Examples with ParallelInstructionNode org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode used on opensource projects

Example 21 with ParallelInstructionNode

use of org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode in project beam by apache.

the class ReplacePgbkWithPrecombineFunction method apply.

@Override
public MutableNetwork<Node, Edge> apply(MutableNetwork<Node, Edge> network) {
    Networks.replaceDirectedNetworkNodes(network, (Node node) -> {
        if (!isPrecombinePgbk(node)) {
            return node;
        }
        // Turn the Pgbk into a ParDo with the combine function as a UserFn.
        ParallelInstructionNode castNode = ((ParallelInstructionNode) node);
        ParallelInstruction parallelInstruction = castNode.getParallelInstruction();
        Map<String, Object> cloudUserFnSpec = parallelInstruction.getPartialGroupByKey().getValueCombiningFn();
        addString(cloudUserFnSpec, WorkerPropertyNames.PHASE, CombinePhase.ADD);
        ParDoInstruction newParDoInstruction = new ParDoInstruction();
        newParDoInstruction.setUserFn(cloudUserFnSpec);
        ParallelInstruction newParallelInstruction = parallelInstruction.clone();
        newParallelInstruction.setPartialGroupByKey(null);
        newParallelInstruction.setParDo(newParDoInstruction);
        return ParallelInstructionNode.create(newParallelInstruction, ExecutionLocation.UNKNOWN);
    });
    return network;
}

Example 22 with ParallelInstructionNode

use of org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode in project beam by apache.

the class CloneAmbiguousFlattensFunction method cloneFlatten.

/**
 * A helper function which performs the actual cloning procedure, which means creating the runner
 * and SDK versions of both the ambiguous flatten and its PCollection, attaching the old flatten's
 * predecessors and successors properly, and then removing the ambiguous flatten from the network.
 */
private void cloneFlatten(Node flatten, MutableNetwork<Node, Edge> network) {
    // Start by creating the clones of the flatten and its PCollection.
    InstructionOutputNode flattenOut = (InstructionOutputNode) Iterables.getOnlyElement(network.successors(flatten));
    ParallelInstruction flattenInstruction = ((ParallelInstructionNode) flatten).getParallelInstruction();
    Node runnerFlatten = ParallelInstructionNode.create(flattenInstruction, ExecutionLocation.RUNNER_HARNESS);
    Node runnerFlattenOut = InstructionOutputNode.create(flattenOut.getInstructionOutput(), flattenOut.getPcollectionId());
    network.addNode(runnerFlatten);
    network.addNode(runnerFlattenOut);
    Node sdkFlatten = ParallelInstructionNode.create(flattenInstruction, ExecutionLocation.SDK_HARNESS);
    Node sdkFlattenOut = InstructionOutputNode.create(flattenOut.getInstructionOutput(), flattenOut.getPcollectionId());
    network.addNode(sdkFlatten);
    network.addNode(sdkFlattenOut);
    for (Edge edge : ImmutableList.copyOf(network.edgesConnecting(flatten, flattenOut))) {
        network.addEdge(runnerFlatten, runnerFlattenOut, edge.clone());
        network.addEdge(sdkFlatten, sdkFlattenOut, edge.clone());
    }
    // Copy over predecessor edges to both cloned nodes.
    for (Node predecessor : network.predecessors(flatten)) {
        for (Edge edge : ImmutableList.copyOf(network.edgesConnecting(predecessor, flatten))) {
            network.addEdge(predecessor, runnerFlatten, edge.clone());
            network.addEdge(predecessor, sdkFlatten, edge.clone());
        }
    }
    // Copy over successor edges depending on execution locations of successors.
    for (Node successor : network.successors(flattenOut)) {
        // Connect successor to SDK harness only if sure it executes in SDK.
        Node selectedOutput = executesInSdkHarness(successor) ? sdkFlattenOut : runnerFlattenOut;
        for (Edge edge : ImmutableList.copyOf(network.edgesConnecting(flattenOut, successor))) {
            network.addEdge(selectedOutput, successor, edge.clone());
        }
    }
    network.removeNode(flatten);
    network.removeNode(flattenOut);
}

Example 23 with ParallelInstructionNode

use of org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode in project beam by apache.

the class CloneAmbiguousFlattensFunction method apply.

