitHub mirror : https://github.com/apache/incubator-wayang-website.git ","version":"Next","tagName":"h3"},{"title":"Issues, Bugs and Features","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#issues-bugs-and-features","content":" If you have a specific bug to report or feature request, we suggest opening an issue in Jira. ","version":"Next","tagName":"h3"},{"title":"Team","type":0,"sectionRef":"#","url":"/docs/community/team","content":"","keywords":"","version":"Next"},{"title":"Committers and Recent Contributors","type":1,"pageTitle":"Team","url":"/docs/community/team#committers-and-recent-contributors","content":" Name\tRole\tApache ID\tOrganizationAlexander Alten\tPMC, Committer\taloalt\tScalytics Calvin Kirs\tPMC, Committer\tkirs\tSelectDB, ASF Zoi Kaoudi\tPMC, Committer\tzkaoudi\tITU Copenhagen, Scalytics Kaustubh Beedkar\tPMC, Committer\tkbeedkar\tScalytics, IIT Dehli Mirko Kaempf\tPMC, Committer\tkamir\tEcolytiq Juri Petersen\tPMC, Committer\tjuri\tITU Copenhagen Mads Sejer Pedersen\tPMC, Committer\tsejer\tITU Copenhagen Jean-Baptiste Onofré\tPMC, Committer\tjbonofre\tDremio, ASF Yasser Idris\tPMC, Committer\tyidris\tExpensePoint, Alexandria University Anis Troudi\tCommitter\tatroudi Christofer Dutz\tCommitter\tchrisdutz\tASF Mingxi Liu\tContributor East China Normal University Michalis Vargiamis\tContributor Scalytics Jeppe Halvorsen\tContributor ITU Copenhagen, Falck Christoffer Kristensen\tContributor ITU Copenhagen, Stretch Qonnect Aurélien Bertrand\tContributor Maastricht University, TecAlliance Aditya Goel\tContributor IIT Delhi Dudipala Vigneshwar Reddy\tContributor IIT Delhi Akshit Goel\tContributor IIT Delhi Vedanta Neogi\tContributor IIT Delhi Christos-Evangelos Lampropoulos\tContributor University of Patras Bertty Contreras\tContributor\tbertty Jorge Quiané\tContributor\tquiaru Rodrigo Pardo Meza\tContributor\trpardomeza Glaucia Esppenchutz\tContributor\tglauesppen Gábor Gévay\tContributor\tggevay\t You can reach committers directly at <apache-id>@apache.org ","version":"Next","tagName":"h2"},{"title":"Mentors during the incubation process","type":1,"pageTitle":"Team","url":"/docs/community/team#mentors-during-the-incubation-process","content":" The following people were very kind to mentor the project while in incubation. Name\tApache IDBernd Fondermann\tberndf Christofer Dutz\tcdutz Jean-Baptiste Onofré\tjbonofre Lars George\tlarsgeorge ","version":"Next","tagName":"h2"},{"title":"Donations to Wayang","type":1,"pageTitle":"Team","url":"/docs/community/team#donations-to-wayang","content":" The Logo was donated by Brian Vera ","version":"Next","tagName":"h2"},{"title":"API Documentation","type":0,"sectionRef":"#","url":"/docs/guide/api-documentation","content":"API Documentation The latest Javadocs can be found here.","keywords":"","version":"Next"},{"title":"Adding new operators in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/adding-operators","content":"","keywords":"","version":"Next"},{"title":"Step 1: Add a Wayang operator","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-1-add-a-wayang-operator","content":" Wayang operators are located under the wayang-basic in the org.apache.wayang.basic.operators package. An operator needs to extend from one of the following abstract classes: UnaryToUnaryOperator, BinaryToUnaryOperator, UnarySource, UnarySink. For a unary to unary operator, see for example here. For enhanced performance in Wayang, consider adding a cardinality estimator by overriding the createCardinalityEstimator() function as here. ","version":"Next","tagName":"h2"},{"title":"Step 2: Add the (platform-specific) execution operators","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-2-add-the-platform-specific-execution-operators","content":" Execution operators are located under the corresponding module of wayang-platforms. For instance, Java execution operators are located in the org.apache.wayang.java.operators package of the wayang-java module. An execution operator needs to extend from its corresponding Wayang operator and implement the corresponding platform operator interface. For the above MapOperator, the following is the corresponding JavaMapOperator. For enhanced performance in Wayang, consider adding a load function as well: For this you need to overwrite the getLoadProfileEstimatorConfigurationKey() function and provide the right key that will then be read from a properties file. For the JavaMapOperator it's: wayang.java.map.load. Then add in the corresponding properties file (e.g., this is for the java executor) the template which is the mathematical formula that represents the cost of this operator and an instantiation of it. See here for the example of the map operator. ","version":"Next","tagName":"h2"},{"title":"Step 3: Add mappings","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-3-add-mappings","content":" Create mappings from the Wayang operator to the platform-specific execution operators. The mappings are located in the corresponding execution module in the org.apache.wayang.java.operators package. For the above MapOperator and JavaMapOperator, see here. After that you need to declare this mapping in Wayang in the corresponding Mappings class. ","version":"Next","tagName":"h2"},{"title":"Step 4: Expand the Java scala-like API","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-4-expand-the-java-scala-like-api","content":" Once you created a new operator you need to expose it to the API so that users can use it as a function in the dataflow job they create. For this, you need to go to the module wayang-api/wayang-api-scala-java and expand the JavaPlanBuilder.scala file to include a new source operator or the DataQuantaBuilder for non-source operators. ","version":"Next","tagName":"h2"},{"title":"Developing in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/developing-in-wayang","content":"","keywords":"","version":"Next"},{"title":"Compile the module you modified","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#compile-the-module-you-modified","content":" Within the root directory of Wayang, compile only the module you modified for faster compilation: mvn clean install -DskipTests -pl <modified_module> or change the directory to your module and compile there: cd <modified_module> && mvn clean install Important: before making a Pull Request make sure all modules compile and all tests are passing: mvn clean install ","version":"Next","tagName":"h2"},{"title":"Package the project","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#package-the-project","content":" mvn clean package -pl :wayang-assembly -Pdistribution ","version":"Next","tagName":"h2"},{"title":"Execute your code","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#execute-your-code","content":" Before executing your code, make sure the required environment variables are set correctly. cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Apache Wayang Release Odysse","type":0,"sectionRef":"#","url":"/blog/wayang-release-odysse","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#intro","content":" The ASF provides a robust infrastructure for open communities of software developers. We can share ideas, combine forces, contribute code, docs, review-energy, art work, and from time to time we can nail it down. A release defines an intermediate result of the continuous community work. How we do such a release in the Apache Wayang team is an essential aspect towards graduation. First of all, there are some references to take into account, such as: https://www.apache.org/legal/release-policy.htmlhttps://maven.apache.org/maven-release/maven-release-plugin/ Assuming you are (P)PMC, and assuming that you have the right permissions for such a release, you can follow the path as described in this guide: https://plc4x.apache.org/developers/release/release.html I tried to follow exactly this procedure, several times. I failed. Here I share the current status of my release attempts. I plan a longer tour, and do not want to block the project for a long time. Hence I create this draft, and I hope we can unblock this project as soon as possible. ","version":null,"tagName":"h2"},{"title":"Status:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#status","content":" I am not able to conduct the mvn release:perform step. Anything before worked, sometimes only after some digging, but it worked. We assume, that due to my membership in two ASF incubator projects I am not able to upload the artefacts to the Nexus repository (H1). It can be, that I have not the correct user and password in my settings.xml file (H2). But I tested a manual login to the nexus server https://repository.apache.org/service/local/staging/deploy/maven2 with success. And beyond that I have no idea how I can verify this detail alone. ","version":null,"tagName":"h2"},{"title":"Idea / Proposal","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#idea--proposal","content":" (1) It would be great, if someone - who has done a release in any other ASF project or in Apache Wayang - could follow the steps I share, so that we can check where the problem hides itself. (2) As a follow-up task, I suggest to add a Release Guide to the Apache Wayang project, including release manager onboarding steps, and checklists for the particular project, derived from the referenced sources which are listed above. But for now it is all about sharing the status (as I did serveral times on multiple chanels, including JIRA, Slack, Mailing lists) and finding a solution for Apache Wayang release 1.0. ","version":null,"tagName":"h2"},{"title":"Latest Error:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#latest-error","content":" mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h2"},{"title":"Activity Log","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#activity-log","content":" mvn release:clean mvn versions:set -DnewVersion=1.0.0-RC2 mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: You don't have a SNAPSHOT project in the reactor projects list. -> [Help 1] mvn versions:commit mvn versions:set -DnewVersion=1.0.0-RC2-SNAPSHOT mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Cannot prepare the release because you have local modifications : git status git add . git commit -m "prepare for release 1.0.0-RC2-SNAPSHOT" git push mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -DdryRun=true mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -XXX Caused by: org.eclipse.aether.transfer.NoRepositoryConnectorException: Blocked mirror for repositories: [repository.jboss.org (http://repository.jboss.org/nexus/content/groups/public/, default, releases)] ","version":null,"tagName":"h2"},{"title":"Dependency on JDK-11 during release","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#dependency-on-jdk-11-during-release","content":" FIXED with local JDK11 Setup brew install openjdk@11 curl -s "https://get.sdkman.io" | bash source "$HOME/.sdkman/bin/sdkman-init.sh" which java which java /Users/kamir/.sdkman/candidates/java/current/bin/java ➜ GITHUB.active export JAVA_HOME=/Users/kamir/.sdkman/candidates/java/current ➜ GITHUB.active mvn clean -XXX export JAVA_HOME= sdk install java 11.0.24-amzn sdk home java 11.0.24-amzn /usr/libexec/java_home -v 11 jenv add /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home jenv global 11.0 jenv shell 11.0 jenv local 11.0 java -version ","version":null,"tagName":"h3"},{"title":"Manual update of release-version","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#manual-update-of-release-version","content":" During the release procedure, do I have to set the version here in this configuration section manually? <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-release-plugin</artifactId> <version>3.0.1</version> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>0.7.1</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>wayang-0.7.1</tag> <scmReleaseCommitComment>@{prefix} prepare release 0.7.1</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> </plugin> It seems that these properties must be updated manually. ","version":null,"tagName":"h3"},{"title":"Warning regarding \"illegal reflective access operation\"","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#warning-regarding-illegal-reflective-access-operation","content":" [ERROR] WARNING: An illegal reflective access operation has occurred [ERROR] WARNING: Illegal reflective access by org.codehaus.groovy.reflection.CachedClass (file:/Users/mkaempf/.m2/repository/org/codehaus/groovy/groovy-all/2.4.9/groovy-all-2.4.9.jar) to method java.lang.Object.finalize() [ERROR] WARNING: Please consider reporting this to the maintainers of org.codehaus.groovy.reflection.CachedClass [ERROR] WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations [ERROR] WARNING: All illegal access operations will be denied in a future release This is still an OPEN ISSUE ! ","version":null,"tagName":"h3"},{"title":"RAT Check fails","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#rat-check-fails","content":" [INFO] [ERROR] Failed to execute goal org.apache.rat:apache-rat-plugin:0.13:check (license-check) on project wayang: Too many files with unapproved license: 1 See RAT report in: /Users/mkaempf/GITHUB.private/incubator-wayang/target/rat.txt -> [Help 1] ***************************************************** Printing headers for text files without a valid license header... ===================================================== == File: .java-version ===================================================== 11.0 FIXED by adding .java-versions to .gitignore ","version":null,"tagName":"h3"},{"title":"Tag could not be created in SCM.","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#tag-could-not-be-created-in-scm","content":" [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Unable to tag SCM [ERROR] Provider message: [ERROR] The git-tag command failed. [ERROR] Command output: [ERROR] fatal: tag 'wayang-0.7.1' already exists FIXED by manual changes in pom.xml. <scm> <connection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</connection> <developerConnection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</developerConnection> <url>https://github.com/apache/incubator-wayang</url> <tag>1.0.0-RC2-SNAPSHOT</tag> </scm> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>1.0.0-RC2-SNAPSHOT</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>1.0.0-RC2-SNAPSHOT</tag> <scmReleaseCommitComment>@{prefix} prepare release 1.0.0-RC2-SNAPSHOT</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> mvn release:prepare -Darguments='-DskipTests=True -Dresume=True' mvn clean package mvn release:perform -X -DskipTests - you need your password for the keystore to sign the build artefacts. So far so good. But now the sun went down. [INFO] [ERROR] Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException -> [Help 1] [INFO] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.plugin.MojoExecutionException: ArtifactDeployerException [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:201) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.shared.transfer.artifact.deploy.ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:126) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:278) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.transfer.ArtifactTransferException: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.connector.basic.ArtifactTransportListener.transferFailed (ArtifactTransportListener.java:44) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:417) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: java.lang.NullPointerException [INFO] at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) [INFO] at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) [INFO] at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) [INFO] at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) [INFO] at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) [INFO] at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) [INFO] at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) [INFO] at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) [INFO] at org.apache.http.impl.execchain.RetryExec.execute (RetryExec.java:89) [INFO] at org.apache.http.impl.execchain.ServiceUnavailableRetryExec.execute (ServiceUnavailableRetryExec.java:85) [INFO] at org.apache.http.impl.execchain.RedirectExec.execute (RedirectExec.java:110) [INFO] at org.apache.http.impl.client.InternalHttpClient.doExecute (InternalHttpClient.java:185) [INFO] at org.apache.http.impl.client.CloseableHttpClient.execute (CloseableHttpClient.java:72) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.execute (HttpTransporter.java:485) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.implPut (HttpTransporter.java:469) [INFO] at org.eclipse.aether.spi.connector.transport.AbstractTransporter.put (AbstractTransporter.java:107) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$PutTaskRunner.runTask (BasicRepositoryConnector.java:564) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:414) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] [ERROR] [INFO] [ERROR] [INFO] [ERROR] For more information about the errors and possible solutions, please read the following articles: [INFO] [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [INFO] [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [INFO] [DEBUG] Shutting down 'file-lock' factory [INFO] [DEBUG] Shutting down 'rwlock-local' factory [INFO] [DEBUG] Shutting down 'semaphore-local' factory [INFO] [DEBUG] Shutting down 'noop' factory [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary for Apache Wayang (incubating) 1.0.0-RC3-SNAPSHOT: [INFO] [INFO] Apache Wayang (incubating) ......................... FAILURE [ 23.626 s] [INFO] Wayang Commons ..................................... SKIPPED [INFO] wayang-utils-profile-db ............................ SKIPPED [INFO] Wayang Core ........................................ SKIPPED [INFO] Wayang Basic ....................................... SKIPPED [INFO] Wayang Platform .................................... SKIPPED [INFO] Wayang Platform Java ............................... SKIPPED [INFO] Wayang Platform Spark .............................. SKIPPED [INFO] Wayang Platform JDBC Template ...................... SKIPPED [INFO] Wayang Platform Postgres ........................... SKIPPED [INFO] Wayang Platform SQLite3 ............................ SKIPPED [INFO] Wayang Platform Giraph ............................. SKIPPED [INFO] Wayang Platform Apache Flink ....................... SKIPPED [INFO] Wayang Platform Generic Jdbc ....................... SKIPPED [INFO] Wayang API ......................................... SKIPPED [INFO] Wayang API Scala-Java .............................. SKIPPED [INFO] Wayang Integration Test ............................ SKIPPED [INFO] Wayang API Python .................................. SKIPPED [INFO] wayang-api-sql ..................................... SKIPPED [INFO] Wayang Profiler .................................... SKIPPED [INFO] Wayang Extensions .................................. SKIPPED [INFO] wayang-iejoin ...................................... SKIPPED [INFO] Wayang - Common resources .......................... SKIPPED [INFO] wayang-benchmark ................................... SKIPPED [INFO] Wayang ML4all ...................................... SKIPPED [INFO] Wayang Project Assembly ............................ SKIPPED [INFO] ------------------------------------------------------------------------ [INFO] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Total time: 24.093 s [INFO] Finished at: 2024-06-25T10:53:32+02:00 [INFO] ------------------------------------------------------------------------ [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 -> [Help 1] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.plugin.MojoExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:198) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.ReleaseExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:115) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.exec.MavenExecutorException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.exec.InvokerMavenExecutor.executeGoals (InvokerMavenExecutor.java:129) at org.apache.maven.shared.release.exec.AbstractMavenExecutor.executeGoals (AbstractMavenExecutor.java:70) at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:105) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [ERROR] [ERROR] [ERROR] For more information about the errors and possible solutions, please read the following articles: [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [DEBUG] Shutting down 'file-lock' factory [DEBUG] Shutting down 'rwlock-local' factory [DEBUG] Shutting down 'semaphore-local' factory [DEBUG] Shutting down 'noop' factory The null pointer exception indicates problems in settings.xml. This could be fixed. But now, the error changes with and I am still not able to stage the build results. mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h3"},{"title":"Checklist for next iteration:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#checklist-for-next-iteration","content":" The error you're encountering indicates that there is a problem with deploying the artifacts using the maven-deploy-plugin. The root cause of the error is a NullPointerException during the deployment process, specifically related to the org.eclipse.aether.transfer.ArtifactTransferException. Here's a step-by-step guide to troubleshoot and resolve this issue: ","version":null,"tagName":"h2"},{"title":"1. Check Maven Settings","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#1-check-maven-settings","content":" Ensure that your Maven settings (settings.xml) are correctly configured for deployment. Verify that the repository settings and credentials are correctly specified. [DONE] ","version":null,"tagName":"h3"},{"title":"2. Verify Repository URL","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#2-verify-repository-url","content":" Make sure the repository URL in your pom.xml or settings.xml is correct and reachable. The URL should point to the correct staging repository for deployment. [OPEN] - I did not touch it, so I guess it is correct in the pom.xml. ","version":null,"tagName":"h3"},{"title":"3. Maven Version Compatibility","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#3-maven-version-compatibility","content":" Verify that you are using a compatible version of Maven. Sometimes, upgrading or downgrading Maven can resolve such issues. Apache Maven 3.9.6 (bc0240f3c744dd6b6ec2920b3cd08dcc295161ae) Maven home: /usr/local/Cellar/maven/3.9.6/libexec Java version: 11.0.15.1, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home Default locale: en_DE, platform encoding: US-ASCII OS name: "mac os x", version: "13.2.1", arch: "x86_64", family: "mac" [DONE] ","version":null,"tagName":"h3"},{"title":"4. Check for Network Issues","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#4-check-for-network-issues","content":" Ensure there are no network issues that might be causing problems in connecting to the repository. Sometimes, network configurations, firewalls, or proxy settings can interfere with the deployment process. [DONE] ","version":null,"tagName":"h3"},{"title":"5. Update Maven Plugins","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#5-update-maven-plugins","content":" Ensure that you are using the latest versions of the Maven plugins. Sometimes, bugs in older versions can cause unexpected issues. [OPEN] ","version":null,"tagName":"h3"},{"title":"6. Configure the maven-deploy-plugin in pom.xml","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#6-configure-the-maven-deploy-plugin-in-pomxml","content":" Make sure the maven-deploy-plugin is correctly configured in your pom.xml. Here's an example configuration: <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>3.0.0-M1</version> <configuration> <repositoryId>apache.releases.https</repositoryId> <url>https://repository.apache.org/service/local/staging/deploy/maven2</url> </configuration> </plugin> ","version":null,"tagName":"h3"},{"title":"7. Increase Verbose Logging","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#7-increase-verbose-logging","content":" Enable verbose logging to get more details about the error. You can do this by adding the -X flag when running the Maven command: mvn clean deploy -X [DONE] ","version":null,"tagName":"h3"},{"title":"8. Retry with a Clean Local Repository","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#8-retry-with-a-clean-local-repository","content":" Sometimes, a corrupt local repository can cause issues. Try cleaning your local Maven repository and re-running the deployment: mvn clean install -U mvn deploy [OPEN] - Does not match to an authentication issue. ","version":null,"tagName":"h3"},{"title":"9. Check for Missing Credentials","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#9-check-for-missing-credentials","content":" Ensure that the credentials for the repository are correctly set up in your settings.xml: <servers> <server> <id>apache.releases.https</id> <username>your-username</username> <password>your-password</password> </server> </servers> [DONE] ","version":null,"tagName":"h3"},{"title":"10. Review the Full Stack Trace","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#10-review-the-full-stack-trace","content":" The full stack trace indicates a NullPointerException: Caused by: java.lang.NullPointerException at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) ... This suggests that there might be an issue with how credentials are being handled. Double-check that the credentials are being correctly passed and processed. [DONE] ","version":null,"tagName":"h3"},{"title":"Installing and building Apache Wayang","type":0,"sectionRef":"#","url":"/docs/guide/installation","content":"","keywords":"","version":"Next"},{"title":"Clone repository","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#clone-repository","content":" git clone https://github.com/apache/incubator-wayang.git ","version":"Next","tagName":"h2"},{"title":"Create binaries","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#create-binaries","content":" Running following commands to build Wayang and generate the tar.gz cd incubator-wayang ./mvnw clean install -DskipTests ./mvnw clean package -pl :wayang-assembly -Pdistribution Then you can find the wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz under wayang-assembly/target directory. ","version":"Next","tagName":"h2"},{"title":"Prepare the environment","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#prepare-the-environment","content":" ","version":"Next","tagName":"h2"},{"title":"Wayang","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#wayang","content":" tar -xvf wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz cd wayang-0.7.1-SNAPSHOT In linux echo "export WAYANG_HOME=$(pwd)" >> ~/.bashrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.bashrc source ~/.bashrc In MacOS echo "export WAYANG_HOME=$(pwd)" >> ~/.zshrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.zshrc source ~/.zshrc ","version":"Next","tagName":"h3"},{"title":"Others","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#others","content":" You need to install Apache Spark version 3 or higher. Don’t forget to set the SPARK_HOME environment variable.You need to install Apache Hadoop version 3 or higher. Don’t forget to set the HADOOP_HOME environment variable. ","version":"Next","tagName":"h3"},{"title":"Run the program","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#run-the-program","content":" To execute the WordCount example with Apache Wayang, you need to execute your program with the 'wayang-submit' command: cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.apps.wordcount.Main java file://$(pwd)/README.md Then you should be able to see the output of the Wordcount example. Compiling Apache Wayang Apache Wayang (incubating) has different dependencies, for compiling, it needs to add some profile in the compilation to enable maven works properly. mvn clean compile The line before is because the plugin the Antlr is not needed in all the modules, as well it has happened with Scala language. When maven compiles one or more modules using those plugins in the compilation time, it needs to add. The modules are: wayang-api-scala-javawayang-core (Antlr)wayang-iejoinwayang-sparkwayang-profilerwayang-tests-integration Executing Coverage mvn clean verify jacoco:report the final report is placed on ./target/aggregate.exec/aggregate.exec ","version":"Next","tagName":"h2"},{"title":"How the optimizer works (WIP)","type":0,"sectionRef":"#","url":"/docs/guide/optimizer","content":"","keywords":"","version":"Next"},{"title":"Configuration Files","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#configuration-files","content":" Description of configuration files and how to edit them ","version":"Next","tagName":"h2"},{"title":"Key Configuration Parameters","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#key-configuration-parameters","content":" List of key parameters and how they affect performance ","version":"Next","tagName":"h2"},{"title":"Tuning