聊聊flink的SpoutWrapper（flink proctime）

序

本文主要研究一下flink的SpoutWrapper

SpoutWrapper

flink-storm_2.11-1.6.2-sources.jar!/org/apache/flink/storm/wrappers/SpoutWrapper.java

/**
 * A {@link SpoutWrapper} wraps an {@link IRichSpout} in order to execute it within a Flink Streaming program. It
 * takes the spout's output tuples and transforms them into Flink tuples of type {@code OUT} (see
 * {@link SpoutCollector} for supported types).<br>
 * <br>
 * Per default, {@link SpoutWrapper} calls the wrapped spout's {@link IRichSpout#nextTuple() nextTuple()} method in
 * an infinite loop.<br>
 * Alternatively, {@link SpoutWrapper} can call {@link IRichSpout#nextTuple() nextTuple()} for a finite number of
 * times and terminate automatically afterwards (for finite input streams). The number of {@code nextTuple()} calls can
 * be specified as a certain number of invocations or can be undefined. In the undefined case, {@link SpoutWrapper}
 * terminates if no record was emitted to the output collector for the first time during a call to
 * {@link IRichSpout#nextTuple() nextTuple()}.<br>
 * If the given spout implements {@link FiniteSpout} interface and {@link #numberOfInvocations} is not provided or
 * is {@code null}, {@link SpoutWrapper} calls {@link IRichSpout#nextTuple() nextTuple()} method until
 * {@link FiniteSpout#reachedEnd()} returns true.
 */
public final class SpoutWrapper<OUT> extends RichParallelSourceFunction<OUT> implements StoppableFunction {
 //......
?
 /** The number of {@link IRichSpout#nextTuple()} calls. */
 private Integer numberOfInvocations; // do not use int -> null indicates an infinite loop
?
 /**
 * Instantiates a new {@link SpoutWrapper} that calls the {@link IRichSpout#nextTuple() nextTuple()} method of
 * the given {@link IRichSpout spout} a finite number of times. The output type will be one of {@link Tuple0} to
 * {@link Tuple25} depending on the spout's declared number of attributes.
 *
 * @param spout
 * The {@link IRichSpout spout} to be used.
 * @param numberOfInvocations
 * The number of calls to {@link IRichSpout#nextTuple()}. If value is negative, {@link SpoutWrapper}
 * terminates if no tuple was emitted for the first time. If value is {@code null}, finite invocation is
 * disabled.
 * @throws IllegalArgumentException
 * If the number of declared output attributes is not with range [0;25].
 */
 public SpoutWrapper(final IRichSpout spout, final Integer numberOfInvocations)
 throws IllegalArgumentException {
 this(spout, (Collection<String>) null, numberOfInvocations);
 }
?
 /**
 * Instantiates a new {@link SpoutWrapper} that calls the {@link IRichSpout#nextTuple() nextTuple()} method of
 * the given {@link IRichSpout spout} in an infinite loop. The output type will be one of {@link Tuple0} to
 * {@link Tuple25} depending on the spout's declared number of attributes.
 *
 * @param spout
 * The {@link IRichSpout spout} to be used.
 * @throws IllegalArgumentException
 * If the number of declared output attributes is not with range [0;25].
 */
 public SpoutWrapper(final IRichSpout spout) throws IllegalArgumentException {
 this(spout, (Collection<String>) null, null);
 }
?
 @Override
 public final void run(final SourceContext<OUT> ctx) throws Exception {
 final GlobalJobParameters config = super.getRuntimeContext().getExecutionConfig()
 .getGlobalJobParameters();
 StormConfig stormConfig = new StormConfig();
?
 if (config != null) {
 if (config instanceof StormConfig) {
 stormConfig = (StormConfig) config;
 } else {
 stormConfig.putAll(config.toMap());
 }
 }
?
 final TopologyContext stormTopologyContext = WrapperSetupHelper.createTopologyContext(
 (StreamingRuntimeContext) super.getRuntimeContext(), this.spout, this.name,
 this.stormTopology, stormConfig);
?
 SpoutCollector<OUT> collector = new SpoutCollector<OUT>(this.numberOfAttributes,
 stormTopologyContext.getThisTaskId(), ctx);
?
 this.spout.open(stormConfig, stormTopologyContext, new SpoutOutputCollector(collector));
 this.spout.activate();
?
 if (numberOfInvocations == null) {
 if (this.spout instanceof FiniteSpout) {
 final FiniteSpout finiteSpout = (FiniteSpout) this.spout;
?
 while (this.isRunning && !finiteSpout.reachedEnd()) {
 finiteSpout.nextTuple();
 }
 } else {
 while (this.isRunning) {
 this.spout.nextTuple();
 }
 }
 } else {
 int counter = this.numberOfInvocations;
 if (counter >= 0) {
 while ((--counter >= 0) && this.isRunning) {
 this.spout.nextTuple();
 }
 } else {
 do {
 collector.tupleEmitted = false;
 this.spout.nextTuple();
 } while (collector.tupleEmitted && this.isRunning);
 }
 }
 }
?
 /**
 * {@inheritDoc}
 *
 * <p>Sets the {@link #isRunning} flag to {@code false}.
 */
 @Override
 public void cancel() {
 this.isRunning = false;
 }
?
 /**
 * {@inheritDoc}
 *
 * <p>Sets the {@link #isRunning} flag to {@code false}.
 */
 @Override
 public void stop() {
 this.isRunning = false;
 }
?
 @Override
 public void close() throws Exception {
 this.spout.close();
 }
}

