聊聊flink KeyedStream的reduce操做

時間  2019-12-14
標籤 聊聊 flink keyedstream reduce 简体版
原文   原文鏈接

序

本文主要研究一下flink KeyedStream的reduce操做java

實例

@Test
    public void testWordCount() throws Exception {
        // Checking input parameters
//        final ParameterTool params = ParameterTool.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 = env.fromElements(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)
                        .reduce(new ReduceFunction<Tuple2<String, Integer>>() {
                            @Override
                            public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1, Tuple2<String, Integer> value2) {
                                System.out.println("value1:"+value1.f1+";value2:"+value2.f1);
                                return new Tuple2<>(value1.f0, value1.f1 + value2.f1);
                            }
                        });

        // emit result
        System.out.println("Printing result to stdout. Use --output to specify output path.");
        counts.print();

        // execute program
        env.execute("Streaming WordCount");
    }
  • 這裏對KeyedStream進行reduce操做，自定義了ReduceFunction，在reduce方法裏頭累加word的計數

KeyedStream.reduce

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/KeyedStream.javaweb

@Public
public class KeyedStream<T, KEY> extends DataStream<T> {
	//......

	/**
	 * Applies a reduce transformation on the grouped data stream grouped on by
	 * the given key position. The {@link ReduceFunction} will receive input
	 * values based on the key value. Only input values with the same key will
	 * go to the same reducer.
	 *
	 * @param reducer
	 *            The {@link ReduceFunction} that will be called for every
	 *            element of the input values with the same key.
	 * @return The transformed DataStream.
	 */
	public SingleOutputStreamOperator<T> reduce(ReduceFunction<T> reducer) {
		return transform("Keyed Reduce", getType(), new StreamGroupedReduce<T>(
				clean(reducer), getType().createSerializer(getExecutionConfig())));
	}

	@Override
	@PublicEvolving
	public <R> SingleOutputStreamOperator<R> transform(String operatorName,
			TypeInformation<R> outTypeInfo, OneInputStreamOperator<T, R> operator) {

		SingleOutputStreamOperator<R> returnStream = super.transform(operatorName, outTypeInfo, operator);

		// inject the key selector and key type
		OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>) returnStream.getTransformation();
		transform.setStateKeySelector(keySelector);
		transform.setStateKeyType(keyType);

		return returnStream;
	}

	//......
}
  • KeyedStream的reduce方法調用了transform方法，而構造的OneInputStreamOperator爲StreamGroupedReduce

ReduceFunction

flink-core-1.7.0-sources.jar!/org/apache/flink/api/common/functions/ReduceFunction.javaapache

@Public
@FunctionalInterface
public interface ReduceFunction<T> extends Function, Serializable {

	/**
	 * The core method of ReduceFunction, combining two values into one value of the same type.
	 * The reduce function is consecutively applied to all values of a group until only a single value remains.
	 *
	 * @param value1 The first value to combine.
	 * @param value2 The second value to combine.
	 * @return The combined value of both input values.
	 *
	 * @throws Exception This method may throw exceptions. Throwing an exception will cause the operation
	 *                   to fail and may trigger recovery.
	 */
	T reduce(T value1, T value2) throws Exception;
}
  • ReduceFunction定義了reduce方法，它主要是用來將兩個同類型的值操做爲一個同類型的值，第一個參數爲前面reduce的結果，第二參數爲當前的元素

Task.run

flink-runtime_2.11-1.7.0-sources.jar!/org/apache/flink/runtime/taskmanager/Task.javabootstrap

/**
 * 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() {

		// ----------------------------
		//  Initial State transition
		// ----------------------------
		//......

		// all resource acquisitions and registrations from here on
		// need to be undone in the end
		Map<String, Future<Path>> distributedCacheEntries = new HashMap<>();
		AbstractInvokable invokable = null;

		try {

			// 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
			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();

			//......
		}
		catch (Throwable t) {
			//......
		}
		finally {
			//......
		}	
	}
}
  • Task的run方法會調用invokable.invoke()，這裏的invokable爲OneInputStreamTask，而OneInputStreamTask繼承了StreamTask，這裏實際調用的invoke()方法是StreamTask裏頭的

StreamTask.invoke

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/runtime/tasks/StreamTask.javaapi

@Internal
public abstract class StreamTask<OUT, OP extends StreamOperator<OUT>>
		extends AbstractInvokable
		implements AsyncExceptionHandler {

	//......

	protected abstract void run() throws Exception;

	@Override
	public final void invoke() throws Exception {

		boolean disposed = false;
		try {
			// -------- Initialize ---------
			LOG.debug("Initializing {}.", getName());

			asyncOperationsThreadPool = Executors.newCachedThreadPool();

			CheckpointExceptionHandlerFactory cpExceptionHandlerFactory = createCheckpointExceptionHandlerFactory();

			synchronousCheckpointExceptionHandler = cpExceptionHandlerFactory.createCheckpointExceptionHandler(
				getExecutionConfig().isFailTaskOnCheckpointError(),
				getEnvironment());

			asynchronousCheckpointExceptionHandler = new AsyncCheckpointExceptionHandler(this);

			stateBackend = createStateBackend();
			checkpointStorage = stateBackend.createCheckpointStorage(getEnvironment().getJobID());

