【Flink博客阅读】 Flink 作业执行深度解析(WordCount) 读后实战总结

深藏阁楼爱情的钟 2023-02-20 02:08 71阅读 0赞

# Flink 作业执行解析 #

所有有关Flink作业执行的介绍都包含以下的这个流程，今天我们就是实战一些这些转换是如何完成的？

Code

StreamGraph

JobGraph

ExecutionGraph

物理执行计划

*  `StreamGraph` Class representing the streaming topology. It contains all the information necessary to build the jobgraph for the execution. 这个类表示流处理的拓扑结构，包含构造`JobGraph`的所有信息，从而满足任务执行。
 *  `JobGraph`: `JobGraph`代表`Flink`的 `dataflow`程序，处于`JobManager`接受的底层。来自更高级别`API`的所有程序都将转换为`JobGraphs`。在此之前，都是在client里面进行运行的。并且可以根据ExplainPlan获取执行计划
 *  `ExecutionGraph`: `ExecutionGraph`是JobGraph的并行化版本，是调度层(Schduler)最核心的数据结构。
 *  物理执行计划

![image-20200608223431142][]

*  StreamGraph： 流处理节点拓扑图

![image-20200621123036155][]

*  JobGraph： Flink的数据流图。

![image-20200621112119230][]

*  JobGraph 属性

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70][]

*  Operator ： 算子，理解为function定义。
 *  Transformation： 转换，包含输入、算子、与输出，理解为一个完整的流程，function runtime。

## 示例程序 ##

`flink-examples-streaming`工程下面的`org.apache.flink.streaming.examples.wordcount.WordCount`

public static void main(String[] args) throws Exception { 
    
    		// Checking input parameters
    		final MultipleParameterTool params = MultipleParameterTool.fromArgs(args);
    
    		// set up the execution environment
    		final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
    		// make parameters available in the web interface
    		env.getConfig().setGlobalJobParameters(params);
    
    		// get input data
    		DataStream<String> text = null;
    		if (params.has("input")) { 
    			// union all the inputs from text files
    			for (String input : params.getMultiParameterRequired("input")) { 
    				if (text == null) { 
    					text = env.readTextFile(input);
    				} else { 
    					text = text.union(env.readTextFile(input));
    				}
    			}
    			Preconditions.checkNotNull(text, "Input DataStream should not be null.");
    		} else { 
    			System.out.println("Executing WordCount example with default input data set.");
    			System.out.println("Use --input to specify file input.");
    			// get default test text data
    			text = env.fromElements(WordCountData.WORDS);
    		}
    
    		DataStream<Tuple2<String, Integer>> counts =
    			// split up the lines in pairs (2-tuples) containing: (word,1)
    			text.flatMap(new Tokenizer())
    			// group by the tuple field "0" and sum up tuple field "1"
    			.keyBy(0).sum(1);
    
    		// emit result
    		if (params.has("output")) { 
    			counts.writeAsText(params.get("output"));
    		} else { 
    			System.out.println("Printing result to stdout. Use --output to specify output path.");
    			counts.print();
    		}
    		// execute program
    		env.execute("Streaming WordCount");
    	}

## Flink Client ##

### WordCount程序 ###

text = env.fromElements(WordCountData.WORDS);
    
    DataStream<Tuple2<String, Integer>> counts =
    			// split up the lines in pairs (2-tuples) containing: (word,1)
    			text.flatMap(new Tokenizer())
    			// group by the tuple field "0" and sum up tuple field "1"
    			.keyBy(0).sum(1);

### 初始化 ###

初始化是之前当前程序的定义，主要是整个数据流的定义元数据收集

#### Source ####

![image-20200621114157046][]

#### FlatMap ####

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 1][]

*  (1) 首先调用DataStream的FlatMap方法 => transform 方法

DataStream\#doTransform

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 2][]

*  （2）创建Transformation（包含输入实例、算子 、输出）
    
    ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 3][]
    
    ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 4][]
 *  （3）创建结果流
    
    ![20200621174359902.png][]
 *  （4）添加算子到当前上下文
    
        public void addOperator(Transformation<?> transformation) {
        		Preconditions.checkNotNull(transformation, "transformation must not be null.");
        		this.transformations.add(transformation);
        	}
 *  （5）返回结果流

#### keyBy ####

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 5][]

*  创建一个KeyStream 返回
    
    ![image-20200621121716437][]

#### sum ####

sum还是在当前的dataStream流上面。

> 需要注意的是，有些 transform 操作并不会生成StreamNode 如 PartitionTransformtion，而是生成个虚拟节点。

![image-20200621121844254][]

调用transform即可flatmap一样。会把自己作为transformations。

![image-20200621121834097][]

