[case47]聊聊flink的BoltWrapper

序

本文主要研究一下flink的BoltWrapper

BoltWrapper

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

/**
 * A {@link BoltWrapper} wraps an {@link IRichBolt} in order to execute the Storm bolt within a Flink Streaming program.
 * It takes the Flink input tuples of type {@code IN} and transforms them into {@link StormTuple}s that the bolt can
 * process. Furthermore, it takes the bolt's output tuples and transforms them into Flink tuples of type {@code OUT}
 * (see {@link AbstractStormCollector} for supported types).<br/>
 * <br/>
 * <strong>Works for single input streams only! See {@link MergedInputsBoltWrapper} for multi-input stream
 * Bolts.</strong>
 */
public class BoltWrapper<IN, OUT> extends AbstractStreamOperator<OUT> implements OneInputStreamOperator<IN, OUT> {

    @Override
    public void open() throws Exception {
        super.open();

        this.flinkCollector = new TimestampedCollector<>(this.output);

        GlobalJobParameters config = getExecutionConfig().getGlobalJobParameters();
        StormConfig stormConfig = new StormConfig();

        if (config != null) {
            if (config instanceof StormConfig) {
                stormConfig = (StormConfig) config;
            } else {
                stormConfig.putAll(config.toMap());
            }
        }

        this.topologyContext = WrapperSetupHelper.createTopologyContext(
                getRuntimeContext(), this.bolt, this.name, this.stormTopology, stormConfig);

        final OutputCollector stormCollector = new OutputCollector(new BoltCollector<OUT>(
                this.numberOfAttributes, this.topologyContext.getThisTaskId(), this.flinkCollector));

        if (this.stormTopology != null) {
            Map<GlobalStreamId, Grouping> inputs = this.topologyContext.getThisSources();

            for (GlobalStreamId inputStream : inputs.keySet()) {
                for (Integer tid : this.topologyContext.getComponentTasks(inputStream
                        .get_componentId())) {
                    this.inputComponentIds.put(tid, inputStream.get_componentId());
                    this.inputStreamIds.put(tid, inputStream.get_streamId());
                    this.inputSchemas.put(tid,
                            this.topologyContext.getComponentOutputFields(inputStream));
                }
            }
        }

        this.bolt.prepare(stormConfig, this.topologyContext, stormCollector);
    }

    @Override
    public void dispose() throws Exception {
        super.dispose();
        this.bolt.cleanup();
    }

    @Override
    public void processElement(final StreamRecord<IN> element) throws Exception {
        this.flinkCollector.setTimestamp(element);

        IN value = element.getValue();

        if (this.stormTopology != null) {
            Tuple tuple = (Tuple) value;
            Integer producerTaskId = tuple.getField(tuple.getArity() - 1);

            this.bolt.execute(new StormTuple<>(value, this.inputSchemas.get(producerTaskId),
                    producerTaskId, this.inputStreamIds.get(producerTaskId), this.inputComponentIds
                    .get(producerTaskId), MessageId.makeUnanchored()));

        } else {
            this.bolt.execute(new StormTuple<>(value, this.inputSchemas.get(null), -1, null, null,
                    MessageId.makeUnanchored()));
        }
    }


}

• flink用BoltWrapper來包裝storm的IRichBolt，它實現了OneInputStreamOperator接口，繼承AbstractStreamOperator類
• OneInputStreamOperator接口繼承了StreamOperator接口，額外定義了processElement、processWatermark、processLatencyMarker三個接口
• AbstractStreamOperator類實現的是StreamOperator接口，可是裏頭幫忙實現了processWatermark、processLatencyMarker這兩個接口
• BoltWrapper裏頭主要是實現OneInputStreamOperator接口的processElement方法，而後是覆蓋StreamOperator接口定義的open及dispose方法
• open方法有個要點就是調用bolt的prepare方法，傳入包裝BoltCollector的OutputCollector，經過BoltCollector來收集bolt發射的數據到flink，它使用的是flink的TimestampedCollector

BoltCollector

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

/**
 * A {@link BoltCollector} is used by {@link BoltWrapper} to provided an Storm compatible
 * output collector to the wrapped bolt. It transforms the emitted Storm tuples into Flink tuples
 * and emits them via the provide {@link Output} object.
 */
class BoltCollector<OUT> extends AbstractStormCollector<OUT> implements IOutputCollector {

    /** The Flink output Collector. */
    private final Collector<OUT> flinkOutput;

    /**
     * Instantiates a new {@link BoltCollector} that emits Flink tuples to the given Flink output object. If the
     * number of attributes is negative, any output type is supported (ie, raw type). If the number of attributes is
     * between 0 and 25, the output type is {@link Tuple0} to {@link Tuple25}, respectively.
     *
     * @param numberOfAttributes
     *            The number of attributes of the emitted tuples per output stream.
     * @param taskId
     *            The ID of the producer task (negative value for unknown).
     * @param flinkOutput
     *            The Flink output object to be used.
     * @throws UnsupportedOperationException
     *             if the specified number of attributes is greater than 25
     */
    BoltCollector(final HashMap<String, Integer> numberOfAttributes, final int taskId,
            final Collector<OUT> flinkOutput) throws UnsupportedOperationException {
        super(numberOfAttributes, taskId);
        assert (flinkOutput != null);
        this.flinkOutput = flinkOutput;
    }