Tips","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#tuning-tips","content":" Tips on tuning Apache Wayang for different scenarios ","version":"Next","tagName":"h2"},{"title":"ML4all: scalable ML system for everyone","type":0,"sectionRef":"#","url":"/docs/guide/ml4all","content":"","keywords":"","version":"Next"},{"title":"Abstraction","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#abstraction","content":" ML4all abstracts most ML algorithms with seven operators: (1) Transform receives a data point to transform (e.g., normalize it) and outputs a new data point. (2) Stage initializes all the required global param- eters (e.g., centroids for the k-means algorithm). (3) Compute performs user-defined computations on the input data point and returns a new data point. For example, it can compute the nearest cen- troid for each input data point. (4) Update updates the global parameters based on a user-defined formula. For example, it can update the new centroids based on the output computed by the Compute operator. (5) Sample takes as input the size of the desired sample and the data points to sample from and re- turns a reduced set of sampled data points. (6) Converge specifies a function that outputs a convergence dataset required for determining whether the iterations should continue or stop. (7) Loop specifies the stopping condition on the convergence dataset. Similar to MapReduce, where users need to implement a map and reduce function, users of ML4all wishing to develop their own algorithm should implement the above interfaces. The interfaces can be found in org.apache.wayang.ml4all.abstraction.api. Examples for KMeans clustering and stochastic gradient descent can be found in org.apache.wayang.ml4all.algorithms. ","version":"Next","tagName":"h2"},{"title":"Example runs","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#example-runs","content":" Kmeans: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunKMeans java,spark <url_path_to_file>/USCensus1990-sample.input 3 68 0 1 SGD: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunSGD spark <url_path_to_file>/adult.zeros.input 100827 123 10 0.001 ","version":"Next","tagName":"h2"},{"title":"What is Apache Wayang™?","type":0,"sectionRef":"#","url":"/docs/introduction/about","content":"","keywords":"","version":"Next"},{"title":"Architecture and Software stack","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#architecture-and-software-stack","content":" Apache Wayang's unique architecture, unlike traditional DBMSs, decouples the physical planning and execution layers, empowering developers to express their data processing logic in a platform-agnostic fashion. This separation of concerns allows developers to focus on the algorithmic aspects of their applications without being constrained by the intricacies of specific processing platforms. At the bottom layers of the software stack, there are the different data storage mediums and the supported data processing platforms. On top of these, Wayang’s core consists of the following main components: the optimizer, the executor, the monitor, and platform-specific drivers. Wayang currently supports two main APIs: the Java one and the Scala one. A Python API is also supported with limited operator coverage for the moment. Besides using any of the supported languages, users can directly input SQL queries via the SQL library, which transforms them into a Wayang plan. Wayang also comes with an ML library for running ML tasks. Users can directly utilize the provided algorithms or can implement their own algorithm using a simple ML abstraction. To enable support for more programming languages in an efficient way, Wayang will soon come with a Polyglot library. Below you can see on the left, a Wayang plan representing the stochastic gradient descent algorithm, which used in most deep learning tasks. On the right, you can see how the optimizer decided to execute it. Orange nodes are the operators that ran on Spark and green the operators executed as a single Java process. ","version":"Next","tagName":"h3"},{"title":"Query Optimizer","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#query-optimizer","content":" Wayang's query optimizer is the principal component of the core. It receives as input a Wayang plan and outputs an execution (platform-specific) plan (see example plan above) with the goal of minimizing the total cost. The metric for the cost can be anything, from execution runtime to monetary cost or energy consumption. To achieve this, the optimizer first “inflates” the plan: For each node that corresponds to a Wayang operator, it adds all the corresponding execution (platform-specific) operators. Once the inflated plan is created, the optimizer attaches not only the operator’s costs but also the costs for moving intermediate data from one platform to another. By default, Wayang uses linear cost formulas to estimate these costs but one can plug their own optimizer, e.g., an ML-based one. At the last step of query optimization, our enumeration algorithm considers available options to output the optimal execution plan w.r.t. a defined cost. Note that users can control the optimizer by specifying in their code where an operator has to be executed via the withTargetPlatform(plat) call on the desired operator. Then, the optimizer takes into consideration the decisions of the user and outputs an execution plan by navigating a reduced search space during the plan enumeration. ","version":"Next","tagName":"h3"},{"title":"History","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#history","content":" Wayang (formely called Rheem) is the product of many years of top quality research. Below you can find the main publications: Apache Wayang: A Unified Data Analytics Framework. SIGMOD Rec. 52(3): 30-35 (2023) pdf Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework. SIGMOD Conference Companion 2025: 35-38 pdf ML-based Cross-Platform Query Optimization. ICDE 2020: 1489-1500 pdf RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems. VLDB J. 29(6): 1287-1310 (2020) pdf RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -. Proc. VLDB Endow. 11(11): 1414-1427 (2018) pdf ","version":"Next","tagName":"h3"},{"title":"Using Wayang's docker image","type":0,"sectionRef":"#","url":"/docs/guide/wayang-docker","content":"","keywords":"","version":"Next"},{"title":"Step 1: Creating a docker-compose file","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-1-creating-a-docker-compose-file","content":" This guide assumes knowledge aboutdocker anddocker compose. We provide a pre-built docker imagethat contains the necessary tooling in order to run or develop Apache Wayang. The tools necessary for this are: Java 11Apache SparkHadoopMaven A docker-compose.yml containing the following services will suffice to run TPC-H benchmarks: name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always Placing this file in the root directory of Wayang's source will mount volumes containing the application and its dependencies after installation into the app container. ","version":"Next","tagName":"h2"},{"title":"Step 2: Connecting to the app container","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-2-connecting-to-the-app-container","content":" In order to get a interactive bash session that allows running commands inside of the app container, run the following: docker exec -it apache-wayang-app bash ","version":"Next","tagName":"h2"},{"title":"Step 3: Compiling Wayang and running benchmarks","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-3-compiling-wayang-and-running-benchmarks","content":" Within the root directory of Wayang (/var/www/html in our container), run the following command to install all packages in Wayang: mvn clean install -DskipTests Packaging the project to build the executable: mvn clean package -pl :wayang-assembly -Pdistribution Execute your code: cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-incubating-dist.tar.gz cd wayang-0.7.1 ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Optional: Add clusters for additional platforms to your docker setup","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#optional-add-clusters-for-additional-platforms-to-your-docker-setup","content":" name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always spark-master: image: cluster-apache-spark:3.0.2 spark-worker-a: image: cluster-apache-spark:3.0.2 depends_on: - spark-master environment: - SPARK_MASTER=spark://spark-master:7077 - SPARK_WORKER_CORES=1 - SPARK_WORKER_MEMORY=1G - SPARK_DRIVER_MEMORY=1G - SPARK_EXECUTOR_MEMORY=1G - SPARK_WORKLOAD=worker - SPARK_LOCAL_IP=spark-worker-a ","version":"Next","tagName":"h2"},{"title":"Guide to Development and Usage with Apache Wayang (incubating)","type":0,"sectionRef":"#","url":"/docs/guide/examples","content":"","keywords":"","version":"Next"},{"title":"Example 1: Machine Learning for query optimization in Apache Wayang","type":1,"pageTitle":"Guide to Development and Usage with Apache Wayang (incubating)","url":"/docs/guide/examples#example-1-machine-learning-for-query-optimization-in-apache-wayang","content":" Apache Wayang can be customized with concrete implementations of the EstimatableCost interface in order to optimize for a desired metric. The implementation can be enabled by providing it to a Configuration. public class CustomEstimatableCost implements EstimatableCost { /* Provide concrete implementations to match desired cost function(s) * by implementing the interface in this class. */ } public class WordCount { public static void main(String[] args) { /* Create a Wayang context and specify the platforms Wayang will consider */ Configuration config = new Configuration(); /* Provision of a EstimatableCost that implements the interface.*/ config.setCostModel(new CustomEstimatableCost()); WayangContext wayangContext = new WayangContext(config) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); /*... omitted */ } } In combination with an encoding scheme and a third party package to load ML models, the following example shows how to predict runtimes of query execution plans runtimes in Apache Wayang (incubating): public class MLCost implements EstimatableCost { public EstimatableCostFactory getFactory() { return new Factory(); } public static class Factory implements EstimatableCostFactory { @Override public EstimatableCost makeCost() { return new MLCost(); } } @Override public ProbabilisticDoubleInterval getEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } @Override public ProbabilisticDoubleInterval getParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } /** Returns a squashed cost estimate. */ @Override public double getSquashedEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public double getSquashedParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public Tuple<List<ProbabilisticDoubleInterval>, List<Double>> getParallelOperatorJunctionAllCostEstimate(PlanImplementation plan, Operator operator) { List<ProbabilisticDoubleInterval> intervalList = new ArrayList<ProbabilisticDoubleInterval>(); List<Double> doubleList = new ArrayList<Double>(); intervalList.add(this.getEstimate(plan, true)); doubleList.add(this.getSquashedEstimate(plan, true)); return new Tuple<>(intervalList, doubleList); } public PlanImplementation pickBestExecutionPlan( Collection<PlanImplementation> executionPlans, ExecutionPlan existingPlan, Set<Channel> openChannels, Set<ExecutionStage> executedStages) { final PlanImplementation bestPlanImplementation = executionPlans.stream() .reduce((p1, p2) -> { final double t1 = p1.getSquashedCostEstimate(); final double t2 = p2.getSquashedCostEstimate(); return t1 < t2 ? p1 : p2; }) .orElseThrow(() -> new WayangException("Could not find an execution plan.")); return bestPlanImplementation; } } Third-party packages such as OnnxRuntime can be used to load pre-trained .onnx files that contain desired ML models. public class OrtMLModel { private static OrtMLModel INSTANCE; private OrtSession session; private OrtEnvironment env; private final Map<String, OnnxTensor> inputMap = new HashMap<>(); private final Set<String> requestedOutputs = new HashSet<>(); public static OrtMLModel getInstance(Configuration configuration) throws OrtException { if (INSTANCE == null) { INSTANCE = new OrtMLModel(configuration); } return INSTANCE; } private OrtMLModel(Configuration configuration) throws OrtException { this.loadModel(configuration.getStringProperty("wayang.ml.model.file")); } public void loadModel(String filePath) throws OrtException { if (this.env == null) { this.env = OrtEnvironment.getEnvironment(); } if (this.session == null) { this.session = env.createSession(filePath, new OrtSession.SessionOptions()); } } public void closeSession() throws OrtException { this.session.close(); this.env.close(); } /** * @param encodedVector * @return NaN on error, and a predicted cost on any other value. * @throws OrtException */ public double runModel(Vector<Long> encodedVector) throws OrtException { double costPrediction; OnnxTensor tensor = OnnxTensor.createTensor(env, encodedVector); this.inputMap.put("input", tensor); this.requestedOutputs.add("output"); BiFunction<Result, String, Double> unwrapFunc = (r, s) -> { try { return ((double[]) r.get(s).get().getValue())[0]; } catch (OrtException e) { return Double.NaN; } }; try (Result r = session.run(inputMap, requestedOutputs)) { costPrediction = unwrapFunc.apply(r, "output"); } return costPrediction; } } ","version":"Next","tagName":"h2"},{"title":"Benchmarking Wayang","type":0,"sectionRef":"#","url":"/docs/introduction/benchmark","content":"","keywords":"","version":"Next"},{"title":"Running a Relational Query Across Multiple Independent Databases (Federation)","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#running-a-relational-query-across-multiple-independent-databases-federation","content":" In this use case, we showcase how Apache Wayang addresses the common challenge of analyzing data scattered across diverse systems, a prevalent issue in many data-driven projects. Through Wayang, developers can seamlessly perform analytics on relational data residing in multiple systems. The in-situ data processing approach employed by Wayang introduces data privacy-preserving mechanisms into distributed data analytics tasks. Datasets: To test the capabilities of Wayang, we utilized the TPC-H benchmark, which comprises five datasets/relations. The lineitem and orders relations are stored in HDFS, while customer, supplier, and region are in Postgres, and nation is in S3 or the local file system. Our testing covered a range of dataset sizes, from 1GB to 100GB, to ensure our product's scalability and robustness. Query/task: We evaluate the performance of the complex SQL TPC-H query 5: SELECT N_NAME, SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) AS REVENUE FROM CUSTOMER, ORDERS, LINEITEM, SUPPLIER, NATION, REGION WHERE C_CUSTKEY = O_CUSTKEY AND L_ORDERKEY = O_ORDERKEY AND L_SUPPKEY = S_SUPPKEY AND C_NATIONKEY = S_NATIONKEY AND S_NATIONKEY = N_NATIONKEY AND N_REGIONKEY = R_REGIONKEY AND R_NAME = 'ASIA' AND O_ORDERDATE >= '1994-01-01' AND O_ORDERDATE < DATEADD(YY, 1, cast('1994-01-01' as date)) GROUP BY N_NAME ORDER BY REVENUE DESC Baselines: We compared Wayang with two widely-used systems for storing and querying relational data: Spark and Postgres. To conduct a fair comparison, we first moved all datasets to the system being tested (Spark or Postgres). Results: The results of our tests, presented in Figure 1, show the execution time in seconds of the SQL query 5. Note that the time required to transfer data to Spark or Postgres was excluded from the results. As seen in Figure 1, Wayang significantly outperformed Postgres while retaining a runtime very close to Spark. It's important to note that for Spark to function, we needed twice the time to extract datasets from Postgres and transfer them to Spark. Wayang achieved such impressive performance by seamlessly combining Postgres with Spark: Wayang's query optimizer chose to perform all selections and projections on the data stored in Postgres before extracting data to join with the relations in HDFS. Additionally, the optimizer determined that executing the join between lineitem and supplier in Spark would be beneficial, as it could distribute the computational load of joining to multiple workers. All of this was done without the need for the user to specify where each operation should be executed. ","version":"Next","tagName":"h3"},{"title":"Reducing Execution Costs for Machine Learning Tasks Using Multiple Systems","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#reducing-execution-costs-for-machine-learning-tasks-using-multiple-systems","content":" To evaluate Apache Wayang's ability to accelerate machine learning tasks while optimizing costs by leveraging multiple systems, we conducted a comprehensive benchmark. The benchmark involved storing all data within a single repository, a scenario where selectively offloading data to more powerful engines can significantly enhance performance. To assess this feature, we employed a widely used machine learning algorithm, stochastic gradient descent, to perform classification tasks. Datasets: We use two real-world datasets that we downloaded from the UCI machine learning repository, namely higgs and rcv1. higgs consists of ~11 million data points with 28 features each and rcv1 contains ~677 thousand data points with ~47 thousand features each. In addition, we construct a synthetic dataset so that we can stress our product with even larger datasets, specifically with 88 million data points of 100 features each. Query/task: We test the performance of training classification models for higgs and the synthetic dataset and a regression model rcv1. All three training models use stochastic gradient descent but with a different loss function. We use Hinge loss to simulate support vector machines for the classification tasks and the logistic loss for the regression task. Baselines: We compare Wayang against MLlib from Apache Spark and SystemML from IBM, two very popular machine learning libraries. Results: Our results, shown in Figure 2, demonstrate that Wayang outperforms both baselines by more than an order of magnitude in runtime performance for large datasets. Apache Wayang's intelligent optimizer dynamically utilizes a hybrid approach, seamlessly blending Spark and local Java execution, to significantly improve the performance of the stochastic gradient descent algorithm. This optimization strategy involves leveraging Spark for efficient preprocessing and data preparation, while transitioning to local Java execution for the subsequent gradient computation phase. During this phase, the dataset shrinks considerably, making it more efficient to process using a single machine. This optimization technique, while not readily apparent without specialized expertise, is seamlessly implemented by Apache Wayang, requiring no additional user intervention. ","version":"Next","tagName":"h3"},{"title":"Optimizing Big Data Analytics by Adapting Platforms to Data and Task Characteristics","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#optimizing-big-data-analytics-by-adapting-platforms-to-data-and-task-characteristics","content":" We demonstrate the platform adaptation capabilities of Apache Wayang, showcasing how it optimizes analytical tasks by tailoring execution to the specific data and task characteristics. Our approach involves dynamically selecting the optimal processing engine based on the nature of the data and the complexity of the analytical operation. This flexibility allows Wayang to deliver enhanced performance and efficiency, particularly for complex analytical workflows that demand the strengths of multiple processing engines. Datasets: We use Wikipedia abstracts and store them in HDFS. We vary the dataset size from 1GB to 800GB. Query/task: We test our product with a widely popular big data analytical task, namely WordCount. It counts the number of distinct words in a text corpus. Different variations of wordcount are useful in various text mining applications, such as term frequency, word clouds, etc. Baselines: We use three different platforms where Wordcount can be executed: Apache Spark, Apache Flink, and a single node Java program. We then set Wayang to be able to automatically choose which of the three platforms to use for each dataset. Results: The benchmark results, presented in Figure 3, provide evidence of Apache Wayang's platform adaptation prowess, consistently selecting the fastest available platform for various dataset sizes in our WordCount example. By incorporating execution cost modeling into the query optimizer, Wayang eliminates the need for manual platform selection or migration, allowing users to solely focus on their analytical tasks. Our platform adaptation feature seamlessly identifies the optimal platform for each query or task based on data and query characteristics, ensuring faster execution and enhanced efficiency in big data analytics. ","version":"Next","tagName":"h3"},{"title":"Features","type":0,"sectionRef":"#","url":"/docs/introduction/features","content":"","keywords":"","version":"Next"},{"title":"Cross platform enablement","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cross-platform-enablement","content":" Apache Wayang's cross-platform optimizer stands out as its most distinguishing feature. It not only selects the most suitable processing engine for each task, but also empowers users to execute a single task across multiple platforms. This capability is achieved through an extensible set of graph transformations applied to the Apache Wayang plan, which identifies alternative execution plans that potentially offer better performance. These alternative plans are then evaluated using platform-specific cost models, which can either be user-defined or learned from historical data. These cost models are parameterized based on the underlying hardware configuration, such as the number of computing nodes for distributed operations. ","version":"Next","tagName":"h3"},{"title":"Scalable ML (ML4All)","type":1,"pageTitle":"Features","url":"/docs/introduction/features#scalable-ml-ml4all","content":" ML4all is part of Wayang, enhancing machine learning workflows. ML4all alleviates users from the complexities of algorithm selection and low-level implementation details, enabling them to concentrate on the core aspects of their machine learning applications. By employing an innovative abstraction, ML4all addresses a broad spectrum of machine learning tasks and utilizes a cost-based optimizer to dynamically identify the optimal gradient descent algorithm for each scenario. Notably, ML4all demonstrates performance improvements of two orders of magnitude over existing systems, enabling the processing of large datasets that were previously impractical. ","version":"Next","tagName":"h3"},{"title":"High Efficiency","type":1,"pageTitle":"Features","url":"/docs/introduction/features#high-efficiency","content":" Apache Wayang's suite of optimized operators and sophisticated query optimization techniques enables it to effectively process large-scale and small-scale datasets. By employing a data processing abstraction based on UDFs, Wayang allows applications to convey semantic information about their functions, optimization hints (such as iteration counts), constraints (e.g., physical placement of operators), and alternative execution plans. The optimizer incorporates these insights to refine the execution plan, aiming to achieve optimal performance in a comprehensive manner. ","version":"Next","tagName":"h3"},{"title":"Flexibility","type":1,"pageTitle":"Features","url":"/docs/introduction/features#flexibility","content":" Apache Wayang offers a collection of operators that applications utilize to define their tasks. A crucial aspect of Apache Wayang is its flexible operator mapping structure, which enables developers to effortlessly add, modify, or remove mappings between Wayang and execution operators. This flexibility empowers developers to seamlessly integrate additional Wayang and execution operators into their applications. ","version":"Next","tagName":"h3"},{"title":"Cost Saving","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cost-saving","content":" Developers can focus on building their applications without the need to understand the complexities of underlying platforms. This simplifies the development process and removes the requirement for developers to be experts in big data infrastructures. Apache Wayang automatically determines the most suitable data processing platforms for specific tasks and deploys applications accordingly. ","version":"Next","tagName":"h3"},{"title":"Additional Features","type":1,"pageTitle":"Features","url":"/docs/introduction/features#additional-features","content":" Zero-copy: cross-platform in-situ data processingHigh performance: A highly-extensible API framework to generate DAG based federated execution plane at runtime to speed up data processing, providing 50-180x speed up by: reduce data movement to single platformsreduce ETL overhead to perform large-scale analyticsreduce data duplication and storageexecute data processing on the best available technology, including local JVM stream processing Data application agnosticity run data tasks on multiple platforms without re-platforming the code (move jobs from Hadoop to Spark without changing the code at large)implement a sustainable AI strategy by using data pools in-situ Enterprise ready federated learning (in development) ","version":"Next","tagName":"h3"},{"title":"Releases","type":0,"sectionRef":"#","url":"/docs/start/download","content":"","keywords":"","version":"Next"},{"title":"1.1.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#110-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"1.0.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#100-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"0.7.1 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#071-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"Frameworks supported","type":1,"pageTitle":"Releases","url":"/docs/start/download#frameworks-supported","content":" Apache Flink v1.7.1Apache Giraph v1.2.0-hadoop2GraphChi v0.2.2 (only available with scala 11.x)Java Streams (version depends on the java version)JDBC-TemplatePostgres v9.4.1208 (Implementation JDBC-Template)Apache Spark v3.1.2 (scala 12.x) and v2.4.8 (scala 11.x)SQLite3 v3.8.11.2 (implementation JDBC-Template) NOTE: The supported platforms for Scala may vary depending on the Scala version.\\ ","version":"Next","tagName":"h3"},{"title":"Previous releases","type":1,"pageTitle":"Releases","url":"/docs/start/download#previous-releases","content":" Older releases can be found here ","version":"Next","tagName":"h3"},{"title":"Getting started","type":0,"sectionRef":"#","url":"/docs/guide/getting-started","content":"","keywords":"","version":"Next"},{"title":"Requirements","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#requirements","content":" Apache Wayang (incubating) is built upon the foundations of Java 11 and Scala 2.12, providing a robust and versatile platform for data processing applications. If you intend to build Wayang from source, you will also need to have Apache Maven, the popular build automation tool, installed on your system. Additionally, be mindful that some of the processing platforms supported by Wayang may have their own specific installation requirements. ","version":"Next","tagName":"h2"},{"title":"Get Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#get-wayang","content":" Apache Wayang is readily available through Maven Central, facilitating seamless integration into your development workflow. For instance, to utilize Wayang in your Maven-based project, simply add the following dependency to your project's POM file: <dependency> <groupId>org.apache.wayang</groupId> <artifactId>wayang-***</artifactId> <version>0.7.1</version> </dependency> Note the ***: Wayang ships with multiple modules that can be included in your app, depending on how you want to use it: wayang-core: provides core data structures and the optimizer (required)wayang-basic: provides common operators and data types for your apps (recommended)wayang-api: provides an easy-to-use Scala and Java API to assemble Wayang plans (recommended)wayang-java, wayang-spark, wayang-graphchi, wayang-sqlite3, wayang-postgres: adapters for the various supported processing platformswayang-profiler: provides functionality to learn operator and UDF cost functions from historical execution data For the sake of version flexibility, you still have to include your Hadoop (hadoop-hdfs and hadoop-common) and Spark (spark-core and spark-graphx) version of choice. In addition, you can obtain the most recent snapshot version of Wayang via Sonatype's snapshot repository. Just included <repositories> <repository> <id>sonatype-snapshots</id> <name>Sonatype Snapshot Repository</name> <url>https://oss.sonatype.org/content/repositories/snapshots</url> </repository> <repositories> If you need to rebuild Wayang, e.g., to use a different Scala version, you can simply do so via Maven: Adapt the version variables (e.g., spark.version) in the main pom.xml file.Build Wayang with the adapted versions. $ mvn clean install Note the standalone profile to fix Hadoop and Spark versions, so that Wayang apps do not explicitly need to declare the corresponding dependencies. Also, note the distro profile, which assembles a binary Wayang distribution. To activate these profiles, you need to specify them when running maven, i.e., mvn clean install -P<profile name> ","version":"Next","tagName":"h3"},{"title":"Configure Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#configure-wayang","content":" To enable Apache Wayang's smooth operation, you need to equip it with details about your processing platforms' capabilities and how to interact with them. A default configuration is available for initial testing, but creating a properties file is generally preferable for fine-tuning the configuration to suit your specific requirements. To harness this personalized configuration effortlessly, launch your application via $ java -Dwayang.configuration=url://to/my/wayang.properties ... Essential configuration settings: General settings wayang.core.log.enabled (= true): whether to log execution statistics to allow learning better cardinality and cost estimators for the optimizerwayang.core.log.executions (= ~/.wayang/executions.json) where to log execution times of operator groupswayang.core.log.cardinalities (= ~/.wayang/cardinalities.json) where to log cardinality measurementswayang.core.optimizer.instrumentation (= org.apache.wayang.core.profiling.OutboundInstrumentationStrategy): where to measure cardinalities in Wayang plans; other options are org.apache.wayang.core.profiling.NoInstrumentationStrategy and org.apache.wayang.core.profiling.FullInstrumentationStrategywayang.core.optimizer.reoptimize (= false): whether to progressively optimize Wayang planswayang.basic.tempdir (= file:///tmp): where to store temporary files, in particular for inter-platform communication Java Streams wayang.java.cpu.mhz (= 2700): clock frequency of processor the JVM runs on in MHzwayang.java.