• SpoutWrapper继承了RichParallelSourceFunction类，实现了StoppableFunction接口的stop方法
• SpoutWrapper的run方法创建了flink的SpoutCollector作为storm的SpoutOutputCollector的构造器参数，之后调用spout的open方法，把包装了SpoutCollector(flink)的SpoutOutputCollector传递给spout，用来收集spout发射的数据
• 之后就是根据numberOfInvocations参数来调用spout.nextTuple()方法来发射数据；numberOfInvocations是控制调用spout的nextTuple的次数，它可以在创建SpoutWrapper的时候在构造器中设置，如果使用没有numberOfInvocations参数的构造器，则该值为null，表示infinite loop
• flink对storm的spout有进行封装，提供了FiniteSpout接口，它有个reachedEnd接口用来判断数据是否发送完毕，来将storm的spout改造为finite模式；这里如果使用的是storm原始的spout，则就是一直循环调用nextTuple方法
• 如果有设置numberOfInvocations而且大于等于0，则根据指定的次数来调用nextTuple方法；如果该值小于0，则根据collector.tupleEmitted值来判断是否终止循环

SpoutCollector

flink-storm_2.11-1.6.2-sources.jar!/org/apache/flink/storm/wrappers/SpoutCollector.java

/**
 * A {@link SpoutCollector} is used by {@link SpoutWrapper} to provided an Storm
 * compatible output collector to the wrapped spout. It transforms the emitted Storm tuples into
 * Flink tuples and emits them via the provide {@link SourceContext} object.
 */
class SpoutCollector<OUT> extends AbstractStormCollector<OUT> implements ISpoutOutputCollector {
?
 /** The Flink source context object. */
 private final SourceContext<OUT> flinkContext;
?
 /**
 * Instantiates a new {@link SpoutCollector} that emits Flink tuples to the given Flink source context. If the
 * number of attributes is specified as zero, any output type is supported. If the number of attributes is between 0
 * to 25, the output type is {@link Tuple0} to {@link Tuple25}, respectively.
 *
 * @param numberOfAttributes
 * The number of attributes of the emitted tuples.
 * @param taskId
 * The ID of the producer task (negative value for unknown).
 * @param flinkContext
 * The Flink source context to be used.
 * @throws UnsupportedOperationException
 * if the specified number of attributes is greater than 25
 */
 SpoutCollector(final HashMap<String, Integer> numberOfAttributes, final int taskId,
 final SourceContext<OUT> flinkContext) throws UnsupportedOperationException {
 super(numberOfAttributes, taskId);
 assert (flinkContext != null);
 this.flinkContext = flinkContext;
 }
?
 @Override
 protected List<Integer> doEmit(final OUT flinkTuple) {
 this.flinkContext.collect(flinkTuple);
 // TODO
 return null;
 }
?
 @Override
 public void reportError(final Throwable error) {
 // not sure, if Flink can support this
 }
?
 @Override
 public List<Integer> emit(final String streamId, final List<Object> tuple, final Object messageId) {
 return this.tansformAndEmit(streamId, tuple);
 }
?
 @Override
 public void emitDirect(final int taskId, final String streamId, final List<Object> tuple, final Object messageId) {
 throw new UnsupportedOperationException("Direct emit is not supported by Flink");
 }
?
 public long getPendingCount() {
 return 0;
 }
?
}

• SpoutCollector实现了storm的ISpoutOutputCollector接口，实现了该接口定义的emit、emitDirect、getPendingCount、reportError方法；flink目前不支持emitDirect方法，另外getPendingCount也始终返回0，reportError方法是个空操作
• doEmit里头调用flinkContext.collect(flinkTuple)来发射数据，该方法为protected，主要是给tansformAndEmit调用的
• tansformAndEmit方法由父类AbstractStormCollector提供