#### 最终配置总览 ####

![image-20200621122025735][]

![image-20200621122136802][]

### 执行提交 ###

env.execute("Streaming WordCount");
    
    ===>
    
         public JobExecutionResult execute(String jobName) throws Exception { 
    		Preconditions.checkNotNull(jobName, "Streaming Job name should not be null.");
    
    		return execute(getStreamGraph(jobName));
    	}

#### 生成StreamGraph（Pipeline） ####

public StreamGraph getStreamGraph(String jobName, boolean clearTransformations) {
    		StreamGraph streamGraph = getStreamGraphGenerator().setJobName(jobName).generate();
    		if (clearTransformations) {
    			this.transformations.clear();
    		}
    		return streamGraph;
    	}

Generate全图

拓扑图的生成逻辑，循环处理每一个节点

![image-20200621122533549][]

Generate Transformation

这里对操作符的类型进行判断，并以此调用相应的处理逻辑.简而言之，

处理的核心：是递归的将该节点和节点的上游节点加入图

private Collection<Integer> transform(Transformation<?> transform) { 
    
    		if (alreadyTransformed.containsKey(transform)) { 
    			return alreadyTransformed.get(transform);
    		}
    
    		LOG.debug("Transforming " + transform);
    
    		if (transform.getMaxParallelism() <= 0) { 
    
    			// if the max parallelism hasn't been set, then first use the job wide max parallelism
    			// from the ExecutionConfig.
    			int globalMaxParallelismFromConfig = executionConfig.getMaxParallelism();
    			if (globalMaxParallelismFromConfig > 0) { 
    				transform.setMaxParallelism(globalMaxParallelismFromConfig);
    			}
    		}
    
    		// call at least once to trigger exceptions about MissingTypeInfo
    		transform.getOutputType();
    
    		Collection<Integer> transformedIds;
    		if (transform instanceof OneInputTransformation<?, ?>) { 
    			transformedIds = transformOneInputTransform((OneInputTransformation<?, ?>) transform);
    		} else if (transform instanceof TwoInputTransformation<?, ?, ?>) { 
    			transformedIds = transformTwoInputTransform((TwoInputTransformation<?, ?, ?>) transform);
    		} else if (transform instanceof SourceTransformation<?>) { 
    			// .......... 省略
    
    		// need this check because the iterate transformation adds itself before
    		// transforming the feedback edges
    		if (!alreadyTransformed.containsKey(transform)) { 
    			alreadyTransformed.put(transform, transformedIds);
    		}
    
    		if (transform.getBufferTimeout() >= 0) { 
    			streamGraph.setBufferTimeout(transform.getId(), transform.getBufferTimeout());
    		} else { 
    			streamGraph.setBufferTimeout(transform.getId(), defaultBufferTimeout);
    		}
    
    		if (transform.getUid() != null) { 
    			streamGraph.setTransformationUID(transform.getId(), transform.getUid());
    		}
    		if (transform.getUserProvidedNodeHash() != null) { 
    			streamGraph.setTransformationUserHash(transform.getId(), transform.getUserProvidedNodeHash());
    		}
    
    		if (!streamGraph.getExecutionConfig().hasAutoGeneratedUIDsEnabled()) { 
    			if (transform instanceof PhysicalTransformation &&
    					transform.getUserProvidedNodeHash() == null &&
    					transform.getUid() == null) { 
    				throw new IllegalStateException("Auto generated UIDs have been disabled " +
    					"but no UID or hash has been assigned to operator " + transform.getName());
    			}
    		}
    
    		if (transform.getMinResources() != null && transform.getPreferredResources() != null) { 
    			streamGraph.setResources(transform.getId(), transform.getMinResources(), transform.getPreferredResources());
    		}
    
    		streamGraph.setManagedMemoryWeight(transform.getId(), transform.getManagedMemoryWeight());
    
    		return transformedIds;
    	}

创建节点并加入图

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 6][]

##### 生成结果 #####

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 7][]

#### 生成JobGraph ####

执行

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 8][]

PipelinExecutor

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 9][]

execute 执行

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 10][]

生成jobGraph

![image-20200621161237478][]

![image-20200621161149714][]

*  使用PipeLineTranslator生成JobGraph

![image-20200621161440395][]

![image-20200621161500468][]

Job Graph生成逻辑

![image-20200621161545796][]

##### 生成结果 #####

![image-20200621162619847][]

可以看到上面 key-aggregate-sink合并为了一个chain。

JobGraph 对象结构如上图所示，taskVertices 中只存在三个 TaskVertex，Sink operator 被嵌到 Keyed operator 中去了。

>  *  ![image-20200621161902396][]
>     
>     ![image-20200621161942724][]
> 
> ![image-20200621161959454][]

