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<item>
+<title>Flink Community Update - June'20</title>
+<description><p>And suddenly it’s June. The previous month has been calm on the surface, but quite hectic underneath — the final testing phase for Flink 1.11 is moving at full speed, Stateful Functions 2.1 is out in the wild and Flink has made it into Google Season of Docs 2020.</p>
+
+<p>To top it off, a piece of good news: <a href="https://www.flink-forward.org/global-2020">Flink Forward</a> is back on October 19-22 as a free virtual event!</p>
+
+<div class="page-toc">
+<ul id="markdown-toc">
+ <li><a href="#the-past-month-in-flink" id="markdown-toc-the-past-month-in-flink">The Past Month in Flink</a> <ul>
+ <li><a href="#flink-stateful-functions-21-release" id="markdown-toc-flink-stateful-functions-21-release">Flink Stateful Functions 2.1 Release</a></li>
+ <li><a href="#testing-is-on-for-flink-111" id="markdown-toc-testing-is-on-for-flink-111">Testing is ON for Flink 1.11</a></li>
+ <li><a href="#flink-minor-releases" id="markdown-toc-flink-minor-releases">Flink Minor Releases</a> <ul>
+ <li><a href="#flink-1101" id="markdown-toc-flink-1101">Flink 1.10.1</a></li>
+ </ul>
+ </li>
+ <li><a href="#new-committers-and-pmc-members" id="markdown-toc-new-committers-and-pmc-members">New Committers and PMC Members</a> <ul>
+ <li><a href="#new-committers" id="markdown-toc-new-committers">New Committers</a></li>
+ </ul>
+ </li>
+ </ul>
+ </li>
+ <li><a href="#the-bigger-picture" id="markdown-toc-the-bigger-picture">The Bigger Picture</a> <ul>
+ <li><a href="#flink-forward-global-virtual-conference-2020" id="markdown-toc-flink-forward-global-virtual-conference-2020">Flink Forward Global Virtual Conference 2020</a></li>
+ <li><a href="#google-season-of-docs-2020" id="markdown-toc-google-season-of-docs-2020">Google Season of Docs 2020</a></li>
+ </ul>
+ </li>
+</ul>
+
+</div>
+
+<h1 id="the-past-month-in-flink">The Past Month in Flink</h1>
+
+<h2 id="flink-stateful-functions-21-release">Flink Stateful Functions 2.1 Release</h2>
+
+<p>It might seem like <a href="https://flink.apache.org/news/2020/04/07/release-statefun-2.0.0.html">Stateful Functions 2.0 was announced</a> only a handful of weeks ago (and it was!), but the Flink community has just released Stateful Functions 2.1! This release introduces two new features: state expiration for any kind of persisted state and support for UNIX Domain Sockets (UDS) to improve the performance of inter-container communication in co-located deployments; as well as other important changes that improve the overall stability and testability of the project. You can read the <a href="https://flink.apache.org/news/2020/06/09/release-statefun-2.1.0.html">announcement blogpost</a> for more details on the release!</p>
+
+<p>As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration. If you’d like to get involved, we’re always <a href="https://github.com/apache/flink-statefun#contributing">looking for new contributors</a> — especially around SDKs for other languages (e.g. Go, Rust, Javascript).</p>
+
+<hr />
+
+<h2 id="testing-is-on-for-flink-111">Testing is ON for Flink 1.11</h2>
+
+<p>Things have been pretty quiet in the Flink community, as all efforts shifted to testing the newest features shipping with Flink 1.11. While we wait for a voting Release Candidate (RC) to be out, you can check the progress of testing in <a href="https://issues.apache.org/jira/secure/RapidBoard.jspa?rapidView=364&amp;projectKey=FLINK">this JIRA burndown board</a> and learn more about some of the <a href="https://flink.apache.org/news/2020/05/07/community-update.html#warming-up-for-flink-111">upcoming features</a> in these Flink Forward videos:</p>
+
+<ul>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=ssEmeLcL5Uk">Rethinking of fault tolerance in Flink: what lies ahead?</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=t7fAN3xNJ3Q">It’s finally here: Python on Flink &amp; Flink on Zeppelin</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=KDD8e4GE12w">A deep dive into Flink SQL</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=4ce1H9CRyEc">Production-Ready Flink and Hive Integration - what story you can tell now?</a></p>
+ </li>
+</ul>
+
+<p>We encourage the wider community to also get involved in testing once the voting RC is out. Keep an eye on the <a href="https://flink.apache.org/community.html#mailing-lists">@dev mailing list</a> for updates!</p>
+
+<hr />
+
+<h2 id="flink-minor-releases">Flink Minor Releases</h2>
+
+<h3 id="flink-1101">Flink 1.10.1</h3>
+
+<p>The community released Flink 1.10.1, covering some outstanding bugs in Flink 1.10. You can find more in the <a href="https://flink.apache.org/news/2020/05/12/release-1.10.1.html">announcement blogpost</a>!</p>
+
+<hr />
+
+<h2 id="new-committers-and-pmc-members">New Committers and PMC Members</h2>
+
+<p>The Apache Flink community has welcomed <strong>2 new Committers</strong> since the last update. Congratulations!</p>
+
+<h3 id="new-committers">New Committers</h3>
+
+<div class="row">
+ <div class="col-lg-3">
+ <div class="text-center">
+ <img class="img-circle" src="https://avatars3.githubusercontent.com/u/4471524?s=400&amp;v=4" width="90" height="90" />
+ <p>Benchao Li</p>
+ </div>
+ </div>
+ <div class="col-lg-3">
+ <div class="text-center">
+ <img class="img-circle" src="https://avatars0.githubusercontent.com/u/6509172?s=400&amp;v=4" width="90" height="90" />
+ <p>Xintong Song</p>
+ </div>
+ </div>
+</div>
+
+<hr />
+
+<h1 id="the-bigger-picture">The Bigger Picture</h1>
+
+<h2 id="flink-forward-global-virtual-conference-2020">Flink Forward Global Virtual Conference 2020</h2>
+
+<p>After a first successful <a href="https://www.youtube.com/playlist?list=PLDX4T_cnKjD0ngnBSU-bYGfgVv17MiwA7">virtual conference</a> last April, Flink Forward will be hosting a second free virtual edition on October 19-22. This time around, the conference will feature two days of hands-on training and two full days of conference talks!</p>
+
+<p>Got a Flink story to share? Maybe your recent adventures with Stateful Functions? The <a href="https://www.flink-forward.org/global-2020/call-for-presentations">Call for Presentations is now open</a> and accepting submissions from the community until <strong>June 19th, 11:59 PM CEST</strong>.</p>
+
+<div style="line-height:60%;">
+ <br />
+</div>
+
+<center>
+<img src="/img/blog/2020-06-10-community-update/FlinkForward_Banner_CFP_Global_2020.png" width="600px" alt="Flink Forward Global 2020" />
+</center>
+
+<div style="line-height:60%;">
+ <br />
+</div>
+
+<hr />
+
+<h2 id="google-season-of-docs-2020">Google Season of Docs 2020</h2>
+
+<p>In the last update, we announced that Flink was applying to <a href="https://developers.google.com/season-of-docs">Google Season of Docs (GSoD)</a> again this year. The good news: we’ve made it into the shortlist of accepted projects! This represents an invaluable opportunity for the Flink community to collaborate with technical writers to improve the Table API &amp; SQL documentation. We’re honored to have seen a great number of people reach out over the last couple of weeks, and look forward to receiving applications from this week on!</p>
+
+<p>If you’re interested in learning more about our project idea or want to get involved in GSoD as a technical writer, check out the <a href="https://flink.apache.org/news/2020/05/04/season-of-docs.html">announcement blogpost</a> and <a href="https://developers.google.com/season-of-docs/docs/tech-writer-application-hints">submit your application</a>. The deadline for GSoD applications is <strong>July 9th, 18:00 UTC</strong>.</p>
+
+<hr />
+
+<p>If you’d like to keep a closer eye on what’s happening in the community, subscribe to the Flink <a href="https://flink.apache.org/community.html#mailing-lists">@community mailing list</a> to get fine-grained weekly updates, upcoming event announcements and more.</p>
+</description>
+<pubDate>Thu, 11 Jun 2020 10:00:00 +0200</pubDate>
+<link>https://flink.apache.org/news/2020/06/11/community-update.html</link>
+<guid isPermaLink="true">/news/2020/06/11/community-update.html</guid>
+</item>
+
+<item>
<title>Stateful Functions 2.1.0 Release Announcement</title>
<description><p>The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 2.1.0! This release introduces new features around state expiration and performance improvements for co-located deployments, as well as other important changes that improve the stability and testability of the project. As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration.</p>
@@ -16823,196 +16954,5 @@
<guid isPermaLink="true">/news/2015/05/14/Community-update-April.html</guid>
</item>
-<item>
-<title>Juggling with Bits and Bytes</title>
-<description><h2 id="how-apache-flink-operates-on-binary-data">How Apache Flink operates on binary data</h2>
-
-<p>Nowadays, a lot of open-source systems for analyzing large data sets are implemented in Java or other JVM-based programming languages. The most well-known example is Apache Hadoop, but also newer frameworks such as Apache Spark, Apache Drill, and also Apache Flink run on JVMs. A common challenge that JVM-based data analysis engines face is to store large amounts of data in memory - both for caching and for efficient processing such as sorting and joining of data. Managing the JVM memory well makes the difference between a system that is hard to configure and has unpredictable reliability and performance and a system that behaves robustly with few configuration knobs.</p>
-
-<p>In this blog post we discuss how Apache Flink manages memory, talk about its custom data de/serialization stack, and show how it operates on binary data.</p>
-
-<h2 id="data-objects-lets-put-them-on-the-heap">Data Objects? Let’s put them on the heap!</h2>
-
-<p>The most straight-forward approach to process lots of data in a JVM is to put it as objects on the heap and operate on these objects. Caching a data set as objects would be as simple as maintaining a list containing an object for each record. An in-memory sort would simply sort the list of objects.
