Examples with CoderTypeInformation org.apache.beam.runners.flink.translation.types.CoderTypeInformation used on opensource projects

Example 1 with CoderTypeInformation

use of org.apache.beam.runners.flink.translation.types.CoderTypeInformation in project beam by apache.

the class ExecutableStageDoFnOperatorTest method testWatermarkHandling.

@Test
public void testWatermarkHandling() throws Exception {
    TupleTag<Integer> mainOutput = new TupleTag<>("main-output");
    DoFnOperator.MultiOutputOutputManagerFactory<Integer> outputManagerFactory = new DoFnOperator.MultiOutputOutputManagerFactory(mainOutput, VoidCoder.of(), new SerializablePipelineOptions(FlinkPipelineOptions.defaults()));
    ExecutableStageDoFnOperator<KV<String, Integer>, Integer> operator = getOperator(mainOutput, Collections.emptyList(), outputManagerFactory, WindowingStrategy.of(FixedWindows.of(Duration.millis(10))), StringUtf8Coder.of(), WindowedValue.getFullCoder(KvCoder.of(StringUtf8Coder.of(), VarIntCoder.of()), IntervalWindow.getCoder()));
    KeyedOneInputStreamOperatorTestHarness<String, WindowedValue<KV<String, Integer>>, WindowedValue<Integer>> testHarness = new KeyedOneInputStreamOperatorTestHarness<>(operator, val -> val.getValue().getKey(), new CoderTypeInformation<>(StringUtf8Coder.of(), FlinkPipelineOptions.defaults()));
    RemoteBundle bundle = Mockito.mock(RemoteBundle.class);
    when(bundle.getInputReceivers()).thenReturn(ImmutableMap.<String, FnDataReceiver<WindowedValue>>builder().put("input", Mockito.mock(FnDataReceiver.class)).build());
    when(bundle.getTimerReceivers()).thenReturn(ImmutableMap.<KV<String, String>, FnDataReceiver<WindowedValue>>builder().put(KV.of("transform", "timer"), Mockito.mock(FnDataReceiver.class)).put(KV.of("transform", "timer2"), Mockito.mock(FnDataReceiver.class)).put(KV.of("transform", "timer3"), Mockito.mock(FnDataReceiver.class)).build());
    when(stageBundleFactory.getBundle(any(), any(), any(), any(), any(), any())).thenReturn(bundle);
    testHarness.open();
    assertThat(operator.getCurrentOutputWatermark(), is(BoundedWindow.TIMESTAMP_MIN_VALUE.getMillis()));
    // No bundle has been started, watermark can be freely advanced
    testHarness.processWatermark(0);
    assertThat(operator.getCurrentOutputWatermark(), is(0L));
    // Trigger a new bundle
    IntervalWindow intervalWindow = new IntervalWindow(new Instant(0), new Instant(9));
    WindowedValue<KV<String, Integer>> windowedValue = WindowedValue.of(KV.of("one", 1), Instant.now(), intervalWindow, PaneInfo.NO_FIRING);
    testHarness.processElement(new StreamRecord<>(windowedValue));
    // The output watermark should be held back during the bundle
    testHarness.processWatermark(1);
    assertThat(operator.getEffectiveInputWatermark(), is(1L));
    assertThat(operator.getCurrentOutputWatermark(), is(0L));
    // After the bundle has been finished, the watermark should be advanced
    operator.invokeFinishBundle();
    assertThat(operator.getCurrentOutputWatermark(), is(1L));