##### 提交作业 #####

这里相当于把本地作业提交到集群上面。就是一个作业元数据上传的过程。底层的RestClient使用的是Netty。

ClientUtils#submitJob

public static JobExecutionResult submitJob(
    			ClusterClient<?> client,
    			JobGraph jobGraph) throws ProgramInvocationException {
    		checkNotNull(client);
    		checkNotNull(jobGraph);
    		try {
    			return client
    				.submitJob(jobGraph)
    				.thenApply(DetachedJobExecutionResult::new)
    				.get();
    		} catch (InterruptedException | ExecutionException e) {
    			ExceptionUtils.checkInterrupted(e);
    			throw new ProgramInvocationException("Could not run job in detached mode.", jobGraph.getJobID(), e);
    		}
    	}

通过 RestClusterClient 提交到集群上

public CompletableFuture<JobID> submitJob(@Nonnull JobGraph jobGraph) {
             // 生成二进制包，用于作业恢复
    		CompletableFuture<java.nio.file.Path> jobGraphFileFuture = CompletableFuture.supplyAsync(() -> {
    			try {
    				final java.nio.file.Path jobGraphFile = Files.createTempFile("flink-jobgraph", ".bin");
    				try (ObjectOutputStream objectOut = new ObjectOutputStream(Files.newOutputStream(jobGraphFile))) {
    					objectOut.writeObject(jobGraph);
    				}
    				return jobGraphFile;
    			} catch (IOException e) {
    				throw new CompletionException(new FlinkException("Failed to serialize JobGraph.", e));
    			}
    		}, executorService);
             // 查询所有需要上传的包
    		CompletableFuture<Tuple2<JobSubmitRequestBody, Collection<FileUpload>>> requestFuture = jobGraphFileFuture.thenApply(jobGraphFile -> {
    			List<String> jarFileNames = new ArrayList<>(8);
    			List<JobSubmitRequestBody.DistributedCacheFile> artifactFileNames = new ArrayList<>(8);
    			Collection<FileUpload> filesToUpload = new ArrayList<>(8);
    
    			filesToUpload.add(new FileUpload(jobGraphFile, RestConstants.CONTENT_TYPE_BINARY));
    
    			for (Path jar : jobGraph.getUserJars()) {
    				jarFileNames.add(jar.getName());
    				filesToUpload.add(new FileUpload(Paths.get(jar.toUri()), RestConstants.CONTENT_TYPE_JAR));
    			}
    
    			for (Map.Entry<String, DistributedCache.DistributedCacheEntry> artifacts : jobGraph.getUserArtifacts().entrySet()) {
    				final Path artifactFilePath = new Path(artifacts.getValue().filePath);
    				try {
    					// Only local artifacts need to be uploaded.
    					if (!artifactFilePath.getFileSystem().isDistributedFS()) {
    						artifactFileNames.add(new JobSubmitRequestBody.DistributedCacheFile(artifacts.getKey(), artifactFilePath.getName()));
    						filesToUpload.add(new FileUpload(Paths.get(artifacts.getValue().filePath), RestConstants.CONTENT_TYPE_BINARY));
    					}
    				} catch (IOException e) {
    					throw new CompletionException(
    						new FlinkException("Failed to get the FileSystem of artifact " + artifactFilePath + ".", e));
    				}
    			}
    
    			final JobSubmitRequestBody requestBody = new JobSubmitRequestBody(
    				jobGraphFile.getFileName().toString(),
    				jarFileNames,
    				artifactFileNames);
    
    			return Tuple2.of(requestBody, Collections.unmodifiableCollection(filesToUpload));
    		});
             // 上传jar包，提交作业
    		final CompletableFuture<JobSubmitResponseBody> submissionFuture = requestFuture.thenCompose(
    			requestAndFileUploads -> sendRetriableRequest(
    				JobSubmitHeaders.getInstance(),
    				EmptyMessageParameters.getInstance(),
    				requestAndFileUploads.f0,
    				requestAndFileUploads.f1,
    				isConnectionProblemOrServiceUnavailable())
    		);
             // 删除生成的jobGraph文件
    		submissionFuture
    			.thenCombine(jobGraphFileFuture, (ignored, jobGraphFile) -> jobGraphFile)
    			.thenAccept(jobGraphFile -> {
    			try {
    				Files.delete(jobGraphFile);
    			} catch (IOException e) {
    				LOG.warn("Could not delete temporary file {}.", jobGraphFile, e);
    			}
    		});
            // 返回jobId
    		return submissionFuture
    			.thenApply(ignore -> jobGraph.getJobID())
    			.exceptionally(
    				(Throwable throwable) -> {
    					throw new CompletionException(new JobSubmissionException(jobGraph.getJobID(), "Failed to submit JobGraph.", ExceptionUtils.stripCompletionException(throwable)));
    				});
    	}
    	