@Override
public MutableNetwork<Node, Edge> apply(MutableNetwork<Node, Edge> network) {
    // Important: The cloning technique only works when the flatten being cloned has no ambiguous
    // descendants, so to ensure this is always true we iterate through the network in reverse
    // topological order.
    Set<Node> sortedNodesSet = Networks.topologicalOrder(network);
    Node[] sortedNodes = sortedNodesSet.toArray(new Node[sortedNodesSet.size()]);
    for (int i = sortedNodes.length - 1; i >= 0; i--) {
        Node node = sortedNodes[i];
        if (node instanceof ParallelInstructionNode && ((ParallelInstructionNode) node).getParallelInstruction().getFlatten() != null && ((ParallelInstructionNode) node).getExecutionLocation() == ExecutionLocation.AMBIGUOUS) {
            cloneFlatten(node, network);
        }
    }
    return network;
}

Example 24 with ParallelInstructionNode

use of org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode in project beam by apache.

the class StreamingDataflowWorker method process.

private void process(final SdkWorkerHarness worker, final ComputationState computationState, final Instant inputDataWatermark, @Nullable final Instant outputDataWatermark, @Nullable final Instant synchronizedProcessingTime, final Work work) {
    final Windmill.WorkItem workItem = work.getWorkItem();
    final String computationId = computationState.getComputationId();
    final ByteString key = workItem.getKey();
    work.setState(State.PROCESSING);
    {
        StringBuilder workIdBuilder = new StringBuilder(33);
        workIdBuilder.append(Long.toHexString(workItem.getShardingKey()));
        workIdBuilder.append('-');
        workIdBuilder.append(Long.toHexString(workItem.getWorkToken()));
        DataflowWorkerLoggingMDC.setWorkId(workIdBuilder.toString());
    }
    DataflowWorkerLoggingMDC.setStageName(computationId);
    LOG.debug("Starting processing for {}:\n{}", computationId, work);
    Windmill.WorkItemCommitRequest.Builder outputBuilder = initializeOutputBuilder(key, workItem);
    // Before any processing starts, call any pending OnCommit callbacks.  Nothing that requires
    // cleanup should be done before this, since we might exit early here.
    callFinalizeCallbacks(workItem);
    if (workItem.getSourceState().getOnlyFinalize()) {
        outputBuilder.setSourceStateUpdates(Windmill.SourceState.newBuilder().setOnlyFinalize(true));
        work.setState(State.COMMIT_QUEUED);
        commitQueue.put(new Commit(outputBuilder.build(), computationState, work));
        return;
    }
    long processingStartTimeNanos = System.nanoTime();
    final MapTask mapTask = computationState.getMapTask();
    StageInfo stageInfo = stageInfoMap.computeIfAbsent(mapTask.getStageName(), s -> new StageInfo(s, mapTask.getSystemName(), this));
    ExecutionState executionState = null;
    try {
        executionState = computationState.getExecutionStateQueue(worker).poll();
        if (executionState == null) {
            MutableNetwork<Node, Edge> mapTaskNetwork = mapTaskToNetwork.apply(mapTask);
            if (LOG.isDebugEnabled()) {
                LOG.debug("Network as Graphviz .dot: {}", Networks.toDot(mapTaskNetwork));
            }
            ParallelInstructionNode readNode = (ParallelInstructionNode) Iterables.find(mapTaskNetwork.nodes(), node -> node instanceof ParallelInstructionNode && ((ParallelInstructionNode) node).getParallelInstruction().getRead() != null);
            InstructionOutputNode readOutputNode = (InstructionOutputNode) Iterables.getOnlyElement(mapTaskNetwork.successors(readNode));
            DataflowExecutionContext.DataflowExecutionStateTracker executionStateTracker = new DataflowExecutionContext.DataflowExecutionStateTracker(ExecutionStateSampler.instance(), stageInfo.executionStateRegistry.getState(NameContext.forStage(mapTask.getStageName()), "other", null, ScopedProfiler.INSTANCE.emptyScope()), stageInfo.deltaCounters, options, computationId);
            StreamingModeExecutionContext context = new StreamingModeExecutionContext(pendingDeltaCounters, computationId, readerCache, !computationState.getTransformUserNameToStateFamily().isEmpty() ? computationState.getTransformUserNameToStateFamily() : stateNameMap, stateCache.forComputation(computationId), stageInfo.metricsContainerRegistry, executionStateTracker, stageInfo.executionStateRegistry, maxSinkBytes);
            DataflowMapTaskExecutor mapTaskExecutor = mapTaskExecutorFactory.create(worker.getControlClientHandler(), worker.getGrpcDataFnServer(), sdkHarnessRegistry.beamFnDataApiServiceDescriptor(), worker.getGrpcStateFnServer(), mapTaskNetwork, options, mapTask.getStageName(), readerRegistry, sinkRegistry, context, pendingDeltaCounters, idGenerator);