AbstractStormCollector.tansformAndEmit

flink-storm_2.11-1.6.2-sources.jar!/org/apache/flink/storm/wrappers/AbstractStormCollector.java

 /**
 * Transforms a Storm tuple into a Flink tuple of type {@code OUT} and emits this tuple via {@link #doEmit(Object)}
 * to the specified output stream.
 *
 * @param The
 * The output stream id.
 * @param tuple
 * The Storm tuple to be emitted.
 * @return the return value of {@link #doEmit(Object)}
 */
 @SuppressWarnings("unchecked")
 protected final List<Integer> tansformAndEmit(final String streamId, final List<Object> tuple) {
 List<Integer> taskIds;
?
 int numAtt = this.numberOfAttributes.get(streamId);
 int taskIdIdx = numAtt;
 if (this.taskId >= 0 && numAtt < 0) {
 numAtt = 1;
 taskIdIdx = 0;
 }
 if (numAtt >= 0) {
 assert (tuple.size() == numAtt);
 Tuple out = this.outputTuple.get(streamId);
 for (int i = 0; i < numAtt; ++i) {
 out.setField(tuple.get(i), i);
 }
 if (this.taskId >= 0) {
 out.setField(this.taskId, taskIdIdx);
 }
 if (this.split) {
 this.splitTuple.streamId = streamId;
 this.splitTuple.value = out;
?
 taskIds = doEmit((OUT) this.splitTuple);
 } else {
 taskIds = doEmit((OUT) out);
 }
?
 } else {
 assert (tuple.size() == 1);
 if (this.split) {
 this.splitTuple.streamId = streamId;
 this.splitTuple.value = tuple.get(0);
?
 taskIds = doEmit((OUT) this.splitTuple);
 } else {
 taskIds = doEmit((OUT) tuple.get(0));
 }
 }
 this.tupleEmitted = true;
?
 return taskIds;
 }

• AbstractStormCollector.tansformAndEmit，这里主要处理了split的场景，即一个spout declare了多个stream，最后都通过子类SpoutCollector.doEmit来发射数据
• 如果split为true，则传给doEmit方法的是splitTuple，即SplitStreamType，它记录了streamId及其value
• 如果split为false，则传给doEmit方法的是Tuple类型，即相当于SplitStreamType中的value，相比于SplitStreamType少了streamId信息

Task.run

flink-runtime_2.11-1.6.2-sources.jar!/org/apache/flink/runtime/taskmanager/Task.java

/**
 * The Task represents one execution of a parallel subtask on a TaskManager.
 * A Task wraps a Flink operator (which may be a user function) and
 * runs it, providing all services necessary for example to consume input data,
 * produce its results (intermediate result partitions) and communicate
 * with the JobManager.
 *
 * <p>The Flink operators (implemented as subclasses of
 * {@link AbstractInvokable} have only data readers, -writers, and certain event callbacks.
 * The task connects those to the network stack and actor messages, and tracks the state
 * of the execution and handles exceptions.
 *
 * <p>Tasks have no knowledge about how they relate to other tasks, or whether they
 * are the first attempt to execute the task, or a repeated attempt. All of that
 * is only known to the JobManager. All the task knows are its own runnable code,
 * the task's configuration, and the IDs of the intermediate results to consume and
 * produce (if any).
 *
 * <p>Each Task is run by one dedicated thread.
 */
public class Task implements Runnable, TaskActions, CheckpointListener {
 //......
?
 /**
 * The core work method that bootstraps the task and executes its code.
 */
 @Override
 public void run() {
 //......
 // now load and instantiate the task's invokable code
 invokable = loadAndInstantiateInvokable(userCodeClassLoader, nameOfInvokableClass, env);
?
 // ----------------------------------------------------------------
 // actual task core work
 // ----------------------------------------------------------------
?
 // we must make strictly sure that the invokable is accessible to the cancel() call
 // by the time we switched to running.
 this.invokable = invokable;
?
 // switch to the RUNNING state, if that fails, we have been canceled/failed in the meantime
 if (!transitionState(ExecutionState.DEPLOYING, ExecutionState.RUNNING)) {
 throw new CancelTaskException();
 }
?
 // notify everyone that we switched to running
 notifyObservers(ExecutionState.RUNNING, null);
 taskManagerActions.updateTaskExecutionState(new TaskExecutionState(jobId, executionId, ExecutionState.RUNNING));
?
 // make sure the user code classloader is accessible thread-locally
 executingThread.setContextClassLoader(userCodeClassLoader);
?
 // run the invokable
 invokable.invoke();
?
 //......
 }
}

