前言
我们知道，Flink 程序的执行是在我们调用 env.execute() 后才会真正开始。事实上，我们在编写业务代码时，是在勾勒程序执行的 DAG 图，当调用 execute 时这些逻辑才会开始工作，那么在我们调用 execute 函数后到底发生了什么？
执行流程
构造拓扑图
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 从 kafka 获取数据
String brokers;
String groupId;
String topic;
ParameterTool param = ParameterTool.fromArgs(args);
if (param.getNumberOfParameters() > 0){
brokers = param.get("brokers");
groupId = param.get("groupId");
topic = param.get("topic");
} else {
brokers = "";
groupId = "";
topic = "";
}
// 消费 kafka，接入数据源
DataStream<String> dataStream = env.addSource(KafkaConsumer.consumer(brokers, groupId, topic));
SingleOutputStreamOperator<ActionStat> userStat = dataStream.map(new MyMap())
.filter(user -> (user.userId != null && user.articleId != null && "AppClick".equals(user.action)))
.keyBy("userId")
.timeWindow(Time.milliseconds(5000))
.aggregate(new AggDiY());
userStat.print();
env.execute("filnk-test");
首先，必须先要初始化我们的执行环境，这里本地调试通常会生成 LocalStreamEnvironment 环境，而生产上通常是 RemoteStreamEnvironment 环境，这是 StreamExecutionEnvironment 的两个子类：
public class LocalStreamEnvironment extends StreamExecutionEnvironment {...}
public class RemoteStreamEnvironment extends StreamExecutionEnvironment {...}
接下来我们在本地跟一下代码的执行流程：
public JobExecutionResult execute(String jobName) throws Exception {
// transform the streaming program into a JobGraph
StreamGraph streamGraph = getStreamGraph();
streamGraph.setJobName(jobName);
JobGraph jobGraph = streamGraph.getJobGraph();
jobGraph.setAllowQueuedScheduling(true);
Configuration configuration = new Configuration();
configuration.addAll(jobGraph.getJobConfiguration());
configuration.setString(TaskManagerOptions.MANAGED_MEMORY_SIZE, "0");
// add (and override) the settings with what the user defined
configuration.addAll(this.configuration);
if (!configuration.contains(RestOptions.BIND_PORT)) {
configuration.setString(RestOptions.BIND_PORT, "0");
}
int numSlotsPerTaskManager = configuration.getInteger(TaskManagerOptions.NUM_TASK_SLOTS, jobGraph.getMaximumParallelism());
MiniClusterConfiguration cfg = new MiniClusterConfiguration.Builder()
.setConfiguration(configuration)
.setNumSlotsPerTaskManager(numSlotsPerTaskManager)
.build();
if (LOG.isInfoEnabled()) {
LOG.info("Running job on local embedded Flink mini cluster");
}
MiniCluster miniCluster = new MiniCluster(cfg);
try {
miniCluster.start();
configuration.setInteger(RestOptions.PORT, miniCluster.getRestAddress().get().getPort());
return miniCluster.executeJobBlocking(jobGraph);
}
finally {
transformations.clear();
miniCluster.close();
}
}
当我们调用 execute 开始执行程序时，会先生成 StreamGraph，此时 StreamExecutionEnvironment 会在内部初始化一个 transformations 用来存放每一步的操作：
// 创建 transformations
protected final List<StreamTransformation<?>> transformations = new ArrayList<>();
// 遍历 transformations，开始生成 streamGraph
public StreamGraph getStreamGraph() {
if (transformations.size() <= 0) {
throw new IllegalStateException("No operators defined in streaming topology. Cannot execute.");
}
return StreamGraphGenerator.generate(this, transformations);
}
private StreamGraph generateInternal(List<StreamTransformation<?>> transformations) {
for (StreamTransformation<?> transformation: transformations) {
transform(transformation);
}
return streamGraph;
}
private Collection<Integer> transform(StreamTransformation<?> 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 = env.getConfig().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<?>) {
transformedIds = transformSource((SourceTransformation<?>) transform);
} else if (transform instanceof SinkTransformation<?>) {
transformedIds = transformSink((SinkTransformation<?>) transform);
} else if (transform instanceof UnionTransformation<?>) {
transformedIds = transformUnion((UnionTransformation<?>) transform);
} else if (transform instanceof SplitTransformation<?>) {
transformedIds = transformSplit((SplitTransformation<?>) transform);
} else if (transform instanceof SelectTransformation<?>) {
transformedIds = transformSelect((SelectTransformation<?>) transform);
} else if (transform instanceof FeedbackTransformation<?>) {
transformedIds = transformFeedback((FeedbackTransformation<?>) transform);
} else if (transform instanceof CoFeedbackTransformation<?>) {
transformedIds = transformCoFeedback((CoFeedbackTransformation<?>) transform);
} else if (transform instanceof PartitionTransformation<?>) {
transformedIds = transformPartition((PartitionTransformation<?>) transform);
} else if (transform instanceof SideOutputTransformation<?>) {
transformedIds = transformSideOutput((SideOutputTransformation<?>) transform);
