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class="margin-top--md">有关最新文档，请参阅最新稳定版本<b><a href="/zh-CN/docs/latest/engine_usage/spark">latest version</a></b> (<!-- -->1.1.2<!-- -->).</div></div><div class="docItemContainer_oiyr"><article><span class="theme-doc-version-badge badge badge--secondary">Version: <!-- -->1.0.3</span><div class="tocCollapsible_aw-L theme-doc-toc-mobile tocMobile_Tx6Y"><button type="button" class="clean-btn tocCollapsibleButton_zr6a">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Spark 引擎</h1></header><p>本文主要介绍在Linkis1.0中，spark引擎的配置、部署和使用。</p><h2><a aria-hidden="true" tabindex="-1" class="anchor anchor__h2 anchorWithStickyNavbar_y2LR" id="1spark引擎使用前的环境配置"></a>1.Spark引擎使用前的环境配置<a class="hash-link" href="#1spark引擎使用前的环境配置" title="Direct link to heading">#</a></h2><p>如果您希望在您的服务器上使用spark引擎，您需要保证以下的环境变量已经设置正确并且引擎的启动用户是有这些环境变量的。</p><p>强烈建议您在执行spark任务之前，检查下执行用户的这些环境变量。</p><table><thead><tr><th>环境变量名</th><th>环境变量内容</th><th>备注</th></tr></thead><tbody><tr><td>JAVA_HOME</td><td>JDK安装路径</td><td>必须</td></tr><tr><td>HADOOP_HOME</td><td>Hadoop安装路径</td><td>必须</td></tr><tr><td>HADOOP_CONF_DIR</td><td>Hadoop配置路径</td><td>必须</td></tr><tr><td>HIVE<!-- -->_<!-- -->CONF_DIR</td><td>Hive配置路径</td><td>必须</td></tr><tr><td>SPARK_HOME</td><td>Spark安装路径</td><td>必须</td></tr><tr><td>SPARK_CONF_DIR</td><td>Spark配置路径</td><td>必须</td></tr><tr><td>python</td><td>python</td><td>建议使用anaconda的python作为默认python</td></tr></tbody></table><p>表1-1 环境配置清单</p><h2><a aria-hidden="true" tabindex="-1" class="anchor anchor__h2 anchorWithStickyNavbar_y2LR" id="2spark引擎的配置和部署"></a>2.Spark引擎的配置和部署<a class="hash-link" href="#2spark引擎的配置和部署" title="Direct link to heading">#</a></h2><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="21-spark版本的选择和编译"></a>2.1 spark版本的选择和编译<a class="hash-link" href="#21-spark版本的选择和编译" title="Direct link to heading">#</a></h3><p>理论上Linkis1.0支持的spark2.x以上的所有版本。默认支持的版本Spark2.4.3。如果您想使用您的spark版本，如spark2.1.0，则您仅仅需要将插件spark的版本进行修改，然后进行编译即可。具体的，您可以找到linkis-engineplugin-spark模块，将\<spark.version<!-- -->><!-- -->标签进行改成2.1.0，然后单独编译此模块即可。</p><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="22-spark-engineconn部署和加载"></a>2.2 spark engineConn部署和加载<a class="hash-link" href="#22-spark-engineconn部署和加载" title="Direct link to heading">#</a></h3><p>如果您已经编译完了您的spark引擎的插件已经编译完成，那么您需要将新的插件放置到指定的位置中才能加载，具体可以参考下面这篇文章</p><p><a href="/zh-CN/docs/1.0.3/deployment/engine_conn_plugin_installation">EngineConnPlugin引擎插件安装</a> </p><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="23-spark引擎的标签"></a>2.3 spark引擎的标签<a class="hash-link" href="#23-spark引擎的标签" title="Direct link to heading">#</a></h3><p>Linkis1.0是通过标签来进行的，所以需要在我们数据库中插入数据，插入的方式如下文所示。</p><p><a href="/zh-CN/docs/1.0.3/deployment/engine_conn_plugin_installation">EngineConnPlugin引擎插件安装 > 2.2 管理台Configuration配置修改（可选）</a> </p><h2><a aria-hidden="true" tabindex="-1" class="anchor anchor__h2 anchorWithStickyNavbar_y2LR" id="3spark引擎的使用"></a>3.spark引擎的使用<a class="hash-link" href="#3spark引擎的使用" title="Direct link to heading">#</a></h2><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="准备操作队列设置"></a>准备操作，队列设置<a class="hash-link" href="#准备操作队列设置" title="Direct link to heading">#</a></h3><p>因为spark的执行是需要队列的资源，所以用户在执行之前，必须要设置自己能够执行的队列。</p><p><img src="/zh-CN/assets/images/queue-set-e89c51e5b7d25d78a78580b122e4e64c.png"></p><p>图3-1 队列设置 |
| 您也可以通过在提交参数的StartUpMap里面添加队列的值：<code>startupMap.put("wds.linkis.rm.yarnqueue", "dws")</code></p><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="31-通过linkis-sdk进行使用"></a>3.1 通过Linkis SDK进行使用<a class="hash-link" href="#31-通过linkis-sdk进行使用" title="Direct link to heading">#</a></h3><p>Linkis提供了Java和Scala 的SDK向Linkis服务端提交任务. 具体可以参考 <a href="/zh-CN/docs/1.0.3/user_guide/sdk_manual">JAVA SDK Manual</a>. |
