Examples with TimestampCombiner org.apache.beam.sdk.transforms.windowing.TimestampCombiner used on opensource projects

Example 1 with TimestampCombiner

use of org.apache.beam.sdk.transforms.windowing.TimestampCombiner in project beam by apache.

the class WindowingStrategyTranslation method fromProto.

/**
   * Converts from {@link RunnerApi.WindowingStrategy} to the SDK's {@link WindowingStrategy} using
   * the provided components to dereferences identifiers found in the proto.
   */
public static WindowingStrategy<?, ?> fromProto(RunnerApi.WindowingStrategy proto, Components components) throws InvalidProtocolBufferException {
    SdkFunctionSpec windowFnSpec = proto.getWindowFn();
    WindowFn<?, ?> windowFn = windowFnFromProto(windowFnSpec);
    TimestampCombiner timestampCombiner = timestampCombinerFromProto(proto.getOutputTime());
    AccumulationMode accumulationMode = fromProto(proto.getAccumulationMode());
    Trigger trigger = TriggerTranslation.fromProto(proto.getTrigger());
    ClosingBehavior closingBehavior = fromProto(proto.getClosingBehavior());
    Duration allowedLateness = Duration.millis(proto.getAllowedLateness());
    return WindowingStrategy.of(windowFn).withAllowedLateness(allowedLateness).withMode(accumulationMode).withTrigger(trigger).withTimestampCombiner(timestampCombiner).withClosingBehavior(closingBehavior);
}

Example 2 with TimestampCombiner

use of org.apache.beam.sdk.transforms.windowing.TimestampCombiner in project beam by apache.

the class CopyOnAccessInMemoryStateInternalsTest method testWatermarkHoldStateWithUnderlying.

@Test
public void testWatermarkHoldStateWithUnderlying() {
    CopyOnAccessInMemoryStateInternals<String> underlying = CopyOnAccessInMemoryStateInternals.withUnderlying(key, null);
    TimestampCombiner timestampCombiner = TimestampCombiner.EARLIEST;
    StateNamespace namespace = new StateNamespaceForTest("foo");
    StateTag<WatermarkHoldState> stateTag = StateTags.watermarkStateInternal("wmstate", timestampCombiner);
    WatermarkHoldState underlyingValue = underlying.state(namespace, stateTag);
    assertThat(underlyingValue.read(), nullValue());
    underlyingValue.add(new Instant(250L));
    assertThat(underlyingValue.read(), equalTo(new Instant(250L)));
    CopyOnAccessInMemoryStateInternals<String> internals = CopyOnAccessInMemoryStateInternals.withUnderlying(key, underlying);
    WatermarkHoldState copyOnAccessState = internals.state(namespace, stateTag);
    assertThat(copyOnAccessState.read(), equalTo(new Instant(250L)));
    copyOnAccessState.add(new Instant(100L));
    assertThat(copyOnAccessState.read(), equalTo(new Instant(100L)));
    assertThat(underlyingValue.read(), equalTo(new Instant(250L)));
    copyOnAccessState.add(new Instant(500L));
    assertThat(copyOnAccessState.read(), equalTo(new Instant(100L)));
    WatermarkHoldState reReadUnderlyingValue = underlying.state(namespace, stateTag);
    assertThat(underlyingValue.read(), equalTo(reReadUnderlyingValue.read()));
}

Example 3 with TimestampCombiner

use of org.apache.beam.sdk.transforms.windowing.TimestampCombiner in project beam by apache.

the class SparkGlobalCombineFn method createAccumulator.

private Iterable<WindowedValue<AccumT>> createAccumulator(WindowedValue<InputT> input) {
    // sort exploded inputs.
    Iterable<WindowedValue<InputT>> sortedInputs = sortByWindows(input.explodeWindows());
    TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner();
    WindowFn<?, BoundedWindow> windowFn = windowingStrategy.getWindowFn();
    //--- inputs iterator, by window order.
    final Iterator<WindowedValue<InputT>> iterator = sortedInputs.iterator();
    WindowedValue<InputT> currentInput = iterator.next();
    BoundedWindow currentWindow = Iterables.getFirst(currentInput.getWindows(), null);
    // first create the accumulator and accumulate first input.
    AccumT accumulator = combineFn.createAccumulator(ctxtForInput(currentInput));
    accumulator = combineFn.addInput(accumulator, currentInput.getValue(), ctxtForInput(currentInput));
    // keep track of the timestamps assigned by the TimestampCombiner.
    Instant windowTimestamp = timestampCombiner.assign(currentWindow, windowingStrategy.getWindowFn().getOutputTime(currentInput.getTimestamp(), currentWindow));
    // accumulate the next windows, or output.
    List<WindowedValue<AccumT>> output = Lists.newArrayList();
    // if merging, merge overlapping windows, e.g. Sessions.
    final boolean merging = !windowingStrategy.getWindowFn().isNonMerging();
    while (iterator.hasNext()) {
        WindowedValue<InputT> nextValue = iterator.next();
        BoundedWindow nextWindow = Iterables.getOnlyElement(nextValue.getWindows());
        boolean mergingAndIntersecting = merging && isIntersecting((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
        if (mergingAndIntersecting || nextWindow.equals(currentWindow)) {
            if (mergingAndIntersecting) {
                // merge intersecting windows.
                currentWindow = merge((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
            }
            // keep accumulating and carry on ;-)
            accumulator = combineFn.addInput(accumulator, nextValue.getValue(), ctxtForInput(nextValue));
            windowTimestamp = timestampCombiner.merge(currentWindow, windowTimestamp, windowingStrategy.getWindowFn().getOutputTime(nextValue.getTimestamp(), currentWindow));
        } else {
            // moving to the next window, first add the current accumulation to output
            // and initialize the accumulator.
            output.add(WindowedValue.of(accumulator, windowTimestamp, currentWindow, PaneInfo.NO_FIRING));
            // re-init accumulator, window and timestamp.
            accumulator = combineFn.createAccumulator(ctxtForInput(nextValue));
            accumulator = combineFn.addInput(accumulator, nextValue.getValue(), ctxtForInput(nextValue));
            currentWindow = nextWindow;
            windowTimestamp = timestampCombiner.assign(currentWindow, windowFn.getOutputTime(nextValue.getTimestamp(), currentWindow));
        }
    }
    // add last accumulator to the output.
    output.add(WindowedValue.of(accumulator, windowTimestamp, currentWindow, PaneInfo.NO_FIRING));
    return output;
}