-However, this approach has a few notable drawbacks. First of all it is not trivial to watch and control heap memory usage when a lot of objects are created and invalidated constantly. Memory overallocation instantly kills the JVM with an <code>OutOfMemoryError</code>. Another aspect is garbage collection on multi-GB JVMs which are flooded with new objects. The overhead of garbage collection in such environments can easily reach 50% and more. Finally, Java objects come with a certain space overhead depending on the JVM and platform. For data sets with many small objects this can significantly reduce the effectively usable amount of memory. Given proficient system design and careful, use-case specific system parameter tuning, heap memory usage can be more or less controlled and <code>OutOfMemoryErrors</code> avoided. However, such setups are rather fragile especially if data characteristics or the execution environment change.</p>
-
-<h2 id="what-is-flink-doing-about-that">What is Flink doing about that?</h2>
-
-<p>Apache Flink has its roots at a research project which aimed to combine the best technologies of MapReduce-based systems and parallel database systems. Coming from this background, Flink has always had its own way of processing data in-memory. Instead of putting lots of objects on the heap, Flink serializes objects into a fixed number of pre-allocated memory segments. Its DBMS-style sort and join algorithms operate as much as possible on this binary data to keep the de/serialization overhead at a minimum. If more data needs to be processed than can be kept in memory, Flink’s operators partially spill data to disk. In fact, a lot of Flink’s internal implementations look more like C/C++ rather than common Java. The following figure gives a high-level overview of how Flink stores data serialized in memory segments and spills to disk if necessary.</p>
-
-<center>
-<img src="/img/blog/memory-mgmt.png" style="width:90%;margin:15px" />
-</center>
-
-<p>Flink’s style of active memory management and operating on binary data has several benefits:</p>
-
-<ol>
- <li><strong>Memory-safe execution &amp; efficient out-of-core algorithms.</strong> Due to the fixed amount of allocated memory segments, it is trivial to monitor remaining memory resources. In case of memory shortage, processing operators can efficiently write larger batches of memory segments to disk and later them read back. Consequently, <code>OutOfMemoryErrors</code> are effectively prevented.</li>
- <li><strong>Reduced garbage collection pressure.</strong> Because all long-lived data is in binary representation in Flink’s managed memory, all data objects are short-lived or even mutable and can be reused. Short-lived objects can be more efficiently garbage-collected, which significantly reduces garbage collection pressure. Right now, the pre-allocated memory segments are long-lived objects on the JVM heap, but the Flink community is actively working on allocating off-heap memory for this purpose. This effort will result in much smaller JVM heaps and facilitate even faster garbage collection cycles.</li>
- <li><strong>Space efficient data representation.</strong> Java objects have a storage overhead which can be avoided if the data is stored in a binary representation.</li>
- <li><strong>Efficient binary operations &amp; cache sensitivity.</strong> Binary data can be efficiently compared and operated on given a suitable binary representation. Furthermore, the binary representations can put related values, as well as hash codes, keys, and pointers, adjacently into memory. This gives data structures with usually more cache efficient access patterns.</li>
-</ol>
-
-<p>These properties of active memory management are very desirable in a data processing systems for large-scale data analytics but have a significant price tag attached. Active memory management and operating on binary data is not trivial to implement, i.e., using <code>java.util.HashMap</code> is much easier than implementing a spillable hash-table backed by byte arrays and a custom serialization stack. Of course Apache Flink is not the only JVM-based data processing system that operates on serialized binary data. Projects such as <a href="http://drill.apache.org/">Apache Drill</a>, <a href="http://ignite.incubator.apache.org/">Apache Ignite (incubating)</a> or <a href="http://projectgeode.org/">Apache Geode (incubating)</a> apply similar techniques and it was recently announced that also <a href="http://spark.apache.org/">Apache Spark</a> will evolve into this direction with <a href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html">Project Tungsten</a>.</p>
-
-<p>In the following we discuss in detail how Flink allocates memory, de/serializes objects, and operates on binary data. We will also show some performance numbers comparing processing objects on the heap and operating on binary data.</p>
-
-<h2 id="how-does-flink-allocate-memory">How does Flink allocate memory?</h2>
-
-<p>A Flink worker, called TaskManager, is composed of several internal components such as an actor system for coordination with the Flink master, an IOManager that takes care of spilling data to disk and reading it back, and a MemoryManager that coordinates memory usage. In the context of this blog post, the MemoryManager is of most interest.</p>
-
-<p>The MemoryManager takes care of allocating, accounting, and distributing MemorySegments to data processing operators such as sort and join operators. A <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/core/memory/MemorySegment.java">MemorySegment</a> is Flink’s distribution unit of memory and is backed by a regular Java byte array (size is 32 KB by default). A MemorySegment provides very efficient write and read access to its backed byte array using Java’s unsafe methods. You can think of a MemorySegment as a custom-tailored version of Java’s NIO ByteBuffer. In order to operate on multiple MemorySegments like on a larger chunk of consecutive memory, Flink uses logical views that implement Java’s <code>java.io.DataOutput</code> and <code>java.io.DataInput</code> interfaces.</p>
-
-<p>MemorySegments are allocated once at TaskManager start-up time and are destroyed when the TaskManager is shut down. Hence, they are reused and not garbage-collected over the whole lifetime of a TaskManager. After all internal data structures of a TaskManager have been initialized and all core services have been started, the MemoryManager starts creating MemorySegments. By default 70% of the JVM heap that is available after service initialization is allocated by the MemoryManager. It is also possible to configure an absolute amount of managed memory. The remaining JVM heap is used for objects that are instantiated during task processing, including objects created by user-defined functions. The following figure shows the memory distribution in the TaskManager JVM after startup.</p>
-
-<center>
-<img src="/img/blog/memory-alloc.png" style="width:60%;margin:15px" />
-</center>
-
-<h2 id="how-does-flink-serialize-objects">How does Flink serialize objects?</h2>
-
-<p>The Java ecosystem offers several libraries to convert objects into a binary representation and back. Common alternatives are standard Java serialization, <a href="https://github.com/EsotericSoftware/kryo">Kryo</a>, <a href="http://avro.apache.org/">Apache Avro</a>, <a href="http://thrift.apache.org/">Apache Thrift</a>, or Google’s <a href="https://github.com/google/protobuf">Protobuf</a>. Flink includes its own custom serialization framework in order to control the binary representation of data. This is important because operating on binary data such as comparing or even manipulating binary data requires exact knowledge of the serialization layout. Further, configuring the serialization layout with respect to operations that are performed on binary data can yield a significant performance boost. Flink’s serialization stack also leverages the fact, that the type of the objects which are going through de/serialization are exactly known before a program is executed.</p>
-
-<p>Flink programs can process data represented as arbitrary Java or Scala objects. Before a program is optimized, the data types at each processing step of the program’s data flow need to be identified. For Java programs, Flink features a reflection-based type extraction component to analyze the return types of user-defined functions. Scala programs are analyzed with help of the Scala compiler. Flink represents each data type with a <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/api/common/typeinfo/TypeInformation.java">TypeInformation</a>. Flink has TypeInformations for several kinds of data types, including:</p>
-
-<ul>
- <li>BasicTypeInfo: Any (boxed) Java primitive type or java.lang.String.</li>
- <li>BasicArrayTypeInfo: Any array of a (boxed) Java primitive type or java.lang.String.</li>
- <li>WritableTypeInfo: Any implementation of Hadoop’s Writable interface.</li>