    // Bundle finished, watermark can be freely advanced
    testHarness.processWatermark(2);
    assertThat(operator.getEffectiveInputWatermark(), is(2L));
    assertThat(operator.getCurrentOutputWatermark(), is(2L));
    // Trigger a new bundle
    testHarness.processElement(new StreamRecord<>(windowedValue));
    // cleanup timer
    assertThat(testHarness.numEventTimeTimers(), is(1));
    // Set at timer
    Instant timerTarget = new Instant(5);
    Instant timerTarget2 = new Instant(6);
    operator.getLockToAcquireForStateAccessDuringBundles().lock();
    BiConsumer<String, Instant> timerConsumer = (timerId, timestamp) -> operator.setTimer(Timer.of(windowedValue.getValue().getKey(), "", windowedValue.getWindows(), timestamp, timestamp, PaneInfo.NO_FIRING), TimerInternals.TimerData.of("", TimerReceiverFactory.encodeToTimerDataTimerId("transform", timerId), StateNamespaces.window(IntervalWindow.getCoder(), intervalWindow), timestamp, timestamp, TimeDomain.EVENT_TIME));
    timerConsumer.accept("timer", timerTarget);
    timerConsumer.accept("timer2", timerTarget2);
    assertThat(testHarness.numEventTimeTimers(), is(3));
    // Advance input watermark past the timer
    // Check the output watermark is held back
    long targetWatermark = timerTarget.getMillis() + 100;
    testHarness.processWatermark(targetWatermark);
    // Do not yet advance the output watermark because we are still processing a bundle
    assertThat(testHarness.numEventTimeTimers(), is(3));
    assertThat(operator.getCurrentOutputWatermark(), is(2L));
    // Check that the timers are fired but the output watermark is advanced no further than
    // the minimum timer timestamp of the previous bundle because we are still processing a
    // bundle which might contain more timers.
    // Timers can create loops if they keep rescheduling themselves when firing
    // Thus, we advance the watermark asynchronously to allow for checkpointing to run
    operator.invokeFinishBundle();
    assertThat(testHarness.numEventTimeTimers(), is(3));
    testHarness.setProcessingTime(testHarness.getProcessingTime() + 1);
    assertThat(testHarness.numEventTimeTimers(), is(0));
    assertThat(operator.getCurrentOutputWatermark(), is(5L));
    // Output watermark is advanced synchronously when the bundle finishes,
    // no more timers are scheduled
    operator.invokeFinishBundle();
    assertThat(operator.getCurrentOutputWatermark(), is(targetWatermark));
    assertThat(testHarness.numEventTimeTimers(), is(0));
    // Watermark is advanced in a blocking fashion on close, not via a timers
    // Create a bundle with a pending timer to simulate that
    testHarness.processElement(new StreamRecord<>(windowedValue));
    timerConsumer.accept("timer3", new Instant(targetWatermark));
    assertThat(testHarness.numEventTimeTimers(), is(1));
    // This should be blocking until the watermark reaches Long.MAX_VALUE.
    testHarness.close();
    assertThat(testHarness.numEventTimeTimers(), is(0));
    assertThat(operator.getCurrentOutputWatermark(), is(Long.MAX_VALUE));
}

