/**
* Tests that the given {@link GroupAlsoByWindowsDoFn} implementation combines elements per
* session window correctly according to the provided {@link CombineFn}.
*/
public static void combinesElementsPerSessionWithEndOfWindowTimestamp(
GroupAlsoByWindowsDoFnFactory<String, Long, Long> gabwFactory,
CombineFn<Long, ?, Long> combineFn)
throws Exception {
WindowingStrategy<?, IntervalWindow> windowingStrategy =
WindowingStrategy.of(Sessions.withGapDuration(Duration.millis(10)))
.withOutputTimeFn(OutputTimeFns.outputAtEndOfWindow());
BoundedWindow secondWindow = window(15, 25);
List<WindowedValue<KV<String, Long>>> result =
runGABW(
gabwFactory,
windowingStrategy,
"k",
WindowedValue.of(1L, new Instant(0), Arrays.asList(window(0, 10)), PaneInfo.NO_FIRING),
WindowedValue.of(2L, new Instant(5), Arrays.asList(window(5, 15)), PaneInfo.NO_FIRING),
WindowedValue.of(4L, new Instant(15), Arrays.asList(secondWindow), PaneInfo.NO_FIRING));
assertThat(result, hasSize(2));
BoundedWindow firstResultWindow = window(0, 15);
TimestampedValue<KV<String, Long>> item0 = getOnlyElementInWindow(result, firstResultWindow);
assertThat(item0.getValue().getValue(), equalTo(combineFn.apply(ImmutableList.of(1L, 2L))));
assertThat(item0.getTimestamp(), equalTo(firstResultWindow.maxTimestamp()));
TimestampedValue<KV<String, Long>> item1 = getOnlyElementInWindow(result, secondWindow);
assertThat(item1.getValue().getValue(), equalTo(combineFn.apply(ImmutableList.of(4L))));
assertThat(item1.getTimestamp(), equalTo(secondWindow.maxTimestamp()));
}
/**
* Tests that the given GABW implementation correctly groups elements into merged sessions with
* output timestamps at the end of the merged window.
*/
public static void groupsElementsInMergedSessionsWithLatestTimestamp(
GroupAlsoByWindowsDoFnFactory<String, String, Iterable<String>> gabwFactory)
throws Exception {
WindowingStrategy<?, IntervalWindow> windowingStrategy =
WindowingStrategy.of(Sessions.withGapDuration(Duration.millis(10)))
.withOutputTimeFn(OutputTimeFns.outputAtLatestInputTimestamp());
BoundedWindow unmergedWindow = window(15, 25);
List<WindowedValue<KV<String, Iterable<String>>>> result =
runGABW(
gabwFactory,
windowingStrategy,
"k",
WindowedValue.of(
"v1", new Instant(0), Arrays.asList(window(0, 10)), PaneInfo.NO_FIRING),
WindowedValue.of(
"v2", new Instant(5), Arrays.asList(window(5, 15)), PaneInfo.NO_FIRING),
WindowedValue.of(
"v3", new Instant(15), Arrays.asList(unmergedWindow), PaneInfo.NO_FIRING));
assertThat(result, hasSize(2));
BoundedWindow mergedWindow = window(0, 15);
TimestampedValue<KV<String, Iterable<String>>> item0 =
getOnlyElementInWindow(result, mergedWindow);
assertThat(item0.getValue().getValue(), containsInAnyOrder("v1", "v2"));
assertThat(item0.getTimestamp(), equalTo(new Instant(5)));
TimestampedValue<KV<String, Iterable<String>>> item1 =
getOnlyElementInWindow(result, unmergedWindow);
assertThat(item1.getValue().getValue(), contains("v3"));
assertThat(item1.getTimestamp(), equalTo(new Instant(15)));
}
/**
* Tests that for a simple sequence of elements on the same key, the given GABW implementation
* correctly groups them according to fixed windows and also sets the output timestamp according
* to the policy {@link OutputTimeFns#outputAtEndOfWindow()}.