} else {
throw new IllegalStateException("Unknown transformation: " + transform);
}
// 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());
}
if (transform.getUid() != null) {
streamGraph.setTransformationUID(transform.getId(), transform.getUid());
}
if (transform.getUserProvidedNodeHash() != null) {
streamGraph.setTransformationUserHash(transform.getId(), transform.getUserProvidedNodeHash());
}
if (transform.getMinResources() != null && transform.getPreferredResources() != null) {
streamGraph.setResources(transform.getId(), transform.getMinResources(), transform.getPreferredResources());
}
return transformedIds;
}
在遍历 List<StreamTransformation> 生成 StreamGraph 的时候，会递归调用 StreamGraphGenerator 的 transform 方法。StreamTransformations 被转换为 StreamGraph 中的节点 StreamNode，并为上下游节点添加边 StreamEdge。这里会针对不同的 transformation 有不同的处理，我们以第一项 OneInputTransformation 为例来说明下游执行流程:
private <IN, OUT> Collection<Integer> transformOneInputTransform(OneInputTransformation<IN, OUT> transform) {
Collection<Integer> inputIds = transform(transform.getInput());
// the recursive call might have already transformed this
if (alreadyTransformed.containsKey(transform)) {
return alreadyTransformed.get(transform);
}
String slotSharingGroup = determineSlotSharingGroup(transform.getSlotSharingGroup(), inputIds);
streamGraph.addOperator(transform.getId(),
slotSharingGroup,
transform.getCoLocationGroupKey(),
transform.getOperator(),
transform.getInputType(),
transform.getOutputType(),
transform.getName());
if (transform.getStateKeySelector() != null) {
TypeSerializer<?> keySerializer = transform.getStateKeyType().createSerializer(env.getConfig());
streamGraph.setOneInputStateKey(transform.getId(), transform.getStateKeySelector(), keySerializer);
}
streamGraph.setParallelism(transform.getId(), transform.getParallelism());
streamGraph.setMaxParallelism(transform.getId(), transform.getMaxParallelism());
for (Integer inputId: inputIds) {
streamGraph.addEdge(inputId, transform.getId(), 0);
}
return Collections.singleton(transform.getId());
}
这里执行包括以下几个步骤：
- 执行 transform， 保证上游的转换已经结束。
- 执行 determineSlotSharingGroup ，确定资源共享组，如果没有指定，则默认的是 default。
- 执行 addOperator ，向 StreamGraph 中添加 Operator 操作, 这里会生成相应的 StreamNode。
protected StreamNode addNode(Integer vertexID,
String slotSharingGroup,
@Nullable String coLocationGroup,
Class<? extends AbstractInvokable> vertexClass,
StreamOperator<?> operatorObject,
String operatorName) {
if (streamNodes.containsKey(vertexID)) {
throw new RuntimeException("Duplicate vertexID " + vertexID);
}
StreamNode vertex = new StreamNode(environment,
vertexID,
slotSharingGroup,
coLocationGroup,
operatorObject,
operatorName,
new ArrayList<OutputSelector<?>>(),
vertexClass);
streamNodes.put(vertexID, vertex);
return vertex;
}
- 执行 addEdge ，将上下游 StreamNode 关联起来，生成 StreamEdge 。
public void addEdge(Integer upStreamVertexID, Integer downStreamVertexID, int typeNumber) {
addEdgeInternal(upStreamVertexID,
downStreamVertexID,
typeNumber,
null,
new ArrayList<String>(),
null);
}
private void addEdgeInternal(Integer upStreamVertexID,
Integer downStreamVertexID,
int typeNumber,
StreamPartitioner<?> partitioner,
List<String> outputNames,
OutputTag outputTag) {
if (virtualSideOutputNodes.containsKey(upStreamVertexID)) {
int virtualId = upStreamVertexID;
upStreamVertexID = virtualSideOutputNodes.get(virtualId).f0;
if (outputTag == null) {
outputTag = virtualSideOutputNodes.get(virtualId).f1;
}
addEdgeInternal(upStreamVertexID, downStreamVertexID, typeNumber, partitioner, null, outputTag);
} else if (virtualSelectNodes.containsKey(upStreamVertexID)) {
int virtualId = upStreamVertexID;
upStreamVertexID = virtualSelectNodes.get(virtualId).f0;
if (outputNames.isEmpty()) {
// selections that happen downstream override earlier selections
outputNames = virtualSelectNodes.get(virtualId).f1;
}
addEdgeInternal(upStreamVertexID, downStreamVertexID, typeNumber, partitioner, outputNames, outputTag);
} else if (virtualPartitionNodes.containsKey(upStreamVertexID)) {
int virtualId = upStreamVertexID;
upStreamVertexID = virtualPartitionNodes.get(virtualId).f0;
if (partitioner == null) {
partitioner = virtualPartitionNodes.get(virtualId).f1;
}
addEdgeInternal(upStreamVertexID, downStreamVertexID, typeNumber, partitioner, outputNames, outputTag);
} else {
StreamNode upstreamNode = getStreamNode(upStreamVertexID);
StreamNode downstreamNode = getStreamNode(downStreamVertexID);
// If no partitioner was specified and the parallelism of upstream and downstream
// operator matches use forward partitioning, use rebalance otherwise.