    	// 提交到集群
    	private <M extends MessageHeaders<R, P, U>, U extends MessageParameters, R extends RequestBody, P extends ResponseBody> CompletableFuture<P>
    	sendRetriableRequest(M messageHeaders, U messageParameters, R request, Collection<FileUpload> filesToUpload, Predicate<Throwable> retryPredicate) {
    		return retry(() -> getWebMonitorBaseUrl().thenCompose(webMonitorBaseUrl -> {
    			try {
    				return restClient.sendRequest(webMonitorBaseUrl.getHost(), webMonitorBaseUrl.getPort(), messageHeaders, messageParameters, request, filesToUpload);
    			} catch (IOException e) {
    				throw new CompletionException(e);
    			}
    		}), retryPredicate);
    	}

## FlinkCluster Server ##

### Dispatcher 接收请求 ###

![image-20200621163428029][]

![image-20200621164048723][]

#### 内部执行 ####

![image-20200621164308850][]

#### 持久化和启动作业 ####

![image-20200621164513430][]

##### 持久化作业 #####

##### 创建JobManagerRunner（JobManagerRunnerImpl） #####

![image-20200621164832663][]

属性

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMwNzYwNDQ_size_16_color_FFFFFF_t_70 11]

#### 翻译JobGraph => ExecutionGraph ####

![image-20200621173634085][]

**ExecutionGraphBuilder\# buildGraph逻辑**

*  create a new execution graph, if none exists so far
 *  set the basic properties
 *  initialize the vertices that have a master initialization hook
 *  file output formats create directories here, input formats create splits
 *  topologically sort the job vertices and attach the graph to the existing one
    
     *  将 JobGraph 里面的 jobVertex 从 Source 节点开始排序。
     *  在 executionGraph.attachJobGraph(sortedTopology)方法里面，根据 JobVertex 生成 ExecutionJobVertex，在 ExecutionJobVertex 构造方法里面，根据 jobVertex 的 IntermediateDataSet 构建 IntermediateResult，根据 jobVertex 并发构建 ExecutionVertex，ExecutionVertex 构建的时候，构建 IntermediateResultPartition（每一个 Execution 构建 IntermediateResult 数个IntermediateResultPartition ）；将创建的 ExecutionJobVertex 与前置的 IntermediateResult 连接起来。
     *  构建 ExecutionEdge ，连接到前面的 IntermediateResultPartition，最终从 ExecutionGraph 到物理执行计划。
 *  configure the state checkpointing
 *  create all the metrics for the Execution Graph

##### 实际启动作业 #####

![image-20200621164714082][]

#### 执行作业 ####

JobMaster

![image-20200621165828687][]

调度器执行

resetAndStartScheduler =》

![image-20200621170153978][]

![image-20200621170324945][]

## 总结 ##

--------------------

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*  第一层 StreamGraph 从 Source 节点开始，每一次 transform 生成一个 StreamNode，两个 StreamNode 通过 StreamEdge 连接在一起,形成 StreamNode 和 StreamEdge 构成的DAG。
 *  第二层 JobGraph，依旧从 Source 节点开始，然后去遍历寻找能够嵌到一起的 operator，如果能够嵌到一起则嵌到一起，不能嵌到一起的单独生成 jobVertex，通过 JobEdge 链接上下游 JobVertex，最终形成 JobVertex 层面的 DAG。
 *  JobVertex DAG 提交到任务以后，从 Source 节点开始排序,根据 JobVertex 生成ExecutionJobVertex，根据 jobVertex的IntermediateDataSet 构建IntermediateResult，然后 IntermediateResult 构建上下游的依赖关系，形成 ExecutionJobVertex 层面的 DAG 即 ExecutionGraph。
 *  最后通过 ExecutionGraph 层到物理执行层。

## 参考资料 ##

https://ververica.cn/developers/advanced-tutorial-2-flink-job-execution-depth-analysis/ Flink 作业执行深度解析

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[image-20200621112119230]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly93dWh1bGFsYS5vc3MtY24tc2hhbmdoYWkuYWxpeXVuY3MuY29tL2Jsb2cvaW1hZ2UtMjAyMDA2MjExMTIxMTkyMzAucG5n?x-oss-process=image/format,png
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[image-20200621114157046]: https://imgconvert.csdnimg.cn/aHR0cHM6Ly93dWh1bGFsYS5vc3MtY24tc2hhbmdoYWkuYWxpeXVuY3MuY29tL2Jsb2cvaW1hZ2UtMjAyMDA2MjExMTQxNTcwNDYucG5n?x-oss-process=image/format,png
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