*/
public static void groupsElementsIntoFixedWindowsWithEndOfWindowTimestamp(
GroupAlsoByWindowsDoFnFactory<String, String, Iterable<String>> gabwFactory)
throws Exception {
WindowingStrategy<?, IntervalWindow> windowingStrategy =
WindowingStrategy.of(FixedWindows.of(Duration.millis(10)))
.withOutputTimeFn(OutputTimeFns.outputAtEndOfWindow());
List<WindowedValue<KV<String, Iterable<String>>>> result =
runGABW(
gabwFactory,
windowingStrategy,
"key",
WindowedValue.of(
"v1", new Instant(1), Arrays.asList(window(0, 10)), PaneInfo.NO_FIRING),
WindowedValue.of(
"v2", new Instant(2), Arrays.asList(window(0, 10)), PaneInfo.NO_FIRING),
WindowedValue.of(
"v3", new Instant(13), Arrays.asList(window(10, 20)), PaneInfo.NO_FIRING));
assertThat(result, hasSize(2));
TimestampedValue<KV<String, Iterable<String>>> item0 =
getOnlyElementInWindow(result, window(0, 10));
assertThat(item0.getValue().getValue(), containsInAnyOrder("v1", "v2"));
assertThat(item0.getTimestamp(), equalTo(window(0, 10).maxTimestamp()));
TimestampedValue<KV<String, Iterable<String>>> item1 =
getOnlyElementInWindow(result, window(10, 20));
assertThat(item1.getValue().getValue(), contains("v3"));
assertThat(item1.getTimestamp(), equalTo(window(10, 20).maxTimestamp()));
}
/**
* Tests that for a simple sequence of elements on the same key, the given GABW implementation
* correctly groups and combines them according to sliding windows.
*
* <p>In the input here, each element occurs in multiple windows.
*/
public static void combinesElementsInSlidingWindows(
GroupAlsoByWindowsDoFnFactory<String, Long, Long> gabwFactory,
CombineFn<Long, ?, Long> combineFn)
throws Exception {
WindowingStrategy<?, IntervalWindow> windowingStrategy =
WindowingStrategy.of(SlidingWindows.of(Duration.millis(20)).every(Duration.millis(10)))
.withOutputTimeFn(OutputTimeFns.outputAtEarliestInputTimestamp());
List<WindowedValue<KV<String, Long>>> result =
runGABW(
gabwFactory,
windowingStrategy,
"k",
WindowedValue.of(
1L,
new Instant(5),
Arrays.asList(window(-10, 10), window(0, 20)),
PaneInfo.NO_FIRING),
WindowedValue.of(
2L,
new Instant(15),
Arrays.asList(window(0, 20), window(10, 30)),
PaneInfo.NO_FIRING),
WindowedValue.of(
4L,
new Instant(18),
Arrays.asList(window(0, 20), window(10, 30)),
PaneInfo.NO_FIRING));
assertThat(result, hasSize(3));
TimestampedValue<KV<String, Long>> item0 = getOnlyElementInWindow(result, window(-10, 10));
assertThat(item0.getValue().getKey(), equalTo("k"));
assertThat(item0.getValue().getValue(), equalTo(combineFn.apply(ImmutableList.of(1L))));
assertThat(item0.getTimestamp(), equalTo(new Instant(5L)));
TimestampedValue<KV<String, Long>> item1 = getOnlyElementInWindow(result, window(0, 20));
assertThat(item1.getValue().getKey(), equalTo("k"));
assertThat(item1.getValue().getValue(), equalTo(combineFn.apply(ImmutableList.of(1L, 2L, 4L))));
// Timestamp adjusted by WindowFn to exceed the end of the prior sliding window
assertThat(item1.getTimestamp(), equalTo(new Instant(10L)));
TimestampedValue<KV<String, Long>> item2 = getOnlyElementInWindow(result, window(10, 30));
assertThat(item2.getValue().getKey(), equalTo("k"));
assertThat(item2.getValue().getValue(), equalTo(combineFn.apply(ImmutableList.of(2L, 4L))));
// Timestamp adjusted by WindowFn to exceed the end of the prior sliding window
assertThat(item2.getTimestamp(), equalTo(new Instant(20L)));
}
@SuppressWarnings("ConstantConditions")
JavaDStream<WindowedValue<T>> getDStream() {
if (dStream == null) {
WindowedValue.ValueOnlyWindowedValueCoder<T> windowCoder =
WindowedValue.getValueOnlyCoder(coder);
// create the DStream from queue
Queue<JavaRDD<WindowedValue<T>>> rddQueue = new LinkedBlockingQueue<>();
JavaRDD<WindowedValue<T>> lastRDD = null;
for (Iterable<T> v : values) {
Iterable<WindowedValue<T>> windowedValues =
Iterables.transform(v, WindowingHelpers.<T>windowValueFunction());
JavaRDD<WindowedValue<T>> rdd =
jssc.sc()
.parallelize(CoderHelpers.toByteArrays(windowedValues, windowCoder))
.map(CoderHelpers.fromByteFunction(windowCoder));
rddQueue.offer(rdd);
lastRDD = rdd;
}
// create DStream from queue, one at a time,
// with last as default in case batches repeat (graceful stops for example).