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to JVM in ms/MB Apache Spark spark.master (= local): Spark master various other Spark settings are supported, e.g., spark.executor.memory, spark.serializer, ... wayang.spark.cpu.mhz (= 2700): clock frequency of processor the Spark workers run on in MHzwayang.spark.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to the Spark workers in ms/MBwayang.spark.network.ms-per-mb (= 8.6): average network throughput of the Spark workers in ms/MBwayang.spark.init.ms (= 4500): time it takes Spark to initialize in ms GraphChi wayang.graphchi.cpu.mhz (= 2700): clock frequency of processor GraphChi runs on in MHzwayang.graphchi.cpu.cores (= 2): number of cores GraphChi runs onwayang.graphchi.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to GraphChi in ms/MB SQLite wayang.sqlite3.jdbc.url: JDBC URL to use SQLitewayang.sqlite3.jdbc.user: optional user namewayang.sqlite3.jdbc.password: optional passwordwayang.sqlite3.cpu.mhz (= 2700): clock frequency of processor SQLite runs on in MHzwayang.sqlite3.cpu.cores (= 2): number of cores SQLite runs on PostgreSQL wayang.postgres.jdbc.url: JDBC URL to use PostgreSQLwayang.postgres.jdbc.user: optional user namewayang.postgres.jdbc.password: optional passwordwayang.postgres.cpu.mhz (= 2700): clock frequency of processor PostgreSQL runs on in MHzwayang.postgres.cpu.cores (= 2): number of cores PostgreSQL runs on To effectively define your applications with Apache Wayang, utilize its Scala or Java API, conveniently found within the wayang-api module. For clear illustrations, refer to the provided examples below. ","version":"Next","tagName":"h3"},{"title":"Cost Functions","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#cost-functions","content":" Wayang provides a utility to learn cost functions from historical execution data. Specifically, Wayang can learn configurations for load profile estimators (that estimate CPU load, disk load etc.) for both operators and UDFs, as long as the configuration provides a template for those estimators. As an example, the JavaMapOperator draws its load profile estimator configuration via the configuration key wayang.java.map.load. Now, it is possible to specify a load profile estimator template in the configuration under the key <original key>.template, e.g.: wayang.java.map.load.template = {\\ "in":1, "out":1,\\ "cpu":"?*in0"\\ } This template encapsulates a load profile estimator that requires at minimum one input cardinality and one output cardinality. Furthermore, it simulates CPU load by assuming a direct relationship with the input cardinality. However, more complex functions are possible. In particular, you can use the variables in0, in1, ... and out0, out1, ... to incorporate the input and output cardinalities, respectively;operator properties, such as numIterations for the PageRankOperator implementations;the operators +, -, *, /, %, ^, and parantheses;the functions min(x0, x1, ...)), max(x0, x1, ...), abs(x), log(x, base), ln(x), ld(x);and the constants e and pi. While Apache Wayang provides templates for all execution operators, you will need to explicitly define your user-defined functions (UDFs) by specifying their cost functions, which are based on configuration parameters. This involves creating an initial specification and template for each UDF. As soon as execution data has been collected, you can initiate: java ... org.apache.wayang.profiler.ga.GeneticOptimizerApp [configuration URL [execution log]] This tool will attempt to determine suitable values for the question marks (?) within the load profile estimator templates, aligning them with the collected execution data and pre-defined configuration entries for the load profile estimators. These optimized values can then be directly incorporated into your configuration. ","version":"Next","tagName":"h2"},{"title":"Examples","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#examples","content":" For some executable examples, have a look at this repository. ","version":"Next","tagName":"h2"},{"title":"WordCount","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#wordcount","content":" Java API import org.apache.wayang.api.JavaPlanBuilder; import org.apache.wayang.basic.data.Tuple2; import org.apache.wayang.core.api.Configuration; import org.apache.wayang.core.api.WayangContext; import org.apache.wayang.core.optimizer.cardinality.DefaultCardinalityEstimator; import org.apache.wayang.java.Java; import org.apache.wayang.spark.Spark; import java.util.Collection; import java.util.Arrays; public class WordcountJava { public static void main(String[] args){ // Settings String inputUrl = "file:/tmp.txt"; // Get a plan builder. WayangContext wayangContext = new WayangContext(new Configuration()) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); JavaPlanBuilder planBuilder = new JavaPlanBuilder(wayangContext) .withJobName(String.format("WordCount (%s)", inputUrl)) .withUdfJarOf(WordcountJava.class); // Start building the WayangPlan. Collection<Tuple2<String, Integer>> wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(line -> Arrays.asList(line.split("\\\\W+"))) .withSelectivity(10, 100, 0.9) .withName("Split words") // Filter empty tokens. .filter(token -> !token.isEmpty()) .withSelectivity(0.99, 0.99, 0.99) .withName("Filter empty words") // Attach counter to each word. .map(word -> new Tuple2<>(word.toLowerCase(), 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey( Tuple2::getField0, (t1, t2) -> new Tuple2<>(t1.getField0(), t1.getField1() + t2.getField1()) ) .withCardinalityEstimator(new DefaultCardinalityEstimator(0.9, 1, false, in -> Math.round(0.01 * in[0]))) .withName("Add counters") // Execute the plan and collect the results. .collect(); System.out.println(wordcounts); } } Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark object WordcountScala { def main(args: Array[String]) { // Settings val inputUrl = "file:/tmp.txt" // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"WordCount ($inputUrl)") .withUdfJarsOf(this.getClass) val wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(_.split("\\\\W+"), selectivity = 10).withName("Split words") // Filter empty tokens. .filter(_.nonEmpty, selectivity = 0.99).withName("Filter empty words") // Attach counter to each word. .map(word => (word.toLowerCase, 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey(_._1, (c1, c2) => (c1._1, c1._2 + c2._2)).withName("Add counters") .withCardinalityEstimator((in: Long) => math.round(in * 0.01)) // Execute the plan and collect the results. .collect() println(wordcounts) } } ","version":"Next","tagName":"h3"},{"title":"k-means","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#k-means","content":" Wayang is also capable of iterative processing, which is, e.g., very important for machine learning algorithms, such as k-means. Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.core.function.FunctionDescriptor.ExtendedSerializableFunction import org.apache.wayang.core.function.ExecutionContext import org.apache.wayang.core.optimizer.costs.LoadProfileEstimators import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark import scala.util.Random import scala.collection.JavaConversions._ object kmeans { def main(args: Array[String]) { // Settings val inputUrl = "file:/kmeans.txt" val k = 5 val iterations = 100 val configuration = new Configuration // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"k-means ($inputUrl, k=$k, $iterations iterations)") .withUdfJarsOf(this.getClass) case class Point(x: Double, y: Double) case class TaggedPoint(x: Double, y: Double, cluster: Int) case class TaggedPointCounter(x: Double, y: Double, cluster: Int, count: Long) { def add_points(that: TaggedPointCounter) = TaggedPointCounter(this.x + that.x, this.y + that.y, this.cluster, this.count + that.count) def average = TaggedPointCounter(x / count, y / count, cluster, 0) } // Read and parse the input file(s). val points = planBuilder .readTextFile(inputUrl).withName("Read file") .map { line => val fields = line.split(",") Point(fields(0).toDouble, fields(1).toDouble) }.withName("Create points") // Create initial centroids. val random = new Random val initialCentroids = planBuilder .loadCollection(for (i <- 1 to k) yield TaggedPointCounter(random.nextGaussian(), random.nextGaussian(), i, 0)).withName("Load random centroids") // Declare UDF to select centroid for each data point. class SelectNearestCentroid extends ExtendedSerializableFunction[Point, TaggedPointCounter] { /** Keeps the broadcasted centroids. */ var centroids: Iterable[TaggedPointCounter] = _ override def open(executionCtx: ExecutionContext) = { centroids = executionCtx.getBroadcast[TaggedPointCounter]("centroids") } override def apply(point: Point): TaggedPointCounter = { var minDistance = Double.PositiveInfinity var nearestCentroidId = -1 for (centroid <- centroids) { val distance = Math.pow(Math.pow(point.x - centroid.x, 2) + Math.pow(point.y - centroid.y, 2), 0.5) if (distance < minDistance) { minDistance = distance nearestCentroidId = centroid.cluster } } new TaggedPointCounter(point.x, point.y, nearestCentroidId, 1) } } // Do the k-means loop. val finalCentroids = initialCentroids.repeat(iterations, { currentCentroids => points .mapJava(new SelectNearestCentroid, udfLoad = LoadProfileEstimators.createFromSpecification( "my.udf.costfunction.key", configuration )) .withBroadcast(currentCentroids, "centroids").withName("Find nearest centroid") .reduceByKey(_.cluster, _.add_points(_)).withName("Add up points") .withCardinalityEstimator(k) .map(_.average).withName("Average points") }).withName("Loop") // Collect the results. .collect() println(finalCentroids) } } ","version":"Next","tagName":"h3"}],"options":{"id":"default"}}
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+{"searchDocs":[{"title":"Apache Kafka meets Wayang - Part 1","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-1","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#intro","content":" This article is the first of a four part series about federated data analysis using Apache Wayang. The first article starts with an introduction of a typical data colaboration scenario which will emerge in our digital future. In part two and three we will share a summary of our Apache Kafka client implementation for Apache Wayang. We started with the Java Platform (part 2) and the Apache Spark implementation follows (W.I.P.) in part three. The use case behind this work is an imaginary data collaboration scenario. We see this example and the demand for a solution already in many places. For us this is motivation enough to propose a solution. This would also allow us to do more local data processing, and businesses can stop moving data around the world, but rather care about data locality while they expose and share specific information to others by using data federation. This reduces complexity of data management and cost dramatically. For this purpose, we illustrate a cross organizational data sharing scenario from the finance sector soon. This analysis pattern will also be relevant in the context of data analysis along supply chains, another typical example where data from many stakeholder together is needed but never managed in one place, for good reasons. Data federation can help us to unlock the hidden value of all those isolated data lakes. ","version":null,"tagName":"h2"},{"title":"A cross organizational data sharing scenario","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 1","url":"/blog/kafka-meets-wayang-1#a-cross-organizational-data-sharing-scenario","content":" Our goal is the implementation of a cross organization decentralized data processing scenario, in which protected local data should be processed in combination with public data from public sources in a collaborative manner. Instead of copying all data into a central data lake or a central data platform we decided to use federated analytics. Apache Wayang is the tool we work with. In our case, the public data is hosted on publicly available websites or data pods. A client can use the HTTP(S) protocol to read the data which is given in a well defined format. For simplicity we decided to use CSV format. When we look into the data of each participant we have a different perspective. Our processing procedure should calculate a particular metric on the local data of each participant. An example of such a metric is the average spending of all users on a particular product category per month. This can vary from partner to partner, hence, we want to be able to calculate a peer-group comparison so that each partner can see its own metric compared with a global average calculated from contributions by all partners. Such a process requires global averaging and local averaging. And due to governance constraints, we can’t bring all raw data together in one place. Instead, we want to use Apache Wayang for this purpose. We simplify the procedure and split it into two phases. Phase one is the process, which allows each participant to calculate the local metrics. This requires only local data. The second phase requires data from all collaborating partners. The monthly sum and counter values per partner and category are needed in one place by all other parties. Hence, the algorithm of the first phase stores the local results locally, and the contributions to the global results in an externally accessible Kafka topic. We assume this is done by each of the partners. Now we have a scenario, in which an Apache Wayang process must be able to read data from multiple Apache Kafka topics from multiple Apache Kafka clusters but finally writes into a single Kafka topic, which then can be accessed by all the participating clients. The illustration shows the data flows in such a scenario. Jobs with red border are executed by the participants in isolation within their own data processing environments. But they share some of the data, using publicly accessible Kafka topics, marked by A. Job 4 is the Apache Wayang job in our focus: here we intent to read data from 3 different source systems, and write results into a fourth system (marked as B), which can be accesses by all participants again. With this in mind we want to implement an Apache Wayang application which implements the illustrated Job 4. Since as of today, there is now KafkaSource and KafkaSink available in Apache Wayang, an implementation of both will be our first step. Our assumption is, that in the beginning, there won’t be much data. Apache Spark is not required to cope with the load, but we expect, that in the future, a single Java application would not be able to handle our workload. Hence, we want to utilize the Apache Wayang abstraction over multiple processing platforms, starting with Java. Later, we want to switch to Apache Spark. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 2","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-2","content":"","keywords":"","version":null},{"title":"Apache Wayang’s Read & Write Path for Kafka topics","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#apache-wayangs-read--write-path-for-kafka-topics","content":" To describe the read and write paths for data in the context of the created Apache Wayang code snippet, the primary classes and interfaces we need to understand are as follows: WayangContext: This class is essential for initializing the Wayang processing environment. It allows you to configure the execution environment and register plugins that define which platforms Wayang can use for data processing tasks, such as Java.basicPlugin() for local Java execution. JavaPlanBuilder: This class is used to build and define the data processing pipeline (or plan) in Wayang. It provides a fluent API to specify the operations to be performed on the data, from reading the input to processing it and writing the output. ","version":null,"tagName":"h2"},{"title":"Read Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#read-path","content":" The read path describes how data is ingested from a source into the Wayang processing pipeline: Reading from Kafka Topic: The method readKafkaTopic(topicName) is used to ingest data from a specified Kafka topic. This is the starting point of the data processing pipeline, where topicName represents the name of the Kafka topic from which data is read. Data Tokenization and Preparation: Once the data is read from Kafka, it undergoes several transformations such as Splitting, Filtering, and Mapping. What follows are the procedures known as Reducing, Grouping, Co-Grouping, and Counting. ","version":null,"tagName":"h3"},{"title":"Write Path","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#write-path","content":" Writing to Kafka Topic: The final step in the pipeline involves writing the processed data back to a Kafka topic using .writeKafkaTopic(...). This method takes parameters that specify the target Kafka topic, a serialization function to format the data as strings, and additional configuration for load profile estimation, which optimizes the writing process. This read-write path provides a comprehensive flow of data from ingestion from Kafka, through various processing steps, and finally back to Kafka, showcasing a full cycle of data processing within Apache Wayang's abstracted environment and is implemented in our example program shown in listing 1. ","version":null,"tagName":"h3"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#implementation-of-input--and-output-operators","content":" The next section shows how a new pair of operators can be implemented to extend Apache Wayang’s capabilities on the input and output side. We created the Kafka Source and Kafka Sink components so that our cross organizational data collaboration scenario can be implemented using data streaming infrastructure. Level 1 – Wayang execution plan with abstract operators The implementation of our Kafka Source and Kafka Sink components for Apache Wayang requires new methods and classes on three layers. First of all in the API package. Here we use the JavaPlanBuilder to expose the function for selecting a Kafka topic as the source to be used by client. The class JavaPlanBuilder in package org.apache.wayang.api in the project wayang-api/wayang-api-scala-java exposes our new functionality to our external client. An instance of the JavaPlanBuilder is used to define the data processing pipeline. We use its readKafkaTopic() which specifies the source Kafka topic to read from, and for the write path we use the writeKafkaTopic() method. Both Methods do only trigger activities in the background. For the output side, we use the DataQuantaBuilder class, which offers an implementation of the writeKafkaTopic function. This function is designed to send processed data, referred to as DataQuanta, to a specified Kafka topic. Essentially, it marks the final step in a data processing sequence constructed using the Apache Wayang framework. In the DataQuanta class we implemented the methods writeKafkaTopic and writeKafkaTopicJava which use the KafkaTopicSink class. In this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation The second layer builds the bridge between the WayangContext and PlanBuilders which work together with DataQuanta and the DataQuantaBuilder. Also, the mapping between the abstract components and the specific implementations are defined in this layer. Therefore, the mappings package has a class Mappings in which all relevant input and output operators are listed. We use it to register the KafkaSourceMapping and a KafkaSinkMapping for the particular platform, Java in our case. These classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In our case this leads to a Java program running on a JVM which is set up by the Apache Wayang framework using the logical components of the execution plan. Those mappings link the real implementation of our operators the ones used in an execution plan. The JavaKafkaTopicSource and the JavaKafkaTopicSink extend the KafkaTopicSource and KafkaTopicSink so that the lower level implementation of those classes become available within Wayang’s Java Platform context. In this layer, the KafkaConsumer class and the KafkaProducer class are used, but both are configured and instantiated in the next layer underneath. All this is done in the project wayang-plarforms/wayang-java. Layer 3 – Input/Output Connector Layer The KafkaTopicSource and KafkaTopicSink classes build the third layer of our implementation. Both are implemented in Java programming language. In this layer, the real Kafka-Client logic is defined. Details about consumer and producers, client configuration, and schema handling have to be handled here. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#summary","content":" Both classes in the third layer implement the Kafka client logic which is needed by the Wayang-execution plan when external data flows should be established. The layer above handles the mapping of the components at startup time. All this wiring is needed to keep Wayang open and flexible so that multiple external systems can be used in a variety of combinations and using multiple target platforms in combinations. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 2","url":"/blog/kafka-meets-wayang-2#outlook","content":" The next part of the article series will cover the creation of an Kafka Source and Sink component for the Apache Spark platform, which allows our use case to scale. Finally, in part four we bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Apache Kafka meets Wayang - Part 3","type":0,"sectionRef":"#","url":"/blog/kafka-meets-wayang-3","content":"","keywords":"","version":null},{"title":"The goal of this implementation","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#the-goal-of-this-implementation","content":" We want to process data from Apache Kafka topics, which are hosted on Confluent cloud. In our example scenario, data is available in multiple different clusters, in different regions and owned by different organizations. Each organization uses the "stream sharing" feature provided by Confluent cloud. This way, the operator of our central processing job has been granted appropriate permissions. The plaftorm provided the necessary configuration properties, including access coordinates and credentials in the name of the topic owner to us. The following illustration has already been introduced in part one of the blog series, but for clarity we repeat it here. Today, we focus on Job 4 in the image. We implement a program which uses data federation based on multiple sources. Each source allows us to read the data from that particular topic so that we can process it in a different governance context. In this example it is a public processing context, in which data from multiple private processing contexts are used together. This use case is already prepared for high processing loads We can utilize the scalability capabilities of Apache Spark or simply use a Java program for initial tests of the solution. Switching between both is done in one line of code in Apache Wayang. Again, we start with a WayangContext, as shown by examples in the Wayang code repository. WayangContext wayangContext = new WayangContext().with(Spark.basicPlugin()); We simply switched the backend system towards Apache Spark by using the WayangContext with Spark.basicPlugin(). The JavaPlanBuilder and all other logic of our example job won't be touched. In order to make this working we will now implement the Mappings and the Operators for the Apache Spark platform module. ","version":null,"tagName":"h2"},{"title":"Implementation of Input- and Output Operators","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#implementation-of-input--and-output-operators","content":" We reuse the Kafka Source and Kafka Sink components which have been created for the JavaKafkaSource and JavaKafkaSink. Hence we work with Wayang's Java API. Level 1 – Wayang execution plan with abstract operators Since the JavaPlanBuilder already exposes the function for selecting a Kafka topic as source and the DataQuantaBuilder class exposes the writeKafkaTopic function we can move on quickly. Remember, in this API layer we use the Scala programming language, but we utilize the Java classes, implemented in the layer below. Level 2 – Wiring between Platform Abstraction and Implementation As in the case with the Java Platform, in the second layer we build a bridge between the WayangContext and the PlanBuilders, which work together with DataQuanta and the DataQuantaBuilder. We must provide the mapping between the abstract components and the specific implementations in this layer. Therefore, the mappings package in project wayang-platforms/wayang-spark has a class Mappings in which our KafkaTopicSinkMapping and KafkaTopicSourceMapping will be registered. Again, these classes allow the Apache Wayang framework to use the Java implementation of the KafkaTopicSource component (and KafkaTopicSink respectively). While the Wayang execution plan uses the higher abstractions, here on the “platform level” we have to link the specific implementation for the target platform. In this case this leads to an Apache Spark job, running on a Spark cluster which is set up by the Apache Wayang framework using the logical components of the execution plan, and the Apache Spark configuration provided at runtime. A mapping links an operator implementation to the abstraction used in an execution plan. We define two new mappings for our purpose, namely KafkaTopicSourceMapping, and KafkaTopicSinkMapping, both could be reused from last round. For the Spark platform we simply replace the occurences of JavaPlatform with SparkPlatform. Furthermore, we create an implementation of the SparkKafkaTopicSource and SparkKafkaTopicSink. Layer 3 – Input/Output Connector Layer Let's quickly recap, how does Apache Spark interacts with Apache Kafka? There is already an integration which gives us a DataSet using the Spark SQL framework. For Spark Streaming, there is also a Kafka integration using the SparkSession's readStream() function. Kafka client properties are provided as key value pairs k and v by using the option( k, v ) function. For writing into a topic, we can use the writeStream() function. But from a first look, it seems to be not the best fit. Another approach is possible. We can use simple RDDs to process data previously consumed from Apache Kafka. This is a more low-level approach compared to using Datasets with Spark Structured Streaming, and it typically involves using the Kafka RDD API provided by Spark. This approach is less common with newer versions of Spark, as Structured Streaming provides a higher-level abstraction that simplifies stream processing. However, we might need that approach for the integration with Apache Wayang. For now, we will focus on the lower level approach and plan to consume data from Kafka using a Kafka client, and then we parallelize the records in an RDD. This allows us to reuse KafkaTopicSource and KafkaTopicSink classes we built last time. Those were made specifically for a simple non parallel