• Task的run方法会调用invokable.invoke()，这里的invokable为StreamTask

StreamTask

flink-streaming-java_2.11-1.6.2-sources.jar!/org/apache/flink/streaming/runtime/tasks/StreamTask.java

/**
 * Base class for all streaming tasks. A task is the unit of local processing that is deployed
 * and executed by the TaskManagers. Each task runs one or more {@link StreamOperator}s which form
 * the Task's operator chain. Operators that are chained together execute synchronously in the
 * same thread and hence on the same stream partition. A common case for these chains
 * are successive map/flatmap/filter tasks.
 *
 * <p>The task chain contains one "head" operator and multiple chained operators.
 * The StreamTask is specialized for the type of the head operator: one-input and two-input tasks,
 * as well as for sources, iteration heads and iteration tails.
 *
 * <p>The Task class deals with the setup of the streams read by the head operator, and the streams
 * produced by the operators at the ends of the operator chain. Note that the chain may fork and
 * thus have multiple ends.
 *
 * <p>The life cycle of the task is set up as follows:
 * <pre>{@code
 * -- setInitialState -> provides state of all operators in the chain
 *
 * -- invoke()
 * |
 * +----> Create basic utils (config, etc) and load the chain of operators
 * +----> operators.setup()
 * +----> task specific init()
 * +----> initialize-operator-states()
 * +----> open-operators()
 * +----> run()
 * +----> close-operators()
 * +----> dispose-operators()
 * +----> common cleanup
 * +----> task specific cleanup()
 * }</pre>
 *
 * <p>The {@code StreamTask} has a lock object called {@code lock}. All calls to methods on a
 * {@code StreamOperator} must be synchronized on this lock object to ensure that no methods
 * are called concurrently.
 *
 * @param <OUT>
 * @param <OP>
 */
@Internal
public abstract class StreamTask<OUT, OP extends StreamOperator<OUT>>
 extends AbstractInvokable
 implements AsyncExceptionHandler {
?
 //......
?
 @Override
 public final void invoke() throws Exception {
?
 boolean disposed = false;
 try {
 //......
?
 // let the task do its work
 isRunning = true;
 run();
?
 // if this left the run() method cleanly despite the fact that this was canceled,
 // make sure the "clean shutdown" is not attempted
 if (canceled) {
 throw new CancelTaskException();
 }
?
 LOG.debug("Finished task {}", getName());
?
 //......
 }
 finally {
 // clean up everything we initialized
 isRunning = false;
?
 //......
 }
 }
}

• StreamTask的invoke方法里头调用子类的run方法，这里子类为StoppableSourceStreamTask

StoppableSourceStreamTask

flink-streaming-java_2.11-1.6.2-sources.jar!/org/apache/flink/streaming/runtime/tasks/StoppableSourceStreamTask.java

/**
 * Stoppable task for executing stoppable streaming sources.
 *
 * @param <OUT> Type of the produced elements
 * @param <SRC> Stoppable source function
 */
public class StoppableSourceStreamTask<OUT, SRC extends SourceFunction<OUT> & StoppableFunction>
 extends SourceStreamTask<OUT, SRC, StoppableStreamSource<OUT, SRC>> implements StoppableTask {
?
 private volatile boolean stopped;
?
 public StoppableSourceStreamTask(Environment environment) {
 super(environment);
 }
?
 @Override
 protected void run() throws Exception {
 if (!stopped) {
 super.run();
 }
 }
?
 @Override
 public void stop() {
 stopped = true;
 if (this.headOperator != null) {
 this.headOperator.stop();
 }
 }
}

• StoppableSourceStreamTask继承了SourceStreamTask，主要是实现了StoppableTask的stop方法，它的run方法由其直接父类SourceStreamTask来实现

SourceStreamTask

flink-streaming-java_2.11-1.6.2-sources.jar!/org/apache/flink/streaming/runtime/tasks/SourceStreamTask.java

/**
 * {@link StreamTask} for executing a {@link StreamSource}.
 *
 * <p>One important aspect of this is that the checkpointing and the emission of elements must never
 * occur at the same time. The execution must be serial. This is achieved by having the contract
 * with the StreamFunction that it must only modify its state or emit elements in
 * a synchronized block that locks on the lock Object. Also, the modification of the state
 * and the emission of elements must happen in the same block of code that is protected by the
 * synchronized block.
 *
 * @param <OUT> Type of the output elements of this source.
 * @param <SRC> Type of the source function for the stream source operator
 * @param <OP> Type of the stream source operator
 */
@Internal
public class SourceStreamTask<OUT, SRC extends SourceFunction<OUT>, OP extends StreamSource<OUT, SRC>>
 extends StreamTask<OUT, OP> {
?
 //......
?
 @Override
 protected void run() throws Exception {
 headOperator.run(getCheckpointLock(), getStreamStatusMaintainer());
 }
}