- <li>TupleTypeInfo: Any Flink tuple (Tuple1 to Tuple25). Flink tuples are Java representations for fixed-length tuples with typed fields.</li>
- <li>CaseClassTypeInfo: Any Scala CaseClass (including Scala tuples).</li>
- <li>PojoTypeInfo: Any POJO (Java or Scala), i.e., an object with all fields either being public or accessible through getters and setter that follow the common naming conventions.</li>
- <li>GenericTypeInfo: Any data type that cannot be identified as another type.</li>
-</ul>
-
-<p>Each TypeInformation provides a serializer for the data type it represents. For example, a BasicTypeInfo returns a serializer that writes the respective primitive type, the serializer of a WritableTypeInfo delegates de/serialization to the write() and readFields() methods of the object implementing Hadoop’s Writable interface, and a GenericTypeInfo returns a serializer that delegates serialization to Kryo. Object serialization to a DataOutput which is backed by Flink MemorySegments goes automatically through Java’s efficient unsafe operations. For data types that can be used as keys, i.e., compared and hashed, the TypeInformation provides TypeComparators. TypeComparators compare and hash objects and can - depending on the concrete data type - also efficiently compare binary representations and extract fixed-length binary key prefixes.</p>
-
-<p>Tuple, Pojo, and CaseClass types are composite types, i.e., containers for one or more possibly nested data types. As such, their serializers and comparators are also composite and delegate the serialization and comparison of their member data types to the respective serializers and comparators. The following figure illustrates the serialization of a (nested) <code>Tuple3&lt;Integer, Double, Person&gt;</code> object where <code>Person</code> is a POJO and defined as follows:</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="kd">public</span> <span class="kd">class</span> <span class="nc">Person</span> <span class="o">{</span>
- <span class="kd">public</span> <span class="kt">int</span> <span class="n">id</span><span class="o">;</span>
- <span class="kd">public</span> <span class="n">String</span> <span class="n">name</span><span class="o">;</span>
-<span class="o">}</span></code></pre></div>
-
-<center>
-<img src="/img/blog/data-serialization.png" style="width:80%;margin:15px" />
-</center>
-
-<p>Flink’s type system can be easily extended by providing custom TypeInformations, Serializers, and Comparators to improve the performance of serializing and comparing custom data types.</p>
-
-<h2 id="how-does-flink-operate-on-binary-data">How does Flink operate on binary data?</h2>
-
-<p>Similar to many other data processing APIs (including SQL), Flink’s APIs provide transformations to group, sort, and join data sets. These transformations operate on potentially very large data sets. Relational database systems feature very efficient algorithms for these purposes since several decades including external merge-sort, merge-join, and hybrid hash-join. Flink builds on this technology, but generalizes it to handle arbitrary objects using its custom serialization and comparison stack. In the following, we show how Flink operates with binary data by the example of Flink’s in-memory sort algorithm.</p>
-
-<p>Flink assigns a memory budget to its data processing operators. Upon initialization, a sort algorithm requests its memory budget from the MemoryManager and receives a corresponding set of MemorySegments. The set of MemorySegments becomes the memory pool of a so-called sort buffer which collects the data that is be sorted. The following figure illustrates how data objects are serialized into the sort buffer.</p>
-
-<center>
-<img src="/img/blog/sorting-binary-data-1.png" style="width:90%;margin:15px" />
-</center>
-
-<p>The sort buffer is internally organized into two memory regions. The first region holds the full binary data of all objects. The second region contains pointers to the full binary object data and - depending on the key data type - fixed-length sort keys. When an object is added to the sort buffer, its binary data is appended to the first region, and a pointer (and possibly a key) is appended to the second region. The separation of actual data and pointers plus fixed-length keys is done for two purposes. It enables efficient swapping of fix-length entries (key+pointer) and also reduces the data that needs to be moved when sorting. If the sort key is a variable length data type such as a String, the fixed-length sort key must be a prefix key such as the first n characters of a String. Note, not all data types provide a fixed-length (prefix) sort key. When serializing objects into the sort buffer, both memory regions are extended with MemorySegments from the memory pool. Once the memory pool is empty and no more objects can be added, the sort buffer is completely filled and can be sorted. Flink’s sort buffer provides methods to compare and swap elements. This makes the actual sort algorithm pluggable. By default, Flink uses a Quicksort implementation which can fall back to HeapSort.
-The following figure shows how two objects are compared.</p>
-
-<center>
-<img src="/img/blog/sorting-binary-data-2.png" style="width:80%;margin:15px" />
-</center>
-
-<p>The sort buffer compares two elements by comparing their binary fix-length sort keys. The comparison is successful if either done on a full key (not a prefix key) or if the binary prefix keys are not equal. If the prefix keys are equal (or the sort key data type does not provide a binary prefix key), the sort buffer follows the pointers to the actual object data, deserializes both objects and compares the objects. Depending on the result of the comparison, the sort algorithm decides whether to swap the compared elements or not. The sort buffer swaps two elements by moving their fix-length keys and pointers. The actual data is not moved. Once the sort algorithm finishes, the pointers in the sort buffer are correctly ordered. The following figure shows how the sorted data is returned from the sort buffer.</p>
-
-<center>
-<img src="/img/blog/sorting-binary-data-3.png" style="width:80%;margin:15px" />
-</center>
-
-<p>The sorted data is returned by sequentially reading the pointer region of the sort buffer, skipping the sort keys and following the sorted pointers to the actual data. This data is either deserialized and returned as objects or the binary representation is copied and written to disk in case of an external merge-sort (see this <a href="http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">blog post on joins in Flink</a>).</p>
-
-<h2 id="show-me-numbers">Show me numbers!</h2>
-
-<p>So, what does operating on binary data mean for performance? We’ll run a benchmark that sorts 10 million <code>Tuple2&lt;Integer, String&gt;</code> objects to find out. The values of the Integer field are sampled from a uniform distribution. The String field values have a length of 12 characters and are sampled from a long-tail distribution. The input data is provided by an iterator that returns a mutable object, i.e., the same tuple object instance is returned with different field values. Flink uses this technique when reading data from memory, network, or disk to avoid unnecessary object instantiations. The benchmarks are run in a JVM with 900 MB heap size which is approximately the required amount of memory to store and sort 10 million tuple objects on the heap without dying of an <code>OutOfMemoryError</code>. We sort the tuples on the Integer field and on the String field using three sorting methods:</p>
-
-<ol>
- <li><strong>Object-on-heap.</strong> The tuples are stored in a regular <code>java.util.ArrayList</code> with initial capacity set to 10 million entries and sorted using Java’s regular collection sort.</li>
- <li><strong>Flink-serialized.</strong> The tuple fields are serialized into a sort buffer of 600 MB size using Flink’s custom serializers, sorted as described above, and finally deserialized again. When sorting on the Integer field, the full Integer is used as sort key such that the sort happens entirely on binary data (no deserialization of objects required). For sorting on the String field a 8-byte prefix key is used and tuple objects are deserialized if the prefix keys are equal.</li>
- <li><strong>Kryo-serialized.</strong> The tuple fields are serialized into a sort buffer of 600 MB size using Kryo serialization and sorted without binary sort keys. This means that each pair-wise comparison requires two object to be deserialized.</li>
-</ol>
-
-<p>All sort methods are implemented using a single thread. The reported times are averaged over ten runs. After each run, we call <code>System.gc()</code> to request a garbage collection run which does not go into measured execution time. The following figure shows the time to store the input data in memory, sort it, and read it back as objects.</p>
-
-<center>
-<img src="/img/blog/sort-benchmark.png" style="width:90%;margin:15px" />
-</center>
-
