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                        <h1 class="page-title"><a href="/show-203312639.html" title="Flink的Job启动TaskManager端（源码分析）">Flink的Job启动TaskManager端（源码分析）</a></h1>
            
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<p><span style="color:#363636;">先来看一下，TaskManager端用来接收JobManager发送过来的TDD对象的RPC接口</span></p>
<p><span style="color:#363636;">在TaskExecutor.java中</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_lkYm5" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpjNU1pOWpNMkppT1RRd1ptRXpNakkzWXpCa05UUXdOemhtT0dJM1lqVmlOREkzT0M1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这个方法用于接收了一个TaskDeploymentDescriptor对象用于启动任务（上一篇知道这里executionGraph的每一个并行度都会调用deploy方法生成一个TDD）</span></p>
<p><span style="color:#363636;">来看一下具体接收到以后做了什么</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_SlGvL" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpJd01DOHlZMlkwWWpNM05UTXpOVE5pTm1ZMk9XWTBaR0U1TXpSbU16YzRNakpqT0M1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_XCTIp" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpjeU5TOHpPV1ptTVRSaFlXUm1PREExTURGaE0yWXhZV1kwT0RCaVpEZzJOV1l5TlM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">创建了一个Task并且将其内部的一个线程启动起来了</span></p>
<p><span style="color:#363636;">注意这里从TDD中得到了InputGate,Partition的信息，用于创建InputGate,ResultPartition</span></p>
<p><span style="color:#363636;">InputGate用于对接上游产生的数据（消费）</span></p>
<p><span style="color:#363636;">ResultPartition用于往下游发送自己产生的数据（生产）</span></p>
<p><span style="color:#363636;">来看一下Task创建,在Task的构造方法中</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_CW715" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpFeE5TODVZakJoTXprNVpXVXhaak00TkdSa1l6VmpOalV5TmpNMU1qTmxOakptWWk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这里看到创建了对应往下游发送数据的ResultPartition</span></p>
<p><span style="color:#363636;">ResultPartition中创建的SubPartition具体分为</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_Bfsp9" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpVMk55ODFObUkxWkRrMU16RXhaRGRqTW1FNE1qUmpPREJqTmpnMllqYzJZemxpTnk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">可以看到就是说三个参数分别对应</span></p>
<p><span style="color:#363636;">　　PIPELINED    可以边消费边生产，是有背压的，这个partition没有buffer数量的限制</span></p>
<p><span style="color:#363636;">　　  　　（因为背压的控制是通过，接收数据端公用同一个指定大小的bufferPool,以后背压的时候讲）</span></p>
<p><span style="color:#363636;">　　其他同理</span></p>
<p><span style="color:#363636;">这里看一下不同类型的ResultPartitionType是创建的什么subpartitions</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_eEBYe" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpJNU9TOHdaakpqWkRFNE1HUmhPR0psWldJeFpqazRNVEV4TkdZNE0yWTRPV1F6TXk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">BLOCKING  这种创建了一个SpillableSubpartition并且传进去了一个ioManager（这个ioManager以后io管理细讲）</span></p>
<p><span style="color:#363636;">大致看了一下就是说这种Subpartition是会落盘的</span></p>
<p><span style="color:#363636;">PIPELINED  而这种方式是完全基于内存的</span></p>
<p><span style="color:#363636;">根据上游的信息创建好ResultPartition以后</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_iLk0e" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpRdlpEVXlOVE0wT0RBMVlXWmlObVEzWVdJMVpUWmlNR0U1TmpKaFlqQmlPV011Y0c1bg==" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">接着创建了InputGate用于接收上游的数据，并且在create方法中</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" id="aimg_Ty2h8" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpVeE5pOHpOMlU1TlROaVl6Z3dZMlZsTkRZd1ltVTBNVGRtWlRBME1HRTFZMlk0TkM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">会根据partition的位置创建对应的channel，这里可以分为</span></p>
<p><span style="color:#363636;">Local      就是说下游和自己是在同一台机器</span></p>
<p><span style="color:#363636;">Remote  下游是需要通过网络发送的</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THprMU5pOWxabVE1T0dJd09EbGlZelJoTURkbVlXVm1PV1E1TWpKak5UZ3lNR1l5WXk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">并且在这里将inputGate和它所有的inputChannels关联了起来</span></p>