            ReadOperation readOperation = mapTaskExecutor.getReadOperation();
            // Disable progress updates since its results are unused  for streaming
            // and involves starting a thread.
            readOperation.setProgressUpdatePeriodMs(ReadOperation.DONT_UPDATE_PERIODICALLY);
            Preconditions.checkState(mapTaskExecutor.supportsRestart(), "Streaming runner requires all operations support restart.");
            Coder<?> readCoder;
            readCoder = CloudObjects.coderFromCloudObject(CloudObject.fromSpec(readOutputNode.getInstructionOutput().getCodec()));
            Coder<?> keyCoder = extractKeyCoder(readCoder);
            // If using a custom source, count bytes read for autoscaling.
            if (CustomSources.class.getName().equals(readNode.getParallelInstruction().getRead().getSource().getSpec().get("@type"))) {
                NameContext nameContext = NameContext.create(mapTask.getStageName(), readNode.getParallelInstruction().getOriginalName(), readNode.getParallelInstruction().getSystemName(), readNode.getParallelInstruction().getName());
                readOperation.receivers[0].addOutputCounter(new OutputObjectAndByteCounter(new IntrinsicMapTaskExecutorFactory.ElementByteSizeObservableCoder<>(readCoder), mapTaskExecutor.getOutputCounters(), nameContext).setSamplingPeriod(100).countBytes("dataflow_input_size-" + mapTask.getSystemName()));
            }
            executionState = new ExecutionState(mapTaskExecutor, context, keyCoder, executionStateTracker);
        }
        WindmillStateReader stateReader = new WindmillStateReader(metricTrackingWindmillServer, computationId, key, workItem.getShardingKey(), workItem.getWorkToken());
        StateFetcher localStateFetcher = stateFetcher.byteTrackingView();
        // If the read output KVs, then we can decode Windmill's byte key into a userland
        // key object and provide it to the execution context for use with per-key state.
        // Otherwise, we pass null.
        // 
        // The coder type that will be present is:
        // WindowedValueCoder(TimerOrElementCoder(KvCoder))
        @Nullable Coder<?> keyCoder = executionState.getKeyCoder();
        @Nullable Object executionKey = keyCoder == null ? null : keyCoder.decode(key.newInput(), Coder.Context.OUTER);
        if (workItem.hasHotKeyInfo()) {
            Windmill.HotKeyInfo hotKeyInfo = workItem.getHotKeyInfo();
            Duration hotKeyAge = Duration.millis(hotKeyInfo.getHotKeyAgeUsec() / 1000);
            // The MapTask instruction is ordered by dependencies, such that the first element is
            // always going to be the shuffle task.
            String stepName = computationState.getMapTask().getInstructions().get(0).getName();
            if (options.isHotKeyLoggingEnabled() && keyCoder != null) {
                hotKeyLogger.logHotKeyDetection(stepName, hotKeyAge, executionKey);
            } else {
                hotKeyLogger.logHotKeyDetection(stepName, hotKeyAge);
            }
        }
        executionState.getContext().start(executionKey, workItem, inputDataWatermark, outputDataWatermark, synchronizedProcessingTime, stateReader, localStateFetcher, outputBuilder);
        // Blocks while executing work.
        executionState.getWorkExecutor().execute();
        Iterables.addAll(this.pendingMonitoringInfos, executionState.getWorkExecutor().extractMetricUpdates());
        commitCallbacks.putAll(executionState.getContext().flushState());
        // Release the execution state for another thread to use.
        computationState.getExecutionStateQueue(worker).offer(executionState);
        executionState = null;
        // Add the output to the commit queue.
        work.setState(State.COMMIT_QUEUED);
        WorkItemCommitRequest commitRequest = outputBuilder.build();
        int byteLimit = maxWorkItemCommitBytes;
        int commitSize = commitRequest.getSerializedSize();
        int estimatedCommitSize = commitSize < 0 ? Integer.MAX_VALUE : commitSize;
        // Detect overflow of integer serialized size or if the byte limit was exceeded.
        windmillMaxObservedWorkItemCommitBytes.addValue(estimatedCommitSize);
        if (commitSize < 0 || commitSize > byteLimit) {
            KeyCommitTooLargeException e = KeyCommitTooLargeException.causedBy(computationId, byteLimit, commitRequest);
            reportFailure(computationId, workItem, e);
            LOG.error(e.toString());
            // Drop the current request in favor of a new, minimal one requesting truncation.