			// if the clock is not already set, then assign a default TimeServiceProvider
			if (timerService == null) {
				ThreadFactory timerThreadFactory = new DispatcherThreadFactory(TRIGGER_THREAD_GROUP,
					"Time Trigger for " + getName(), getUserCodeClassLoader());

				timerService = new SystemProcessingTimeService(this, getCheckpointLock(), timerThreadFactory);
			}

			operatorChain = new OperatorChain<>(this, streamRecordWriters);
			headOperator = operatorChain.getHeadOperator();

			// task specific initialization
			init();

			// save the work of reloading state, etc, if the task is already canceled
			if (canceled) {
				throw new CancelTaskException();
			}

			// -------- Invoke --------
			LOG.debug("Invoking {}", getName());

			// we need to make sure that any triggers scheduled in open() cannot be
			// executed before all operators are opened
			synchronized (lock) {

				// both the following operations are protected by the lock
				// so that we avoid race conditions in the case that initializeState()
				// registers a timer, that fires before the open() is called.

				initializeState();
				openAllOperators();
			}

			// final check to exit early before starting to run
			if (canceled) {
				throw new CancelTaskException();
			}

			// 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 {
			//......
		}
	}
}
  • StreamTask的invoke方法會調用run方法，該方法爲抽象方法，由子類實現，這裏就是OneInputStreamTask的run方法

OneInputStreamTask.run

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/runtime/tasks/OneInputStreamTask.javaapp

@Internal
public class OneInputStreamTask<IN, OUT> extends StreamTask<OUT, OneInputStreamOperator<IN, OUT>> {

	private StreamInputProcessor<IN> inputProcessor;

	private volatile boolean running = true;

	private final WatermarkGauge inputWatermarkGauge = new WatermarkGauge();

	/**
	 * Constructor for initialization, possibly with initial state (recovery / savepoint / etc).
	 *
	 * @param env The task environment for this task.
	 */
	public OneInputStreamTask(Environment env) {
		super(env);
	}

	/**
	 * Constructor for initialization, possibly with initial state (recovery / savepoint / etc).
	 *
	 * <p>This constructor accepts a special {@link ProcessingTimeService}. By default (and if
	 * null is passes for the time provider) a {@link SystemProcessingTimeService DefaultTimerService}
	 * will be used.
	 *
	 * @param env The task environment for this task.
	 * @param timeProvider Optionally, a specific time provider to use.
	 */
	@VisibleForTesting
	public OneInputStreamTask(
			Environment env,
			@Nullable ProcessingTimeService timeProvider) {
		super(env, timeProvider);
	}

	@Override
	public void init() throws Exception {
		StreamConfig configuration = getConfiguration();

		TypeSerializer<IN> inSerializer = configuration.getTypeSerializerIn1(getUserCodeClassLoader());
		int numberOfInputs = configuration.getNumberOfInputs();

		if (numberOfInputs > 0) {
			InputGate[] inputGates = getEnvironment().getAllInputGates();

			inputProcessor = new StreamInputProcessor<>(
					inputGates,
					inSerializer,
					this,
					configuration.getCheckpointMode(),
					getCheckpointLock(),
					getEnvironment().getIOManager(),
					getEnvironment().getTaskManagerInfo().getConfiguration(),
					getStreamStatusMaintainer(),
					this.headOperator,
					getEnvironment().getMetricGroup().getIOMetricGroup(),
					inputWatermarkGauge);
		}
		headOperator.getMetricGroup().gauge(MetricNames.IO_CURRENT_INPUT_WATERMARK, this.inputWatermarkGauge);
		// wrap watermark gauge since registered metrics must be unique
		getEnvironment().getMetricGroup().gauge(MetricNames.IO_CURRENT_INPUT_WATERMARK, this.inputWatermarkGauge::getValue);
	}

	@Override
	protected void run() throws Exception {
		// cache processor reference on the stack, to make the code more JIT friendly
		final StreamInputProcessor<IN> inputProcessor = this.inputProcessor;

		while (running && inputProcessor.processInput()) {
			// all the work happens in the "processInput" method
		}
	}

	@Override
	protected void cleanup() throws Exception {
		if (inputProcessor != null) {
			inputProcessor.cleanup();
		}
	}

	@Override
	protected void cancelTask() {
		running = false;
	}
}
  • OneInputStreamTask的run方法會不斷循環調用inputProcessor.processInput()，inputProcessor這裏爲StreamInputProcessor

StreamInputProcessor.processInput

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/runtime/io/StreamInputProcessor.javaasync

@Internal
public class StreamInputProcessor<IN> {

	//......