-<p>We see that Flink’s sort on binary data using its own serializers significantly outperforms the other two methods. Comparing to the object-on-heap method, we see that loading the data into memory is much faster. Since we actually collect the objects, there is no opportunity to reuse the object instances, but have to re-create every tuple. This is less efficient than Flink’s serializers (or Kryo serialization). On the other hand, reading objects from the heap comes for free compared to deserialization. In our benchmark, object cloning was more expensive than serialization and deserialization combined. Looking at the sorting time, we see that also sorting on the binary representation is faster than Java’s collection sort. Sorting data that was serialized using Kryo without binary sort key, is much slower than both other methods. This is due to the heavy deserialization overhead. Sorting the tuples on their String field is faster than sorting on the Integer field due to the long-tailed value distribution which significantly reduces the number of pair-wise comparisons. To get a better feeling of what is happening during sorting we monitored the executing JVM using VisualVM. The following screenshots show heap memory usage, garbage collection activity and CPU usage over the execution of 10 runs.</p>
-
-<table width="100%">
- <tr>
- <th></th>
- <th><center><b>Garbage Collection</b></center></th>
- <th><center><b>Memory Usage</b></center></th>
- </tr>
- <tr>
- <td><b>Object-on-Heap (int)</b></td>
- <td><img src="/img/blog/objHeap-int-gc.png" style="width:80%" /></td>
- <td><img src="/img/blog/objHeap-int-mem.png" style="width:80%" /></td>
- </tr>
- <tr>
- <td><b>Flink-Serialized (int)</b></td>
- <td><img src="/img/blog/flinkSer-int-gc.png" style="width:80%" /></td>
- <td><img src="/img/blog/flinkSer-int-mem.png" style="width:80%" /></td>
- </tr>
- <tr>
- <td><b>Kryo-Serialized (int)</b></td>
- <td><img src="/img/blog/kryoSer-int-gc.png" style="width:80%" /></td>
- <td><img src="/img/blog/kryoSer-int-mem.png" style="width:80%" /></td>
- </tr>
-</table>
-
-<p>The experiments run single-threaded on an 8-core machine, so full utilization of one core only corresponds to a 12.5% overall utilization. The screenshots show that operating on binary data significantly reduces garbage collection activity. For the object-on-heap approach, the garbage collector runs in very short intervals while filling the sort buffer and causes a lot of CPU usage even for a single processing thread (sorting itself does not trigger the garbage collector). The JVM garbage collects with multiple parallel threads, explaining the high overall CPU utilization. On the other hand, the methods that operate on serialized data rarely trigger the garbage collector and have a much lower CPU utilization. In fact the garbage collector does not run at all if the tuples are sorted on the Integer field using the flink-serialized method because no objects need to be deserialized for pair-wise comparisons. The kryo-serialized method requires slightly more garbage collection since it does not use binary sort keys and deserializes two objects for each comparison.</p>
-
-<p>The memory usage charts shows that the flink-serialized and kryo-serialized constantly occupy a high amount of memory (plus some objects for operation). This is due to the pre-allocation of MemorySegments. The actual memory usage is much lower, because the sort buffers are not completely filled. The following table shows the memory consumption of each method. 10 million records result in about 280 MB of binary data (object data plus pointers and sort keys) depending on the used serializer and presence and size of a binary sort key. Comparing this to the memory requirements of the object-on-heap approach we see that operating on binary data can significantly improve memory efficiency. In our benchmark more than twice as much data can be sorted in-memory if serialized into a sort buffer instead of holding it as objects on the heap.</p>
-
-<table width="100%">
- <tr>
- <th>Occupied Memory</th>
- <th>Object-on-Heap</th>
- <th>Flink-Serialized</th>
- <th>Kryo-Serialized</th>
- </tr>
- <tr>
- <td><b>Sort on Integer</b></td>
- <td>approx. 700 MB (heap)</td>
- <td>277 MB (sort buffer)</td>
- <td>266 MB (sort buffer)</td>
- </tr>
- <tr>
- <td><b>Sort on String</b></td>
- <td>approx. 700 MB (heap)</td>
- <td>315 MB (sort buffer)</td>
- <td>266 MB (sort buffer)</td>
- </tr>
-</table>
-
-<p><br /></p>
-
-<p>To summarize, the experiments verify the previously stated benefits of operating on binary data.</p>
-
-<h2 id="were-not-done-yet">We’re not done yet!</h2>
-
-<p>Apache Flink features quite a bit of advanced techniques to safely and efficiently process huge amounts of data with limited memory resources. However, there are a few points that could make Flink even more efficient. The Flink community is working on moving the managed memory to off-heap memory. This will allow for smaller JVMs, lower garbage collection overhead, and also easier system configuration. With Flink’s Table API, the semantics of all operations such as aggregations and projections are known (in contrast to black-box user-defined functions). Hence we can generate code for Table API operations that directly operates on binary data. Further improvements include serialization layouts which are tailored towards the operations that are applied on the binary data and code generation for serializers and comparators.</p>
-
-<p>The groundwork (and a lot more) for operating on binary data is done but there is still some room for making Flink even better and faster. If you are crazy about performance and like to juggle with lot of bits and bytes, join the Flink community!</p>
-
-<h2 id="tldr-give-me-three-things-to-remember">TL;DR; Give me three things to remember!</h2>
-
-<ul>
- <li>Flink’s active memory management avoids nasty <code>OutOfMemoryErrors</code> that kill your JVMs and reduces garbage collection overhead.</li>
- <li>Flink features a highly efficient data de/serialization stack that facilitates operations on binary data and makes more data fit into memory.</li>
- <li>Flink’s DBMS-style operators operate natively on binary data yielding high performance in-memory and destage gracefully to disk if necessary.</li>
-</ul>
-</description>
-<pubDate>Mon, 11 May 2015 12:00:00 +0200</pubDate>
-<link>https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html</link>
-<guid isPermaLink="true">/news/2015/05/11/Juggling-with-Bits-and-Bytes.html</guid>
-</item>
-
</channel>
</rss>
diff --git a/content/blog/index.html b/content/blog/index.html
index 5cf1b73..eb6fba3 100644
--- a/content/blog/index.html
+++ b/content/blog/index.html
@@ -196,6 +196,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></h2>
+
+ <p>11 Jun 2020
+ Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
+
+ <p>And suddenly it’s June. The previous month has been calm on the surface, but quite hectic underneath — the final testing phase for Flink 1.11 is moving at full speed, Stateful Functions 2.1 is out in the wild and Flink has made it into Google Season of Docs 2020.</p>
+
+ <p><a href="/news/2020/06/11/community-update.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></h2>
<p>09 Jun 2020
@@ -321,19 +334,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2020/04/01/community-update.html">Flink Community Update - April'20</a></h2>
-
- <p>01 Apr 2020
- Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
-
- <p>While things slow down around us, the Apache Flink community is privileged to remain as active as ever. This blogpost combs through the past few months to give you an update on the state of things in Flink — from core releases to Stateful Functions; from some good old community stats to a new development blog.</p>
-
- <p><a href="/news/2020/04/01/community-update.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -366,6 +366,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page10/index.html b/content/blog/page10/index.html
index 32b61e6..bdc80d3 100644
--- a/content/blog/page10/index.html
+++ b/content/blog/page10/index.html
@@ -196,6 +196,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2015/12/11/storm-compatibility.html">Storm Compatibility in Apache Flink: How to run existing Storm topologies on Flink</a></h2>
+
+ <p>11 Dec 2015 by Matthias J. Sax (<a href="https://twitter.com/">@MatthiasJSax</a>)
+ </p>
+
+ <p>In this blog post, we describe Flink's compatibility package for <a href="https://storm.apache.org">Apache Storm</a> that allows to embed Spouts (sources) and Bolts (operators) in a regular Flink streaming job. Furthermore, the compatibility package provides a Storm compatible API in order to execute whole Storm topologies with (almost) no code adaption.</p>
+
+ <p><a href="/news/2015/12/11/storm-compatibility.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2015/12/04/Introducing-windows.html">Introducing Stream Windows in Apache Flink</a></h2>
<p>04 Dec 2015 by Fabian Hueske (<a href="https://twitter.com/">@fhueske</a>)