// if the stream is empty, avoid creating a default empty RDD.
// mainly for unit test so no reason to have this configurable.
dStream =
lastRDD != null
? jssc.queueStream(rddQueue, true, lastRDD)
: jssc.queueStream(rddQueue, true);
}
return dStream;
}
@Override
@SuppressWarnings("unchecked")
public void translateNode(
Flatten.FlattenPCollectionList<T> transform, FlinkBatchTranslationContext context) {
List<TaggedPValue> allInputs = context.getInputs(transform);
DataSet<WindowedValue<T>> result = null;
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
DataSource<String> dummySource = context.getExecutionEnvironment().fromElements("dummy");
result =
dummySource
.flatMap(
new FlatMapFunction<String, WindowedValue<T>>() {
@Override
public void flatMap(String s, Collector<WindowedValue<T>> collector)
throws Exception {
// never return anything
}
})
.returns(
new CoderTypeInformation<>(
WindowedValue.getFullCoder(
(Coder<T>) VoidCoder.of(), GlobalWindow.Coder.INSTANCE)));
} else {
for (TaggedPValue taggedPc : allInputs) {
checkArgument(
taggedPc.getValue() instanceof PCollection,
"Got non-PCollection input to flatten: %s of type %s",
taggedPc.getValue(),
taggedPc.getValue().getClass().getSimpleName());
PCollection<T> collection = (PCollection<T>) taggedPc.getValue();
DataSet<WindowedValue<T>> current = context.getInputDataSet(collection);
if (result == null) {
result = current;
} else {
result = result.union(current);
}
}
}
// insert a dummy filter, there seems to be a bug in Flink
// that produces duplicate elements after the union in some cases
// if we don't
result =
result
.filter(
new FilterFunction<WindowedValue<T>>() {
@Override
public boolean filter(WindowedValue<T> tWindowedValue) throws Exception {
return true;
}
})
.name("UnionFixFilter");
context.setOutputDataSet(context.getOutput(transform), result);
}
/** Fires any available timers. */
private void fireTimers() throws Exception {
try {
for (FiredTimers transformTimers : evaluationContext.extractFiredTimers()) {
Collection<TimerData> delivery = transformTimers.getTimers();
KeyedWorkItem<?, Object> work =
KeyedWorkItems.timersWorkItem(transformTimers.getKey().getKey(), delivery);
@SuppressWarnings({"unchecked", "rawtypes"})
CommittedBundle<?> bundle =
evaluationContext
.createKeyedBundle(
transformTimers.getKey(),
(PCollection) transformTimers.getTransform().getInput())
.add(WindowedValue.valueInGlobalWindow(work))
.commit(evaluationContext.now());
scheduleConsumption(
transformTimers.getTransform(),
bundle,
new TimerIterableCompletionCallback(delivery));
state.set(ExecutorState.ACTIVE);
}
} catch (Exception e) {
LOG.error("Internal Error while delivering timers", e);
throw e;
}
}
@Override
protected void outputWithTimestampAndWindow(
OutputT value,
Instant timestamp,
Collection<? extends BoundedWindow> windows,
PaneInfo pane) {
collector.collect(WindowedValue.of(value, timestamp, windows, pane));
}
@Override
public void outputWindowedValue(
KV<K, Iterable<V>> output,
Instant timestamp,
Collection<? extends BoundedWindow> windows,
PaneInfo pane) {
bundle.add(WindowedValue.of(output, timestamp, windows, pane));
}
/**
* Tests that for a simple sequence of elements on the same key, the given GABW implementation
* correctly groups them into sliding windows.
*
* <p>In the input here, each element occurs in multiple windows.