Example 4 with TimestampCombiner

use of org.apache.beam.sdk.transforms.windowing.TimestampCombiner in project beam by apache.

the class SparkGlobalCombineFn method combOp.

/**
   * Implement Spark's combOp function in:
   * <p>
   * {@link org.apache.spark.api.java.JavaRDD#aggregate}.
   * </p>
   */
Iterable<WindowedValue<AccumT>> combOp(Iterable<WindowedValue<AccumT>> a1, Iterable<WindowedValue<AccumT>> a2) {
    // concatenate accumulators.
    Iterable<WindowedValue<AccumT>> accumulators = Iterables.concat(a1, a2);
    // if empty, return an empty accumulators iterable.
    if (!accumulators.iterator().hasNext()) {
        return Lists.newArrayList();
    }
    // sort accumulators, no need to explode since inputs were exploded.
    Iterable<WindowedValue<AccumT>> sortedAccumulators = sortByWindows(accumulators);
    @SuppressWarnings("unchecked") TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner();
    //--- accumulators iterator, by window order.
    final Iterator<WindowedValue<AccumT>> iterator = sortedAccumulators.iterator();
    // get the first accumulator and assign it to the current window's accumulators.
    WindowedValue<AccumT> currentValue = iterator.next();
    BoundedWindow currentWindow = Iterables.getFirst(currentValue.getWindows(), null);
    List<AccumT> currentWindowAccumulators = Lists.newArrayList();
    currentWindowAccumulators.add(currentValue.getValue());
    // keep track of the timestamps assigned by the TimestampCombiner,
    // in createCombiner we already merge the timestamps assigned
    // to individual elements, here we will just merge them.
    List<Instant> windowTimestamps = Lists.newArrayList();
    windowTimestamps.add(currentValue.getTimestamp());
    // accumulate the next windows, or output.
    List<WindowedValue<AccumT>> output = Lists.newArrayList();
    // if merging, merge overlapping windows, e.g. Sessions.
    final boolean merging = !windowingStrategy.getWindowFn().isNonMerging();
    while (iterator.hasNext()) {
        WindowedValue<AccumT> nextValue = iterator.next();
        BoundedWindow nextWindow = Iterables.getOnlyElement(nextValue.getWindows());
        boolean mergingAndIntersecting = merging && isIntersecting((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
        if (mergingAndIntersecting || nextWindow.equals(currentWindow)) {
            if (mergingAndIntersecting) {
                // merge intersecting windows.
                currentWindow = merge((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
            }
            // add to window accumulators.
            currentWindowAccumulators.add(nextValue.getValue());
            windowTimestamps.add(nextValue.getTimestamp());
        } else {
            // before moving to the next window,
            // add the current accumulation to the output and initialize the accumulation.
            // merge the timestamps of all accumulators to merge.
            Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
            // merge accumulators.
            // transforming a KV<K, Iterable<AccumT>> into a KV<K, Iterable<AccumT>>.
            // for the (possibly merged) window.
            Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
            WindowedValue<Iterable<AccumT>> preMergeWindowedValue = WindowedValue.of(accumsToMerge, mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
            // applying the actual combiner onto the accumulators.
            AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge, ctxtForInput(preMergeWindowedValue));
            WindowedValue<AccumT> postMergeWindowedValue = preMergeWindowedValue.withValue(accumulated);
            // emit the accumulated output.
            output.add(postMergeWindowedValue);
            // re-init accumulator, window and timestamps.
            currentWindowAccumulators.clear();
            currentWindowAccumulators.add(nextValue.getValue());
            currentWindow = nextWindow;
            windowTimestamps.clear();
            windowTimestamps.add(nextValue.getTimestamp());
        }
    }
    // merge the last chunk of accumulators.
    Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
    Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
    WindowedValue<Iterable<AccumT>> preMergeWindowedValue = WindowedValue.of(accumsToMerge, mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
    AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge, ctxtForInput(preMergeWindowedValue));
    WindowedValue<AccumT> postMergeWindowedValue = preMergeWindowedValue.withValue(accumulated);
    output.add(postMergeWindowedValue);
    return output;
}

Example 5 with TimestampCombiner

use of org.apache.beam.sdk.transforms.windowing.TimestampCombiner in project beam by apache.

the class SparkKeyedCombineFn method mergeCombiners.