@@ -333,20 +346,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></h2>
-
- <p>11 May 2015 by Fabian Hüske (<a href="https://twitter.com/">@fhueske</a>)
- </p>
-
- <p><p>Nowadays, a lot of open-source systems for analyzing large data sets are implemented in Java or other JVM-based programming languages. The most well-known example is Apache Hadoop, but also newer frameworks such as Apache Spark, Apache Drill, and also Apache Flink run on JVMs. A common challenge that JVM-based data analysis engines face is to store large amounts of data in memory - both for caching and for efficient processing such as sorting and joining of data. Managing the JVM memory well makes the difference between a system that is hard to configure and has unpredictable reliability and performance and a system that behaves robustly with few configuration knobs.</p>
-<p>In this blog post we discuss how Apache Flink manages memory, talk about its custom data de/serialization stack, and show how it operates on binary data.</p></p>
-
- <p><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -379,6 +378,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page11/index.html b/content/blog/page11/index.html
index 26bb52a..a78b694 100644
--- a/content/blog/page11/index.html
+++ b/content/blog/page11/index.html
@@ -196,6 +196,20 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></h2>
+
+ <p>11 May 2015 by Fabian Hüske (<a href="https://twitter.com/">@fhueske</a>)
+ </p>
+
+ <p><p>Nowadays, a lot of open-source systems for analyzing large data sets are implemented in Java or other JVM-based programming languages. The most well-known example is Apache Hadoop, but also newer frameworks such as Apache Spark, Apache Drill, and also Apache Flink run on JVMs. A common challenge that JVM-based data analysis engines face is to store large amounts of data in memory - both for caching and for efficient processing such as sorting and joining of data. Managing the JVM memory well makes the difference between a system that is hard to configure and has unpredictable reliability and performance and a system that behaves robustly with few configuration knobs.</p>
+<p>In this blog post we discuss how Apache Flink manages memory, talk about its custom data de/serialization stack, and show how it operates on binary data.</p></p>
+
+ <p><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2015/04/13/release-0.9.0-milestone1.html">Announcing Flink 0.9.0-milestone1 preview release</a></h2>
<p>13 Apr 2015
@@ -340,21 +354,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2014/11/04/release-0.7.0.html">Apache Flink 0.7.0 available</a></h2>
-
- <p>04 Nov 2014
- </p>
-
- <p><p>We are pleased to announce the availability of Flink 0.7.0. This release includes new user-facing features as well as performance and bug fixes, brings the Scala and Java APIs in sync, and introduces Flink Streaming. A total of 34 people have contributed to this release, a big thanks to all of them!</p>
-
-</p>
-
- <p><a href="/news/2014/11/04/release-0.7.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -387,6 +386,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page12/index.html b/content/blog/page12/index.html
index 8f8ef22..da58d39 100644
--- a/content/blog/page12/index.html
+++ b/content/blog/page12/index.html
@@ -196,6 +196,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2014/11/04/release-0.7.0.html">Apache Flink 0.7.0 available</a></h2>
+
+ <p>04 Nov 2014
+ </p>
+
+ <p><p>We are pleased to announce the availability of Flink 0.7.0. This release includes new user-facing features as well as performance and bug fixes, brings the Scala and Java APIs in sync, and introduces Flink Streaming. A total of 34 people have contributed to this release, a big thanks to all of them!</p>
+
+</p>
+
+ <p><a href="/news/2014/11/04/release-0.7.0.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2014/10/03/upcoming_events.html">Upcoming Events</a></h2>
<p>03 Oct 2014
@@ -275,6 +290,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page2/index.html b/content/blog/page2/index.html
index 4458a5c..9460f41 100644
--- a/content/blog/page2/index.html
+++ b/content/blog/page2/index.html
@@ -196,6 +196,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2020/04/01/community-update.html">Flink Community Update - April'20</a></h2>
+
+ <p>01 Apr 2020
+ Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
+
+ <p>While things slow down around us, the Apache Flink community is privileged to remain as active as ever. This blogpost combs through the past few months to give you an update on the state of things in Flink — from core releases to Stateful Functions; from some good old community stats to a new development blog.</p>
+
+ <p><a href="/news/2020/04/01/community-update.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/features/2020/03/27/flink-for-data-warehouse.html">Flink as Unified Engine for Modern Data Warehousing: Production-Ready Hive Integration</a></h2>
<p>27 Mar 2020
@@ -318,21 +331,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2019/12/11/release-1.8.3.html">Apache Flink 1.8.3 Released</a></h2>
-
- <p>11 Dec 2019
- Hequn Cheng </p>
-
- <p><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.8 series.</p>
-
-</p>
-
- <p><a href="/news/2019/12/11/release-1.8.3.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -365,6 +363,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page3/index.html b/content/blog/page3/index.html
index 4be9e38..fb8404e 100644
--- a/content/blog/page3/index.html
+++ b/content/blog/page3/index.html
@@ -196,6 +196,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2019/12/11/release-1.8.3.html">Apache Flink 1.8.3 Released</a></h2>
+
+ <p>11 Dec 2019
+ Hequn Cheng </p>
+
+ <p><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.8 series.</p>
+
+</p>
+
+ <p><a href="/news/2019/12/11/release-1.8.3.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2019/12/09/flink-kubernetes-kudo.html">Running Apache Flink on Kubernetes with KUDO</a></h2>
<p>09 Dec 2019
@@ -321,19 +336,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/2019/06/26/broadcast-state.html">A Practical Guide to Broadcast State in Apache Flink</a></h2>
-
- <p>26 Jun 2019
- Fabian Hueske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
-
- <p>Apache Flink has multiple types of operator state, one of which is called Broadcast State. In this post, we explain what Broadcast State is, and show an example of how it can be applied to an application that evaluates dynamic patterns on an event stream.</p>
-
- <p><a href="/2019/06/26/broadcast-state.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -366,6 +368,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page4/index.html b/content/blog/page4/index.html
index 3760181..0c64f0c 100644
--- a/content/blog/page4/index.html
+++ b/content/blog/page4/index.html
@@ -196,6 +196,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/2019/06/26/broadcast-state.html">A Practical Guide to Broadcast State in Apache Flink</a></h2>
+
+ <p>26 Jun 2019
+ Fabian Hueske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
+
+ <p>Apache Flink has multiple types of operator state, one of which is called Broadcast State. In this post, we explain what Broadcast State is, and show an example of how it can be applied to an application that evaluates dynamic patterns on an event stream.</p>
+
+ <p><a href="/2019/06/26/broadcast-state.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/2019/06/05/flink-network-stack.html">A Deep-Dive into Flink's Network Stack</a></h2>
<p>05 Jun 2019
@@ -320,21 +333,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2019/02/25/release-1.6.4.html">Apache Flink 1.6.4 Released</a></h2>
-
- <p>25 Feb 2019
- </p>
-
- <p><p>The Apache Flink community released the fourth bugfix version of the Apache Flink 1.6 series.</p>
-
-</p>
-
- <p><a href="/news/2019/02/25/release-1.6.4.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -367,6 +365,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page5/index.html b/content/blog/page5/index.html
index a12e65e..496ddc2 100644
--- a/content/blog/page5/index.html
+++ b/content/blog/page5/index.html
@@ -196,6 +196,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2019/02/25/release-1.6.4.html">Apache Flink 1.6.4 Released</a></h2>
+
+ <p>25 Feb 2019
+ </p>
+
+ <p><p>The Apache Flink community released the fourth bugfix version of the Apache Flink 1.6 series.</p>
+
+</p>
+
+ <p><a href="/news/2019/02/25/release-1.6.4.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2019/02/15/release-1.7.2.html">Apache Flink 1.7.2 Released</a></h2>
<p>15 Feb 2019
@@ -330,21 +345,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2018/09/20/release-1.5.4.html">Apache Flink 1.5.4 Released</a></h2>
-
- <p>20 Sep 2018
- </p>
-
- <p><p>The Apache Flink community released the fourth bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/09/20/release-1.5.4.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -377,6 +377,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page6/index.html b/content/blog/page6/index.html