    @Override
    protected List<Integer> doEmit(final OUT flinkTuple) {
        this.flinkOutput.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 Collection<Tuple> anchors, final List<Object> tuple) {
        return this.tansformAndEmit(streamId, tuple);
    }

    @Override
    public void emitDirect(final int taskId, final String streamId, final Collection<Tuple> anchors, final List<Object> tuple) {
        throw new UnsupportedOperationException("Direct emit is not supported by Flink");
    }

    @Override
    public void ack(final Tuple input) {}

    @Override
    public void fail(final Tuple input) {}

    @Override
    public void resetTimeout(Tuple var1) {}

}

• BoltCollector實現了storm的IOutputCollector接口，只是ack、fail、resetTimeout、reportError操做都爲空，不支持emitDirect操做
• doEmit方法調用的是flinkOutput.collect(flinkTuple)
• emit方法調用的是tansformAndEmit(streamId, tuple)，它由繼承的父類AbstractStormCollector實現

TimestampedCollector.collect

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

/**
 * Wrapper around an {@link Output} for user functions that expect a {@link Collector}.
 * Before giving the {@link TimestampedCollector} to a user function you must set
 * the timestamp that should be attached to emitted elements. Most operators
 * would set the timestamp of the incoming
 * {@link org.apache.flink.streaming.runtime.streamrecord.StreamRecord} here.
 *
 * @param <T> The type of the elements that can be emitted.
 */
@Internal
public class TimestampedCollector<T> implements Collector<T> {

    private final Output<StreamRecord<T>> output;

    private final StreamRecord<T> reuse;

    /**
     * Creates a new {@link TimestampedCollector} that wraps the given {@link Output}.
     */
    public TimestampedCollector(Output<StreamRecord<T>> output) {
        this.output = output;
        this.reuse = new StreamRecord<T>(null);
    }

    @Override
    public void collect(T record) {
        output.collect(reuse.replace(record));
    }

    public void setTimestamp(StreamRecord<?> timestampBase) {
        if (timestampBase.hasTimestamp()) {
            reuse.setTimestamp(timestampBase.getTimestamp());
        } else {
            reuse.eraseTimestamp();
        }
    }

    public void setAbsoluteTimestamp(long timestamp) {
        reuse.setTimestamp(timestamp);
    }

    public void eraseTimestamp() {
        reuse.eraseTimestamp();
    }

    @Override
    public void close() {
        output.close();
    }
}

• TimestampedCollector實現了flink的Collector接口，這裏頭額外新增了setTimestamp、setAbsoluteTimestamp、eraseTimestamp方法
• 它使用了StreamRecord對象，它裏頭有value、timestamp、hasTimestamp三個屬性，能夠將value與時間戳關聯起來
• 這裏的collect方法調用了StreamRecord的replace返回的對象，replace方法只是更新了value引用，可是裏頭的時間戳沒有更新

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，這裏主要處理了split的場景，即一個bolt declare了多個stream，最後都經過子類BoltCollector.doEmit來發射數據
• 若是split爲true，則傳給doEmit方法的是splitTuple，即SplitStreamType，它記錄了streamId及其value
• 若是split爲false，則傳給doEmit方法的是Tuple類型，即至關於SplitStreamType中的value，相比於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方法會調用invokable.invoke()，這裏的invokable爲StreamTask

StreamTask.invoke

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方法裏頭調用了子類的run方法，這裏子類爲OneInputStreamTask

OneInputStreamTask.run

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

@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
        }
    }

• 該run方法主要是調用inputProcessor.processInput()，這裏的inputProcessor爲StreamInputProcessor

StreamInputProcessor.processInput

flink-streaming-java_2.11-1.6.2-sources.jar!/org/apache/flink/streaming/runtime/io/StreamInputProcessor.java

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;
                    }
                }
            }

            //......
        }
    }

• 該processInput方法，先是經過currentRecordDeserializer.getNextRecord(deserializationDelegate)獲取nextRecord，以後有調用到streamOperator.processElement(record)來處理，這裏的streamOperator爲BoltWrapper

小結

• flink用BoltWrapper來包裝storm的IRichBolt，它實現OneInputStreamOperator接口的processElement方法，在該方法中執行bolt.execute方法；另外在實現StreamOperator的open方法中調用bolt的prepare方法，傳入包裝BoltCollector的OutputCollector，經過BoltCollector來收集bolt.execute時發射的數據到flink，它使用的是flink的TimestampedCollector
• BoltCollector的emit方法內部調用了AbstractStormCollector.tansformAndEmit(它最後調用BoltCollector.doEmit方法來發射)，針對多個stream的場景，封裝了SplitStreamType的tuple給到doEmit方法；若是隻有一個stream，則僅僅將普通的tuple傳給doEmit方法
• flink的Task的run方法會調用StreamTask的invoke方法，而StreamTask的invoke方法會調用子類(這裏子類爲OneInputStreamTask)的run方法，OneInputStreamTask的run方法是不斷循環調用inputProcessor.processInput()，這裏的inputProcessor爲StreamInputProcessor，它的processInput()會調用currentRecordDeserializer.getNextRecord(deserializationDelegate)獲取nextRecord，以後根據條件選擇調用streamOperator.processElement(record)方法，這裏的streamOperator爲BoltWrapper，而BoltWrapper的processElement正好調用storm bolt的execute方法來執行bolt邏輯並使用flink的BoltCollector進行發射

doc

• Storm Compatibility Beta