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StreamExecutionEnvironment
StreamExecutionEnvironment 是 Flink 在流模式下任务执行的上下文，也是我们编写 Flink 程序的入口。根据具体的执行环境不同，StreamExecutionEnvironment 有不同的具体实现类，如 LocalStreamEnvironment, RemoteStreamEnvironment 等。StreamExecutionEnvironment 也提供了用来配置默认并行度、Checkpointing 等机制的方法，这些配置主要都保存在 ExecutionConfig 和 CheckpointConfig 中
在 StreamExecutionEnvironment 内部使用一个 List<StreamTransformation<?>> transformations 来保留生成 DataStream 的所有转换
StreamTransformation 代表了生成 DataStream 的操作,在 DataStream 上面通过算子不断进行转换，就得到了由 StreamTransformation 构成的图。当需要执行的时候，底层的这个图就会被转换成 StreamGraph。
StreamTransformation 在运行时并不一定对应着一个物理转换操作，有一些操作只是逻辑层面上的，比如 split/select/partitioning 等。
每一个 StreamTransformation 都有一个关联的 Id，这个 Id 是全局递增的。除此以外，还有 uid, slotSharingGroup, parallelism 等信息。
StreamTransformation 有很多具体的子类，如SourceTransformation、 OneInputStreamTransformation、TwoInputTransformation、SideOutputTransformation、 SinkTransformation 等等，这些分别对应了DataStream 上的不同转换操作。
public OneInputTransformation(
Transformation<IN> input,
String name,
StreamOperatorFactory<OUT> operatorFactory,
TypeInformation<OUT> outputType,
int parallelism) {
super(name, outputType, parallelism);
this.input = input;
this.operatorFactory = operatorFactory;
}
public TwoInputTransformation(
Transformation<IN1> input1,
Transformation<IN2> input2,
String name,
StreamOperatorFactory<OUT> operatorFactory,
TypeInformation<OUT> outputType,
int parallelism) {
super(name, outputType, parallelism);
this.input1 = input1;
this.input2 = input2;
this.operatorFactory = operatorFactory;
}
DataStream
一个 DataStream 就表征了由同一种类型元素构成的数据流。通过对 DataStream 应用 map/filter 等操作，可以将一个 DataStream 转换为另一个 DataStream，这个转换的过程就是根据不同的操作生成不同的 StreamTransformation，并将其加入 StreamExecutionEnvironment 的 transformations 列表中。
@PublicEvolving
public <R> SingleOutputStreamOperator<R> transform(String operatorName, TypeInformation<R> outTypeInfo, OneInputStreamOperator<T, R> operator) {
transformation.getOutputType();
//构造 StreamTransformation
OneInputTransformation<T, R> resultTransform = new OneInputTransformation<>(
this.transformation,
operatorName,
operator,
outTypeInfo,
environment.getParallelism());
@SuppressWarnings({ "unchecked", "rawtypes" })
SingleOutputStreamOperator<R> returnStream = new SingleOutputStreamOperator(environment, resultTransform);
//加入到 StreamExecutionEnvironment 的列表中
getExecutionEnvironment().addOperator(resultTransform);
return returnStream;
}
DataStream 的子类包括 SingleOutputStreamOperator、 DataStreamSource KeyedStream 、IterativeStream, SplitStream(已弃用)。这里要吐槽一下 SingleOutputStreamOperator 的这个类的命名，太容易和 StreamOperator 混淆了。StreamOperator 的介绍见下一小节。
除了 DataStream 及其子类以外，其它的表征数据流的类还有 ConnectedStreams (两个流连接在一起)、 WindowedStream、AllWindowedStream 。这些数据流之间的转换可以参考 Flink 的官方文档。
StreamOperator
在操作 DataStream 的时候，比如 DataStream#map 等，会要求我们实现一个自定义的处理函数。那么这些信息时如何保存在 StreamTransformation 中的呢？这里就要引入一个新的接口 StreamOperator。
StreamOperator 定义了对一个具体的算子的生命周期的管理，包括：
//生命周期
void setup(StreamTask<?, ?> containingTask, StreamConfig config, Output<StreamRecord<OUT>> output);
void open() throws Exception;
void close() throws Exception;
@Override
void dispose() throws Exception;
//状态管理