Java program, using one Consumer and one Producer. The selected approach does not yet fully take advantage from Spark's parallelism at load time. For higher loads and especially for streaming processing we would have to investigate another approache, using a SparkStreamingContext, but this is out of scope for now. Since we can't reuse the JavaKafkaTopicSource and JavaKafkaTopicSink we rather implement SparkKafkaTopicSource and SparkKafkaTopicSink based on given SparkTextFileSource and SparkTextFileSink which both cary all needed RDD specific logic. ","version":null,"tagName":"h2"},{"title":"Summary","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#summary","content":" As expected, the integration of Apache Spark with Apache Wayang was no magic, thanks to a fluent API design and a well structured architecture of Apache Wayang. We could easily follow the pattern we have worked out in the previous exercise. But a bunch of much more interesting work will follow next. More testing, more serialization schemes, and Kafka Schema Registry support should follow, and full parallelization as well. The code has been submitted to the Apache Wayang repository. ","version":null,"tagName":"h2"},{"title":"Outlook","type":1,"pageTitle":"Apache Kafka meets Wayang - Part 3","url":"/blog/kafka-meets-wayang-3#outlook","content":" The next part of the article series will cover the real world example as described in image 1. We will show how analysts and developers can use the Apache Kafka integration for Apache Wayang to solve cross organizational collaboration issues. Therefore, we will bring all puzzles together, and show the full implementation of the multi organizational data collaboration use case. ","version":null,"tagName":"h2"},{"title":"Wayang and the Federated AI","type":0,"sectionRef":"#","url":"/blog/wayang-federated-ai","content":"","keywords":"","version":null},{"title":"Apache Wayang in the Federated AI world","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#apache-wayang-in-the-federated-ai-world","content":" Apache Wayang (Wayang, for short), a project in an incubation phase at Apache Software Foundation (ASF), integrates big data platforms and tools by removing the complexity of worrying about low-level details. Interestingly, even if it was not designed for, Wayang could also serve as a scalable platform for federated learning: the Wayang community is starting to work on integrating federated learning capabilities. In a federated learning approach, Wayang would allow different local models to be built and exchange its model results across other data centers to combine them into a single enhanced model. ","version":null,"tagName":"h2"},{"title":"A real-world example","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#a-real-world-example","content":" Let's consider a real-world scenario. Hospitals and health organizations have increased their investments in machine/deep learning initiatives to learn more and predict diagnostics. However, due to legal frameworks, sharing patients' information or diagnostics is impossible, and the solution would be to apply federated learning. To solve this problem, we could use Wayang to help to train the models. See the diagram 1 below: As a first step, the data scientists would send an ML task to Wayang, which will work as an abstraction layer to connect to different data processing platforms, sparing the time to build integration code for each. Then, the data platforms process and generate the results that will be sent back to Wayang. Wayang aggregates the results into one "global result" and sends it back to the requestor as a next step. The process repeats until the desired results are achieved. Although it is very much like a Federated learning pipeline, Wayang removes a considerable layer of complexity from the developers by integrating with diverse types of data platforms. It also brings fast development and reduces the need for a deep understanding of data infrastructure or integrations. Developers can focus on the logic and how to execute tasks instead of details about data processors. ","version":null,"tagName":"h2"},{"title":"Follow Wayang","type":1,"pageTitle":"Wayang and the Federated AI","url":"/blog/wayang-federated-ai#follow-wayang","content":" Apache Wayang is in an incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. Thank you! I (Gláucia) want to thank professor Jorge Quiané for the guidance to write this blog post. Thanks for incentivate me to join the project and for the knowledge shared. I will always remember you. ","version":null,"tagName":"h3"},{"title":"Pywayang - Apache Wayang's Python API","type":0,"sectionRef":"#","url":"/blog/wayang-python-api","content":"","keywords":"","version":null},{"title":"Expanding Apache Wayang's APIs","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#expanding-apache-wayangs-apis","content":" Apache Wayang's architecture decouples the process of planning from the resulting execution, allowing users to specify platform agnostic plans through the provided APIs. Python's popularity and convenience for data processing workloads makes it an obvious candidate for a desired API. Previous APIs, such as the Scala API wayang-api-scala-java benefited from the interoperability of Java and Scala that allows to reuse objects from other languages to provide new interfaces. Accessing JVM objects in Python is possible through several libraries, but in doing so, future APIs in other programming languages would need similar libraries and implementations in order to exist. As a contrast to that, providing an API within Apache Wayang that receives input plans from any source and executes them within allows to create plans and submit them in any programming language. The following figure shows the architecture of pywayang: The Python API allows users to specify WayangPlans with UDFs in Python.pywayang then serializes the UDFs and constructs the WayangPlan in JSON format, preparing it to be sent to Apache Wayang's JSON API. When receiving a valid JSON plan, the JSON API uses the optimizer to construct an execution plan. However, since UDFs are defined in Python and thus need to be executed in Python as well, an operators function needs to be wrapped into a WrappedPythonFunction: val mapOperator = new MapPartitionsOperator[Input, Output]( new MapPartitionsDescriptor[Input, Output]( new WrappedPythonFunction[Input, Output]( ByteString.copyFromUtf8(udf) ), classOf[Input], classOf[Output], ) ) This wrapped functional descriptor allows to handle execution of UDFs in Python through a socket connection with the pywayang worker. Input data is sourced from the platform chosen by the optimizer and Apache Wayang handles routing the output data to the next operator. A new API in any programming languages would have to specify two things: A way to create plans that conform to a JSON format specified in the Wayang JSON API.A worker that handles encoding and decoding of user defined functions (UDFs), as they need to be executed on iterables in their respective language. After that, the API can be added as a module in Wayang, so that operators will be wrapped and UDFs can be executed in the desired programming language. ","version":null,"tagName":"h2"},{"title":"Defining WayangPlans in Python","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#defining-wayangplans-in-python","content":" As the "Hello World!" of data processing systems, wordcount will pose as our primary example to display how users can interact with Apache Wayang through the python package pywayang. from pywy.dataquanta import WayangContext from pywy.platforms.java import JavaPlugin from pywy.platforms.spark import SparkPlugin def wordcount(): ctx = WayangContext() \\ .register({JavaPlugin, SparkPlugin}) \\ .textfile("file://README.md") \\ .flatmap(lambda w: w.split()) \\ .filter(lambda w: w.strip() != "") \\ .map(lambda w: (w.lower(), 1)) \\ .reduce_by_key(lambda t: t[0], lambda t1, t2: (t1[0], int(t1[1]) + int(t2[1]))) \\ .store_textfile("file:///wordcount-out-python.txt") if __name__ == "__main__": wordcount() The example displays a mode of operation that resembles the ScalaPlanBuilder and the JavaPlanBuilder. Plans are specified in a functional way, chaining operations until a terminal operation results in execution of the plan. ","version":null,"tagName":"h2"},{"title":"Wayang-API-JSON","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wayang-api-json","content":" The wayang-api-json module provides an executable that starts a REST server. This server accepts a WayangPlan in JSON format. Starting the REST API as a background process can be done by executing the following: mvn clean package -pl :wayang-assembly -Pdistribution cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.api.json.Main & ","version":null,"tagName":"h2"},{"title":"Wrapping pipelines in MapPartition operators","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#wrapping-pipelines-in-mappartition-operators","content":" With this architecture, the execution of an operator comes with an additional overhead, because the UDFs will have to be executed in python. Python operators receive iterators through a socket and also return their result to Wayang through that connection. To minimize the overhead, unary operators that return unary results will be grouped in pipelines. One pipeline of operators will be submitted to the Wayang JSON API as a single MapPartition operator. This means that the UDFs specified in this pipeline can be chained and only on call from Wayang to the Python worker will have to be made for a given pipeline. ","version":null,"tagName":"h2"},{"title":"Coming soon","type":1,"pageTitle":"Pywayang - Apache Wayang's Python API","url":"/blog/wayang-python-api#coming-soon","content":" As the Python API is currently in development and we are applying finishing touches, this article serves as an outlook for what users can expect to see soon. Author: juripetersen ","version":null,"tagName":"h2"},{"title":"Integrating ML platforms in Wayang","type":0,"sectionRef":"#","url":"/blog/wayang-tensorflow","content":"","keywords":"","version":null},{"title":"Step 1: Introducing a Model","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-1-introducing-a-model","content":" With respect to the model, we followed Wayang’s abstraction philosophy: We created a Model interface to be used as input or output by Wayang operators and then extended it for the platform-specific operators. Different model interfaces can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model A platform-specific model needs to be instantiated to be used as the output of a training operator and as input for an inference operator. You can see an example of the SparkMLModel here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/model/SparkMLModel.java ","version":null,"tagName":"h2"},{"title":"Step 2: Introducing Training Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-2-introducing-training-operators","content":" We added the desired Wayang (platform-agnostic) training operators which are binary to unary operators, taking as input the X and y values and outputting a Model. You can find an example of a LinearRegressionOperator here: https://github.com/apache/incubator-wayang/blob/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/LinearRegressionOperator.java Platform-specific execution operators, such as SparkLinearRegressionOperator, can be easily added as any other execution operator: extending the corresponding Wayang operator and providing the mappings from the Wayang to the execution operator. See, for example, the SparkLinearRegressionOperator: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkLinearRegressionOperator.java ","version":null,"tagName":"h2"},{"title":"Step 3: Introducing Prediction Operators","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#step-3-introducing-prediction-operators","content":" Additionally, we created a PredictOperator, a BinaryToUnary Wayang (platform-agnostic) operator which takes as input the data quanta and a model and outputs the data quanta with the predictions output by the model. https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/PredictOperator.java Then, a concrete platform-specific operator extends from the abstract one. See the SparkPredictOperator for an example: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-spark/src/main/java/org/apache/wayang/spark/operators/ml/SparkPredictOperator.java ","version":null,"tagName":"h2"},{"title":"Deep Learning Models","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#deep-learning-models","content":" Unlike traditional machine learning models, the definition of deep learning models is more flexible. Users can combine different blocks (e.g., fully connected blocks, convolutional blocks) to build their desired models. The whole model can be represented as a graph on which the vertices represent blocks and the edges represent connections between blocks. In this case, we built a DLModel class that implements the Model interface, which contains a user-defined, platform-agnostic graph of the model: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/model/DLModel.java For training, we implemented the platform-agnostic DLModelTrainingOperator Wayang operator: https://github.com/apache/incubator-wayang/tree/main/wayang-commons/wayang-basic/src/main/java/org/apache/wayang/basic/operators/DLTrainingOperator.java ","version":null,"tagName":"h2"},{"title":"New ML platform -- Tensorflow Integration","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#new-ml-platform----tensorflow-integration","content":" We have added Tensorflow as a new platform by creating a new module (wayang-tensorflow) inside the wayang-platforms parent module and implementing a Tensorflow driver. The TensorflowExecutor driver is responsible for creating and destroying Tensorflow resources, such as a model graph and a model parameter context. When a training task scheduled on Tensorflow, it will be mapped to TensorflowDLModelTrainingOperator. In this process, the DLModel will be converted to TensorflowModel, which means that the user-defined model graph will be converted to a Tensorflow model graph. Likewise, for inference, the PredictOperator will be mapped to TensorflowPredictOperator. All the code for the tensorflow platform can be found here: https://github.com/apache/incubator-wayang/tree/main/wayang-platforms/wayang-tensorflow/src/main/java/org/apache/wayang/tensorflow ","version":null,"tagName":"h2"},{"title":"Acknowledgement","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#acknowledgement","content":" The source code for the support of ML operators and the Tensorflow integration has been contributed by Mingxi Liu. ","version":null,"tagName":"h3"},{"title":"Follow Wayang","type":1,"pageTitle":"Integrating ML platforms in Wayang","url":"/blog/wayang-tensorflow#follow-wayang","content":" Apache Wayang is in incubation phase and has a potential roadmap of implementations coming soon (including the federated learning aspect as well as an SQL interface and a novel data debugging functionality). If you want to hear or join the community, consult the linkhttps://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas, write documentation, or fix bugs. ","version":null,"tagName":"h3"},{"title":"Apache Wayang vs. Presto/Trino","type":0,"sectionRef":"#","url":"/blog/wayang-vs-trino","content":"","keywords":"","version":null},{"title":"Key Distinctions","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#key-distinctions","content":" Trino/Presto is a query engine for distributed SQL query processing. It is composed of a coordinator and multiple workers. The coordinator consists of a query optimizer and a scheduler, while the workers are responsible for performing the necessary query processing. Data is fetched from external systems via a Connector API, i.e., Trino/Presto supports multiple data sources. Notably,query processing is is conducted exclusively by Trino/Presto workers, not the external systems. In contrast, Wayang is a middleware for integrating diverse data platforms, including but not limited to query engines. This means that Wayang leverages the processing capabilities of the underlying data platforms to complete a given job, with no actual query processing taking place within Wayang itself. Below you can graphically see the difference between the two systems. Note that not all available data sources or data platforms are illustrated for simplicity reasons. Below you can see how Wayang integrates data platforms and utilizes them for any data processing required. Below you can see how Trino unifies different data sources and then performs data processing in a distributed manner. I hope this makes it clear now. In fact, Trino can be easily plugged to Wayang as a platform and be seamlessly integrated with other data platforms, as shown below. ","version":null,"tagName":"h2"},{"title":"What are the advantages of using Wayang?","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#what-are-the-advantages-of-using-wayang","content":" Wayang brings several benefits thanks to its integration layer: Seamless integration of SQL query engines with ML and other data analysis systems within a single job, eliminating the need to materialize intermediate results. Users are freed from the task of specifying the query engines for an application if they desire. By submitting their Wayang job, the cross-platform optimizer can automatically determine the best data platform to use for improved performance or cost savings. Wayang facilitates cross-platform data processing by utilizing multiple data platforms to execute a query for a single job, optimizing performance and cost efficiency. Data does not have to be transferred outside their original location. ","version":null,"tagName":"h2"},{"title":"Conclusion","type":1,"pageTitle":"Apache Wayang vs. Presto/Trino","url":"/blog/wayang-vs-trino#conclusion","content":" Trino is a distributed SQL query engine which performs all the query processing of an input SQL query in a distributed manner. Wayang, on the other hand, is a data platform integrator which can automatically determine which data platform(s) is best suited for an application. Author: zkaoudi ","version":null,"tagName":"h2"},{"title":"Website updated","type":0,"sectionRef":"#","url":"/blog/website_update","content":"","keywords":"","version":null},{"title":"Cheatsheet","type":1,"pageTitle":"Website updated","url":"/blog/website_update#cheatsheet","content":" List: Line one Line one.oneLine one.two Line two Line two.oneLine two.two Line three ...... Another style for a list: Line oneLine twoLine three ","version":null,"tagName":"h2"},{"title":"Becoming a committer","type":0,"sectionRef":"#","url":"/docs/community/committer","content":"","keywords":"","version":"Next"},{"title":"Sustained contributions to Wayang:","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#sustained-contributions-to-wayang","content":" Committers should have a history of major contributions to Wayang. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within Wayang's engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of Spark. Note that contributions to critical parts of Spark, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement. The type and level of contributions considered may vary by project area – for example, we greatly encourage contributors who want to work on mainly the documentation, or mainly on platform support for specific OSes, storage systems, etc. The (P)PMC also adds new (P)PMC members. (P)PMC members are expected to carry out (P)PMC responsibilities as described in Apache Guidance, including helping vote on releases, enforce Apache project trademarks, take responsibility for legal and license issues, and ensure the project follows Apache project mechanics. The PMC periodically adds committers to the PMC who have shown they understand and can help with these activities. ","version":"Next","tagName":"h3"},{"title":"Review process","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#review-process","content":" All contributions should be reviewed before merging as described in Contributing to Wayang. In particular, if you are working on an area of the codebase you are unfamiliar with, look at the Git history for that code to see who reviewed patches before. You can do this using git log --format=full <filename>, by examining the “Commit” field to see who committed each patch. ","version":"Next","tagName":"h3"},{"title":"When to commit/merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#when-to-commitmerge-a-pull-request","content":" PRs shall not be merged during active, on-topic discussion unless they address issues such as critical security fixes of a public vulnerability. Under extenuating circumstances, PRs may be merged during active, off-topic discussion and the discussion directed to a more appropriate venue. Time should be given prior to merging for those involved with the conversation to explain if they believe they are on-topic. Lazy consensus requires giving time for discussion to settle while understanding that people may not be working on Wayang as their full-time job and may take holidays. It is believed that by doing this, we can limit how often people feel the need to exercise their veto. All -1s with justification merit discussion. A -1 from a non-committer can be overridden only with input from multiple committers, and suitable time must be offered for any committer to raise concerns. A -1 from a committer who cannot be reached requires a consensus vote of the (P)PMC under ASF voting rules to determine the next steps within the ASF guidelines for code vetoes. The Wayang project typically uses a 72h time window to conclude votes. These policies serve to reiterate the core principle that code must not be merged with a pending veto or before a consensus has been reached (lazy or otherwise). It is the (P)PMC’s hope that vetoes continue to be infrequent, and when they occur, that all parties will take the time to build consensus prior to additional feature work. Being a committer means exercising your judgement while working in a community of people with diverse views. There is nothing wrong in getting a second (or third or fourth) opinion when you are uncertain. Thank you for your dedication to the Wayang project; it is appreciated by the developers and users of Wayang. It is hoped that these guidelines do not slow down development; rather, by removing some of the uncertainty, the goal is to make it easier for us to reach consensus. If you have ideas on how to improve these guidelines or other Wayang project operating procedures, you should reach out on the dev@ list to start the discussion. ","version":"Next","tagName":"h2"},{"title":"How we merge a pull request","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#how-we-merge-a-pull-request","content":" Changes pushed to the master branch on Apache cannot be removed; that is, we can’t force-push to it. So please don’t add any test commits or anything like that, only real patches. We typically enforce a review from minimum two committers, who are ideally owners of submodules, like ML4ALL or JDBC. They investigate the PR, add remarks and request changes. Please be not insultet in any way, the team needs to make sure that all code committed to main has a igh quality and does not break current functionality. Ask dev@ if you have trouble with these steps, or want help doing your first merge. Once a PR is merged please leave a comment on the PR stating which branch(es) it has been merged with. ","version":"Next","tagName":"h2"},{"title":"Policy on backporting bug fixes","type":1,"pageTitle":"Becoming a committer","url":"/docs/community/committer#policy-on-backporting-bug-fixes","content":" We go in line with pwendell (Apache Spark PMC): The trade off when backporting is you get to deliver the fix to people running older versions (great!), but you risk introducing new or even worse bugs in maintenance releases (bad!). The decision point is when you have a bug fix and it’s not clear whether it is worth backporting. I think the following facets are important to consider: Backports are an extremely valuable service to the community and should be considered for any bug fix.Introducing a new bug in a maintenance release must be avoided at all costs. It over time would erode confidence in our release process.Distributions or advanced users can always backport risky patches on their own, if they see fit.For me, the consequence of these is that we should backport in the following situations: Both the bug and the fix are well understood and isolated. Code being modified is well tested.The bug being addressed is high priority to the community.The backported fix does not vary widely from the master branch fix.We tend to avoid backports in the converse situations: The bug or fix are not well understood. For instance, it relates to interactions between complex components or third party libraries (e.g. Hadoop libraries). The code is not well tested outside of the immediate bug being fixed. The bug is not clearly a high priority for the community. The backported fix is widely different from the master branch fix. ","version":"Next","tagName":"h3"},{"title":"Mailing List","type":0,"sectionRef":"#","url":"/docs/community/mailinglist","content":"","keywords":"","version":"Next"},{"title":"How to subscribe to a mailing list","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#how-to-subscribe-to-a-mailing-list","content":" Send an email without any contents or subject to LISTNAME-subscribe@wayang.apache.org. (replace LISTNAME with dev, commits, ..) Wait till you receive an email with the subject “confirm subscribe to LISTNAME@wayang.apache.org”. Reply to that email, without editing the subject or including any contents Wait till you receive an email with the subject “WELCOME to LISTNAME@wayang.apache.org”. If you send us an email with a code snippet, make sure that: you do not link to files in external services as such files can change, get deleted, or the link might break and thus make an archived email thread uselessyou paste text instead of screenshots of textyou keep formatting when pasting code in order to keep the code readablethere are enough import statements to avoid ambiguitiesif you are using some platform, please specify the version ","version":"Next","tagName":"h2"},{"title":"Watch the email without subscribe","type":1,"pageTitle":"Mailing List","url":"/docs/community/mailinglist#watch-the-email-without-subscribe","content":" These two channels are public mailing list, and you can get access to them on Website without subscribing. Emails of commits@wayang.apache.orgEmails of dev@wayang.apache.org ","version":"Next","tagName":"h2"},{"title":"Apache Wayang Release Odysse","type":0,"sectionRef":"#","url":"/blog/wayang-release-odysse","content":"","keywords":"","version":null},{"title":"Intro","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#intro","content":" The ASF provides a robust infrastructure for open communities of software developers. We can share ideas, combine forces, contribute code, docs, review-energy, art work, and from time to time we can nail it down. A release defines an intermediate result of the continuous community work. How we do such a release in the Apache Wayang team is an essential aspect towards graduation. First of all, there are some references to take into account, such as: https://www.apache.org/legal/release-policy.htmlhttps://maven.apache.org/maven-release/maven-release-plugin/ Assuming you are (P)PMC, and assuming that you have the right permissions for such a release, you can follow the path as described in this guide: https://plc4x.apache.org/developers/release/release.html I tried to follow exactly this procedure, several times. I failed. Here I share the current status of my release attempts. I plan a longer tour, and do not want to block the project for a long time. Hence I create this draft, and I hope we can unblock this project as soon as possible. ","version":null,"tagName":"h2"},{"title":"Status:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#status","content":" I am not able to conduct the mvn release:perform step. Anything before worked, sometimes only after some digging, but it worked. We assume, that due to my membership in two ASF incubator projects I am not able to upload the artefacts to the Nexus repository (H1). It can be, that I have not the correct user and password in my settings.xml file (H2). But I tested a manual login to the nexus server https://repository.apache.org/service/local/staging/deploy/maven2 with success. And beyond that I have no idea how I can verify this detail alone. ","version":null,"tagName":"h2"},{"title":"Idea / Proposal","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#idea--proposal","content":" (1) It would be great, if someone - who has done a release in any other ASF project or in Apache Wayang - could follow the steps I share, so that we can check where the problem hides itself. (2) As a follow-up task, I suggest to add a Release Guide to the Apache Wayang project, including release manager