	public boolean processInput() throws Exception {
		if (isFinished) {
			return false;
		}
		if (numRecordsIn == null) {
			try {
				numRecordsIn = ((OperatorMetricGroup) streamOperator.getMetricGroup()).getIOMetricGroup().getNumRecordsInCounter();
			} catch (Exception e) {
				LOG.warn("An exception occurred during the metrics setup.", e);
				numRecordsIn = new SimpleCounter();
			}
		}

		while (true) {
			if (currentRecordDeserializer != null) {
				DeserializationResult result = currentRecordDeserializer.getNextRecord(deserializationDelegate);

				if (result.isBufferConsumed()) {
					currentRecordDeserializer.getCurrentBuffer().recycleBuffer();
					currentRecordDeserializer = null;
				}

				if (result.isFullRecord()) {
					StreamElement recordOrMark = deserializationDelegate.getInstance();

					if (recordOrMark.isWatermark()) {
						// handle watermark
						statusWatermarkValve.inputWatermark(recordOrMark.asWatermark(), currentChannel);
						continue;
					} else if (recordOrMark.isStreamStatus()) {
						// handle stream status
						statusWatermarkValve.inputStreamStatus(recordOrMark.asStreamStatus(), currentChannel);
						continue;
					} else if (recordOrMark.isLatencyMarker()) {
						// handle latency marker
						synchronized (lock) {
							streamOperator.processLatencyMarker(recordOrMark.asLatencyMarker());
						}
						continue;
					} else {
						// now we can do the actual processing
						StreamRecord<IN> record = recordOrMark.asRecord();
						synchronized (lock) {
							numRecordsIn.inc();
							streamOperator.setKeyContextElement1(record);
							streamOperator.processElement(record);
						}
						return true;
					}
				}
			}

			//......
		}
	}

	//......
}
  • StreamInputProcessor的processInput方法，會在while true循環裏頭不斷處理nextRecord，這裏根據StreamElement的不一樣類型作不一樣處理，若是是普通的數據，則調用streamOperator.processElement進行處理，這裏的streamOperator爲StreamGroupedReduce

StreamGroupedReduce.processElement

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/operators/StreamGroupedReduce.javaide

/**
 * A {@link StreamOperator} for executing a {@link ReduceFunction} on a
 * {@link org.apache.flink.streaming.api.datastream.KeyedStream}.
 */

@Internal
public class StreamGroupedReduce<IN> extends AbstractUdfStreamOperator<IN, ReduceFunction<IN>>
		implements OneInputStreamOperator<IN, IN> {

	private static final long serialVersionUID = 1L;

	private static final String STATE_NAME = "_op_state";

	private transient ValueState<IN> values;

	private TypeSerializer<IN> serializer;

	public StreamGroupedReduce(ReduceFunction<IN> reducer, TypeSerializer<IN> serializer) {
		super(reducer);
		this.serializer = serializer;
	}

	@Override
	public void open() throws Exception {
		super.open();
		ValueStateDescriptor<IN> stateId = new ValueStateDescriptor<>(STATE_NAME, serializer);
		values = getPartitionedState(stateId);
	}

	@Override
	public void processElement(StreamRecord<IN> element) throws Exception {
		IN value = element.getValue();
		IN currentValue = values.value();

		if (currentValue != null) {
			IN reduced = userFunction.reduce(currentValue, value);
			values.update(reduced);
			output.collect(element.replace(reduced));
		} else {
			values.update(value);
			output.collect(element.replace(value));
		}
	}
}
  • StreamGroupedReduce使用了ValueState存儲reduce操做的結果值，在processElement方法裏頭調用userFunction的reduce操做，userFunction就是用戶自定義的ReduceFunction，而reduce的第一個參數就是ValueState的value，即上一次reduce操做的結果值，而後第二個參數就當前element的value；而在執行完userFunction的reduce操做以後，會將該結果update到ValueState

小結

  • KeyedStream的reduce方法，裏頭調用了transform方法，而構造的OneInputStreamOperator爲StreamGroupedReduce；reduce方法接收的是ReduceFunction，它定義了reduce方法，用來將兩個同類型的值操做爲一個同類型的值
  • Task的run方法會調用invokable.invoke()，這裏的invokable爲OneInputStreamTask，而OneInputStreamTask繼承了StreamTask，這裏實際調用的invoke()方法是StreamTask裏頭的；StreamTask的invoke方法會調用run方法，該方法爲抽象方法，由子類實現，這裏就是OneInputStreamTask的run方法；OneInputStreamTask的run方法，會不斷循環調用inputProcessor.processInput()，inputProcessor這裏爲StreamInputProcessor；StreamInputProcessor的processInput方法，會在while true循環裏頭不斷處理nextRecord，這裏根據StreamElement的不一樣類型作不一樣處理，若是是普通的數據，則調用streamOperator.processElement進行處理，這裏的streamOperator爲StreamGroupedReduce
  • StreamGroupedReduce的processElement方法會調用userFunction的reduce操做，第一個參數就是ValueState的value，即上一次reduce操做的結果值，而後第二個參數就當前element的value；而在執行完userFunction的reduce操做以後，會將該結果update到ValueState

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