OperatorSnapshotFutures snapshotState(
long checkpointId,
long timestamp,
CheckpointOptions checkpointOptions,
CheckpointStreamFactory storageLocation) throws Exception;
void initializeState() throws Exception;
//其它方法暂时省略
StreamOperator 的两个子接口 OneInputStreamOperator 和 TwoInputStreamOperator 则提供了操作数据流中具体元素的方法，而 AbstractUdfStreamOperator 这个抽象子类则提供了自定义处理函数对应的算子的基本实现:
#OneInputStreamOperator
void processElement(StreamRecord<IN> element) throws Exception;
void processWatermark(Watermark mark) throws Exception;
void processLatencyMarker(LatencyMarker latencyMarker) throws Exception;
#TwoInputStreamOperator
void processElement1(StreamRecord<IN1> element) throws Exception;
void processElement2(StreamRecord<IN2> element) throws Exception;
void processWatermark1(Watermark mark) throws Exception;
void processWatermark2(Watermark mark) throws Exception;
#AbstractStreamOperator
提供了initializeState、snapshotState等基本方法的实现
至于具体到诸如 map/fliter 等操作对应的 StreamOperator，基本都是在 AbstractUdfStreamOperator 的基础上实现的。以 StreamMap 为例
@Internal
public class StreamMap<IN, OUT>
extends AbstractUdfStreamOperator<OUT, MapFunction<IN, OUT>>
implements OneInputStreamOperator<IN, OUT> {
private static final long serialVersionUID = 1L;
public StreamMap(MapFunction<IN, OUT> mapper) {
super(mapper);
chainingStrategy = ChainingStrategy.ALWAYS;
}
@Override
public void processElement(StreamRecord<IN> element) throws Exception {
output.collect(element.replace(userFunction.map(element.getValue())));
}
}
通过 DataStream –> StreamTransformation –> StreamOperator 这样的依赖关系，就可以完成 DataStream 的转换，并且保留数据流和应用在流上的算子之间的关系
StreamGraph
StreamGraphGenerator 会基于 StreamExecutionEnvironment 的 transformations 列表来生成 StreamGraph
在遍历 List<StreamTransformation> 生成 StreamGraph 的时候，会递归调用StreamGraphGenerator#transform方法。对于每一个 StreamTransformation, 确保当前其上游已经完成转换。StreamTransformations 被转换为 StreamGraph 中的节点 StreamNode，并为上下游节点添加边 StreamEdge
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);
}
对于不同类型的 StreamTransformation，分别调用对应的转换方法，以 最典型的 transformOneInputTransform 为例：
private <IN, OUT> Collection<Integer> transformOneInputTransform(OneInputTransformation<IN, OUT> transform) {
//首先确保上游节点完成转换
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.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());
}
对于不同类型的 StreamTransformation，分别调用对应的转换方法，以 最典型的 transformOneInputTransform 为例：
private <IN, OUT> Collection<Integer> transformOneInputTransform(OneInputTransformation<IN, OUT> transform) {
//首先确保上游节点完成转换
Collection<Integer> inputIds = transform(transform.getInput());
// 由于是递归调用的，可能已经完成了转换
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());
}
StreamGraph 中对应的添加节点和边的方法:
protected StreamNode addNode(Integer vertexID,
@Nullable String slotSharingGroup,
@Nullable String coLocationGroup,
Class<? extends AbstractInvokable> 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<OutputSelector<?>>(),
vertexClass);
//创建 StreamNode，这里保存了 StreamOperator 和 vertexClass 信息
streamNodes.put(vertexID, vertex);
return vertex;
}
在 StreamNode 中，保存了对应的 StreamOperator (从 StreamTransformation 得到)，并且还引入了变量 jobVertexClass 来表示该节点在 TaskManager 中运行时的实际任务类型
Class<? extends AbstractInvokable> jobVertexClass