index 5a0a6cf..bbea907 100644
--- a/content/blog/page6/index.html
+++ b/content/blog/page6/index.html
@@ -196,6 +196,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2018/09/20/release-1.5.4.html">Apache Flink 1.5.4 Released</a></h2>
+
+ <p>20 Sep 2018
+ </p>
+
+ <p><p>The Apache Flink community released the fourth bugfix version of the Apache Flink 1.5 series.</p>
+
+</p>
+
+ <p><a href="/news/2018/09/20/release-1.5.4.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2018/08/21/release-1.5.3.html">Apache Flink 1.5.3 Released</a></h2>
<p>21 Aug 2018
@@ -328,19 +343,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/features/2018/01/30/incremental-checkpointing.html">Managing Large State in Apache Flink: An Intro to Incremental Checkpointing</a></h2>
-
- <p>30 Jan 2018
- Stefan Ricther (<a href="https://twitter.com/StefanRRicther">@StefanRRicther</a>) & Chris Ward (<a href="https://twitter.com/chrischinch">@chrischinch</a>)</p>
-
- <p>Flink 1.3.0 introduced incremental checkpointing, making it possible for applications with large state to generate checkpoints more efficiently.</p>
-
- <p><a href="/features/2018/01/30/incremental-checkpointing.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -373,6 +375,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page7/index.html b/content/blog/page7/index.html
index 6362ef2..632f4dc 100644
--- a/content/blog/page7/index.html
+++ b/content/blog/page7/index.html
@@ -196,6 +196,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/features/2018/01/30/incremental-checkpointing.html">Managing Large State in Apache Flink: An Intro to Incremental Checkpointing</a></h2>
+
+ <p>30 Jan 2018
+ Stefan Ricther (<a href="https://twitter.com/StefanRRicther">@StefanRRicther</a>) & Chris Ward (<a href="https://twitter.com/chrischinch">@chrischinch</a>)</p>
+
+ <p>Flink 1.3.0 introduced incremental checkpointing, making it possible for applications with large state to generate checkpoints more efficiently.</p>
+
+ <p><a href="/features/2018/01/30/incremental-checkpointing.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2017/12/21/2017-year-in-review.html">Apache Flink in 2017: Year in Review</a></h2>
<p>21 Dec 2017
@@ -331,20 +344,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2017/04/04/dynamic-tables.html">Continuous Queries on Dynamic Tables</a></h2>
-
- <p>04 Apr 2017 by Fabian Hueske, Shaoxuan Wang, and Xiaowei Jiang
- </p>
-
- <p><p>Flink's relational APIs, the Table API and SQL, are unified APIs for stream and batch processing, meaning that a query produces the same result when being evaluated on streaming or static data.</p>
-<p>In this blog post we discuss the future of these APIs and introduce the concept of Dynamic Tables. Dynamic tables will significantly expand the scope of the Table API and SQL on streams and enable many more advanced use cases. We discuss how streams and dynamic tables relate to each other and explain the semantics of continuously evaluating queries on dynamic tables.</p></p>
-
- <p><a href="/news/2017/04/04/dynamic-tables.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -377,6 +376,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page8/index.html b/content/blog/page8/index.html
index 55ce291..4cbf258 100644
--- a/content/blog/page8/index.html
+++ b/content/blog/page8/index.html
@@ -196,6 +196,20 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2017/04/04/dynamic-tables.html">Continuous Queries on Dynamic Tables</a></h2>
+
+ <p>04 Apr 2017 by Fabian Hueske, Shaoxuan Wang, and Xiaowei Jiang
+ </p>
+
+ <p><p>Flink's relational APIs, the Table API and SQL, are unified APIs for stream and batch processing, meaning that a query produces the same result when being evaluated on streaming or static data.</p>
+<p>In this blog post we discuss the future of these APIs and introduce the concept of Dynamic Tables. Dynamic tables will significantly expand the scope of the Table API and SQL on streams and enable many more advanced use cases. We discuss how streams and dynamic tables relate to each other and explain the semantics of continuously evaluating queries on dynamic tables.</p></p>
+
+ <p><a href="/news/2017/04/04/dynamic-tables.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2017/03/29/table-sql-api-update.html">From Streams to Tables and Back Again: An Update on Flink's Table & SQL API</a></h2>
<p>29 Mar 2017 by Timo Walther (<a href="https://twitter.com/">@twalthr</a>)
@@ -324,21 +338,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2016/08/08/release-1.1.0.html">Announcing Apache Flink 1.1.0</a></h2>
-
- <p>08 Aug 2016
- </p>
-
- <p><div class="alert alert-success"><strong>Important</strong>: The Maven artifacts published with version 1.1.0 on Maven central have a Hadoop dependency issue. It is highly recommended to use <strong>1.1.1</strong> or <strong>1.1.1-hadoop1</strong> as the Flink version.</div>
-
-</p>
-
- <p><a href="/news/2016/08/08/release-1.1.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -371,6 +370,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/blog/page9/index.html b/content/blog/page9/index.html
index 388e6e8..3bd741d 100644
--- a/content/blog/page9/index.html
+++ b/content/blog/page9/index.html
@@ -196,6 +196,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2016/08/08/release-1.1.0.html">Announcing Apache Flink 1.1.0</a></h2>
+
+ <p>08 Aug 2016
+ </p>
+
+ <p><div class="alert alert-success"><strong>Important</strong>: The Maven artifacts published with version 1.1.0 on Maven central have a Hadoop dependency issue. It is highly recommended to use <strong>1.1.1</strong> or <strong>1.1.1-hadoop1</strong> as the Flink version.</div>
+
+</p>
+
+ <p><a href="/news/2016/08/08/release-1.1.0.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2016/05/24/stream-sql.html">Stream Processing for Everyone with SQL and Apache Flink</a></h2>
<p>24 May 2016 by Fabian Hueske (<a href="https://twitter.com/">@fhueske</a>)
@@ -325,19 +340,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2015/12/11/storm-compatibility.html">Storm Compatibility in Apache Flink: How to run existing Storm topologies on Flink</a></h2>
-
- <p>11 Dec 2015 by Matthias J. Sax (<a href="https://twitter.com/">@MatthiasJSax</a>)
- </p>
-
- <p>In this blog post, we describe Flink's compatibility package for <a href="https://storm.apache.org">Apache Storm</a> that allows to embed Spouts (sources) and Bolts (operators) in a regular Flink streaming job. Furthermore, the compatibility package provides a Storm compatible API in order to execute whole Storm topologies with (almost) no code adaption.</p>
-
- <p><a href="/news/2015/12/11/storm-compatibility.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -370,6 +372,16 @@
<ul id="markdown-toc">
+ <li><a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></li>
diff --git a/content/img/blog/2020-06-10-community-update/FlinkForward_Banner_CFP_Global_2020.png b/content/img/blog/2020-06-10-community-update/FlinkForward_Banner_CFP_Global_2020.png
new file mode 100644
index 0000000..8813d88
--- /dev/null
+++ b/content/img/blog/2020-06-10-community-update/FlinkForward_Banner_CFP_Global_2020.png
Binary files differ
diff --git a/content/index.html b/content/index.html
index e867f72..b4f74a2 100644
--- a/content/index.html
+++ b/content/index.html
@@ -568,6 +568,9 @@
<dl>
+ <dt> <a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></dt>
+ <dd>And suddenly it’s June. The previous month has been calm on the surface, but quite hectic underneath — the final testing phase for Flink 1.11 is moving at full speed, Stateful Functions 2.1 is out in the wild and Flink has made it into Google Season of Docs 2020.</dd>
+
<dt> <a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></dt>
<dd><p>The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 2.1.0! This release introduces new features around state expiration and performance improvements for co-located deployments, as well as other important changes that improve the stability and testability of the project. As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration.</p>
@@ -583,11 +586,6 @@
<dt> <a href="/news/2020/05/04/season-of-docs.html">Applying to Google Season of Docs 2020</a></dt>
<dd>The Flink community is thrilled to share that the project is applying again to Google Season of Docs (GSoD) this year! If you’re unfamiliar with the program, GSoD is a great initiative organized by Google Open Source to pair technical writers with mentors to work on documentation for open source projects. Does working shoulder to shoulder with the Flink community on documentation sound exciting? We’d love to hear from you!</dd>
-
- <dt> <a href="/news/2020/04/24/release-1.9.3.html">Apache Flink 1.9.3 Released</a></dt>
- <dd><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.9 series.</p>
-
-</dd>
</dl>
diff --git a/content/news/2020/06/11/community-update.html b/content/news/2020/06/11/community-update.html
new file mode 100644
index 0000000..4781b40