*/
public static void groupsElementsIntoSlidingWindowsWithMinTimestamp(
GroupAlsoByWindowsDoFnFactory<String, String, Iterable<String>> gabwFactory)
throws Exception {
WindowingStrategy<?, IntervalWindow> windowingStrategy =
WindowingStrategy.of(SlidingWindows.of(Duration.millis(20)).every(Duration.millis(10)))
.withOutputTimeFn(OutputTimeFns.outputAtEarliestInputTimestamp());
List<WindowedValue<KV<String, Iterable<String>>>> result =
runGABW(
gabwFactory,
windowingStrategy,
"key",
WindowedValue.of(
"v1",
new Instant(5),
Arrays.asList(window(-10, 10), window(0, 20)),
PaneInfo.NO_FIRING),
WindowedValue.of(
"v2",
new Instant(15),
Arrays.asList(window(0, 20), window(10, 30)),
PaneInfo.NO_FIRING));
assertThat(result, hasSize(3));
TimestampedValue<KV<String, Iterable<String>>> item0 =
getOnlyElementInWindow(result, window(-10, 10));
assertThat(item0.getValue().getValue(), contains("v1"));
assertThat(item0.getTimestamp(), equalTo(new Instant(5)));
TimestampedValue<KV<String, Iterable<String>>> item1 =
getOnlyElementInWindow(result, window(0, 20));
assertThat(item1.getValue().getValue(), containsInAnyOrder("v1", "v2"));
// Timestamp adjusted by WindowFn to exceed the end of the prior sliding window
assertThat(item1.getTimestamp(), equalTo(new Instant(10)));
TimestampedValue<KV<String, Iterable<String>>> item2 =
getOnlyElementInWindow(result, window(10, 30));
assertThat(item2.getValue().getValue(), contains("v2"));
// Timestamp adjusted by WindowFn to exceed the end of the prior sliding window
assertThat(item2.getTimestamp(), equalTo(new Instant(20)));
}
@SuppressWarnings("ConstantConditions")
public JavaRDD<WindowedValue<T>> getRDD() {
if (rdd == null) {
WindowedValue.ValueOnlyWindowedValueCoder<T> windowCoder =
WindowedValue.getValueOnlyCoder(coder);
rdd =
jsc.parallelize(CoderHelpers.toByteArrays(windowedValues, windowCoder))
.map(CoderHelpers.fromByteFunction(windowCoder));
}
return rdd;
}
@Override
public void translateNode(
ParDo.BoundMulti<InputT, OutputT> transform, FlinkBatchTranslationContext context) {
DoFn<InputT, OutputT> doFn = transform.getFn();
rejectStateAndTimers(doFn);
DataSet<WindowedValue<InputT>> inputDataSet =
context.getInputDataSet(context.getInput(transform));
List<TaggedPValue> outputs = context.getOutputs(transform);
Map<TupleTag<?>, Integer> outputMap = Maps.newHashMap();
// put the main output at index 0, FlinkMultiOutputDoFnFunction expects this
outputMap.put(transform.getMainOutputTag(), 0);
int count = 1;
for (TaggedPValue taggedValue : outputs) {
if (!outputMap.containsKey(taggedValue.getTag())) {
outputMap.put(taggedValue.getTag(), count++);
}
}
// assume that the windowing strategy is the same for all outputs
WindowingStrategy<?, ?> windowingStrategy = null;
// collect all output Coders and create a UnionCoder for our tagged outputs
List<Coder<?>> outputCoders = Lists.newArrayList();
for (TaggedPValue taggedValue : outputs) {
checkState(
taggedValue.getValue() instanceof PCollection,
"Within ParDo, got a non-PCollection output %s of type %s",
taggedValue.getValue(),
taggedValue.getValue().getClass().getSimpleName());
PCollection<?> coll = (PCollection<?>) taggedValue.getValue();
outputCoders.add(coll.getCoder());
windowingStrategy = coll.getWindowingStrategy();
}
if (windowingStrategy == null) {
throw new IllegalStateException("No outputs defined.");
}
UnionCoder unionCoder = UnionCoder.of(outputCoders);
TypeInformation<WindowedValue<RawUnionValue>> typeInformation =
new CoderTypeInformation<>(
WindowedValue.getFullCoder(
unionCoder, windowingStrategy.getWindowFn().windowCoder()));
List<PCollectionView<?>> sideInputs = transform.getSideInputs();
// construct a map from side input to WindowingStrategy so that
// the OldDoFn runner can map main-input windows to side input windows
Map<PCollectionView<?>, WindowingStrategy<?, ?>> sideInputStrategies = new HashMap<>();
for (PCollectionView<?> sideInput : sideInputs) {
sideInputStrategies.put(sideInput, sideInput.getWindowingStrategyInternal());
}
@SuppressWarnings("unchecked")
FlinkMultiOutputDoFnFunction<InputT, OutputT> doFnWrapper =