onboarding steps, and checklists for the particular project, derived from the referenced sources which are listed above. But for now it is all about sharing the status (as I did serveral times on multiple chanels, including JIRA, Slack, Mailing lists) and finding a solution for Apache Wayang release 1.0. ","version":null,"tagName":"h2"},{"title":"Latest Error:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#latest-error","content":" mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h2"},{"title":"Activity Log","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#activity-log","content":" mvn release:clean mvn versions:set -DnewVersion=1.0.0-RC2 mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: You don't have a SNAPSHOT project in the reactor projects list. -> [Help 1] mvn versions:commit mvn versions:set -DnewVersion=1.0.0-RC2-SNAPSHOT mvn versions:commit mvn release:prepare -Darguments='-DskipTests=True' [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Cannot prepare the release because you have local modifications : git status git add . git commit -m "prepare for release 1.0.0-RC2-SNAPSHOT" git push mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -DdryRun=true mvn release:prepare -Darguments='-DskipTests=True -Dresume=False' -XXX Caused by: org.eclipse.aether.transfer.NoRepositoryConnectorException: Blocked mirror for repositories: [repository.jboss.org (http://repository.jboss.org/nexus/content/groups/public/, default, releases)] ","version":null,"tagName":"h2"},{"title":"Dependency on JDK-11 during release","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#dependency-on-jdk-11-during-release","content":" FIXED with local JDK11 Setup brew install openjdk@11 curl -s "https://get.sdkman.io" | bash source "$HOME/.sdkman/bin/sdkman-init.sh" which java which java /Users/kamir/.sdkman/candidates/java/current/bin/java ➜ GITHUB.active export JAVA_HOME=/Users/kamir/.sdkman/candidates/java/current ➜ GITHUB.active mvn clean -XXX export JAVA_HOME= sdk install java 11.0.24-amzn sdk home java 11.0.24-amzn /usr/libexec/java_home -v 11 jenv add /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home jenv global 11.0 jenv shell 11.0 jenv local 11.0 java -version ","version":null,"tagName":"h3"},{"title":"Manual update of release-version","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#manual-update-of-release-version","content":" During the release procedure, do I have to set the version here in this configuration section manually? <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-release-plugin</artifactId> <version>3.0.1</version> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>0.7.1</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>wayang-0.7.1</tag> <scmReleaseCommitComment>@{prefix} prepare release 0.7.1</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> </plugin> It seems that these properties must be updated manually. ","version":null,"tagName":"h3"},{"title":"Warning regarding \"illegal reflective access operation\"","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#warning-regarding-illegal-reflective-access-operation","content":" [ERROR] WARNING: An illegal reflective access operation has occurred [ERROR] WARNING: Illegal reflective access by org.codehaus.groovy.reflection.CachedClass (file:/Users/mkaempf/.m2/repository/org/codehaus/groovy/groovy-all/2.4.9/groovy-all-2.4.9.jar) to method java.lang.Object.finalize() [ERROR] WARNING: Please consider reporting this to the maintainers of org.codehaus.groovy.reflection.CachedClass [ERROR] WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations [ERROR] WARNING: All illegal access operations will be denied in a future release This is still an OPEN ISSUE ! ","version":null,"tagName":"h3"},{"title":"RAT Check fails","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#rat-check-fails","content":" [INFO] [ERROR] Failed to execute goal org.apache.rat:apache-rat-plugin:0.13:check (license-check) on project wayang: Too many files with unapproved license: 1 See RAT report in: /Users/mkaempf/GITHUB.private/incubator-wayang/target/rat.txt -> [Help 1] ***************************************************** Printing headers for text files without a valid license header... ===================================================== == File: .java-version ===================================================== 11.0 FIXED by adding .java-versions to .gitignore ","version":null,"tagName":"h3"},{"title":"Tag could not be created in SCM.","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#tag-could-not-be-created-in-scm","content":" [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:prepare (default-cli) on project wayang: Unable to tag SCM [ERROR] Provider message: [ERROR] The git-tag command failed. [ERROR] Command output: [ERROR] fatal: tag 'wayang-0.7.1' already exists FIXED by manual changes in pom.xml. <scm> <connection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</connection> <developerConnection>scm:git:https://gitbox.apache.org/repos/asf/incubator-wayang.git</developerConnection> <url>https://github.com/apache/incubator-wayang</url> <tag>1.0.0-RC2-SNAPSHOT</tag> </scm> <configuration> <autoVersionSubmodules>true</autoVersionSubmodules> <autoResolveSnapshots>all</autoResolveSnapshots> <releaseProfiles>apache-release</releaseProfiles> <!--<pushChanges>false</pushChanges>--> <!--<dryRun>true</dryRun>--> <releaseVersion>1.0.0-RC2-SNAPSHOT</releaseVersion> <updateWorkingCopyVersions>true</updateWorkingCopyVersions> <updateDependencies>true</updateDependencies> <tag>1.0.0-RC2-SNAPSHOT</tag> <scmReleaseCommitComment>@{prefix} prepare release 1.0.0-RC2-SNAPSHOT</scmReleaseCommitComment> <tagNameFormat>apache-@{project.artifactId}-@{project.version}-incubating</tagNameFormat> <tagNameFormat>v${project.version}</tagNameFormat> </configuration> mvn release:prepare -Darguments='-DskipTests=True -Dresume=True' mvn clean package mvn release:perform -X -DskipTests - you need your password for the keystore to sign the build artefacts. So far so good. But now the sun went down. [INFO] [ERROR] Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException -> [Help 1] [INFO] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-deploy-plugin:3.0.0-M1:deploy (default-deploy) on project wayang: ArtifactDeployerException [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.plugin.MojoExecutionException: ArtifactDeployerException [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:201) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.apache.maven.shared.transfer.artifact.deploy.ArtifactDeployerException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:126) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:278) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: org.eclipse.aether.transfer.ArtifactTransferException: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): NullPointerException [INFO] at org.eclipse.aether.connector.basic.ArtifactTransportListener.transferFailed (ArtifactTransportListener.java:44) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:417) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] Caused by: java.lang.NullPointerException [INFO] at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) [INFO] at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) [INFO] at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) [INFO] at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) [INFO] at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) [INFO] at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) [INFO] at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) [INFO] at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) [INFO] at org.apache.http.impl.execchain.RetryExec.execute (RetryExec.java:89) [INFO] at org.apache.http.impl.execchain.ServiceUnavailableRetryExec.execute (ServiceUnavailableRetryExec.java:85) [INFO] at org.apache.http.impl.execchain.RedirectExec.execute (RedirectExec.java:110) [INFO] at org.apache.http.impl.client.InternalHttpClient.doExecute (InternalHttpClient.java:185) [INFO] at org.apache.http.impl.client.CloseableHttpClient.execute (CloseableHttpClient.java:72) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.execute (HttpTransporter.java:485) [INFO] at org.eclipse.aether.transport.http.HttpTransporter.implPut (HttpTransporter.java:469) [INFO] at org.eclipse.aether.spi.connector.transport.AbstractTransporter.put (AbstractTransporter.java:107) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$PutTaskRunner.runTask (BasicRepositoryConnector.java:564) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector$TaskRunner.run (BasicRepositoryConnector.java:414) [INFO] at org.eclipse.aether.connector.basic.BasicRepositoryConnector.put (BasicRepositoryConnector.java:297) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:271) [INFO] at org.eclipse.aether.internal.impl.DefaultDeployer.deploy (DefaultDeployer.java:202) [INFO] at org.eclipse.aether.internal.impl.DefaultRepositorySystem.deploy (DefaultRepositorySystem.java:393) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.Maven31ArtifactDeployer.deploy (Maven31ArtifactDeployer.java:122) [INFO] at org.apache.maven.shared.transfer.artifact.deploy.internal.DefaultArtifactDeployer.deploy (DefaultArtifactDeployer.java:79) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:190) [INFO] at org.apache.maven.shared.transfer.project.deploy.internal.DefaultProjectDeployer.deploy (DefaultProjectDeployer.java:134) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.deployProject (DeployMojo.java:193) [INFO] at org.apache.maven.plugins.deploy.DeployMojo.execute (DeployMojo.java:159) [INFO] at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) [INFO] at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) [INFO] at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) [INFO] at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) [INFO] at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) [INFO] at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) [INFO] at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) [INFO] at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) [INFO] at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) [INFO] at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) [INFO] at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) [INFO] at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) [INFO] at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) [INFO] at java.lang.reflect.Method.invoke (Method.java:566) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) [INFO] at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [INFO] [ERROR] [INFO] [ERROR] [INFO] [ERROR] For more information about the errors and possible solutions, please read the following articles: [INFO] [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [INFO] [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [INFO] [DEBUG] Shutting down 'file-lock' factory [INFO] [DEBUG] Shutting down 'rwlock-local' factory [INFO] [DEBUG] Shutting down 'semaphore-local' factory [INFO] [DEBUG] Shutting down 'noop' factory [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary for Apache Wayang (incubating) 1.0.0-RC3-SNAPSHOT: [INFO] [INFO] Apache Wayang (incubating) ......................... FAILURE [ 23.626 s] [INFO] Wayang Commons ..................................... SKIPPED [INFO] wayang-utils-profile-db ............................ SKIPPED [INFO] Wayang Core ........................................ SKIPPED [INFO] Wayang Basic ....................................... SKIPPED [INFO] Wayang Platform .................................... SKIPPED [INFO] Wayang Platform Java ............................... SKIPPED [INFO] Wayang Platform Spark .............................. SKIPPED [INFO] Wayang Platform JDBC Template ...................... SKIPPED [INFO] Wayang Platform Postgres ........................... SKIPPED [INFO] Wayang Platform SQLite3 ............................ SKIPPED [INFO] Wayang Platform Giraph ............................. SKIPPED [INFO] Wayang Platform Apache Flink ....................... SKIPPED [INFO] Wayang Platform Generic Jdbc ....................... SKIPPED [INFO] Wayang API ......................................... SKIPPED [INFO] Wayang API Scala-Java .............................. SKIPPED [INFO] Wayang Integration Test ............................ SKIPPED [INFO] Wayang API Python .................................. SKIPPED [INFO] wayang-api-sql ..................................... SKIPPED [INFO] Wayang Profiler .................................... SKIPPED [INFO] Wayang Extensions .................................. SKIPPED [INFO] wayang-iejoin ...................................... SKIPPED [INFO] Wayang - Common resources .......................... SKIPPED [INFO] wayang-benchmark ................................... SKIPPED [INFO] Wayang ML4all ...................................... SKIPPED [INFO] Wayang Project Assembly ............................ SKIPPED [INFO] ------------------------------------------------------------------------ [INFO] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Total time: 24.093 s [INFO] Finished at: 2024-06-25T10:53:32+02:00 [INFO] ------------------------------------------------------------------------ [ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 -> [Help 1] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-release-plugin:3.0.1:perform (default-cli) on project wayang: Maven execution failed, exit code: 1 at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:333) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.plugin.MojoExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:198) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.ReleaseExecutionException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:115) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) Caused by: org.apache.maven.shared.release.exec.MavenExecutorException: Maven execution failed, exit code: 1 at org.apache.maven.shared.release.exec.InvokerMavenExecutor.executeGoals (InvokerMavenExecutor.java:129) at org.apache.maven.shared.release.exec.AbstractMavenExecutor.executeGoals (AbstractMavenExecutor.java:70) at org.apache.maven.shared.release.phase.AbstractRunGoalsPhase.execute (AbstractRunGoalsPhase.java:105) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.runLogic (RunPerformGoalsPhase.java:127) at org.apache.maven.shared.release.phase.RunPerformGoalsPhase.execute (RunPerformGoalsPhase.java:59) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:325) at org.apache.maven.shared.release.DefaultReleaseManager.perform (DefaultReleaseManager.java:268) at org.apache.maven.plugins.release.PerformReleaseMojo.execute (PerformReleaseMojo.java:196) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo (DefaultBuildPluginManager.java:126) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute2 (MojoExecutor.java:328) at org.apache.maven.lifecycle.internal.MojoExecutor.doExecute (MojoExecutor.java:316) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:212) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:174) at org.apache.maven.lifecycle.internal.MojoExecutor.access$000 (MojoExecutor.java:75) at org.apache.maven.lifecycle.internal.MojoExecutor$1.run (MojoExecutor.java:162) at org.apache.maven.plugin.DefaultMojosExecutionStrategy.execute (DefaultMojosExecutionStrategy.java:39) at org.apache.maven.lifecycle.internal.MojoExecutor.execute (MojoExecutor.java:159) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:105) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject (LifecycleModuleBuilder.java:73) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build (SingleThreadedBuilder.java:53) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute (LifecycleStarter.java:118) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:261) at org.apache.maven.DefaultMaven.doExecute (DefaultMaven.java:173) at org.apache.maven.DefaultMaven.execute (DefaultMaven.java:101) at org.apache.maven.cli.MavenCli.execute (MavenCli.java:906) at org.apache.maven.cli.MavenCli.doMain (MavenCli.java:283) at org.apache.maven.cli.MavenCli.main (MavenCli.java:206) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0 (Native Method) at jdk.internal.reflect.NativeMethodAccessorImpl.invoke (NativeMethodAccessorImpl.java:62) at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke (DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke (Method.java:566) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced (Launcher.java:283) at org.codehaus.plexus.classworlds.launcher.Launcher.launch (Launcher.java:226) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode (Launcher.java:407) at org.codehaus.plexus.classworlds.launcher.Launcher.main (Launcher.java:348) [ERROR] [ERROR] [ERROR] For more information about the errors and possible solutions, please read the following articles: [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException [DEBUG] Shutting down adapter factory; available factories [file-lock, rwlock-local, semaphore-local, noop]; available name mappers [discriminating, file-gav, file-hgav, file-static, gav, static] [DEBUG] Shutting down 'file-lock' factory [DEBUG] Shutting down 'rwlock-local' factory [DEBUG] Shutting down 'semaphore-local' factory [DEBUG] Shutting down 'noop' factory The null pointer exception indicates problems in settings.xml. This could be fixed. But now, the error changes with and I am still not able to stage the build results. mvn release:perform -X -DskipTests [INFO] Caused by: org.eclipse.aether.deployment.DeploymentException: Failed to deploy artifacts: Could not transfer artifact org.apache.wayang:wayang:pom:1.0.0-RC2 from/to apache.releases.https (https://repository.apache.org/service/local/staging/deploy/maven2): status code: 401, reason phrase: Unauthorized (401) ","version":null,"tagName":"h3"},{"title":"Checklist for next iteration:","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#checklist-for-next-iteration","content":" The error you're encountering indicates that there is a problem with deploying the artifacts using the maven-deploy-plugin. The root cause of the error is a NullPointerException during the deployment process, specifically related to the org.eclipse.aether.transfer.ArtifactTransferException. Here's a step-by-step guide to troubleshoot and resolve this issue: ","version":null,"tagName":"h2"},{"title":"1. Check Maven Settings","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#1-check-maven-settings","content":" Ensure that your Maven settings (settings.xml) are correctly configured for deployment. Verify that the repository settings and credentials are correctly specified. [DONE] ","version":null,"tagName":"h3"},{"title":"2. Verify Repository URL","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#2-verify-repository-url","content":" Make sure the repository URL in your pom.xml or settings.xml is correct and reachable. The URL should point to the correct staging repository for deployment. [OPEN] - I did not touch it, so I guess it is correct in the pom.xml. ","version":null,"tagName":"h3"},{"title":"3. Maven Version Compatibility","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#3-maven-version-compatibility","content":" Verify that you are using a compatible version of Maven. Sometimes, upgrading or downgrading Maven can resolve such issues. Apache Maven 3.9.6 (bc0240f3c744dd6b6ec2920b3cd08dcc295161ae) Maven home: /usr/local/Cellar/maven/3.9.6/libexec Java version: 11.0.15.1, vendor: Oracle Corporation, runtime: /Library/Java/JavaVirtualMachines/jdk-11.0.15.1.jdk/Contents/Home Default locale: en_DE, platform encoding: US-ASCII OS name: "mac os x", version: "13.2.1", arch: "x86_64", family: "mac" [DONE] ","version":null,"tagName":"h3"},{"title":"4. Check for Network Issues","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#4-check-for-network-issues","content":" Ensure there are no network issues that might be causing problems in connecting to the repository. Sometimes, network configurations, firewalls, or proxy settings can interfere with the deployment process. [DONE] ","version":null,"tagName":"h3"},{"title":"5. Update Maven Plugins","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#5-update-maven-plugins","content":" Ensure that you are using the latest versions of the Maven plugins. Sometimes, bugs in older versions can cause unexpected issues. [OPEN] ","version":null,"tagName":"h3"},{"title":"6. Configure the maven-deploy-plugin in pom.xml","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#6-configure-the-maven-deploy-plugin-in-pomxml","content":" Make sure the maven-deploy-plugin is correctly configured in your pom.xml. Here's an example configuration: <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-deploy-plugin</artifactId> <version>3.0.0-M1</version> <configuration> <repositoryId>apache.releases.https</repositoryId> <url>https://repository.apache.org/service/local/staging/deploy/maven2</url> </configuration> </plugin> ","version":null,"tagName":"h3"},{"title":"7. Increase Verbose Logging","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#7-increase-verbose-logging","content":" Enable verbose logging to get more details about the error. You can do this by adding the -X flag when running the Maven command: mvn clean deploy -X [DONE] ","version":null,"tagName":"h3"},{"title":"8. Retry with a Clean Local Repository","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#8-retry-with-a-clean-local-repository","content":" Sometimes, a corrupt local repository can cause issues. Try cleaning your local Maven repository and re-running the deployment: mvn clean install -U mvn deploy [OPEN] - Does not match to an authentication issue. ","version":null,"tagName":"h3"},{"title":"9. Check for Missing Credentials","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#9-check-for-missing-credentials","content":" Ensure that the credentials for the repository are correctly set up in your settings.xml: <servers> <server> <id>apache.releases.https</id> <username>your-username</username> <password>your-password</password> </server> </servers> [DONE] ","version":null,"tagName":"h3"},{"title":"10. Review the Full Stack Trace","type":1,"pageTitle":"Apache Wayang Release Odysse","url":"/blog/wayang-release-odysse#10-review-the-full-stack-trace","content":" The full stack trace indicates a NullPointerException: Caused by: java.lang.NullPointerException at java.util.concurrent.ConcurrentHashMap.putVal (ConcurrentHashMap.java:1011) at java.util.concurrent.ConcurrentHashMap.put (ConcurrentHashMap.java:1006) at org.apache.http.impl.client.BasicCredentialsProvider.setCredentials (BasicCredentialsProvider.java:62) at org.eclipse.aether.transport.http.DeferredCredentialsProvider.getCredentials (DeferredCredentialsProvider.java:67) at org.apache.http.client.protocol.RequestAuthCache.doPreemptiveAuth (RequestAuthCache.java:135) at org.apache.http.client.protocol.RequestAuthCache.process (RequestAuthCache.java:110) at org.apache.http.protocol.ImmutableHttpProcessor.process (ImmutableHttpProcessor.java:133) at org.apache.http.impl.execchain.ProtocolExec.execute (ProtocolExec.java:184) ... This suggests that there might be an issue with how credentials are being handled. Double-check that the credentials are being correctly passed and processed. [DONE] ","version":null,"tagName":"h3"},{"title":"How to contribute","type":0,"sectionRef":"#","url":"/docs/community/contribute","content":"","keywords":"","version":"Next"},{"title":"Contributing by helping other users","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-helping-other-users","content":" A great way to contribute to Wayang is to help answer user questions on the user@ mailing list or on StackOverflow. There are always many new Wayang users; taking a few minutes to help answer a question is a very valuable community service. Contributors should subscribe to this list and follow it in order to keep up to date on what’s happening in Wayang. Answering questions is an excellent and visible way to help the community, which also demonstrates your expertise. See the Mailing Lists guide for guidelines about how to effectively participate in discussions on the mailing list, as well as forums like StackOverflow. ","version":"Next","tagName":"h3"},{"title":"Contributing by testing releases","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-testing-releases","content":" Wayang’s release process is community-oriented, and members of the community can vote on new releases on the dev@ mailing list. Wayang users are invited to subscribe to this list to receive announcements, and test their workloads on newer release and provide feedback on any performance or correctness issues found in the newer release. ","version":"Next","tagName":"h3"},{"title":"Contributing by reviewing changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-by-reviewing-changes","content":" Changes to Wayang source code are proposed, reviewed and committed via GitHub pull requests (described later). Anyone can view and comment on active changes here. Reviewing others’ changes is a good way to learn how the change process works and gain exposure to activity in various parts of the code. You can help by reviewing the changes and asking questions or pointing out issues – as simple as typos or small issues of style. ","version":"Next","tagName":"h2"},{"title":"Contributing documentation changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-documentation-changes","content":" To propose a change to release documentation (that is, docs that appear under Developer section), fork the website repo and edit the Markdown source files in Wayang’s docs/ directory, the README file shows how to build the documentation locally to test your changes. The process to propose a doc change is otherwise the same as the process for proposing code changes below. To propose a change to the rest of the documentation (that is, docs that do not appear under Developer section), similarly, edit the Markdown in the wayang-website repository and open a pull request. ","version":"Next","tagName":"h2"},{"title":"Contributing user libraries to Wayang","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-user-libraries-to-wayang","content":" Just as Java and Scala applications can access a huge selection of libraries and utilities, none of which are part of Java or Scala themselves, Wayang aims to support a rich ecosystem of libraries. Many new useful utilities or features belong outside of Spark rather than in the core. For example: query optimizer code and language support probably has to be a part of core Wayang, but, useful machine learning algorithms can happily exist outside of Wayang. To that end, large and independent new functionality is often rejected for inclusion in Wayang itself, but, can and should be hosted as a separate project and repository, and included in the wayang-packages.org collection. ","version":"Next","tagName":"h2"},{"title":"Contributing bug reports","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-bug-reports","content":" Ideally, bug reports are accompanied by a proposed code change to fix the bug. This isn’t always possible, as those who discover a bug may not have the experience to fix it. A bug may be reported by creating a JIRA or a GitHub Issue but without creating a pull request (see below). Bug reports are only useful however if they include enough information to understand, isolate and ideally reproduce the bug. Simply encountering an error does not mean a bug should be reported; as below, search JIRA and search and inquire on the Wayang user / dev mailing lists first. Unreproducible bugs, or simple error reports, may be closed. It’s very helpful if the bug report has a description about how the bug was introduced, by which commit, so that reviewers can easily understand the bug. It also helps committers to decide how far the bug fix should be backported, when the pull request is merged. The pull request to fix the bug should narrow down the problem to the root cause. Performance regression is also one kind of bug. The pull request to fix a performance regression must provide a benchmark to prove the problem is indeed fixed. Note that, data correctness/data loss bugs are very serious. Make sure the corresponding bug report JIRA ticket is labeled as correctness or data-loss. If the bug report doesn’t get enough attention, please send an email to dev@, to draw more attentions. It is possible to propose new features as well. These are generally not helpful unless accompanied by detail, such as a design document and/or code change. Large new contributions should consider wayang-packages.org first (see above), or be discussed on the mailing list first. Feature requests may be rejected, or closed after a long period of inactivity. ","version":"Next","tagName":"h2"},{"title":"Contributing to JIRA maintenance","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-to-jira-maintenance","content":" Given the volume of issues raised in the Apache Wayang JIRA, inevitably some issues are duplicates, or become obsolete and eventually fixed otherwise, or can’t be reproduced, or could benefit from