Example 2 with CoderTypeInformation

use of org.apache.beam.runners.flink.translation.types.CoderTypeInformation in project beam by apache.

the class FlinkBatchPortablePipelineTranslator method pruneOutput.

private static void pruneOutput(DataSet<RawUnionValue> taggedDataset, BatchTranslationContext context, int unionTag, Coder<WindowedValue<?>> outputCoder, String collectionId) {
    TypeInformation<WindowedValue<?>> outputType = new CoderTypeInformation<>(outputCoder, context.getPipelineOptions());
    FlinkExecutableStagePruningFunction pruningFunction = new FlinkExecutableStagePruningFunction(unionTag, context.getPipelineOptions());
    FlatMapOperator<RawUnionValue, WindowedValue<?>> pruningOperator = new FlatMapOperator<>(taggedDataset, outputType, pruningFunction, String.format("ExtractOutput[%s]", unionTag));
    context.addDataSet(collectionId, pruningOperator);
}

Example 3 with CoderTypeInformation

use of org.apache.beam.runners.flink.translation.types.CoderTypeInformation in project beam by apache.

the class FlinkStreamingPortablePipelineTranslator method translateFlatten.

private <T> void translateFlatten(String id, RunnerApi.Pipeline pipeline, StreamingTranslationContext context) {
    RunnerApi.PTransform transform = pipeline.getComponents().getTransformsOrThrow(id);
    Map<String, String> allInputs = transform.getInputsMap();
    if (allInputs.isEmpty()) {
        // create an empty dummy source to satisfy downstream operations
        // we cannot create an empty source in Flink, therefore we have to
        // add the flatMap that simply never forwards the single element
        long shutdownAfterIdleSourcesMs = context.getPipelineOptions().getShutdownSourcesAfterIdleMs();
        DataStreamSource<WindowedValue<byte[]>> dummySource = context.getExecutionEnvironment().addSource(new ImpulseSourceFunction(shutdownAfterIdleSourcesMs));
        DataStream<WindowedValue<T>> result = dummySource.<WindowedValue<T>>flatMap((s, collector) -> {
        // never return anything
        }).returns(new CoderTypeInformation<>(WindowedValue.getFullCoder((Coder<T>) VoidCoder.of(), GlobalWindow.Coder.INSTANCE), context.getPipelineOptions()));
        context.addDataStream(Iterables.getOnlyElement(transform.getOutputsMap().values()), result);
    } else {
        DataStream<T> result = null;
        // Determine DataStreams that we use as input several times. For those, we need to uniquify
        // input streams because Flink seems to swallow watermarks when we have a union of one and
        // the same stream.
        HashMultiset<DataStream<T>> inputCounts = HashMultiset.create();
        for (String input : allInputs.values()) {
            DataStream<T> current = context.getDataStreamOrThrow(input);
            inputCounts.add(current, 1);
        }
        for (String input : allInputs.values()) {
            DataStream<T> current = context.getDataStreamOrThrow(input);
            final int timesRequired = inputCounts.count(current);
            if (timesRequired > 1) {
                current = current.flatMap(new FlatMapFunction<T, T>() {

                    private static final long serialVersionUID = 1L;

                    @Override
                    public void flatMap(T t, Collector<T> collector) {
                        collector.collect(t);
                    }
                });
            }
            result = (result == null) ? current : result.union(current);
        }
        context.addDataStream(Iterables.getOnlyElement(transform.getOutputsMap().values()), result);
    }
}

Example 4 with CoderTypeInformation

use of org.apache.beam.runners.flink.translation.types.CoderTypeInformation in project beam by apache.

the class FlinkStreamingPortablePipelineTranslator method translateStreamingImpulse.

private void translateStreamingImpulse(String id, RunnerApi.Pipeline pipeline, StreamingTranslationContext context) {
    RunnerApi.PTransform pTransform = pipeline.getComponents().getTransformsOrThrow(id);
    TypeInformation<WindowedValue<byte[]>> typeInfo = new CoderTypeInformation<>(WindowedValue.getFullCoder(ByteArrayCoder.of(), GlobalWindow.Coder.INSTANCE), context.getPipelineOptions());
    ObjectMapper objectMapper = new ObjectMapper();
    final int intervalMillis;
    final int messageCount;
    try {
        JsonNode config = objectMapper.readTree(pTransform.getSpec().getPayload().toByteArray());
        intervalMillis = config.path("interval_ms").asInt(100);
        messageCount = config.path("message_count").asInt(0);
    } catch (IOException e) {
        throw new RuntimeException("Failed to parse configuration for streaming impulse", e);
    }
    SingleOutputStreamOperator<WindowedValue<byte[]>> source = context.getExecutionEnvironment().addSource(new StreamingImpulseSource(intervalMillis, messageCount), StreamingImpulseSource.class.getSimpleName()).returns(typeInfo);
    context.addDataStream(Iterables.getOnlyElement(pTransform.getOutputsMap().values()), source);
}

Example 5 with CoderTypeInformation

use of org.apache.beam.runners.flink.translation.types.CoderTypeInformation in project beam by apache.

the class FlinkStreamingPortablePipelineTranslator method translateExecutableStage.