| 对于Spark任务你只需要修改Demo中的EngineConnType和CodeType参数即可:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI java"><pre tabindex="0" class="prism-code language-java codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Map<String, Object> labels = new HashMap<String, Object>();</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> labels.put(LabelKeyConstant.ENGINE_TYPE_KEY, "spark-2.4.3"); // required engineType Label</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> labels.put(LabelKeyConstant.USER_CREATOR_TYPE_KEY, "hadoop-IDE");// required execute user and creator</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> labels.put(LabelKeyConstant.CODE_TYPE_KEY, "sql"); // required codeType py,sql,scala</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="32-通过linkis-cli进行任务提交"></a>3.2 通过Linkis-cli进行任务提交<a class="hash-link" href="#32-通过linkis-cli进行任务提交" title="Direct link to heading">#</a></h3><p>Linkis 1.0后提供了cli的方式提交任务，我们只需要指定对应的EngineConn和CodeType标签类型即可，Spark的使用如下：</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI shell"><pre tabindex="0" class="prism-code language-shell codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)">## codeType对应关系 py-->pyspark sql-->sparkSQL scala-->Spark scala</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">sh</span><span class="token plain"> ./bin/linkis-cli -engineType spark-2.4.3 -codeType sql -code </span><span class="token string" style="color:rgb(255, 121, 198)">"show tables"</span><span class="token plain"> -submitUser hadoop -proxyUser hadoop</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block"> |
| </span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)"># 可以在提交参数通过-confMap wds.linkis.yarnqueue=dws 来指定yarn 队列</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">sh</span><span class="token plain"> ./bin/linkis-cli -engineType spark-2.4.3 -codeType sql -confMap wds.linkis.yarnqueue</span><span class="token operator">=</span><span class="token plain">dws -code </span><span class="token string" style="color:rgb(255, 121, 198)">"show tables"</span><span class="token plain"> -submitUser hadoop -proxyUser hadoop</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>具体使用可以参考： <a href="/zh-CN/docs/1.0.3/user_guide/linkiscli_manual">Linkis CLI Manual</a>.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor anchor__h3 anchorWithStickyNavbar_y2LR" id="33-scriptis的使用方式"></a>3.3 Scriptis的使用方式<a class="hash-link" href="#33-scriptis的使用方式" title="Direct link to heading">#</a></h3><p>Scriptis的使用方式是最简单的，您可以直接进入Scriptis，新建sql、scala或者pyspark脚本进行执行。</p><p>sql的方式是最简单的，您可以新建sql脚本然后编写进行执行，执行的时候，会有进度的显示。如果一开始用户是没有spark引擎的话，sql的执行会启动一个spark会话(这里可能会花一些时间)， |
| SparkSession初始化之后，就可以开始执行sql。</p><p><img src="/zh-CN/assets/images/sparksql-run-d748d4fab0548fa92a6e91f42c911466.png"></p><p>图3-2 sparksql的执行效果截图</p><p>spark-scala的任务，我们已经初始化好了sqlContext等变量，用户可以直接使用这个sqlContext进行sql的执行。</p><p><img src="/zh-CN/assets/images/scala-run-77cd49935a85082d9346d28f3ecf44e3.png"></p><p>图3-3 spark-scala的执行效果图</p><p>类似的，pyspark的方式中，我们也已经初始化好了SparkSession，用户可以直接使用spark.sql的方式进行执行sql。</p><p><img src="/zh-CN/assets/images/pyspakr-run-39cd0bbe6c61d2fc7ad933db99c33d06.png"> |
| 图3-4 pyspark的执行方式</p><h2><a aria-hidden="true" tabindex="-1" class="anchor anchor__h2 anchorWithStickyNavbar_y2LR" id="4spark引擎的用户设置"></a>4.spark引擎的用户设置<a class="hash-link" href="#4spark引擎的用户设置" title="Direct link to heading">#</a></h2><p>除了以上引擎配置，用户还可以进行自定义的设置，比如spark会话executor个数和executor的内存。这些参数是为了用户能够更加自由地设置自己的spark的参数，另外spark其他参数也可以进行修改，比如的pyspark的python版本等。</p><p><img src="/zh-CN/assets/images/spark-conf-2b013d6df48bcafd6b6b672f44039eab.png"></p><p>图4-1 spark的用户自定义配置管理台</p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/apache/incubator-linkis-website/edit/dev/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.0.3/engine_usage/spark.md" target="_blank" rel="noreferrer noopener" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 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href="#2spark引擎的配置和部署" class="table-of-contents__link">2.Spark引擎的配置和部署</a><ul><li><a href="#21-spark版本的选择和编译" class="table-of-contents__link">2.1 spark版本的选择和编译</a></li><li><a href="#22-spark-engineconn部署和加载" class="table-of-contents__link">2.2 spark engineConn部署和加载</a></li><li><a href="#23-spark引擎的标签" class="table-of-contents__link">2.3 spark引擎的标签</a></li></ul></li><li><a href="#3spark引擎的使用" class="table-of-contents__link">3.spark引擎的使用</a><ul><li><a href="#准备操作队列设置" class="table-of-contents__link">准备操作，队列设置</a></li><li><a href="#31-通过linkis-sdk进行使用" class="table-of-contents__link">3.1 通过Linkis SDK进行使用</a></li><li><a href="#32-通过linkis-cli进行任务提交" class="table-of-contents__link">3.2 通过Linkis-cli进行任务提交</a></li><li><a href="#33-scriptis的使用方式" class="table-of-contents__link">3.3 Scriptis的使用方式</a></li></ul></li><li><a href="#4spark引擎的用户设置" class="table-of-contents__link">4.spark引擎的用户设置</a></li></ul></div></div></div></div></main></div></div><footer class="footer"><div class="container"><div 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