--- /dev/null
+++ b/content/news/2020/06/11/community-update.html
@@ -0,0 +1,374 @@
+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
+ <meta name="viewport" content="width=device-width, initial-scale=1">
+ <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
+ <title>Apache Flink: Flink Community Update - June'20</title>
+ <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
+ <link rel="icon" href="/favicon.ico" type="image/x-icon">
+
+ <!-- Bootstrap -->
+ <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/css/bootstrap.min.css">
+ <link rel="stylesheet" href="/css/flink.css">
+ <link rel="stylesheet" href="/css/syntax.css">
+
+ <!-- Blog RSS feed -->
+ <link href="/blog/feed.xml" rel="alternate" type="application/rss+xml" title="Apache Flink Blog: RSS feed" />
+
+ <!-- jQuery (necessary for Bootstrap's JavaScript plugins) -->
+ <!-- We need to load Jquery in the header for custom google analytics event tracking-->
+ <script src="/js/jquery.min.js"></script>
+
+ <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries -->
+ <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+ <!--[if lt IE 9]>
+ <script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js"></script>
+ <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+ <![endif]-->
+ </head>
+ <body>
+
+
+ <!-- Main content. -->
+ <div class="container">
+ <div class="row">
+
+
+ <div id="sidebar" class="col-sm-3">
+
+
+<!-- Top navbar. -->
+ <nav class="navbar navbar-default">
+ <!-- The logo. -->
+ <div class="navbar-header">
+ <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ </button>
+ <div class="navbar-logo">
+ <a href="/">
+ <img alt="Apache Flink" src="/img/flink-header-logo.svg" width="147px" height="73px">
+ </a>
+ </div>
+ </div><!-- /.navbar-header -->
+
+ <!-- The navigation links. -->
+ <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
+ <ul class="nav navbar-nav navbar-main">
+
+ <!-- First menu section explains visitors what Flink is -->
+
+ <!-- What is Stream Processing? -->
+ <!--
+ <li><a href="/streamprocessing1.html">What is Stream Processing?</a></li>
+ -->
+
+ <!-- What is Flink? -->
+ <li><a href="/flink-architecture.html">What is Apache Flink?</a></li>
+
+
+
+ <!-- What is Stateful Functions? -->
+
+ <li><a href="/stateful-functions.html">What is Stateful Functions?</a></li>
+
+ <!-- Use cases -->
+ <li><a href="/usecases.html">Use Cases</a></li>
+
+ <!-- Powered by -->
+ <li><a href="/poweredby.html">Powered By</a></li>
+
+
+
+ <!-- Second menu section aims to support Flink users -->
+
+ <!-- Downloads -->
+ <li><a href="/downloads.html">Downloads</a></li>
+
+ <!-- Getting Started -->
+ <li class="dropdown">
+ <a class="dropdown-toggle" data-toggle="dropdown" href="#">Getting Started<span class="caret"></span></a>
+ <ul class="dropdown-menu">
+ <li><a href="https://ci.apache.org/projects/flink/flink-docs-release-1.10/getting-started/index.html" target="_blank">With Flink <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ <li><a href="https://ci.apache.org/projects/flink/flink-statefun-docs-release-2.1/getting-started/project-setup.html" target="_blank">With Flink Stateful Functions <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ <li><a href="/training.html">Training Course</a></li>
+ </ul>
+ </li>
+
+ <!-- Documentation -->
+ <li class="dropdown">
+ <a class="dropdown-toggle" data-toggle="dropdown" href="#">Documentation<span class="caret"></span></a>
+ <ul class="dropdown-menu">
+ <li><a href="https://ci.apache.org/projects/flink/flink-docs-release-1.10" target="_blank">Flink 1.10 (Latest stable release) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ <li><a href="https://ci.apache.org/projects/flink/flink-docs-master" target="_blank">Flink Master (Latest Snapshot) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ <li><a href="https://ci.apache.org/projects/flink/flink-statefun-docs-release-2.1" target="_blank">Flink Stateful Functions 2.1 (Latest stable release) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ <li><a href="https://ci.apache.org/projects/flink/flink-statefun-docs-master" target="_blank">Flink Stateful Functions Master (Latest Snapshot) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+ </ul>
+ </li>
+
+ <!-- getting help -->
+ <li><a href="/gettinghelp.html">Getting Help</a></li>
+
+ <!-- Blog -->
+ <li class="active"><a href="/blog/"><b>Flink Blog</b></a></li>
+
+
+ <!-- Flink-packages -->
+ <li>
+ <a href="https://flink-packages.org" target="_blank">flink-packages.org <small><span class="glyphicon glyphicon-new-window"></span></small></a>
+ </li>
+
+
+ <!-- Third menu section aim to support community and contributors -->
+
+ <!-- Community -->
+ <li><a href="/community.html">Community & Project Info</a></li>
+
+ <!-- Roadmap -->
+ <li><a href="/roadmap.html">Roadmap</a></li>
+
+ <!-- Contribute -->
+ <li><a href="/contributing/how-to-contribute.html">How to Contribute</a></li>
+
+
+ <!-- GitHub -->
+ <li>
+ <a href="https://github.com/apache/flink" target="_blank">Flink on GitHub <small><span class="glyphicon glyphicon-new-window"></span></small></a>
+ </li>
+
+
+
+ <!-- Language Switcher -->
+ <li>
+
+
+ <!-- link to the Chinese home page when current is blog page -->
+ <a href="/zh">中文版</a>
+
+
+ </li>
+
+ </ul>
+
+ <ul class="nav navbar-nav navbar-bottom">
+ <hr />
+
+ <!-- Twitter -->
+ <li><a href="https://twitter.com/apacheflink" target="_blank">@ApacheFlink <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+
+ <!-- Visualizer -->
+ <li class=" hidden-md hidden-sm"><a href="/visualizer/" target="_blank">Plan Visualizer <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+
+ <hr />
+
+ <li><a href="https://apache.org" target="_blank">Apache Software Foundation <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
+
+ <li>
+ <style>
+ .smalllinks:link {
+ display: inline-block !important; background: none; padding-top: 0px; padding-bottom: 0px; padding-right: 0px; min-width: 75px;
+ }
+ </style>
+
+ <a class="smalllinks" href="https://www.apache.org/licenses/" target="_blank">License</a> <small><span class="glyphicon glyphicon-new-window"></span></small>
+
+ <a class="smalllinks" href="https://www.apache.org/security/" target="_blank">Security</a> <small><span class="glyphicon glyphicon-new-window"></span></small>
+
+ <a class="smalllinks" href="https://www.apache.org/foundation/sponsorship.html" target="_blank">Donate</a> <small><span class="glyphicon glyphicon-new-window"></span></small>
+
+ <a class="smalllinks" href="https://www.apache.org/foundation/thanks.html" target="_blank">Thanks</a> <small><span class="glyphicon glyphicon-new-window"></span></small>
+ </li>
+
+ </ul>
+ </div><!-- /.navbar-collapse -->
+ </nav>
+
+ </div>
+ <div class="col-sm-9">
+ <div class="row-fluid">
+ <div class="col-sm-12">
+ <div class="row">
+ <h1>Flink Community Update - June'20</h1>
+ <p><i></i></p>
+
+ <article>
+ <p>11 Jun 2020 Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
+
+<p>And suddenly it’s June. The previous month has been calm on the surface, but quite hectic underneath — the final testing phase for Flink 1.11 is moving at full speed, Stateful Functions 2.1 is out in the wild and Flink has made it into Google Season of Docs 2020.</p>
+
+<p>To top it off, a piece of good news: <a href="https://www.flink-forward.org/global-2020">Flink Forward</a> is back on October 19-22 as a free virtual event!</p>
+
+<div class="page-toc">
+<ul id="markdown-toc">
+ <li><a href="#the-past-month-in-flink" id="markdown-toc-the-past-month-in-flink">The Past Month in Flink</a> <ul>
+ <li><a href="#flink-stateful-functions-21-release" id="markdown-toc-flink-stateful-functions-21-release">Flink Stateful Functions 2.1 Release</a></li>
+ <li><a href="#testing-is-on-for-flink-111" id="markdown-toc-testing-is-on-for-flink-111">Testing is ON for Flink 1.11</a></li>
+ <li><a href="#flink-minor-releases" id="markdown-toc-flink-minor-releases">Flink Minor Releases</a> <ul>
+ <li><a href="#flink-1101" id="markdown-toc-flink-1101">Flink 1.10.1</a></li>
+ </ul>
+ </li>
+ <li><a href="#new-committers-and-pmc-members" id="markdown-toc-new-committers-and-pmc-members">New Committers and PMC Members</a> <ul>
+ <li><a href="#new-committers" id="markdown-toc-new-committers">New Committers</a></li>
+ </ul>
+ </li>
+ </ul>
+ </li>
+ <li><a href="#the-bigger-picture" id="markdown-toc-the-bigger-picture">The Bigger Picture</a> <ul>
+ <li><a href="#flink-forward-global-virtual-conference-2020" id="markdown-toc-flink-forward-global-virtual-conference-2020">Flink Forward Global Virtual Conference 2020</a></li>
+ <li><a href="#google-season-of-docs-2020" id="markdown-toc-google-season-of-docs-2020">Google Season of Docs 2020</a></li>
+ </ul>
+ </li>
+</ul>
+
+</div>
+
+<h1 id="the-past-month-in-flink">The Past Month in Flink</h1>
+
+<h2 id="flink-stateful-functions-21-release">Flink Stateful Functions 2.1 Release</h2>
+