private <InputT, OutputT> void translateExecutableStage(String id, RunnerApi.Pipeline pipeline, StreamingTranslationContext context) {
    // TODO: Fail on splittable DoFns.
    // TODO: Special-case single outputs to avoid multiplexing PCollections.
    RunnerApi.Components components = pipeline.getComponents();
    RunnerApi.PTransform transform = components.getTransformsOrThrow(id);
    Map<String, String> outputs = transform.getOutputsMap();
    final RunnerApi.ExecutableStagePayload stagePayload;
    try {
        stagePayload = RunnerApi.ExecutableStagePayload.parseFrom(transform.getSpec().getPayload());
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    String inputPCollectionId = stagePayload.getInput();
    final TransformedSideInputs transformedSideInputs;
    if (stagePayload.getSideInputsCount() > 0) {
        transformedSideInputs = transformSideInputs(stagePayload, components, context);
    } else {
        transformedSideInputs = new TransformedSideInputs(Collections.emptyMap(), null);
    }
    Map<TupleTag<?>, OutputTag<WindowedValue<?>>> tagsToOutputTags = Maps.newLinkedHashMap();
    Map<TupleTag<?>, Coder<WindowedValue<?>>> tagsToCoders = Maps.newLinkedHashMap();
    // TODO: does it matter which output we designate as "main"
    final TupleTag<OutputT> mainOutputTag = outputs.isEmpty() ? null : new TupleTag(outputs.keySet().iterator().next());
    // associate output tags with ids, output manager uses these Integer ids to serialize state
    BiMap<String, Integer> outputIndexMap = createOutputMap(outputs.keySet());
    Map<String, Coder<WindowedValue<?>>> outputCoders = Maps.newHashMap();
    Map<TupleTag<?>, Integer> tagsToIds = Maps.newHashMap();
    Map<String, TupleTag<?>> collectionIdToTupleTag = Maps.newHashMap();
    // order output names for deterministic mapping
    for (String localOutputName : new TreeMap<>(outputIndexMap).keySet()) {
        String collectionId = outputs.get(localOutputName);
        Coder<WindowedValue<?>> windowCoder = (Coder) instantiateCoder(collectionId, components);
        outputCoders.put(localOutputName, windowCoder);
        TupleTag<?> tupleTag = new TupleTag<>(localOutputName);
        CoderTypeInformation<WindowedValue<?>> typeInformation = new CoderTypeInformation(windowCoder, context.getPipelineOptions());
        tagsToOutputTags.put(tupleTag, new OutputTag<>(localOutputName, typeInformation));
        tagsToCoders.put(tupleTag, windowCoder);
        tagsToIds.put(tupleTag, outputIndexMap.get(localOutputName));
        collectionIdToTupleTag.put(collectionId, tupleTag);
    }
    final SingleOutputStreamOperator<WindowedValue<OutputT>> outputStream;
    DataStream<WindowedValue<InputT>> inputDataStream = context.getDataStreamOrThrow(inputPCollectionId);
    CoderTypeInformation<WindowedValue<OutputT>> outputTypeInformation = !outputs.isEmpty() ? new CoderTypeInformation(outputCoders.get(mainOutputTag.getId()), context.getPipelineOptions()) : null;
    ArrayList<TupleTag<?>> additionalOutputTags = Lists.newArrayList();
    for (TupleTag<?> tupleTag : tagsToCoders.keySet()) {
        if (!mainOutputTag.getId().equals(tupleTag.getId())) {
            additionalOutputTags.add(tupleTag);
        }
    }
    final Coder<WindowedValue<InputT>> windowedInputCoder = instantiateCoder(inputPCollectionId, components);
    final boolean stateful = stagePayload.getUserStatesCount() > 0 || stagePayload.getTimersCount() > 0;
    final boolean hasSdfProcessFn = stagePayload.getComponents().getTransformsMap().values().stream().anyMatch(pTransform -> pTransform.getSpec().getUrn().equals(PTransformTranslation.SPLITTABLE_PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS_URN));
    Coder keyCoder = null;
    KeySelector<WindowedValue<InputT>, ?> keySelector = null;
    if (stateful || hasSdfProcessFn) {
        // Stateful/SDF stages are only allowed of KV input.
        Coder valueCoder = ((WindowedValue.FullWindowedValueCoder) windowedInputCoder).getValueCoder();
        if (!(valueCoder instanceof KvCoder)) {