more detail, and so on. It’s useful to help identify these issues and resolve them, either by advancing the discussion or even resolving the JIRA. Most contributors are able to directly resolve JIRAs. Use judgment in determining whether you are quite confident the issue should be resolved, although changes can be easily undone. If in doubt, just leave a comment on the JIRA. When resolving JIRAs, observe a few useful conventions: Resolve as Fixed if there’s a change you can point to that resolved the issue Set Fix Version(s), if and only if the resolution is FixedSet Assignee to the person who most contributed to the resolution, which is usually the person who opened the PR that resolved the issue.In case several people contributed, prefer to assign to the more ‘junior’, non-committer contributor For issues that can’t be reproduced against master as reported, resolve as Cannot Reproduce Fixed is reasonable too, if it’s clear what other previous pull request resolved it. Link to it.If the issue is the same as or a subset of another issue, resolved as DuplicateMake sure to link to the JIRA it duplicatesPrefer to resolve the issue that has less activity or discussion as the duplicate If the issue seems clearly obsolete and applies to issues or components that have changed radically since it was opened, resolve as Not a Problem If the issue doesn’t make sense – not actionable, for example, a non-Spark issue, resolve as Invalid If it’s a coherent issue, but there is a clear indication that there is not support or interest in acting on it, then resolve as Won’t Fix Sometimes, a contributor will already have a particular new change or bug in mind. If seeking ideas, consult the list of starter tasks in JIRA, or ask the user@ mailing list. Before proceeding, contributors should evaluate if the proposed change is likely to be relevant, new and actionable: Is it clear that code must change? Proposing a JIRA and pull request is appropriate only when a clear problem or change has been identified. If simply having trouble using Spark, use the mailing lists first, rather than consider filing a JIRA or proposing a change. When in doubt, email user@ first about the possible changeSearch the user@ and dev@ mailing list archives for related discussions. Often, the problem has been discussed before, with a resolution that doesn’t require a code change, or recording what kinds of changes will not be accepted as a resolution.Search JIRA for existing issues: https://issues.apache.org/jira/browse/WAYANGIs the scope of the change matched to the contributor’s level of experience? Anyone is qualified to suggest a typo fix, but refactoring core scheduling logic requires much more understanding of Wayang. Some changes require building up experience first (see above).It’s worth reemphasizing that changes to the core of Wayang, or to highly complex and important modules like SQL and Catalyst, are more difficult to make correctly. They will be subjected to more scrutiny, and held to a higher standard of review than changes to less critical code. ","version":"Next","tagName":"h3"},{"title":"ML4ALL and optimizer-specific contribution guidelines","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#ml4all-and-optimizer-specific-contribution-guidelines","content":" While a rich set of algorithms is an important goal for Wayang, scaling the project requires that maintainability, consistency, and code quality come first. New algorithms should: Be used and accepted (academic citations and concrete use cases can help justify this)Be well documentedHave APIs consistent with other algorithms in WayangCome with a reasonable expectation of developer supportError message guidelinesExceptions thrown in Wayang should be associated with standardized and actionable error messages Error messages should answer the following questions: What was the problem?Why did the problem happen?How can the problem be solved? When writing error messages, you should: Use active voiceAvoid time-based statements, such as promises of future supportUse the present tense to describe the error and provide suggestionsProvide concrete examples if the resolution is unclearAvoid sounding accusatory, judgmental, or insultingBe directDo not use programming jargon in user-facing errors 8 See the error message guidelines for more details. ","version":"Next","tagName":"h3"},{"title":"Code review criteria","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-review-criteria","content":" Before considering how to contribute code, it’s useful to understand how code is reviewed, and why changes may be rejected. See the detailed guide for code reviewers from Google’s Engineering Practices documentation. Simply put, changes that have many or large positives, and few negative effects or risks, are much more likely to be merged, and merged quickly. Risky and less valuable changes are very unlikely to be merged, and may be rejected outright rather than receive iterations of review. Positives Fixes the root cause of a bug in existing functionalityAdds functionality or fixes a problem needed by a large number of usersSimple, targetedMaintains or improves consistency across Python, Java, ScalaEasily tested; has testsReduces complexity and lines of codeChange has already been discussed and is known to committers Negatives, risks Band-aids a symptom of a bug onlyIntroduces complex new functionality, especially an API that needs to be supportedAdds complexity that only helps a niche use caseAdds user-space functionality that does not need to be maintained in Wayang, but could be hosted externally and indexed by wayang-packages.orgChanges a public API or semantics (rarely allowed)Adds large dependenciesChanges versions of existing dependenciesAdds a large amount of codeMakes lots of modifications in one “big bang” change ","version":"Next","tagName":"h3"},{"title":"Contributing code changes","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#contributing-code-changes","content":" Please review the preceding section before proposing a code change. This section documents how to do so. When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so. ","version":"Next","tagName":"h3"},{"title":"Cloning the Apache Wayang™ source code","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#cloning-the-apache-wayang-source-code","content":" If you are interested in working with the newest under-development code or contributing to Apache Spark development, you can check out the master branch from Git: # Master development branch git clone git://github.com/apache/incubator-wayang.git Once you’ve downloaded Wayang, you can find instructions for installing and building it on the Compiling Apache Wayang page. ","version":"Next","tagName":"h3"},{"title":"JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#jira","content":" Generally, Wayang uses JIRA to track logical issues, including bugs and improvements, and uses GitHub pull requests to manage the review and merge of specific code changes. That is, JIRAs are used to describe what should be fixed or changed, and high-level approaches, and pull requests describe how to implement that change in the project’s source code. For example, major design decisions are discussed in JIRA. Find the existing Spark JIRA that the change pertains to. Do not create a new JIRA if creating a change to address an existing issue in JIRA; add to the existing discussion and work insteadLook for existing pull requests that are linked from the JIRA, to understand if someone is already working on the JIRA If the change is new, then it usually needs a new JIRA. However, trivial changes, where the what should change is virtually the same as the how it should change do not require a JIRA. Example: Fix typos in Foo wayang docIf required, create a new JIRA: Provide a descriptive Title. “Update web UI” or “Problem in scheduler” is not sufficient. “Kafka Streaming support fails to handle empty queue in YARN cluster mode” is good.Write a detailed description. For bug reports, this should ideally include a short reproduction of the problem. For new features, it may include a design document.Set required fields: Issue Type. Generally, Bug, Improvement and New Feature are the only types used in Spark.Priority. Set to Major or below; higher priorities are generally reserved for committers to set. The main exception is correctness or data-loss issues, which can be flagged as Blockers. JIRA tends to unfortunately conflate “size” and “importance” in its Priority field values. Their meaning is roughly: Blocker: pointless to release without this change as the release would be unusable to a large minority of users. Correctness and data loss issues should be considered Blockers for their target versions.Critical: a large minority of users are missing important functionality without this, and/or a workaround is difficultMajor: a small minority of users are missing important functionality without this, and there is a workaroundMinor: a niche use case is missing some support, but it does not affect usage or is easily worked aroundTrivial: a nice-to-have change but unlikely to be any problem in practice otherwise ComponentAffects Version. For Bugs, assign at least one version that is known to exhibit the problem or need the changeLabel. Not widely used, except for the following: correctness: a correctness issuedata-loss: a data loss issuerelease-notes: the change’s effects need mention in release notes. The JIRA or pull request should include detail suitable for inclusion in release notes – see “Docs Text” below.starter: small, simple change suitable for new contributors Docs Text: For issues that require an entry in the release notes, this should contain the information that the release manager should include in Release Notes. This should include a short summary of what behavior is impacted, and detail on what behavior changed. It can be provisionally filled out when the JIRA is opened, but will likely need to be updated with final details when the issue is resolved. Do not set the following fields: Fix Version This is assigned by committers only when resolved.Target Version This is assigned by committers to indicate a PR has been accepted for possible fix by the target version. Do not include a patch file; pull requests are used to propose the actual change. If the change is a large change, consider inviting discussion on the issue at dev@ first before proceeding to implement the change. ","version":"Next","tagName":"h3"},{"title":"Pull request","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#pull-request","content":" Before creating a pull request in Apache Wayang, it is important to check if tests can pass on your branch because our GitHub Actions workflows automatically run tests for your pull request/following commits and every run burdens the limited resources of GitHub Actions in Apache Wayang repository. Below steps will take your through the process. Fork the GitHub repository at https://github.com/apache/incubator-wayang if you haven’t alreadyGo to “Actions” tab on your forked repository and enable “Build and test” and “Report test results” workflowsClone your fork and create a new branchConsider whether documentation or tests need to be added or updated as part of the change, and add them as needed. When you add tests, make sure the tests are self-descriptive.Also, you should consider writing a JIRA ID in the tests when your pull request targets to fix a specific issue. In practice, usually it is added when a JIRA type is a bug or a PR adds a couple of tests to an existing test class. See the examples below: @Test public void testCase() { // WAYANG-12345: a short description of the test Consider whether benchmark results should be added or updated as part of the change, and add them as needed by Running benchmarks in your forked repository to generate benchmark results.Run all tests with in your build to verify that the code still compiles, passes tests, and passes style checks. If style checks fail, review the Code Style Guide below.Push commits to your branch. This will trigger “Build and test” and “Report test results” workflows on your forked repository and start testing and validating your changes.Open a pull request against the master branch of apache/incubator-wayang. (Only in special cases would the PR be opened against other branches). This will trigger workflows “On pull request*” (on the Wayang repo) that will look/watch for successful workflow runs on “your” forked repository (it will wait if one is running). The PR title should be of the form [WAYANG-xxxx][COMPONENT] Title, where WAYANG-xxxx is the relevant JIRA number, COMPONENT is one of the PR categories and Title may be the JIRA’s title or a more specific title describing the PR itself.If the pull request is still a work in progress, and so is not ready to be merged, but needs to be pushed to GitHub to facilitate review, then add [WIP] after the component.Consider identifying committers or other contributors who have worked on the code being changed. Find the file(s) in GitHub and click “Blame” to see a line-by-line annotation of who changed the code last. You can add @username in the PR description to ping them immediately.Please state that the contribution is your original work and that you license the work to the project under the project’s open source license. The related JIRA, if any, will be marked as “In Progress” and your pull request will automatically be linked to it. There is no need to be the Assignee of the JIRA to work on it, though you are welcome to comment that you have begun work. ","version":"Next","tagName":"h3"},{"title":"The review process","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#the-review-process","content":" Other reviewers, including committers, may comment on the changes and suggest modifications. Changes can be added by simply pushing more commits to the same branch.Lively, polite, rapid technical debate is encouraged from everyone in the community. The outcome may be a rejection of the entire change.Keep in mind that changes to more critical parts of Spark, like its core and SQL components, will be subjected to more review, and may require more testing and proof of its correctness than other changes.Reviewers can indicate that a change looks suitable for merging with a comment such as: “I think this patch looks good”. Wayang uses the LGTM convention for indicating the strongest level of technical sign-off on a patch: simply comment with the word “LGTM”. It specifically means: “I’ve looked at this thoroughly and take as much ownership as if I wrote the patch myself”. If you comment LGTM you will be expected to help with bugs or follow-up issues on the patch. Consistent, judicious use of LGTMs is a great way to gain credibility as a reviewer with the broader community.Sometimes, other changes will be merged which conflict with your pull request’s changes. The PR can’t be merged until the conflict is resolved. This can be resolved by, for example, adding a remote to keep up with upstream changes by git remote add upstream https://github.com/apache/incubator-wayang.git, running git fetch upstream followed by git rebase upstream/master and resolving the conflicts by hand, then pushing the result to your branch.Try to be responsive to the discussion rather than let days pass between replies ","version":"Next","tagName":"h3"},{"title":"Closing your pull request / JIRA","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#closing-your-pull-request--jira","content":" If a change is accepted, it will be merged and the pull request will automatically be closed, along with the associated JIRA if any Note that in the rare case you are asked to open a pull request against a branch besides master, that you will actually have to close the pull request manuallyThe JIRA will be Assigned to the primary contributor to the change as a way of giving credit. If the JIRA isn’t closed and/or Assigned promptly, comment on the JIRA. If your pull request is ultimately rejected, please close it promptly … because committers can’t close PRs directlyPull requests will be automatically closed by an automated process at Apache after about a week if a committer has made a comment like “mind closing this PR?” This means that the committer is specifically requesting that it be closed. If a pull request has gotten little or no attention, consider improving the description or the change itself and ping likely reviewers again after a few days. Consider proposing a change that’s easier to include, like a smaller and/or less invasive change.If it has been reviewed but not taken up after weeks, after soliciting review from the most relevant reviewers, or, has met with neutral reactions, the outcome may be considered a “soft no”. It is helpful to withdraw and close the PR in this case.If a pull request is closed because it is deemed not the right approach to resolve a JIRA, then leave the JIRA open. However if the review makes it clear that the issue identified in the JIRA is not going to be resolved by any pull request (not a problem, won’t fix) then also resolve the JIRA. ","version":"Next","tagName":"h3"},{"title":"Code style guide","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-style-guide","content":" Please follow the style of the existing codebase. For Python code, Apache Wayang follows PEP 8 with one exception: lines can be up to 100 characters in length, not 79.For R code, Apache Wayang follows Google’s R Style Guide with three exceptions: lines can be up to 100 characters in length, not 80, there is no limit on function name but it has a initial lower case latter and S4 objects/methods are allowed.For Java code, Apache Wayang follows Oracle’s Java code conventions and Scala guidelines below. The latter is preferred.For Scala code, Apache Wayang follows the official Scala style guide. ","version":"Next","tagName":"h2"},{"title":"If in doubt","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#if-in-doubt","content":" If you’re not sure about the right style for something, try to follow the style of the existing codebase. Look at whether there are other examples in the code that use your feature. Feel free to ask on the dev@ list as well and/or ask committers. ","version":"Next","tagName":"h3"},{"title":"Code of conduct","type":1,"pageTitle":"How to contribute","url":"/docs/community/contribute#code-of-conduct","content":" The Apache Wayang project follows the Apache Software Foundation Code of Conduct. The code of conduct applies to all spaces managed by the Apache Software Foundation, including IRC, all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our communities. A code of conduct which is specific to in-person events (ie., conferences) is codified in the published ASF anti-harassment policy. We expect this code of conduct to be honored by everyone who participates in the Apache community formally or informally, or claims any affiliation with the Foundation, in any Foundation-related activities and especially when representing the ASF, in any role. This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. We expect it to be followed in spirit as much as in the letter, so that it can enrich all of us and the technical communities in which we participate. For more information and specific guidelines, refer to the Apache Software Foundation Code of Conduct. This guide was originally released by Apache Spark, the Apache Wayang project adapted the guide. ","version":"Next","tagName":"h2"},{"title":"Maturity Model","type":0,"sectionRef":"#","url":"/docs/community/maturity","content":"Maturity Model ID\tDescription\tStatus\tCode CD10\tThe project produces Open Source software for distribution to the public, at no charge.\tYes. The project source code is licensed under the Apache License 2.0. CD20\tAnyone can easily discover and access the project's code.\tYes. The official website includes GitHub link which can access the project's repository on GitHub directly. CD30\tAnyone using standard, widely-available tools, can build the code in a reproducible way.\tYes. Our webpage and github repo provide "Getting Started" and several examples to allow users build our system. CD40\tThe full history of the project's code is available via a source code control system, in a way that allows anyone to recreate any released version.\tYes. We use git and anyone can view the full history of the project via commit logs and tags of the releases. CD50\tThe source code control system establishes the provenance of each line of code in a reliable way, based on strong authentication of the committer. When third parties contribute code, commit messages provide reliable information about the code provenance.\tYes. The project uses GitHub and managed by Apache Infra and ensures provenance of each line of code to a committer. Third-party contributions are accepted in accordance with the contributing guides. Licenses and Copyright LC10\tThe Apache License, version 2.0, covers the released code.\tYes. All source files are licensed with Apache License v2.0. LICENSE can be found in the GitHub repository. LC20\tLibraries that are mandatory dependencies of the project's code do not create more restrictions than the Apache License does.\tYes. None of the dependencies create more restrictions. LC30\tThe libraries mentioned in LC20 are available as Open Source software.\tYes. All libraries are open source. LC40\tCommitters are bound by an Individual Contributor Agreement (the "Apache iCLA") that defines which code they may commit and how they need to identify code that is not their own.\tYes. All committers have iCLAs. LC50\tThe project clearly defines and documents the copyright ownership of everything that the project produces.\tYes. All source files are with APLv2 header Releases RE10\tReleases consist of source code, distributed using standard and open archive formats that are expected to stay readable in the long term.\tYes. Source release is distributed via dist.apache.org and linked from download page. RE20\tThe project's PPMC (Project Management Committee, see CS10) approves each software release in order to make the release an act of the Foundation.\tYes. All releases have been voted at dev@wayang.apache.org and general@incubator.apache.org, and have at least 3 PPMC member's votes. RE30\tReleases are signed and/or distributed along with digests that anyone can reliably use to validate the downloaded archives.\tYes. All releases are signed, and the KEYS are available. RE40\tThe project can distribute convenience binaries alongside source code, but they are not Apache Releases, they are provided with no guarantee.\tYes. Users can easily build binaries from source code. We do not provide binaries as Apache Releases. RE50\tThe project documents a repeatable release process so that someone new to the project can independently generate the complete set of artifacts required for a release.\tYes. Newcomers can follow our very detailed Release guide to make a new Apache Wayang release, and so far we had 3 different release managers. Quality QU10\tThe project is open and honest about the quality of its code. Various levels of quality and maturity for various modules are natural and acceptable as long as they are clearly communicated.\tYes. The project records all bugs using Github Issues. QU20\tThe project puts a very high priority on producing secure software.\tYes. All security issues will be addressed within 3 days. QU30\tThe project provides a well-documented, secure and private channel to report security issues, along with a documented way of responding to them.\tYes. https://wayang.apache.org/docs/community/security QU40\tThe project puts a high priority on backwards compatibility and aims to document any incompatible changes and provide tools and documentation to help users transition to new features.\tYes. Each release contains a “New features” on the website containing the release notes. The project aims to make no backward incompatible changes within a given major version. QU50\tThe project strives to respond to documented bug reports in a timely manner.\tYes. The community has very small response times to issues and pull requests Community CO10\tThe project has a well-known homepage that points to all the information required to operate according to this maturity model.\tYes. The official website includes all information users need to run Apache Wayang. CO20\tThe community welcomes contributions from anyone who acts in good faith and in a respectful manner, and who adds value to the project.\tYes. The contributing guide provides information for anyone to contribute. CO30\tContributions include source code, documentation, constructive bug reports, constructive discussions, marketing and generally anything that adds value to the project.\tYes. All good contributions including code and non-code are welcomed. CO40\tThe community strives to be meritocratic and gives more rights and responsibilities to contributors who, over time, add value to the project.\tYes. The community has elected 3 new PPMC members/Committers the last year. CO50\tThe project documents how contributors can earn more rights such as commit access or decision power, and applies these principles consistently.\tYes. The website has a clear guide on how to become a committer https://wayang.apache.org/docs/community/committer CO60\tThe community operates based on consensus of its members (see CS10) who have decision power. Dictators, benevolent or not, are not welcome in Apache projects.\tYes. All decisions are made after voting by community members. CO70\tThe project strives to answer user questions in a timely manner.\tYes. Our community answers user questions in a timely manner. Consensus CS10\tThe project maintains a public list of its contributors who have decision power. The project's PPMC (Project Management Committee) consists of those contributors.\tYes. See Team with all PPMC members/committers and contributors. CS20\tDecisions require a consensus among PPMC members and are documented on the project's main communications channel. The PPMC takes community opinions into account, but the PPMC has the final word.\tYes. All decisions are made by votes on dev@wayang.apache.org, and with at least 3 +1 votes from PPMC. CS30\tThe project uses documented voting rules to build consensus when discussion is not sufficient.\tYes. The project uses the standard ASF voting rules. CS40\tIn Apache projects, vetoes are only valid for code commits. The person exercising the veto must justify it with a technical explanation, as per the Apache voting rules defined in CS30.\tYes. Apache Wayang community has not used the veto power yet except for code commits. CS50\tAll "important" discussions happen asynchronously in written form on the project's main communications channel. Offline, face-to-face or private discussions that affect the project are also documented on that channel.\tYes. All important discussions and conclusions are sent to the dev list Independence IN10\tThe project is independent from any corporate or organizational influence.\tYes. The PPMC members of Apache Wayang are from several organizations. IN20\tContributors act as themselves, not as representatives of a corporation or organization.\tYes. The contributors act on their own initiative without representing a corporation or organization. Trademark and Branding TB10\tThe project uses "Apache Foo™" as the project and software product name consistently, with appropriate trademark attributions\tYes. The project uses appropriately the trademark symbol. TB20\tThe project's primary homepage is at projectname.apache.org.\tYes. The project's homepage is at wayang.apache.org TB30\tThe ASF has trademark rights, including any registrations, to the project name, logo, and any other major branding elements.\tYes. ASF has the trademark rights TB40\tThe project monitors for any major misuses of their project's brand by others.\tYes. The PPMC members have already addressed misuses.\t","keywords":"","version":"Next"},{"title":"How to make a release","type":0,"sectionRef":"#","url":"/docs/community/release","content":"","keywords":"","version":"Next"},{"title":"First time release","type":1,"pageTitle":"How to make a release","url":"/docs/community/release#first-time-release","content":" ============================================= Add your Apache credentials in ~.m2/settings.xml If you don’t have this directory or file, you should create a .m2 directory in your user home and inside that create a settings.xml file with at least this content: <?xml version="1.0" encoding="UTF-8"?> <settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.1.0 http://maven.apache.org/xsd/settings-1.1.0.xsd" xmlns="http://maven.apache.org/SETTINGS/1.1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <servers> <!-- Apache Repo Settings --> <server> <id>apache.snapshots.