            // Messages, timers, counters, and other commit content will not be used by the service
            // so we're purposefully dropping them here
            commitRequest = buildWorkItemTruncationRequest(key, workItem, estimatedCommitSize);
        }
        commitQueue.put(new Commit(commitRequest, computationState, work));
        // Compute shuffle and state byte statistics these will be flushed asynchronously.
        long stateBytesWritten = outputBuilder.clearOutputMessages().build().getSerializedSize();
        long shuffleBytesRead = 0;
        for (Windmill.InputMessageBundle bundle : workItem.getMessageBundlesList()) {
            for (Windmill.Message message : bundle.getMessagesList()) {
                shuffleBytesRead += message.getSerializedSize();
            }
        }
        long stateBytesRead = stateReader.getBytesRead() + localStateFetcher.getBytesRead();
        windmillShuffleBytesRead.addValue(shuffleBytesRead);
        windmillStateBytesRead.addValue(stateBytesRead);
        windmillStateBytesWritten.addValue(stateBytesWritten);
        LOG.debug("Processing done for work token: {}", workItem.getWorkToken());
    } catch (Throwable t) {
        if (executionState != null) {
            try {
                executionState.getContext().invalidateCache();
                executionState.getWorkExecutor().close();
            } catch (Exception e) {
                LOG.warn("Failed to close map task executor: ", e);
            } finally {
                // Release references to potentially large objects early.
                executionState = null;
            }
        }
        t = t instanceof UserCodeException ? t.getCause() : t;
        boolean retryLocally = false;
        if (KeyTokenInvalidException.isKeyTokenInvalidException(t)) {
            LOG.debug("Execution of work for computation '{}' on key '{}' failed due to token expiration. " + "Work will not be retried locally.", computationId, key.toStringUtf8());
        } else {
            LastExceptionDataProvider.reportException(t);
            LOG.debug("Failed work: {}", work);
            Duration elapsedTimeSinceStart = new Duration(Instant.now(), work.getStartTime());
            if (!reportFailure(computationId, workItem, t)) {
                LOG.error("Execution of work for computation '{}' on key '{}' failed with uncaught exception, " + "and Windmill indicated not to retry locally.", computationId, key.toStringUtf8(), t);
            } else if (isOutOfMemoryError(t)) {
                File heapDump = memoryMonitor.tryToDumpHeap();
                LOG.error("Execution of work for computation '{}' for key '{}' failed with out-of-memory. " + "Work will not be retried locally. Heap dump {}.", computationId, key.toStringUtf8(), heapDump == null ? "not written" : ("written to '" + heapDump + "'"), t);
            } else if (elapsedTimeSinceStart.isLongerThan(MAX_LOCAL_PROCESSING_RETRY_DURATION)) {
                LOG.error("Execution of work for computation '{}' for key '{}' failed with uncaught exception, " + "and it will not be retried locally because the elapsed time since start {} " + "exceeds {}.", computationId, key.toStringUtf8(), elapsedTimeSinceStart, MAX_LOCAL_PROCESSING_RETRY_DURATION, t);
            } else {
                LOG.error("Execution of work for computation '{}' on key '{}' failed with uncaught exception. " + "Work will be retried locally.", computationId, key.toStringUtf8(), t);
                retryLocally = true;
            }
        }
        if (retryLocally) {
            // Try again after some delay and at the end of the queue to avoid a tight loop.
            sleep(retryLocallyDelayMs);
            workUnitExecutor.forceExecute(work, work.getWorkItem().getSerializedSize());
        } else {
            // Consider the item invalid. It will eventually be retried by Windmill if it still needs to
            // be processed.
            computationState.completeWork(ShardedKey.create(key, workItem.getShardingKey()), workItem.getWorkToken());
        }
    } finally {
        // Update total processing time counters. Updating in finally clause ensures that
        // work items causing exceptions are also accounted in time spent.
        long processingTimeMsecs = TimeUnit.NANOSECONDS.toMillis(System.nanoTime() - processingStartTimeNanos);
        stageInfo.totalProcessingMsecs.addValue(processingTimeMsecs);
        // either here or in DFE.
        if (work.getWorkItem().hasTimers()) {
            stageInfo.timerProcessingMsecs.addValue(processingTimeMsecs);
        }
        DataflowWorkerLoggingMDC.setWorkId(null);
        DataflowWorkerLoggingMDC.setStageName(null);
    }
}