/**
   * Implements Spark's mergeCombiners function in:
   * <p>
   * {@link org.apache.spark.rdd.PairRDDFunctions#combineByKey}.
   * </p>
   */
Iterable<WindowedValue<KV<K, AccumT>>> mergeCombiners(Iterable<WindowedValue<KV<K, AccumT>>> a1, Iterable<WindowedValue<KV<K, AccumT>>> a2) {
    // concatenate accumulators.
    Iterable<WindowedValue<KV<K, AccumT>>> accumulators = Iterables.concat(a1, a2);
    // sort accumulators, no need to explode since inputs were exploded.
    Iterable<WindowedValue<KV<K, AccumT>>> sortedAccumulators = sortByWindows(accumulators);
    @SuppressWarnings("unchecked") TimestampCombiner timestampCombiner = windowingStrategy.getTimestampCombiner();
    //--- accumulators iterator, by window order.
    final Iterator<WindowedValue<KV<K, AccumT>>> iterator = sortedAccumulators.iterator();
    // get the first accumulator and assign it to the current window's accumulators.
    WindowedValue<KV<K, AccumT>> currentValue = iterator.next();
    K key = currentValue.getValue().getKey();
    BoundedWindow currentWindow = Iterables.getFirst(currentValue.getWindows(), null);
    List<AccumT> currentWindowAccumulators = Lists.newArrayList();
    currentWindowAccumulators.add(currentValue.getValue().getValue());
    // keep track of the timestamps assigned by the TimestampCombiner,
    // in createCombiner we already merge the timestamps assigned
    // to individual elements, here we will just merge them.
    List<Instant> windowTimestamps = Lists.newArrayList();
    windowTimestamps.add(currentValue.getTimestamp());
    // accumulate the next windows, or output.
    List<WindowedValue<KV<K, AccumT>>> output = Lists.newArrayList();
    // if merging, merge overlapping windows, e.g. Sessions.
    final boolean merging = !windowingStrategy.getWindowFn().isNonMerging();
    while (iterator.hasNext()) {
        WindowedValue<KV<K, AccumT>> nextValue = iterator.next();
        BoundedWindow nextWindow = Iterables.getOnlyElement(nextValue.getWindows());
        boolean mergingAndIntersecting = merging && isIntersecting((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
        if (mergingAndIntersecting || nextWindow.equals(currentWindow)) {
            if (mergingAndIntersecting) {
                // merge intersecting windows.
                currentWindow = merge((IntervalWindow) currentWindow, (IntervalWindow) nextWindow);
            }
            // add to window accumulators.
            currentWindowAccumulators.add(nextValue.getValue().getValue());
            windowTimestamps.add(nextValue.getTimestamp());
        } else {
            // before moving to the next window,
            // add the current accumulation to the output and initialize the accumulation.
            // merge the timestamps of all accumulators to merge.
            Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
            // merge accumulators.
            // transforming a KV<K, Iterable<AccumT>> into a KV<K, Iterable<AccumT>>.
            // for the (possibly merged) window.
            Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
            WindowedValue<KV<K, Iterable<AccumT>>> preMergeWindowedValue = WindowedValue.of(KV.of(key, accumsToMerge), mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
            // applying the actual combiner onto the accumulators.
            AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge, ctxtForInput(preMergeWindowedValue));
            WindowedValue<KV<K, AccumT>> postMergeWindowedValue = preMergeWindowedValue.withValue(KV.of(key, accumulated));
            // emit the accumulated output.
            output.add(postMergeWindowedValue);
            // re-init accumulator, window and timestamps.
            currentWindowAccumulators.clear();
            currentWindowAccumulators.add(nextValue.getValue().getValue());
            currentWindow = nextWindow;
            windowTimestamps.clear();
            windowTimestamps.add(nextValue.getTimestamp());
        }
    }
    // merge the last chunk of accumulators.
    Instant mergedTimestamp = timestampCombiner.merge(currentWindow, windowTimestamps);
    Iterable<AccumT> accumsToMerge = Iterables.unmodifiableIterable(currentWindowAccumulators);
    WindowedValue<KV<K, Iterable<AccumT>>> preMergeWindowedValue = WindowedValue.of(KV.of(key, accumsToMerge), mergedTimestamp, currentWindow, PaneInfo.NO_FIRING);
    AccumT accumulated = combineFn.mergeAccumulators(accumsToMerge, ctxtForInput(preMergeWindowedValue));
    WindowedValue<KV<K, AccumT>> postMergeWindowedValue = preMergeWindowedValue.withValue(KV.of(key, accumulated));
    output.add(postMergeWindowedValue);
    return output;
}