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> <server> <id>apache.releases.https</id> <username>{user-id}</username> <password>{user-pass}</password> </server> </servers> </settings> The following is about key management. Details are described here You need to generate a key with GnuPG Install GnuPG (e.g., brew install gnupg)Run: gpg --full-generate-keyExport the public key: gpg --export --armor mygpgpublickey Install pinentry (e.g., brew install pinentry-mac) so that you can be promted to enter your credentials via command line Configuration A. Make Apache the default key:APACHEID=your_asf_id KEY_ID=$(gpg --list-keys --with-colons $APACHEID@apache.org | egrep "^pub" | awk -F: '{print $5}') echo "default-key $KEY_ID" >> ~/.gnupg/gpg.conf B. export GPG_TTY=$(tty) echo $GPG_TTY C. Configure gnupg to use standard DNS resolution # resolves common "gpg: keyserver receive failed: Network is unreachable" and # "gpg: keyserver receive failed: No keyserver available" errors echo "standard-resolver" > ~/.gnupg/dirmngr.conf sudo pkill dirmngr D. Set configuration to use SHA512 keys by default: cat <<EOL >> ~/.gnupg/gpg.conf personal-digest-preferences SHA512 cert-digest-algo SHA512 default-preference-list SHA512 SHA384 SHA256 SHA224 AES256 AES192 AES CAST5 ZLIB BZIP2 ZIP Uncompressed EOL Upload the key to one of the key servers gpg --keyserver keyserver.ubuntu.com --send-key $KEY_ID gpg --keyserver keys.openpgp.org --send-key $KEY_ID gpg --keyserver pgp.mit.edu --send-key $KEY_ID Make sure your public key is appended in the KEYS file found in the release server here ============================================= Run mvn release:prepare (you can skip tests with -Darguments='-DskipTests=True, if you have done this before) Run mvn clean release:perform (This step will ask for your apache credentials, if everything related to the keys is configured properly.) Go to: https://repository.apache.org, login, go to Staging Repositories and find the orgapachewayang-{somenumber} and close it with description "Apache Wayang (incubating) 1.0.0-rc5" Make a directory structure as described below and copy the corresponding source files from the directory target/checkout/target. The structure should be: ./KEYS ./1.0.0/ ./1.0.0/rc5 ./1.0.0/rc5/README ./1.0.0/rc5/RELEASE_NOTES ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.asc ./1.0.0/rc5/apache-wayang-incubating-1.0.0-source-release.zip.sha12 Make sure the KEYS file contains your public key. The KEYS file can be found in https://dist.apache.org/repos/dist/release/incubator/wayang/ cd 1.0.0 svn import rc5 https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 -m "Staging of rc5 of Wayang 1.0.0" Send the voting email to the dev list. If the voting passes, send the voting email to the general list. Once the voting in the general list passes, the staged artifacts can be released. This is done by moving them inside the Apache SVN: svn move -m "Release of Apache Wayang (incubating) 1.0.0" https://dist.apache.org/repos/dist/dev/incubator/wayang/1.0.0/rc5 https://dist.apache.org/repos/dist/release/incubator/wayang/1.0.0 Then release the maven artifacts: In order to do this, the release manager logs into Nexus at https://repository.apache.org/, selects the staging repository and clicks on the Release button. Bring all changes of the release branch to the main. You can also remove 1.0.0 from the dev directory of the svn now that the release is out. Create and upload the javadocs: Go to the source code directory and run mvn compile javadoc:javadoc javadoc:aggregate The javadocs can then be found in the target directory Upload the javadocs in the website: https://github.com/apache/incubator-wayang-website/tree/main/static/docs/api/javadocs ","version":"Next","tagName":"h3"},{"title":"Community Hubs","type":0,"sectionRef":"#","url":"/docs/community/repositories","content":"","keywords":"","version":"Next"},{"title":"Source Code","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#source-code","content":" Apache Wayang ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang.git GitHub mirror : https://github.com/apache/incubator-wayang.git Wayang Web Page Repository ASF repository : https://gitbox.apache.org/repos/asf/incubator-wayang-website.git GitHub mirror : https://github.com/apache/incubator-wayang-website.git ","version":"Next","tagName":"h3"},{"title":"Issues, Bugs and Features","type":1,"pageTitle":"Community Hubs","url":"/docs/community/repositories#issues-bugs-and-features","content":" If you have a specific bug to report or feature request, we suggest opening an issue in Jira. ","version":"Next","tagName":"h3"},{"title":"Wayang Security","type":0,"sectionRef":"#","url":"/docs/community/security","content":"","keywords":"","version":"Next"},{"title":"Advisories for Dependencies","type":1,"pageTitle":"Wayang Security","url":"/docs/community/security#advisories-for-dependencies","content":" Many organizations employ security scanning tools to identify components with known security advisories. Although we strongly recommend these tools as they can alert users to potential risks, they often generate false positives. This occurs because a vulnerable dependency may not necessarily impact Apache Wayang if used in a non-exploitable manner. Therefore, advisories regarding Apache Wayang's dependencies are not automatically considered critical. However, if additional analysis indicates that Apache Wayang might be affected by a dependency's vulnerability, please report your findings privately to security@wayang.apache.org. If a dependency advisory is identified, please: Verify if our DependencyCheck suppressions contain relevant details.Check our issue tracker for discussions regarding this advisory.Conduct your own analysis to determine whether Apache Wayang is affected. If affected, report your findings privately through security@wayang.apache.org.If not affected, please contribute by updating the DependencyCheck suppression list, clearly documenting why Apache Wayang is not impacted. ","version":"Next","tagName":"h2"},{"title":"API Documentation","type":0,"sectionRef":"#","url":"/docs/guide/api-documentation","content":"API Documentation The latest Javadocs can be found here.","keywords":"","version":"Next"},{"title":"Developing in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/developing-in-wayang","content":"","keywords":"","version":"Next"},{"title":"Compile the module you modified","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#compile-the-module-you-modified","content":" Within the root directory of Wayang, compile only the module you modified for faster compilation: mvn clean install -DskipTests -pl <modified_module> or change the directory to your module and compile there: cd <modified_module> && mvn clean install Important: before making a Pull Request make sure all modules compile and all tests are passing: mvn clean install ","version":"Next","tagName":"h2"},{"title":"Package the project","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#package-the-project","content":" mvn clean package -pl :wayang-assembly -Pdistribution ","version":"Next","tagName":"h2"},{"title":"Execute your code","type":1,"pageTitle":"Developing in Wayang","url":"/docs/guide/developing-in-wayang#execute-your-code","content":" Before executing your code, make sure the required environment variables are set correctly. cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-SNAPSHOT-incubating-dist.tar.gz cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Adding new operators in Wayang","type":0,"sectionRef":"#","url":"/docs/guide/adding-operators","content":"","keywords":"","version":"Next"},{"title":"Step 1: Add a Wayang operator","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-1-add-a-wayang-operator","content":" Wayang operators are located under the wayang-basic in the org.apache.wayang.basic.operators package. An operator needs to extend from one of the following abstract classes: UnaryToUnaryOperator, BinaryToUnaryOperator, UnarySource, UnarySink. For a unary to unary operator, see for example here. For enhanced performance in Wayang, consider adding a cardinality estimator by overriding the createCardinalityEstimator() function as here. ","version":"Next","tagName":"h2"},{"title":"Step 2: Add the (platform-specific) execution operators","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-2-add-the-platform-specific-execution-operators","content":" Execution operators are located under the corresponding module of wayang-platforms. For instance, Java execution operators are located in the org.apache.wayang.java.operators package of the wayang-java module. An execution operator needs to extend from its corresponding Wayang operator and implement the corresponding platform operator interface. For the above MapOperator, the following is the corresponding JavaMapOperator. For enhanced performance in Wayang, consider adding a load function as well: For this you need to overwrite the getLoadProfileEstimatorConfigurationKey() function and provide the right key that will then be read from a properties file. For the JavaMapOperator it's: wayang.java.map.load. Then add in the corresponding properties file (e.g., this is for the java executor) the template which is the mathematical formula that represents the cost of this operator and an instantiation of it. See here for the example of the map operator. ","version":"Next","tagName":"h2"},{"title":"Step 3: Add mappings","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-3-add-mappings","content":" Create mappings from the Wayang operator to the platform-specific execution operators. The mappings are located in the corresponding execution module in the org.apache.wayang.java.operators package. For the above MapOperator and JavaMapOperator, see here. After that you need to declare this mapping in Wayang in the corresponding Mappings class. ","version":"Next","tagName":"h2"},{"title":"Step 4: Expand the Java scala-like API","type":1,"pageTitle":"Adding new operators in Wayang","url":"/docs/guide/adding-operators#step-4-expand-the-java-scala-like-api","content":" Once you created a new operator you need to expose it to the API so that users can use it as a function in the dataflow job they create. For this, you need to go to the module wayang-api/wayang-api-scala-java and expand the JavaPlanBuilder.scala file to include a new source operator or the DataQuantaBuilder for non-source operators. ","version":"Next","tagName":"h2"},{"title":"Team","type":0,"sectionRef":"#","url":"/docs/community/team","content":"","keywords":"","version":"Next"},{"title":"Committers and Recent Contributors","type":1,"pageTitle":"Team","url":"/docs/community/team#committers-and-recent-contributors","content":" Name\tRole\tApache ID\tOrganizationAlexander Alten\tPMC, Committer\taloalt\tScalytics Calvin Kirs\tPMC, Committer\tkirs\tSelectDB, ASF Zoi Kaoudi\tPMC, Committer\tzkaoudi\tITU Copenhagen, Scalytics Kaustubh Beedkar\tPMC, Committer\tkbeedkar\tScalytics, IIT Dehli Mirko Kaempf\tPMC, Committer\tkamir\tEcolytiq Juri Petersen\tPMC, Committer\tjuri\tITU Copenhagen Mads Sejer Pedersen\tPMC, Committer\tsejer\tITU Copenhagen Jean-Baptiste Onofré\tPMC, Committer\tjbonofre\tDremio, ASF Yasser Idris\tPMC, Committer\tyidris\tExpensePoint, Alexandria University Anis Troudi\tCommitter\tatroudi Christofer Dutz\tCommitter\tchrisdutz\tASF Mingxi Liu\tContributor East China Normal University Michalis Vargiamis\tContributor Scalytics Jeppe Halvorsen\tContributor ITU Copenhagen, Falck Christoffer Kristensen\tContributor ITU Copenhagen, Stretch Qonnect Aurélien Bertrand\tContributor Maastricht University, TecAlliance Aditya Goel\tContributor IIT Delhi Dudipala Vigneshwar Reddy\tContributor IIT Delhi Akshit Goel\tContributor IIT Delhi Vedanta Neogi\tContributor IIT Delhi Christos-Evangelos Lampropoulos\tContributor University of Patras Bertty Contreras\tContributor\tbertty Jorge Quiané\tContributor\tquiaru Rodrigo Pardo Meza\tContributor\trpardomeza Glaucia Esppenchutz\tContributor\tglauesppen Gábor Gévay\tContributor\tggevay\t You can reach committers directly at <apache-id>@apache.org ","version":"Next","tagName":"h2"},{"title":"Mentors during the incubation process","type":1,"pageTitle":"Team","url":"/docs/community/team#mentors-during-the-incubation-process","content":" The following people were very kind to mentor the project while in incubation. Name\tApache IDBernd Fondermann\tberndf Christofer Dutz\tcdutz Jean-Baptiste Onofré\tjbonofre Lars George\tlarsgeorge ","version":"Next","tagName":"h2"},{"title":"Donations to Wayang","type":1,"pageTitle":"Team","url":"/docs/community/team#donations-to-wayang","content":" The Logo was donated by Brian Vera ","version":"Next","tagName":"h2"},{"title":"Installing and building Apache Wayang","type":0,"sectionRef":"#","url":"/docs/guide/installation","content":"","keywords":"","version":"Next"},{"title":"Clone repository","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#clone-repository","content":" git clone https://github.com/apache/incubator-wayang.git ","version":"Next","tagName":"h2"},{"title":"Create binaries","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#create-binaries","content":" Running following commands to build Wayang and generate the tar.gz cd incubator-wayang ./mvnw clean install -DskipTests ./mvnw clean package -pl :wayang-assembly -Pdistribution Then you can find the wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz under wayang-assembly/target directory. ","version":"Next","tagName":"h2"},{"title":"Prepare the environment","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#prepare-the-environment","content":" ","version":"Next","tagName":"h2"},{"title":"Wayang","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#wayang","content":" tar -xvf wayang-assembly-0.7.1-SNAPSHOT-dist.tar.gz cd wayang-0.7.1-SNAPSHOT In linux echo "export WAYANG_HOME=$(pwd)" >> ~/.bashrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.bashrc source ~/.bashrc In MacOS echo "export WAYANG_HOME=$(pwd)" >> ~/.zshrc echo "export PATH=${PATH}:${WAYANG_HOME}/bin" >> ~/.zshrc source ~/.zshrc ","version":"Next","tagName":"h3"},{"title":"Others","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#others","content":" You need to install Apache Spark version 3 or higher. Don’t forget to set the SPARK_HOME environment variable.You need to install Apache Hadoop version 3 or higher. Don’t forget to set the HADOOP_HOME environment variable. ","version":"Next","tagName":"h3"},{"title":"Run the program","type":1,"pageTitle":"Installing and building Apache Wayang","url":"/docs/guide/installation#run-the-program","content":" To execute the WordCount example with Apache Wayang, you need to execute your program with the 'wayang-submit' command: cd wayang-0.7.1-SNAPSHOT ./bin/wayang-submit org.apache.wayang.apps.wordcount.Main java file://$(pwd)/README.md Then you should be able to see the output of the Wordcount example. Compiling Apache Wayang Apache Wayang (incubating) has different dependencies, for compiling, it needs to add some profile in the compilation to enable maven works properly. mvn clean compile The line before is because the plugin the Antlr is not needed in all the modules, as well it has happened with Scala language. When maven compiles one or more modules using those plugins in the compilation time, it needs to add. The modules are: wayang-api-scala-javawayang-core (Antlr)wayang-iejoinwayang-sparkwayang-profilerwayang-tests-integration Executing Coverage mvn clean verify jacoco:report the final report is placed on ./target/aggregate.exec/aggregate.exec ","version":"Next","tagName":"h2"},{"title":"How the optimizer works (WIP)","type":0,"sectionRef":"#","url":"/docs/guide/optimizer","content":"","keywords":"","version":"Next"},{"title":"Configuration Files","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#configuration-files","content":" Description of configuration files and how to edit them ","version":"Next","tagName":"h2"},{"title":"Key Configuration Parameters","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#key-configuration-parameters","content":" List of key parameters and how they affect performance ","version":"Next","tagName":"h2"},{"title":"Tuning Tips","type":1,"pageTitle":"How the optimizer works (WIP)","url":"/docs/guide/optimizer#tuning-tips","content":" Tips on tuning Apache Wayang for different scenarios ","version":"Next","tagName":"h2"},{"title":"ML4all: scalable ML system for everyone","type":0,"sectionRef":"#","url":"/docs/guide/ml4all","content":"","keywords":"","version":"Next"},{"title":"Abstraction","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#abstraction","content":" ML4all abstracts most ML algorithms with seven operators: (1) Transform receives a data point to transform (e.g., normalize it) and outputs a new data point. (2) Stage initializes all the required global param- eters (e.g., centroids for the k-means algorithm). (3) Compute performs user-defined computations on the input data point and returns a new data point. For example, it can compute the nearest cen- troid for each input data point. (4) Update updates the global parameters based on a user-defined formula. For example, it can update the new centroids based on the output computed by the Compute operator. (5) Sample takes as input the size of the desired sample and the data points to sample from and re- turns a reduced set of sampled data points. (6) Converge specifies a function that outputs a convergence dataset required for determining whether the iterations should continue or stop. (7) Loop specifies the stopping condition on the convergence dataset. Similar to MapReduce, where users need to implement a map and reduce function, users of ML4all wishing to develop their own algorithm should implement the above interfaces. The interfaces can be found in org.apache.wayang.ml4all.abstraction.api. Examples for KMeans clustering and stochastic gradient descent can be found in org.apache.wayang.ml4all.algorithms. ","version":"Next","tagName":"h2"},{"title":"Example runs","type":1,"pageTitle":"ML4all: scalable ML system for everyone","url":"/docs/guide/ml4all#example-runs","content":" Kmeans: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunKMeans java,spark <url_path_to_file>/USCensus1990-sample.input 3 68 0 1 SGD: ./bin/wayang-submit org.apache.wayang.ml4all.examples.RunSGD spark <url_path_to_file>/adult.zeros.input 100827 123 10 0.001 ","version":"Next","tagName":"h2"},{"title":"Using Wayang's docker image","type":0,"sectionRef":"#","url":"/docs/guide/wayang-docker","content":"","keywords":"","version":"Next"},{"title":"Step 1: Creating a docker-compose file","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-1-creating-a-docker-compose-file","content":" This guide assumes knowledge aboutdocker anddocker compose. We provide a pre-built docker imagethat contains the necessary tooling in order to run or develop Apache Wayang. The tools necessary for this are: Java 11Apache SparkHadoopMaven A docker-compose.yml containing the following services will suffice to run TPC-H benchmarks: name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always Placing this file in the root directory of Wayang's source will mount volumes containing the application and its dependencies after installation into the app container. ","version":"Next","tagName":"h2"},{"title":"Step 2: Connecting to the app container","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-2-connecting-to-the-app-container","content":" In order to get a interactive bash session that allows running commands inside of the app container, run the following: docker exec -it apache-wayang-app bash ","version":"Next","tagName":"h2"},{"title":"Step 3: Compiling Wayang and running benchmarks","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#step-3-compiling-wayang-and-running-benchmarks","content":" Within the root directory of Wayang (/var/www/html in our container), run the following command to install all packages in Wayang: mvn clean install -DskipTests Packaging the project to build the executable: mvn clean package -pl :wayang-assembly -Pdistribution Execute your code: cd wayang-assembly/target/ tar -xvf apache-wayang-assembly-0.7.1-incubating-dist.tar.gz cd wayang-0.7.1 ./bin/wayang-submit org.apache.wayang.<main_class> <parameters> ","version":"Next","tagName":"h2"},{"title":"Optional: Add clusters for additional platforms to your docker setup","type":1,"pageTitle":"Using Wayang's docker image","url":"/docs/guide/wayang-docker#optional-add-clusters-for-additional-platforms-to-your-docker-setup","content":" name: apache-wayang services: app: container_name: apache-wayang-app image: apache/incubator-wayang:latest ports: - 8888:8888 volumes: - ./:/var/www/html - ./.m2/repository/:/root/.m2/repository tty: true restart: always tpch: container_name: apache-wayang-tpch image: ghcr.io/scalytics/tpch-docker:main tty: true volumes: - ./data/:/data restart: always spark-master: image: cluster-apache-spark:3.0.2 spark-worker-a: image: cluster-apache-spark:3.0.2 depends_on: - spark-master environment: - SPARK_MASTER=spark://spark-master:7077 - SPARK_WORKER_CORES=1 - SPARK_WORKER_MEMORY=1G - SPARK_DRIVER_MEMORY=1G - SPARK_EXECUTOR_MEMORY=1G - SPARK_WORKLOAD=worker - SPARK_LOCAL_IP=spark-worker-a ","version":"Next","tagName":"h2"},{"title":"What is Apache Wayang™?","type":0,"sectionRef":"#","url":"/docs/introduction/about","content":"","keywords":"","version":"Next"},{"title":"Architecture and Software stack","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#architecture-and-software-stack","content":" Apache Wayang's unique architecture, unlike traditional DBMSs, decouples the physical planning and execution layers, empowering developers to express their data processing logic in a platform-agnostic fashion. This separation of concerns allows developers to focus on the algorithmic aspects of their applications without being constrained by the intricacies of specific processing platforms. At the bottom layers of the software stack, there are the different data storage mediums and the supported data processing platforms. On top of these, Wayang’s core consists of the following main components: the optimizer, the executor, the monitor, and platform-specific drivers. Wayang currently supports two main APIs: the Java one and the Scala one. A Python API is also supported with limited operator coverage for the moment. Besides using any of the supported languages, users can directly input SQL queries via the SQL library, which transforms them into a Wayang plan. Wayang also comes with an ML library for running ML tasks. Users can directly utilize the provided algorithms or can implement their own algorithm using a simple ML abstraction. To enable support for more programming languages in an efficient way, Wayang will soon come with a Polyglot library. Below you can see on the left, a Wayang plan representing the stochastic gradient descent algorithm, which used in most deep learning tasks. On the right, you can see how the optimizer decided to execute it. Orange nodes are the operators that ran on Spark and green the operators executed as a single Java process. ","version":"Next","tagName":"h3"},{"title":"Query Optimizer","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#query-optimizer","content":" Wayang's query optimizer is the principal component of the core. It receives as input a Wayang plan and outputs an execution (platform-specific) plan (see example plan above) with the goal of minimizing the total cost. The metric for the cost can be anything, from execution runtime to monetary cost or energy consumption. To achieve this, the optimizer first “inflates” the plan: For each node that corresponds to a Wayang operator, it adds all the corresponding execution (platform-specific) operators. Once the inflated plan is created, the optimizer attaches not only the operator’s costs but also the costs for moving intermediate data from one platform to another. By default, Wayang uses linear cost formulas to estimate these costs but one can plug their own optimizer, e.g., an ML-based one. At the last step of query optimization, our enumeration algorithm considers available options to output the optimal execution plan w.r.t. a defined cost. Note that users can control the optimizer by specifying in their code where an operator has to be executed via the withTargetPlatform(plat) call on the desired operator. Then, the optimizer takes into consideration the decisions of the user and outputs an execution plan by navigating a reduced search space during the plan enumeration. ","version":"Next","tagName":"h3"},{"title":"History","type":1,"pageTitle":"What is Apache Wayang™?","url":"/docs/introduction/about#history","content":" Wayang (formely called Rheem) is the product of many years of top quality research. Below you can find the main publications: Apache Wayang: A Unified Data Analytics Framework. SIGMOD Rec. 52(3): 30-35 (2023) pdf Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework. SIGMOD Conference Companion 2025: 35-38 pdf ML-based Cross-Platform Query Optimization. ICDE 2020: 1489-1500 pdf RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems. VLDB J. 29(6): 1287-1310 (2020) pdf RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -. Proc. VLDB Endow. 