Example 25 with ParallelInstructionNode

use of org.apache.beam.runners.dataflow.worker.graph.Nodes.ParallelInstructionNode in project beam by apache.

the class IntrinsicMapTaskExecutorFactory method createParDoOperation.

private OperationNode createParDoOperation(Network<Node, Edge> network, ParallelInstructionNode node, PipelineOptions options, DataflowExecutionContext<?> executionContext, DataflowOperationContext operationContext) throws Exception {
    ParallelInstruction instruction = node.getParallelInstruction();
    ParDoInstruction parDo = instruction.getParDo();
    TupleTag<?> mainOutputTag = tupleTag(parDo.getMultiOutputInfos().get(0));
    ImmutableMap.Builder<TupleTag<?>, Integer> outputTagsToReceiverIndicesBuilder = ImmutableMap.builder();
    int successorOffset = 0;
    for (Node successor : network.successors(node)) {
        for (Edge edge : network.edgesConnecting(node, successor)) {
            outputTagsToReceiverIndicesBuilder.put(tupleTag(((MultiOutputInfoEdge) edge).getMultiOutputInfo()), successorOffset);
        }
        successorOffset += 1;
    }
    ParDoFn fn = parDoFnFactory.create(options, CloudObject.fromSpec(parDo.getUserFn()), parDo.getSideInputs(), mainOutputTag, outputTagsToReceiverIndicesBuilder.build(), executionContext, operationContext);
    OutputReceiver[] receivers = getOutputReceivers(network, node);
    return OperationNode.create(new ParDoOperation(fn, receivers, operationContext));
}