new FlinkMultiOutputDoFnFunction(
doFn,
windowingStrategy,
sideInputStrategies,
context.getPipelineOptions(),
outputMap);
MapPartitionOperator<WindowedValue<InputT>, WindowedValue<RawUnionValue>> taggedDataSet =
new MapPartitionOperator<>(
inputDataSet, typeInformation, doFnWrapper, transform.getName());
transformSideInputs(sideInputs, taggedDataSet, context);
for (TaggedPValue output : outputs) {
pruneOutput(
taggedDataSet,
context,
outputMap.get(output.getTag()),
(PCollection) output.getValue());
}
}
@Override
public void translateNode(
GroupByKey<K, InputT> transform, FlinkBatchTranslationContext context) {
// for now, this is copied from the Combine.PerKey translater. Once we have the new runner API
// we can replace GroupByKey by a Combine.PerKey with the Concatenate CombineFn
DataSet<WindowedValue<KV<K, InputT>>> inputDataSet =
context.getInputDataSet(context.getInput(transform));
Combine.KeyedCombineFn<K, InputT, List<InputT>, List<InputT>> combineFn =
new Concatenate<InputT>().asKeyedFn();
KvCoder<K, InputT> inputCoder = (KvCoder<K, InputT>) context.getInput(transform).getCoder();
Coder<List<InputT>> accumulatorCoder;
try {
accumulatorCoder =
combineFn.getAccumulatorCoder(
context.getInput(transform).getPipeline().getCoderRegistry(),
inputCoder.getKeyCoder(),
inputCoder.getValueCoder());
} catch (CannotProvideCoderException e) {
throw new RuntimeException(e);
}
WindowingStrategy<?, ?> windowingStrategy =
context.getInput(transform).getWindowingStrategy();
TypeInformation<WindowedValue<KV<K, List<InputT>>>> partialReduceTypeInfo =
new CoderTypeInformation<>(
WindowedValue.getFullCoder(
KvCoder.of(inputCoder.getKeyCoder(), accumulatorCoder),
windowingStrategy.getWindowFn().windowCoder()));
Grouping<WindowedValue<KV<K, InputT>>> inputGrouping =
inputDataSet.groupBy(new KvKeySelector<InputT, K>(inputCoder.getKeyCoder()));
FlinkPartialReduceFunction<K, InputT, List<InputT>, ?> partialReduceFunction;
FlinkReduceFunction<K, List<InputT>, List<InputT>, ?> reduceFunction;
if (windowingStrategy.getWindowFn().isNonMerging()) {
@SuppressWarnings("unchecked")
WindowingStrategy<?, BoundedWindow> boundedStrategy =
(WindowingStrategy<?, BoundedWindow>) windowingStrategy;
partialReduceFunction =
new FlinkPartialReduceFunction<>(
combineFn,
boundedStrategy,
Collections.<PCollectionView<?>, WindowingStrategy<?, ?>>emptyMap(),
context.getPipelineOptions());
reduceFunction =
new FlinkReduceFunction<>(
combineFn,
boundedStrategy,
Collections.<PCollectionView<?>, WindowingStrategy<?, ?>>emptyMap(),
context.getPipelineOptions());
} else {
if (!windowingStrategy.getWindowFn().windowCoder().equals(IntervalWindow.getCoder())) {
throw new UnsupportedOperationException(
"Merging WindowFn with windows other than IntervalWindow are not supported.");
}
@SuppressWarnings("unchecked")
WindowingStrategy<?, IntervalWindow> intervalStrategy =
(WindowingStrategy<?, IntervalWindow>) windowingStrategy;
partialReduceFunction =
new FlinkMergingPartialReduceFunction<>(
combineFn,
intervalStrategy,
Collections.<PCollectionView<?>, WindowingStrategy<?, ?>>emptyMap(),
context.getPipelineOptions());
reduceFunction =
new FlinkMergingReduceFunction<>(
combineFn,
intervalStrategy,
Collections.<PCollectionView<?>, WindowingStrategy<?, ?>>emptyMap(),
context.getPipelineOptions());
}
// Partially GroupReduce the values into the intermediate format AccumT (combine)
GroupCombineOperator<WindowedValue<KV<K, InputT>>, WindowedValue<KV<K, List<InputT>>>>
groupCombine =
new GroupCombineOperator<>(
inputGrouping,
partialReduceTypeInfo,
partialReduceFunction,
"GroupCombine: " + transform.getName());
Grouping<WindowedValue<KV<K, List<InputT>>>> intermediateGrouping =
groupCombine.groupBy(new KvKeySelector<List<InputT>, K>(inputCoder.getKeyCoder()));
// Fully reduce the values and create output format VO
GroupReduceOperator<WindowedValue<KV<K, List<InputT>>>, WindowedValue<KV<K, List<InputT>>>>
outputDataSet =
new GroupReduceOperator<>(
intermediateGrouping, partialReduceTypeInfo, reduceFunction, transform.getName());
context.setOutputDataSet(context.getOutput(transform), outputDataSet);
}