            throw new IllegalStateException(String.format(Locale.ENGLISH, "The element coder for stateful DoFn '%s' must be KvCoder but is: %s", inputPCollectionId, valueCoder.getClass().getSimpleName()));
        }
        if (stateful) {
            keyCoder = ((KvCoder) valueCoder).getKeyCoder();
            keySelector = new KvToByteBufferKeySelector(keyCoder, new SerializablePipelineOptions(context.getPipelineOptions()));
        } else {
            // as the key.
            if (!(((KvCoder) valueCoder).getKeyCoder() instanceof KvCoder)) {
                throw new IllegalStateException(String.format(Locale.ENGLISH, "The element coder for splittable DoFn '%s' must be KVCoder(KvCoder, DoubleCoder) but is: %s", inputPCollectionId, valueCoder.getClass().getSimpleName()));
            }
            keyCoder = ((KvCoder) ((KvCoder) valueCoder).getKeyCoder()).getKeyCoder();
            keySelector = new SdfByteBufferKeySelector(keyCoder, new SerializablePipelineOptions(context.getPipelineOptions()));
        }
        inputDataStream = inputDataStream.keyBy(keySelector);
    }
    DoFnOperator.MultiOutputOutputManagerFactory<OutputT> outputManagerFactory = new DoFnOperator.MultiOutputOutputManagerFactory<>(mainOutputTag, tagsToOutputTags, tagsToCoders, tagsToIds, new SerializablePipelineOptions(context.getPipelineOptions()));
    DoFnOperator<InputT, OutputT> doFnOperator = new ExecutableStageDoFnOperator<>(transform.getUniqueName(), windowedInputCoder, Collections.emptyMap(), mainOutputTag, additionalOutputTags, outputManagerFactory, transformedSideInputs.unionTagToView, new ArrayList<>(transformedSideInputs.unionTagToView.values()), getSideInputIdToPCollectionViewMap(stagePayload, components), context.getPipelineOptions(), stagePayload, context.getJobInfo(), FlinkExecutableStageContextFactory.getInstance(), collectionIdToTupleTag, getWindowingStrategy(inputPCollectionId, components), keyCoder, keySelector);
    final String operatorName = generateNameFromStagePayload(stagePayload);
    if (transformedSideInputs.unionTagToView.isEmpty()) {
        outputStream = inputDataStream.transform(operatorName, outputTypeInformation, doFnOperator);
    } else {
        DataStream<RawUnionValue> sideInputStream = transformedSideInputs.unionedSideInputs.broadcast();
        if (stateful || hasSdfProcessFn) {
            // We have to manually construct the two-input transform because we're not
            // allowed to have only one input keyed, normally. Since Flink 1.5.0 it's
            // possible to use the Broadcast State Pattern which provides a more elegant
            // way to process keyed main input with broadcast state, but it's not feasible
            // here because it breaks the DoFnOperator abstraction.
            TwoInputTransformation<WindowedValue<KV<?, InputT>>, RawUnionValue, WindowedValue<OutputT>> rawFlinkTransform = new TwoInputTransformation(inputDataStream.getTransformation(), sideInputStream.getTransformation(), transform.getUniqueName(), doFnOperator, outputTypeInformation, inputDataStream.getParallelism());
            rawFlinkTransform.setStateKeyType(((KeyedStream) inputDataStream).getKeyType());
            rawFlinkTransform.setStateKeySelectors(((KeyedStream) inputDataStream).getKeySelector(), null);
            outputStream = new SingleOutputStreamOperator(inputDataStream.getExecutionEnvironment(), // we have to cheat around the ctor being protected
            rawFlinkTransform) {
            };
        } else {
            outputStream = inputDataStream.connect(sideInputStream).transform(operatorName, outputTypeInformation, doFnOperator);
        }
    }
    // Assign a unique but consistent id to re-map operator state
    outputStream.uid(transform.getUniqueName());
    if (mainOutputTag != null) {
        context.addDataStream(outputs.get(mainOutputTag.getId()), outputStream);
    }
    for (TupleTag<?> tupleTag : additionalOutputTags) {
        context.addDataStream(outputs.get(tupleTag.getId()), outputStream.getSideOutput(tagsToOutputTags.get(tupleTag)));
    }
}