本文内容是基于Flink 1.9来讲解。在执行Flink任务的时候，会涉及到三个Graph，分别是StreamGraph，JobGraph，ExecutionGraph。其中StreamGraph和JobGraph是在client端生成的，ExecutionGraph是在JobMaster中执行的。
- StreamGraph是根据用户代码生成的最原始执行图，也就是直接翻译用户逻辑得到的图
- JobGraph是对StreamGraph进行优化，比如设置哪些算子可以chain，减少网络开销
- ExecutionGraph是用于作业调度的执行图，对JobGraph加了并行度的概念
本篇文章首先介绍下StreamGraph的生成
1. transformations生成
Flink引擎有很多算子，比如map, flatMap, join等，这些算子都会生成一个transformation。比如对于flatMap算子，咱们跟下源码，看下DataStream#flatMap方法
public SingleOutputStreamOperator flatMap(FlatMapFunction flatMapper) {
TypeInformation outType = TypeExtractor.getFlatMapReturnTypes(clean(flatMapper),
getType(), Utils.getCallLocationName(), true);
return transform("Flat Map", outType, new StreamFlatMap<>(clean(flatMapper)));
}
- 先获取返回值类型相关信息
- transform算子，跟进去源码继续看的话，会首先构建一个OneInputTransformation对象，然后把该对象加入StreamExecutionEnvironment 的 transformations对象中
2. StreamGraph生成入口 StreamGraphGenerator#generate()方法
public StreamGraph generate() {
streamGraph = new StreamGraph(executionConfig, checkpointConfig);
streamGraph.setStateBackend(stateBackend);
streamGraph.setChaining(chaining);
streamGraph.setScheduleMode(scheduleMode);
streamGraph.setUserArtifacts(userArtifacts);
streamGraph.setTimeCharacteristic(timeCharacteristic);
streamGraph.setJobName(jobName);
streamGraph.setBlockingConnectionsBetweenChains(blockingConnectionsBetweenChains);
alreadyTransformed = new HashMap<>();
for (Transformation transformation: transformations) {
transform(transformation);
}
final StreamGraph builtStreamGraph = streamGraph;
alreadyTransformed.clear();
alreadyTransformed = null;
streamGraph = null;
return builtStreamGraph;
}
这个generate方法会对所有的transformations进行转换，咱们接着看下transform逻辑
/**
* Transforms one {@code Transformation}.
*
* This checks whether we already transformed it and exits early in that case. If not it
* delegates to one of the transformation specific methods.
*/
private Collection 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 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());
} 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());
}
return transformedIds;
}
从源码可以看出，transform在构建的时候，会有多种类型，比如分为Source, Sink, OneInput, Split等。比如flatMap，就属于OneInputTransformation，接下来以比较常见的transformOneInputTransform进行介绍。
/**
* Transforms a {@code OneInputTransformation}.
*
* This recursively transforms the inputs, creates a new {@code StreamNode} in the graph and
* wired the inputs to this new node.
*/
private Collection transformOneInputTransform(OneInputTransformation transform) {
Collection 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.getOperatorFactory(),
transform.getInputType(),
transform.getOutputType(),
transform.getName());
if (transform.getStateKeySelector() != null) {
TypeSerializer keySerializer = transform.getStateKeyType().createSerializer(executionConfig);
streamGraph.setOneInputStateKey(transform.getId(), transform.getStateKeySelector(), keySerializer);
}
int parallelism = transform.getParallelism() != ExecutionConfig.PARALLELISM_DEFAULT ?
transform.getParallelism() : executionConfig.getParallelism();
streamGraph.setParallelism(transform.getId(), parallelism);
streamGraph.setMaxParallelism(transform.getId(), transform.getMaxParallelism());
for (Integer inputId: inputIds) {
streamGraph.addEdge(inputId, transform.getId(), 0);
}
return Collections.singleton(transform.getId());
}
- 递归调用该算子所有的input节点
- 把该Operator加入streamGraph中，实际会生成一个StreamNode对象加入streamGraph。
1. 首先调用streamGraph.addOperator
2. 然后调用addNode方法
protected StreamNode addNode(Integer vertexID,
@Nullable String slotSharingGroup,
@Nullable String coLocationGroup,
Class vertexClass,
StreamOperatorFactory operatorFactory,
String operatorName) {
if (streamNodes.containsKey(vertexID)) {
throw new RuntimeException("Duplicate vertexID " + vertexID);
}
StreamNode vertex = new StreamNode(
vertexID,
slotSharingGroup,
coLocationGroup,
operatorFactory,
operatorName,
new ArrayList>(),
vertexClass);
streamNodes.put(vertexID, vertex);
return vertex;
}
这个addNode方法，会把该operator转换成一个StreamNode，然后加到StreamGraph中，vertexID对应transform.getId()
- 为该Operator的所有输入与该Operator之间加上StreamEdge
这样通过 StreamNode 和 SteamEdge，就构建出了 DAG 中的所有节点和边，以及它们之间的连接关系，拓扑结构也就建立了。
3. 小结
StreamGraph其实就是由用户代码中涉及到transformations转换来的，SteamEdge用来表示transformation之间的连接关系，StreamNode用来表示具体的operator。
- 从sink节点开始遍历
- 每个transformation，会在StreamGraph中新创建一个StreamNode，并且把新创建的StreamNode和它所有的input之间添加SteamEdge。
- Partitioning, split/select 和 union 并不会在StreamNode中增加一个真实的StreamNode，而是创建一个具有特殊属性的虚拟节点，比如partitioning, selector等，也就是在边上加了属性信息。