11(11): 1414-1427 (2018) pdf ","version":"Next","tagName":"h3"},{"title":"Benchmarking Wayang","type":0,"sectionRef":"#","url":"/docs/introduction/benchmark","content":"","keywords":"","version":"Next"},{"title":"Running a Relational Query Across Multiple Independent Databases (Federation)","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#running-a-relational-query-across-multiple-independent-databases-federation","content":" In this use case, we showcase how Apache Wayang addresses the common challenge of analyzing data scattered across diverse systems, a prevalent issue in many data-driven projects. Through Wayang, developers can seamlessly perform analytics on relational data residing in multiple systems. The in-situ data processing approach employed by Wayang introduces data privacy-preserving mechanisms into distributed data analytics tasks. Datasets: To test the capabilities of Wayang, we utilized the TPC-H benchmark, which comprises five datasets/relations. The lineitem and orders relations are stored in HDFS, while customer, supplier, and region are in Postgres, and nation is in S3 or the local file system. Our testing covered a range of dataset sizes, from 1GB to 100GB, to ensure our product's scalability and robustness. Query/task: We evaluate the performance of the complex SQL TPC-H query 5: SELECT N_NAME, SUM(L_EXTENDEDPRICE*(1-L_DISCOUNT)) AS REVENUE FROM CUSTOMER, ORDERS, LINEITEM, SUPPLIER, NATION, REGION WHERE C_CUSTKEY = O_CUSTKEY AND L_ORDERKEY = O_ORDERKEY AND L_SUPPKEY = S_SUPPKEY AND C_NATIONKEY = S_NATIONKEY AND S_NATIONKEY = N_NATIONKEY AND N_REGIONKEY = R_REGIONKEY AND R_NAME = 'ASIA' AND O_ORDERDATE >= '1994-01-01' AND O_ORDERDATE < DATEADD(YY, 1, cast('1994-01-01' as date)) GROUP BY N_NAME ORDER BY REVENUE DESC Baselines: We compared Wayang with two widely-used systems for storing and querying relational data: Spark and Postgres. To conduct a fair comparison, we first moved all datasets to the system being tested (Spark or Postgres). Results: The results of our tests, presented in Figure 1, show the execution time in seconds of the SQL query 5. Note that the time required to transfer data to Spark or Postgres was excluded from the results. As seen in Figure 1, Wayang significantly outperformed Postgres while retaining a runtime very close to Spark. It's important to note that for Spark to function, we needed twice the time to extract datasets from Postgres and transfer them to Spark. Wayang achieved such impressive performance by seamlessly combining Postgres with Spark: Wayang's query optimizer chose to perform all selections and projections on the data stored in Postgres before extracting data to join with the relations in HDFS. Additionally, the optimizer determined that executing the join between lineitem and supplier in Spark would be beneficial, as it could distribute the computational load of joining to multiple workers. All of this was done without the need for the user to specify where each operation should be executed. ","version":"Next","tagName":"h3"},{"title":"Reducing Execution Costs for Machine Learning Tasks Using Multiple Systems","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#reducing-execution-costs-for-machine-learning-tasks-using-multiple-systems","content":" To evaluate Apache Wayang's ability to accelerate machine learning tasks while optimizing costs by leveraging multiple systems, we conducted a comprehensive benchmark. The benchmark involved storing all data within a single repository, a scenario where selectively offloading data to more powerful engines can significantly enhance performance. To assess this feature, we employed a widely used machine learning algorithm, stochastic gradient descent, to perform classification tasks. Datasets: We use two real-world datasets that we downloaded from the UCI machine learning repository, namely higgs and rcv1. higgs consists of ~11 million data points with 28 features each and rcv1 contains ~677 thousand data points with ~47 thousand features each. In addition, we construct a synthetic dataset so that we can stress our product with even larger datasets, specifically with 88 million data points of 100 features each. Query/task: We test the performance of training classification models for higgs and the synthetic dataset and a regression model rcv1. All three training models use stochastic gradient descent but with a different loss function. We use Hinge loss to simulate support vector machines for the classification tasks and the logistic loss for the regression task. Baselines: We compare Wayang against MLlib from Apache Spark and SystemML from IBM, two very popular machine learning libraries. Results: Our results, shown in Figure 2, demonstrate that Wayang outperforms both baselines by more than an order of magnitude in runtime performance for large datasets. Apache Wayang's intelligent optimizer dynamically utilizes a hybrid approach, seamlessly blending Spark and local Java execution, to significantly improve the performance of the stochastic gradient descent algorithm. This optimization strategy involves leveraging Spark for efficient preprocessing and data preparation, while transitioning to local Java execution for the subsequent gradient computation phase. During this phase, the dataset shrinks considerably, making it more efficient to process using a single machine. This optimization technique, while not readily apparent without specialized expertise, is seamlessly implemented by Apache Wayang, requiring no additional user intervention. ","version":"Next","tagName":"h3"},{"title":"Optimizing Big Data Analytics by Adapting Platforms to Data and Task Characteristics","type":1,"pageTitle":"Benchmarking Wayang","url":"/docs/introduction/benchmark#optimizing-big-data-analytics-by-adapting-platforms-to-data-and-task-characteristics","content":" We demonstrate the platform adaptation capabilities of Apache Wayang, showcasing how it optimizes analytical tasks by tailoring execution to the specific data and task characteristics. Our approach involves dynamically selecting the optimal processing engine based on the nature of the data and the complexity of the analytical operation. This flexibility allows Wayang to deliver enhanced performance and efficiency, particularly for complex analytical workflows that demand the strengths of multiple processing engines. Datasets: We use Wikipedia abstracts and store them in HDFS. We vary the dataset size from 1GB to 800GB. Query/task: We test our product with a widely popular big data analytical task, namely WordCount. It counts the number of distinct words in a text corpus. Different variations of wordcount are useful in various text mining applications, such as term frequency, word clouds, etc. Baselines: We use three different platforms where Wordcount can be executed: Apache Spark, Apache Flink, and a single node Java program. We then set Wayang to be able to automatically choose which of the three platforms to use for each dataset. Results: The benchmark results, presented in Figure 3, provide evidence of Apache Wayang's platform adaptation prowess, consistently selecting the fastest available platform for various dataset sizes in our WordCount example. By incorporating execution cost modeling into the query optimizer, Wayang eliminates the need for manual platform selection or migration, allowing users to solely focus on their analytical tasks. Our platform adaptation feature seamlessly identifies the optimal platform for each query or task based on data and query characteristics, ensuring faster execution and enhanced efficiency in big data analytics. ","version":"Next","tagName":"h3"},{"title":"Features","type":0,"sectionRef":"#","url":"/docs/introduction/features","content":"","keywords":"","version":"Next"},{"title":"Cross platform enablement","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cross-platform-enablement","content":" Apache Wayang's cross-platform optimizer stands out as its most distinguishing feature. It not only selects the most suitable processing engine for each task, but also empowers users to execute a single task across multiple platforms. This capability is achieved through an extensible set of graph transformations applied to the Apache Wayang plan, which identifies alternative execution plans that potentially offer better performance. These alternative plans are then evaluated using platform-specific cost models, which can either be user-defined or learned from historical data. These cost models are parameterized based on the underlying hardware configuration, such as the number of computing nodes for distributed operations. ","version":"Next","tagName":"h3"},{"title":"Scalable ML (ML4All)","type":1,"pageTitle":"Features","url":"/docs/introduction/features#scalable-ml-ml4all","content":" ML4all is part of Wayang, enhancing machine learning workflows. ML4all alleviates users from the complexities of algorithm selection and low-level implementation details, enabling them to concentrate on the core aspects of their machine learning applications. By employing an innovative abstraction, ML4all addresses a broad spectrum of machine learning tasks and utilizes a cost-based optimizer to dynamically identify the optimal gradient descent algorithm for each scenario. Notably, ML4all demonstrates performance improvements of two orders of magnitude over existing systems, enabling the processing of large datasets that were previously impractical. ","version":"Next","tagName":"h3"},{"title":"High Efficiency","type":1,"pageTitle":"Features","url":"/docs/introduction/features#high-efficiency","content":" Apache Wayang's suite of optimized operators and sophisticated query optimization techniques enables it to effectively process large-scale and small-scale datasets. By employing a data processing abstraction based on UDFs, Wayang allows applications to convey semantic information about their functions, optimization hints (such as iteration counts), constraints (e.g., physical placement of operators), and alternative execution plans. The optimizer incorporates these insights to refine the execution plan, aiming to achieve optimal performance in a comprehensive manner. ","version":"Next","tagName":"h3"},{"title":"Flexibility","type":1,"pageTitle":"Features","url":"/docs/introduction/features#flexibility","content":" Apache Wayang offers a collection of operators that applications utilize to define their tasks. A crucial aspect of Apache Wayang is its flexible operator mapping structure, which enables developers to effortlessly add, modify, or remove mappings between Wayang and execution operators. This flexibility empowers developers to seamlessly integrate additional Wayang and execution operators into their applications. ","version":"Next","tagName":"h3"},{"title":"Cost Saving","type":1,"pageTitle":"Features","url":"/docs/introduction/features#cost-saving","content":" Developers can focus on building their applications without the need to understand the complexities of underlying platforms. This simplifies the development process and removes the requirement for developers to be experts in big data infrastructures. Apache Wayang automatically determines the most suitable data processing platforms for specific tasks and deploys applications accordingly. ","version":"Next","tagName":"h3"},{"title":"Additional Features","type":1,"pageTitle":"Features","url":"/docs/introduction/features#additional-features","content":" Zero-copy: cross-platform in-situ data processingHigh performance: A highly-extensible API framework to generate DAG based federated execution plane at runtime to speed up data processing, providing 50-180x speed up by: reduce data movement to single platformsreduce ETL overhead to perform large-scale analyticsreduce data duplication and storageexecute data processing on the best available technology, including local JVM stream processing Data application agnosticity run data tasks on multiple platforms without re-platforming the code (move jobs from Hadoop to Spark without changing the code at large)implement a sustainable AI strategy by using data pools in-situ Enterprise ready federated learning (in development) ","version":"Next","tagName":"h3"},{"title":"Releases","type":0,"sectionRef":"#","url":"/docs/start/download","content":"","keywords":"","version":"Next"},{"title":"1.1.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#110-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"1.0.0 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#100-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"0.7.1 (incubating)","type":1,"pageTitle":"Releases","url":"/docs/start/download#071-incubating","content":" Source Code: zip Signature: asc Hashes: sha512 Release Notes: notes ","version":"Next","tagName":"h2"},{"title":"Frameworks supported","type":1,"pageTitle":"Releases","url":"/docs/start/download#frameworks-supported","content":" Apache Flink v1.7.1Apache Giraph v1.2.0-hadoop2GraphChi v0.2.2 (only available with scala 11.x)Java Streams (version depends on the java version)JDBC-TemplatePostgres v9.4.1208 (Implementation JDBC-Template)Apache Spark v3.1.2 (scala 12.x) and v2.4.8 (scala 11.x)SQLite3 v3.8.11.2 (implementation JDBC-Template) NOTE: The supported platforms for Scala may vary depending on the Scala version.\\ ","version":"Next","tagName":"h3"},{"title":"Previous releases","type":1,"pageTitle":"Releases","url":"/docs/start/download#previous-releases","content":" Older releases can be found here ","version":"Next","tagName":"h3"},{"title":"Getting started","type":0,"sectionRef":"#","url":"/docs/guide/getting-started","content":"","keywords":"","version":"Next"},{"title":"Requirements","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#requirements","content":" Apache Wayang (incubating) is built upon the foundations of Java 11 and Scala 2.12, providing a robust and versatile platform for data processing applications. If you intend to build Wayang from source, you will also need to have Apache Maven, the popular build automation tool, installed on your system. Additionally, be mindful that some of the processing platforms supported by Wayang may have their own specific installation requirements. ","version":"Next","tagName":"h2"},{"title":"Get Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#get-wayang","content":" Apache Wayang is readily available through Maven Central, facilitating seamless integration into your development workflow. For instance, to utilize Wayang in your Maven-based project, simply add the following dependency to your project's POM file: <dependency> <groupId>org.apache.wayang</groupId> <artifactId>wayang-***</artifactId> <version>0.7.1</version> </dependency> Note the ***: Wayang ships with multiple modules that can be included in your app, depending on how you want to use it: wayang-core: provides core data structures and the optimizer (required)wayang-basic: provides common operators and data types for your apps (recommended)wayang-api: provides an easy-to-use Scala and Java API to assemble Wayang plans (recommended)wayang-java, wayang-spark, wayang-graphchi, wayang-sqlite3, wayang-postgres: adapters for the various supported processing platformswayang-profiler: provides functionality to learn operator and UDF cost functions from historical execution data For the sake of version flexibility, you still have to include your Hadoop (hadoop-hdfs and hadoop-common) and Spark (spark-core and spark-graphx) version of choice. In addition, you can obtain the most recent snapshot version of Wayang via Sonatype's snapshot repository. Just included <repositories> <repository> <id>sonatype-snapshots</id> <name>Sonatype Snapshot Repository</name> <url>https://oss.sonatype.org/content/repositories/snapshots</url> </repository> <repositories> If you need to rebuild Wayang, e.g., to use a different Scala version, you can simply do so via Maven: Adapt the version variables (e.g., spark.version) in the main pom.xml file.Build Wayang with the adapted versions. $ mvn clean install Note the standalone profile to fix Hadoop and Spark versions, so that Wayang apps do not explicitly need to declare the corresponding dependencies. Also, note the distro profile, which assembles a binary Wayang distribution. To activate these profiles, you need to specify them when running maven, i.e., mvn clean install -P<profile name> ","version":"Next","tagName":"h3"},{"title":"Configure Wayang","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#configure-wayang","content":" To enable Apache Wayang's smooth operation, you need to equip it with details about your processing platforms' capabilities and how to interact with them. A default configuration is available for initial testing, but creating a properties file is generally preferable for fine-tuning the configuration to suit your specific requirements. To harness this personalized configuration effortlessly, launch your application via $ java -Dwayang.configuration=url://to/my/wayang.properties ... Essential configuration settings: General settings wayang.core.log.enabled (= true): whether to log execution statistics to allow learning better cardinality and cost estimators for the optimizerwayang.core.log.executions (= ~/.wayang/executions.json) where to log execution times of operator groupswayang.core.log.cardinalities (= ~/.wayang/cardinalities.json) where to log cardinality measurementswayang.core.optimizer.instrumentation (= org.apache.wayang.core.profiling.OutboundInstrumentationStrategy): where to measure cardinalities in Wayang plans; other options are org.apache.wayang.core.profiling.NoInstrumentationStrategy and org.apache.wayang.core.profiling.FullInstrumentationStrategywayang.core.optimizer.reoptimize (= false): whether to progressively optimize Wayang planswayang.basic.tempdir (= file:///tmp): where to store temporary files, in particular for inter-platform communication Java Streams wayang.java.cpu.mhz (= 2700): clock frequency of processor the JVM runs on in MHzwayang.java.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to JVM in ms/MB Apache Spark spark.master (= local): Spark master various other Spark settings are supported, e.g., spark.executor.memory, spark.serializer, ... wayang.spark.cpu.mhz (= 2700): clock frequency of processor the Spark workers run on in MHzwayang.spark.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to the Spark workers in ms/MBwayang.spark.network.ms-per-mb (= 8.6): average network throughput of the Spark workers in ms/MBwayang.spark.init.ms (= 4500): time it takes Spark to initialize in ms GraphChi wayang.graphchi.cpu.mhz (= 2700): clock frequency of processor GraphChi runs on in MHzwayang.graphchi.cpu.cores (= 2): number of cores GraphChi runs onwayang.graphchi.hdfs.ms-per-mb (= 2.7): average throughput from HDFS to GraphChi in ms/MB SQLite wayang.sqlite3.jdbc.url: JDBC URL to use SQLitewayang.sqlite3.jdbc.user: optional user namewayang.sqlite3.jdbc.password: optional passwordwayang.sqlite3.cpu.mhz (= 2700): clock frequency of processor SQLite runs on in MHzwayang.sqlite3.cpu.cores (= 2): number of cores SQLite runs on PostgreSQL wayang.postgres.jdbc.url: JDBC URL to use PostgreSQLwayang.postgres.jdbc.user: optional user namewayang.postgres.jdbc.password: optional passwordwayang.postgres.cpu.mhz (= 2700): clock frequency of processor PostgreSQL runs on in MHzwayang.postgres.cpu.cores (= 2): number of cores PostgreSQL runs on To effectively define your applications with Apache Wayang, utilize its Scala or Java API, conveniently found within the wayang-api module. For clear illustrations, refer to the provided examples below. ","version":"Next","tagName":"h3"},{"title":"Cost Functions","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#cost-functions","content":" Wayang provides a utility to learn cost functions from historical execution data. Specifically, Wayang can learn configurations for load profile estimators (that estimate CPU load, disk load etc.) for both operators and UDFs, as long as the configuration provides a template for those estimators. As an example, the JavaMapOperator draws its load profile estimator configuration via the configuration key wayang.java.map.load. Now, it is possible to specify a load profile estimator template in the configuration under the key <original key>.template, e.g.: wayang.java.map.load.template = {\\ "in":1, "out":1,\\ "cpu":"?*in0"\\ } This template encapsulates a load profile estimator that requires at minimum one input cardinality and one output cardinality. Furthermore, it simulates CPU load by assuming a direct relationship with the input cardinality. However, more complex functions are possible. In particular, you can use the variables in0, in1, ... and out0, out1, ... to incorporate the input and output cardinalities, respectively;operator properties, such as numIterations for the PageRankOperator implementations;the operators +, -, *, /, %, ^, and parantheses;the functions min(x0, x1, ...)), max(x0, x1, ...), abs(x), log(x, base), ln(x), ld(x);and the constants e and pi. While Apache Wayang provides templates for all execution operators, you will need to explicitly define your user-defined functions (UDFs) by specifying their cost functions, which are based on configuration parameters. This involves creating an initial specification and template for each UDF. As soon as execution data has been collected, you can initiate: java ... org.apache.wayang.profiler.ga.GeneticOptimizerApp [configuration URL [execution log]] This tool will attempt to determine suitable values for the question marks (?) within the load profile estimator templates, aligning them with the collected execution data and pre-defined configuration entries for the load profile estimators. These optimized values can then be directly incorporated into your configuration. ","version":"Next","tagName":"h2"},{"title":"Examples","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#examples","content":" For some executable examples, have a look at this repository. ","version":"Next","tagName":"h2"},{"title":"WordCount","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#wordcount","content":" Java API import org.apache.wayang.api.JavaPlanBuilder; import org.apache.wayang.basic.data.Tuple2; import org.apache.wayang.core.api.Configuration; import org.apache.wayang.core.api.WayangContext; import org.apache.wayang.core.optimizer.cardinality.DefaultCardinalityEstimator; import org.apache.wayang.java.Java; import org.apache.wayang.spark.Spark; import java.util.Collection; import java.util.Arrays; public class WordcountJava { public static void main(String[] args){ // Settings String inputUrl = "file:/tmp.txt"; // Get a plan builder. WayangContext wayangContext = new WayangContext(new Configuration()) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); JavaPlanBuilder planBuilder = new JavaPlanBuilder(wayangContext) .withJobName(String.format("WordCount (%s)", inputUrl)) .withUdfJarOf(WordcountJava.class); // Start building the WayangPlan. Collection<Tuple2<String, Integer>> wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(line -> Arrays.asList(line.split("\\\\W+"))) .withSelectivity(10, 100, 0.9) .withName("Split words") // Filter empty tokens. .filter(token -> !token.isEmpty()) .withSelectivity(0.99, 0.99, 0.99) .withName("Filter empty words") // Attach counter to each word. .map(word -> new Tuple2<>(word.toLowerCase(), 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey( Tuple2::getField0, (t1, t2) -> new Tuple2<>(t1.getField0(), t1.getField1() + t2.getField1()) ) .withCardinalityEstimator(new DefaultCardinalityEstimator(0.9, 1, false, in -> Math.round(0.01 * in[0]))) .withName("Add counters") // Execute the plan and collect the results. .collect(); System.out.println(wordcounts); } } Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark object WordcountScala { def main(args: Array[String]) { // Settings val inputUrl = "file:/tmp.txt" // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"WordCount ($inputUrl)") .withUdfJarsOf(this.getClass) val wordcounts = planBuilder // Read the text file. .readTextFile(inputUrl).withName("Load file") // Split each line by non-word characters. .flatMap(_.split("\\\\W+"), selectivity = 10).withName("Split words") // Filter empty tokens. .filter(_.nonEmpty, selectivity = 0.99).withName("Filter empty words") // Attach counter to each word. .map(word => (word.toLowerCase, 1)).withName("To lower case, add counter") // Sum up counters for every word. .reduceByKey(_._1, (c1, c2) => (c1._1, c1._2 + c2._2)).withName("Add counters") .withCardinalityEstimator((in: Long) => math.round(in * 0.01)) // Execute the plan and collect the results. .collect() println(wordcounts) } } ","version":"Next","tagName":"h3"},{"title":"k-means","type":1,"pageTitle":"Getting started","url":"/docs/guide/getting-started#k-means","content":" Wayang is also capable of iterative processing, which is, e.g., very important for machine learning algorithms, such as k-means. Scala API import org.apache.wayang.api._ import org.apache.wayang.core.api.{Configuration, WayangContext} import org.apache.wayang.core.function.FunctionDescriptor.ExtendedSerializableFunction import org.apache.wayang.core.function.ExecutionContext import org.apache.wayang.core.optimizer.costs.LoadProfileEstimators import org.apache.wayang.java.Java import org.apache.wayang.spark.Spark import scala.util.Random import scala.collection.JavaConversions._ object kmeans { def main(args: Array[String]) { // Settings val inputUrl = "file:/kmeans.txt" val k = 5 val iterations = 100 val configuration = new Configuration // Get a plan builder. val wayangContext = new WayangContext(new Configuration) .withPlugin(Java.basicPlugin) .withPlugin(Spark.basicPlugin) val planBuilder = new PlanBuilder(wayangContext) .withJobName(s"k-means ($inputUrl, k=$k, $iterations iterations)") .withUdfJarsOf(this.getClass) case class Point(x: Double, y: Double) case class TaggedPoint(x: Double, y: Double, cluster: Int) case class TaggedPointCounter(x: Double, y: Double, cluster: Int, count: Long) { def add_points(that: TaggedPointCounter) = TaggedPointCounter(this.x + that.x, this.y + that.y, this.cluster, this.count + that.count) def average = TaggedPointCounter(x / count, y / count, cluster, 0) } // Read and parse the input file(s). val points = planBuilder .readTextFile(inputUrl).withName("Read file") .map { line => val fields = line.split(",") Point(fields(0).toDouble, fields(1).toDouble) }.withName("Create points") // Create initial centroids. val random = new Random val initialCentroids = planBuilder .loadCollection(for (i <- 1 to k) yield TaggedPointCounter(random.nextGaussian(), random.nextGaussian(), i, 0)).withName("Load random centroids") // Declare UDF to select centroid for each data point. class SelectNearestCentroid extends ExtendedSerializableFunction[Point, TaggedPointCounter] { /** Keeps the broadcasted centroids. */ var centroids: Iterable[TaggedPointCounter] = _ override def open(executionCtx: ExecutionContext) = { centroids = executionCtx.getBroadcast[TaggedPointCounter]("centroids") } override def apply(point: Point): TaggedPointCounter = { var minDistance = Double.PositiveInfinity var nearestCentroidId = -1 for (centroid <- centroids) { val distance = Math.pow(Math.pow(point.x - centroid.x, 2) + Math.pow(point.y - centroid.y, 2), 0.5) if (distance < minDistance) { minDistance = distance nearestCentroidId = centroid.cluster } } new TaggedPointCounter(point.x, point.y, nearestCentroidId, 1) } } // Do the k-means loop. val finalCentroids = initialCentroids.repeat(iterations, { currentCentroids => points .mapJava(new SelectNearestCentroid, udfLoad = LoadProfileEstimators.createFromSpecification( "my.udf.costfunction.key", configuration )) .withBroadcast(currentCentroids, "centroids").withName("Find nearest centroid") .reduceByKey(_.cluster, _.add_points(_)).withName("Add up points") .withCardinalityEstimator(k) .map(_.average).withName("Average points") }).withName("Loop") // Collect the results. .collect() println(finalCentroids) } } ","version":"Next","tagName":"h3"},{"title":"Guide to Development and Usage with Apache Wayang (incubating)","type":0,"sectionRef":"#","url":"/docs/guide/examples","content":"","keywords":"","version":"Next"},{"title":"Example 1: Machine Learning for query optimization in Apache Wayang","type":1,"pageTitle":"Guide to Development and Usage with Apache Wayang (incubating)","url":"/docs/guide/examples#example-1-machine-learning-for-query-optimization-in-apache-wayang","content":" Apache Wayang can be customized with concrete implementations of the EstimatableCost interface in order to optimize for a desired metric. The implementation can be enabled by providing it to a Configuration. public class CustomEstimatableCost implements EstimatableCost { /* Provide concrete implementations to match desired cost function(s) * by implementing the interface in this class. */ } public class WordCount { public static void main(String[] args) { /* Create a Wayang context and specify the platforms Wayang will consider */ Configuration config = new Configuration(); /* Provision of a EstimatableCost that implements the interface.*/ config.setCostModel(new CustomEstimatableCost()); WayangContext wayangContext = new WayangContext(config) .withPlugin(Java.basicPlugin()) .withPlugin(Spark.basicPlugin()); /*... omitted */ } } In combination with an encoding scheme and a third party package to load ML models, the following example shows how to predict runtimes of query execution plans runtimes in Apache Wayang (incubating): public class MLCost implements EstimatableCost { public EstimatableCostFactory getFactory() { return new Factory(); } public static class Factory implements EstimatableCostFactory { @Override public EstimatableCost makeCost() { return new MLCost(); } } @Override public ProbabilisticDoubleInterval getEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } @Override public ProbabilisticDoubleInterval getParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return ProbabilisticDoubleInterval.ofExactly( model.runModel(OneHotEncoder.encode(plan)) ); } catch(Exception e) { return ProbabilisticDoubleInterval.zero; } } /** Returns a squashed cost estimate. */ @Override public double getSquashedEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public double getSquashedParallelEstimate(PlanImplementation plan, boolean isOverheadIncluded) { try { Configuration config = plan .getOptimizationContext() .getConfiguration(); OrtMLModel model = OrtMLModel.getInstance(config); return model.runModel(OneHotEncoder.encode(plan)); } catch(Exception e) { return 0; } } @Override public Tuple<List<ProbabilisticDoubleInterval>, List<Double>> getParallelOperatorJunctionAllCostEstimate(PlanImplementation plan, Operator operator) { List<ProbabilisticDoubleInterval> intervalList = new ArrayList<ProbabilisticDoubleInterval>(); List<Double> doubleList = new ArrayList<Double>(); intervalList.add(this.getEstimate(plan, true)); doubleList.add(this.getSquashedEstimate(plan, true)); return new Tuple<>(intervalList, doubleList); } public PlanImplementation pickBestExecutionPlan( Collection<PlanImplementation> executionPlans, ExecutionPlan existingPlan, Set<Channel> openChannels, Set<ExecutionStage> executedStages) { final PlanImplementation bestPlanImplementation = executionPlans.stream() .reduce((p1, p2) -> { final double t1 = p1.getSquashedCostEstimate(); final double t2 = p2.getSquashedCostEstimate(); return t1 < t2 ? p1 : p2; }) .orElseThrow(() -> new WayangException("Could not find an execution plan.")); return bestPlanImplementation; } } Third-party packages such as OnnxRuntime can be used to load pre-trained .onnx files that contain desired ML models. public class OrtMLModel { private static OrtMLModel INSTANCE; private OrtSession session; private OrtEnvironment env; private final Map<String, OnnxTensor> inputMap = new HashMap<>(); private final Set<String> requestedOutputs = new HashSet<>(); public static OrtMLModel getInstance(Configuration configuration) throws OrtException { if (INSTANCE == null) { INSTANCE = new OrtMLModel(configuration); } return INSTANCE; } private OrtMLModel(Configuration configuration) throws OrtException { this.loadModel(configuration.getStringProperty("wayang.ml.model.file")); } public void loadModel(String filePath) throws OrtException { if (this.env == null) { this.env = OrtEnvironment.getEnvironment(); } if (this.session == null) { this.session = env.createSession(filePath, new OrtSession.SessionOptions()); } } public void closeSession() throws OrtException { this.session.close(); this.env.close(); } /** * @param encodedVector * @return NaN on error, and a predicted cost on any other value. * @throws OrtException */ public double runModel(Vector<Long> encodedVector) throws OrtException { double costPrediction; OnnxTensor tensor = OnnxTensor.createTensor(env, encodedVector); this.inputMap.put("input", tensor); this.requestedOutputs.add("output"); BiFunction<Result, String, Double> unwrapFunc = (r, s) -> { try { return ((double[]) r.get(s).get().getValue())[0]; } catch (OrtException e) { return Double.NaN; } }; try (Result r = session.run(inputMap, requestedOutputs)) { costPrediction = unwrapFunc.apply(r, "output"); } return costPrediction; } } ","version":"Next","tagName":"h2"}],"options":{"id":"default"}}
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