+<p>It might seem like <a href="https://flink.apache.org/news/2020/04/07/release-statefun-2.0.0.html">Stateful Functions 2.0 was announced</a> only a handful of weeks ago (and it was!), but the Flink community has just released Stateful Functions 2.1! This release introduces two new features: state expiration for any kind of persisted state and support for UNIX Domain Sockets (UDS) to improve the performance of inter-container communication in co-located deployments; as well as other important changes that improve the overall stability and testability of the project. You can read the <a href="https://flink.apache.org/news/2020/06/09/release-statefun-2.1.0.html">announcement blogpost</a> for more details on the release!</p>
+
+<p>As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration. If you’d like to get involved, we’re always <a href="https://github.com/apache/flink-statefun#contributing">looking for new contributors</a> — especially around SDKs for other languages (e.g. Go, Rust, Javascript).</p>
+
+<hr />
+
+<h2 id="testing-is-on-for-flink-111">Testing is ON for Flink 1.11</h2>
+
+<p>Things have been pretty quiet in the Flink community, as all efforts shifted to testing the newest features shipping with Flink 1.11. While we wait for a voting Release Candidate (RC) to be out, you can check the progress of testing in <a href="https://issues.apache.org/jira/secure/RapidBoard.jspa?rapidView=364&projectKey=FLINK">this JIRA burndown board</a> and learn more about some of the <a href="https://flink.apache.org/news/2020/05/07/community-update.html#warming-up-for-flink-111">upcoming features</a> in these Flink Forward videos:</p>
+
+<ul>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=ssEmeLcL5Uk">Rethinking of fault tolerance in Flink: what lies ahead?</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=t7fAN3xNJ3Q">It’s finally here: Python on Flink & Flink on Zeppelin</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=KDD8e4GE12w">A deep dive into Flink SQL</a></p>
+ </li>
+ <li>
+ <p><a href="https://www.youtube.com/watch?v=4ce1H9CRyEc">Production-Ready Flink and Hive Integration - what story you can tell now?</a></p>
+ </li>
+</ul>
+
+<p>We encourage the wider community to also get involved in testing once the voting RC is out. Keep an eye on the <a href="https://flink.apache.org/community.html#mailing-lists">@dev mailing list</a> for updates!</p>
+
+<hr />
+
+<h2 id="flink-minor-releases">Flink Minor Releases</h2>
+
+<h3 id="flink-1101">Flink 1.10.1</h3>
+
+<p>The community released Flink 1.10.1, covering some outstanding bugs in Flink 1.10. You can find more in the <a href="https://flink.apache.org/news/2020/05/12/release-1.10.1.html">announcement blogpost</a>!</p>
+
+<hr />
+
+<h2 id="new-committers-and-pmc-members">New Committers and PMC Members</h2>
+
+<p>The Apache Flink community has welcomed <strong>2 new Committers</strong> since the last update. Congratulations!</p>
+
+<h3 id="new-committers">New Committers</h3>
+
+<div class="row">
+ <div class="col-lg-3">
+ <div class="text-center">
+ <img class="img-circle" src="https://avatars3.githubusercontent.com/u/4471524?s=400&v=4" width="90" height="90" />
+ <p>Benchao Li</p>
+ </div>
+ </div>
+ <div class="col-lg-3">
+ <div class="text-center">
+ <img class="img-circle" src="https://avatars0.githubusercontent.com/u/6509172?s=400&v=4" width="90" height="90" />
+ <p>Xintong Song</p>
+ </div>
+ </div>
+</div>
+
+<hr />
+
+<h1 id="the-bigger-picture">The Bigger Picture</h1>
+
+<h2 id="flink-forward-global-virtual-conference-2020">Flink Forward Global Virtual Conference 2020</h2>
+
+<p>After a first successful <a href="https://www.youtube.com/playlist?list=PLDX4T_cnKjD0ngnBSU-bYGfgVv17MiwA7">virtual conference</a> last April, Flink Forward will be hosting a second free virtual edition on October 19-22. This time around, the conference will feature two days of hands-on training and two full days of conference talks!</p>
+
+<p>Got a Flink story to share? Maybe your recent adventures with Stateful Functions? The <a href="https://www.flink-forward.org/global-2020/call-for-presentations">Call for Presentations is now open</a> and accepting submissions from the community until <strong>June 19th, 11:59 PM CEST</strong>.</p>
+
+<div style="line-height:60%;">
+ <br />
+</div>
+
+<center>
+<img src="/img/blog/2020-06-10-community-update/FlinkForward_Banner_CFP_Global_2020.png" width="600px" alt="Flink Forward Global 2020" />
+</center>
+
+<div style="line-height:60%;">
+ <br />
+</div>
+
+<hr />
+
+<h2 id="google-season-of-docs-2020">Google Season of Docs 2020</h2>
+
+<p>In the last update, we announced that Flink was applying to <a href="https://developers.google.com/season-of-docs">Google Season of Docs (GSoD)</a> again this year. The good news: we’ve made it into the shortlist of accepted projects! This represents an invaluable opportunity for the Flink community to collaborate with technical writers to improve the Table API & SQL documentation. We’re honored to have seen a great number of people reach out over the last couple of weeks, and look forward to receiving applications from this week on!</p>
+
+<p>If you’re interested in learning more about our project idea or want to get involved in GSoD as a technical writer, check out the <a href="https://flink.apache.org/news/2020/05/04/season-of-docs.html">announcement blogpost</a> and <a href="https://developers.google.com/season-of-docs/docs/tech-writer-application-hints">submit your application</a>. The deadline for GSoD applications is <strong>July 9th, 18:00 UTC</strong>.</p>
+
+<hr />
+
+<p>If you’d like to keep a closer eye on what’s happening in the community, subscribe to the Flink <a href="https://flink.apache.org/community.html#mailing-lists">@community mailing list</a> to get fine-grained weekly updates, upcoming event announcements and more.</p>
+
+ </article>
+ </div>
+
+ <div class="row">
+ <div id="disqus_thread"></div>
+ <script type="text/javascript">
+ /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
+ var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname
+
+ /* * * DON'T EDIT BELOW THIS LINE * * */
+ (function() {
+ var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
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+ })();
+ </script>
+ </div>
+ </div>
+</div>
+ </div>
+ </div>
+
+ <hr />
+
+ <div class="row">
+ <div class="footer text-center col-sm-12">
+ <p>Copyright © 2014-2019 <a href="http://apache.org">The Apache Software Foundation</a>. All Rights Reserved.</p>
+ <p>Apache Flink, Flink®, Apache®, the squirrel logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation.</p>
+ <p><a href="/privacy-policy.html">Privacy Policy</a> · <a href="/blog/feed.xml">RSS feed</a></p>
+ </div>
+ </div>
+ </div><!-- /.container -->
+
+ <!-- Include all compiled plugins (below), or include individual files as needed -->
+ <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery.matchHeight/0.7.0/jquery.matchHeight-min.js"></script>
+ <script src="/js/codetabs.js"></script>
+ <script src="/js/stickysidebar.js"></script>
+
+ <!-- Google Analytics -->
+ <script>
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+
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+ ga('send', 'pageview');
+ </script>
+ </body>
+</html>
diff --git a/content/zh/index.html b/content/zh/index.html
index f85b7f1..d89be9a 100644
--- a/content/zh/index.html
+++ b/content/zh/index.html
@@ -565,6 +565,9 @@
<dl>
+ <dt> <a href="/news/2020/06/11/community-update.html">Flink Community Update - June'20</a></dt>
+ <dd>And suddenly it’s June. The previous month has been calm on the surface, but quite hectic underneath — the final testing phase for Flink 1.11 is moving at full speed, Stateful Functions 2.1 is out in the wild and Flink has made it into Google Season of Docs 2020.</dd>
+
<dt> <a href="/news/2020/06/09/release-statefun-2.1.0.html">Stateful Functions 2.1.0 Release Announcement</a></dt>
<dd><p>The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 2.1.0! This release introduces new features around state expiration and performance improvements for co-located deployments, as well as other important changes that improve the stability and testability of the project. As the community around StateFun grows, the release cycle will follow this pattern of smaller and more frequent releases to incorporate user feedback and allow for faster iteration.</p>
@@ -580,11 +583,6 @@
<dt> <a href="/news/2020/05/04/season-of-docs.html">Applying to Google Season of Docs 2020</a></dt>
<dd>The Flink community is thrilled to share that the project is applying again to Google Season of Docs (GSoD) this year! If you’re unfamiliar with the program, GSoD is a great initiative organized by Google Open Source to pair technical writers with mentors to work on documentation for open source projects. Does working shoulder to shoulder with the Flink community on documentation sound exciting? We’d love to hear from you!</dd>
-
- <dt> <a href="/news/2020/04/24/release-1.9.3.html">Apache Flink 1.9.3 Released</a></dt>
- <dd><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.9 series.</p>
-
-</dd>
</dl>