if (partitioner == null && upstreamNode.getParallelism() == downstreamNode.getParallelism()) {
partitioner = new ForwardPartitioner<Object>();
} else if (partitioner == null) {
partitioner = new RebalancePartitioner<Object>();
}
if (partitioner instanceof ForwardPartitioner) {
if (upstreamNode.getParallelism() != downstreamNode.getParallelism()) {
throw new UnsupportedOperationException("Forward partitioning does not allow " +
"change of parallelism. Upstream operation: " + upstreamNode + " parallelism: " + upstreamNode.getParallelism() +
", downstream operation: " + downstreamNode + " parallelism: " + downstreamNode.getParallelism() +
" You must use another partitioning strategy, such as broadcast, rebalance, shuffle or global.");
}
}
StreamEdge edge = new StreamEdge(upstreamNode, downstreamNode, typeNumber, outputNames, partitioner, outputTag);
getStreamNode(edge.getSourceId()).addOutEdge(edge);
getStreamNode(edge.getTargetId()).addInEdge(edge);
}
}
通过上面的操作，Flink 初步勾勒出了拓扑图。
StreamGraph 转化为 JobGraph
在 execute 函数中我们看到 JobGraph jobGraph = streamGraph.getJobGraph() ，这里会根据 StreamGraph 生成相应的 JobGraph，核心代码：
private JobGraph createJobGraph() {
// make sure that all vertices start immediately
jobGraph.setScheduleMode(ScheduleMode.EAGER);
// Generate deterministic hashes for the nodes in order to identify them across
// submission iff they didn't change.
Map<Integer, byte[]> hashes = defaultStreamGraphHasher.traverseStreamGraphAndGenerateHashes(streamGraph);
// Generate legacy version hashes for backwards compatibility
List<Map<Integer, byte[]>> legacyHashes = new ArrayList<>(legacyStreamGraphHashers.size());
for (StreamGraphHasher hasher : legacyStreamGraphHashers) {
legacyHashes.add(hasher.traverseStreamGraphAndGenerateHashes(streamGraph));
}
Map<Integer, List<Tuple2<byte[], byte[]>>> chainedOperatorHashes = new HashMap<>();
setChaining(hashes, legacyHashes, chainedOperatorHashes);
setPhysicalEdges();
setSlotSharingAndCoLocation();
configureCheckpointing();
JobGraphGenerator.addUserArtifactEntries(streamGraph.getEnvironment().getCachedFiles(), jobGraph);
// set the ExecutionConfig last when it has been finalized
try {
jobGraph.setExecutionConfig(streamGraph.getExecutionConfig());
}
catch (IOException e) {
throw new IllegalConfigurationException("Could not serialize the ExecutionConfig." +
"This indicates that non-serializable types (like custom serializers) were registered");
}
return jobGraph;
}
- 执行 setScheduleMode， 确保所有的 vertices 都立即执行。
- 执行 traverseStreamGraphAndGenerateHashes ， 生成对应的 hash 值。
- 执行 setExecutionConfig，配置初始化过的 ExecutionConfig。
执行 jobGraph
miniCluster.executeJobBlocking(jobGraph);
public JobExecutionResult executeJobBlocking(JobGraph job) throws JobExecutionException, InterruptedException {
checkNotNull(job, "job is null");
final CompletableFuture<JobSubmissionResult> submissionFuture = submitJob(job);
final CompletableFuture<JobResult> jobResultFuture = submissionFuture.thenCompose(
(JobSubmissionResult ignored) -> requestJobResult(job.getJobID()));
final JobResult jobResult;
try {
jobResult = jobResultFuture.get();
} catch (ExecutionException e) {
throw new JobExecutionException(job.getJobID(), "Could not retrieve JobResult.", ExceptionUtils.stripExecutionException(e));
}
try {
return jobResult.toJobExecutionResult(Thread.currentThread().getContextClassLoader());
} catch (IOException | ClassNotFoundException e) {
throw new JobExecutionException(job.getJobID(), e);
}
}
通过上面代码生成最终的 JobExecutionResult ，整个执行流程就结束了。
小结
通过对源码进行简单的分析，对我们的 Flink Job 有一个整体的执行概念，帮助我们进一步理解 Flink 程序的工作流程。
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