<p><span style="color:#363636;">创建完inputGate以后Task就初始化完了，然后会被start()起来，来看下Task的run方法</span></p>
<p><span style="color:#363636;">在run方法中</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpJeE9DODNNekE1WW1Oak56VmxOV1V4TXpjeVpEUm1ZMlV6TVRNMk1tSXpNMlpsTWk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这个地方会为初始化inputGate与ResultPartition的bufferPool(以后讲到反压在讲)</span></p>
<p><span style="color:#363636;">继续</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpnMU15OW1ZVEJtTWpCbU9XRmlObUV4TTJReVpqZGxObVUzWXpkalptSmlZVEV4WkM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这里通过反射创建了一个StreamTask的实例</span></p>
<p><span style="color:#363636;">并且</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpneE5DOHdNelUyTkRGa01UTmlNak0yTTJWaFptWXdZek0wTm1abE9UVTFaVGd3WlM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">调用了他的invoke()方法，这里也是Job开始的逻辑，来看一下invoke方法</span></p>
<p><span style="color:#363636;">在invoke方法中</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpZMU5TOWlNVE01Tm1VeE1XTXlZVFEyWmpNNFlXRmtZakpqTjJWa01tWmpOek5pTnk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">只要知道这里会初始化OperatorChain这里包含了我们用户算子的逻辑（这里不细讲，随缘讲到Task操作责任链的时候讲）</span></p>
<p><span style="color:#363636;">然后得到了operatorChain的头headoperator其实这里的头就包含了用户的第一个算子逻辑在里面</span></p>
<p><span style="color:#363636;">然后init()方法中用上面的headoperator初始化了一个inputProcess对象并且关联上了上面创建的inputGate（也是留到责任链讲）</span></p>
<p><span style="color:#363636;">接着</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpjMk15OWhNREJsTlRnd01XSXlaV00wTmpBNFlXVTVaRGt5WmpsbE0yRTVNV0ZsWWk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpReE5pODBNR1JpWVdZMk1XTXlPR1ExTVRsbVpUTmlZakl6TURreU16VXhaR1kyT0M1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这里就是上面在init方法中创建的inputProcess，并且调用了他的processInput方法</span></p>
<p><span style="color:#363636;">重头戏来了，来看一下processInput方法</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpJNU5pOWhNV0l6TkRKbVl6WXdNV1JpWm1OalpUZ3pNRFpsWmpnME9EVmtNR0ZqT0M1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这里有个while（true）也就是说这里会一直循环下去</span></p>
<p><span style="color:#363636;">来看一下他循环做什么</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpZMk1TODFaREE0WlRReU9EazVNems1TnpJME1UVXlNMlkxWlRoa1pqSXpObVEzWkM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">这里！！！！这个streamOperator就是上面构造inputProcess时传入的headOperator</span></p>
<p><span style="color:#363636;">这个processElement方法里面就是调用用户的方法啦</span></p>
<p><span style="color:#363636;">也就是不停的从上游接收到数据以后，调用用户具体的处理逻辑</span></p>
<p><span style="color:#363636;">这里job就启动完成了</span></p>
<p> </p>
<p><span style="color:#363636;">注意这个while循环内既然开始走我们用户的逻辑，那肯定会先从inputGate关联到的上游获取数据</span></p>
<p><span style="color:#363636;">这里就非常重要了，因为接收数据就包含了很多的机制的实现</span></p>
<p><span style="color:#363636;">包含了watermark处理的逻辑，水印对齐的逻辑，水印更新的逻辑，如下</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpFek9TOWtaRE0xTXprMU9EZ3hOVGRqTlRRMU9EQm1PVGhqTldKaE1qaGpOR0poTXk1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">以及idle停滞流逻辑，流状态更新逻辑</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpRM055ODVNak14TWpka09ERTNaak00TURSa01qY3lNV1F3T1RabE1qRTNOekEzWkM1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">以及如何接收数据逻辑，接收端反压的逻辑，barriers对齐的逻辑，checkpoint触发的逻辑</span></p>
<p><span style="color:#363636;"><img alt="Flink的Job启动TaskManager端（源码分析）" src="/default/index/img?u=L2RlZmF1bHQvaW5kZXgvaW1nP3U9YUhSMGNITTZMeTl3YVdGdWMyaGxiaTVqYjIwdmFXMWhaMlZ6THpZeU5DOHpObVk1WXpFMk1qZ3hNekkxTkRSallqY3hNR1kwWXpGaFltVmpPRE0wT0M1d2JtYz0=" title="Flink的Job启动TaskManager端（源码分析）"></span></p>
<p><span style="color:#363636;">所以这个StreamInputProcessor.processInput()方法是一个非常重要的方法，以后随缘更新各种机制的时候也会经常看到</span></p>
<p><span style="color:#363636;">更多技术资讯可关注：gzitcast</span></p>
 
                    
            
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