AbstractInvokable 是所有可以在 TaskManager 中运行的任务的抽象基础类，包括流式任务和批任务。StreamTask 是所有流式任务的基础类，其具体的子类包括 SourceStreamTask, OneInputStreamTask, TwoInputStreamTask 等。
对于一些不包含物理转换操作的 StreamTransformation，如 Partitioning, split/select, union，并不会生成 StreamNode，而是生成一个带有特定属性的虚拟节点。当添加一条有虚拟节点指向下游节点的边时，会找到虚拟节点上游的物理节点，在两个物理节点之间添加边，并把虚拟转换操作的属性附着上去。
以 PartitionTansformation 为例， PartitionTansformation 是 KeyedStream 对应的转换：
//StreamGraphGenerator#transformPartition
private <T> Collection<Integer> transformPartition(PartitionTransformation<T> partition) {
Transformation<T> input = partition.getInput();
//递归地转换上游节点
List<Integer> resultIds = new ArrayList<>();
//上游输入节点
Collection<Integer> transformedIds = transform(input);
for (Integer transformedId: transformedIds) {
int virtualId = Transformation.getNewNodeId();
//添加虚拟的 Partition 节点
streamGraph.addVirtualPartitionNode(
transformedId, virtualId, partition.getPartitioner(), partition.getShuffleMode());
resultIds.add(virtualId);
}
return resultIds;
}
// StreamGraph#addVirtualPartitionNode
public void addVirtualPartitionNode(
Integer originalId,
Integer virtualId,
StreamPartitioner<?> partitioner,
ShuffleMode shuffleMode) {
if (virtualPartitionNodes.containsKey(virtualId)) {
throw new IllegalStateException("Already has virtual partition node with id " + virtualId);
}
//添加一个虚拟节点，后续添加边的时候会连接到实际的物理节点
virtualPartitionNodes.put(virtualId, new Tuple3<>(originalId, partitioner, shuffleMode));
}
前面提到，在每一个物理节点的转换上，会调用 StreamGraph#addEdge 在输入节点和当前节点之间建立边的连接：
private void addEdgeInternal(Integer upStreamVertexID,
Integer downStreamVertexID,
int typeNumber,
StreamPartitioner<?> partitioner,
List<String> outputNames,
OutputTag outputTag,
ShuffleMode shuffleMode) {
//先判断是不是虚拟节点上的边，如果是，则找到虚拟节点上游对应的物理节点
//在两个物理节点之间添加边，并把对应的 StreamPartitioner,或者 OutputTag 等补充信息添加到StreamEdge中
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, shuffleMode);
} 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, shuffleMode);
} else if (virtualPartitionNodes.containsKey(upStreamVertexID)) {
int virtualId = upStreamVertexID;
upStreamVertexID = virtualPartitionNodes.get(virtualId).f0;
if (partitioner == null) {
partitioner = virtualPartitionNodes.get(virtualId).f1;
}
shuffleMode = virtualPartitionNodes.get(virtualId).f2;
addEdgeInternal(upStreamVertexID, downStreamVertexID, typeNumber, partitioner, outputNames, outputTag, shuffleMode);
} else {
//两个物理节点
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.");
}
}
if (shuffleMode == null) {
shuffleMode = ShuffleMode.UNDEFINED;
}
//创建 StreamEdge，保留了 StreamPartitioner 等属性
StreamEdge edge = new StreamEdge(upstreamNode, downstreamNode, typeNumber, outputNames, partitioner, outputTag, shuffleMode);
//分别将StreamEdge添加到上游节点和下游节点
getStreamNode(edge.getSourceId()).addOutEdge(edge);
getStreamNode(edge.getTargetId()).addInEdge(edge);
}
}
这样通过 StreamNode 和 SteamEdge，就得到了 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
参考自
https://ververica.cn/developers/advanced-tutorial-2-flink-job-execution-depth-analysis/
https://blog.jrwang.me/tags/flink/
https://ververica.cn/developers/advanced-tutorial-1-analysis-of-the-core-mechanism-of-runtime/
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