• SourceStreamTask主要是调用StreamSource的run方法

StreamSource

flink-streaming-java_2.11-1.6.2-sources.jar!/org/apache/flink/streaming/api/operators/StreamSource.java

/**
 * {@link StreamOperator} for streaming sources.
 *
 * @param <OUT> Type of the output elements
 * @param <SRC> Type of the source function of this stream source operator
 */
@Internal
public class StreamSource<OUT, SRC extends SourceFunction<OUT>>
 extends AbstractUdfStreamOperator<OUT, SRC> implements StreamOperator<OUT> {
?
 //...... 
?
 public void run(final Object lockingObject, final StreamStatusMaintainer streamStatusMaintainer) throws Exception {
 run(lockingObject, streamStatusMaintainer, output);
 }
?
 public void run(final Object lockingObject,
 final StreamStatusMaintainer streamStatusMaintainer,
 final Output<StreamRecord<OUT>> collector) throws Exception {
?
 final TimeCharacteristic timeCharacteristic = getOperatorConfig().getTimeCharacteristic();
?
 final Configuration configuration = this.getContainingTask().getEnvironment().getTaskManagerInfo().getConfiguration();
 final long latencyTrackingInterval = getExecutionConfig().isLatencyTrackingConfigured()
 ? getExecutionConfig().getLatencyTrackingInterval()
 : configuration.getLong(MetricOptions.LATENCY_INTERVAL);
?
 LatencyMarksEmitter<OUT> latencyEmitter = null;
 if (latencyTrackingInterval > 0) {
 latencyEmitter = new LatencyMarksEmitter<>(
 getProcessingTimeService(),
 collector,
 latencyTrackingInterval,
 this.getOperatorID(),
 getRuntimeContext().getIndexOfThisSubtask());
 }
?
 final long watermarkInterval = getRuntimeContext().getExecutionConfig().getAutoWatermarkInterval();
?
 this.ctx = StreamSourceContexts.getSourceContext(
 timeCharacteristic,
 getProcessingTimeService(),
 lockingObject,
 streamStatusMaintainer,
 collector,
 watermarkInterval,
 -1);
?
 try {
 userFunction.run(ctx);
?
 // if we get here, then the user function either exited after being done (finite source)
 // or the function was canceled or stopped. For the finite source case, we should emit
 // a final watermark that indicates that we reached the end of event-time
 if (!isCanceledOrStopped()) {
 ctx.emitWatermark(Watermark.MAX_WATERMARK);
 }
 } finally {
 // make sure that the context is closed in any case
 ctx.close();
 if (latencyEmitter != null) {
 latencyEmitter.close();
 }
 }
 }

• 它调用了userFunction.run(ctx)，这里的userFunction为SpoutWrapper，从而完成spout的nextTuple的触发

小结

• flink使用SpoutWrapper来包装storm原始的spout，它在run方法里头创建了flink的SpoutCollector作为storm的SpoutOutputCollector的构造器参数，之后调用spout的open方法，把包装了SpoutCollector(flink)的SpoutOutputCollector传递给spout，用来收集spout发射的数据；之后就是根据numberOfInvocations参数来调用spout.nextTuple()方法来发射数据；numberOfInvocations是控制调用spout的nextTuple的次数，它可以在创建SpoutWrapper的时候在构造器中设置，如果使用没有numberOfInvocations参数的构造器，则该值为null，表示infinite loop
• SpoutCollector的emit方法内部调用了AbstractStormCollector.tansformAndEmit(它最后调用SpoutCollector.doEmit方法来发射)，针对多个stream的场景，封装了SplitStreamType的tuple给到doEmit方法；如果只有一个stream，则仅仅将普通的tuple传给doEmit方法
• flink的Task的run方法会调用StreamTask的invoke方法，而StreamTask的invoke方法会调用子类(这里子类为StoppableSourceStreamTask)的run方法，StoppableSourceStreamTask的run方法是直接父类SourceStreamTask来实现的，而它主要是调用了StreamSource的run方法，而StreamSource的run方法调用了userFunction.run(ctx)，这里的userFunction为SpoutWrapper，从而执行spout的nextTuple的逻辑，通过flink的SpoutCollector进行发射

doc

• Storm Compatibility Beta