Examples with FoldingStateDescriptor org.apache.flink.api.common.state.FoldingStateDescriptor used on opensource projects

Example 16 with FoldingStateDescriptor

use of org.apache.flink.api.common.state.FoldingStateDescriptor in project flink by apache.

the class TimeWindowTranslationTest method testFoldEventTimeWindows.

@Test
@SuppressWarnings("rawtypes")
public void testFoldEventTimeWindows() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DataStream<Tuple2<String, Integer>> window1 = source.keyBy(0).timeWindow(Time.of(1000, TimeUnit.MILLISECONDS), Time.of(100, TimeUnit.MILLISECONDS)).fold(new Tuple2<>("", 1), new DummyFolder());
    OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>> transform1 = (OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>>) window1.getTransformation();
    OneInputStreamOperator<Tuple2<String, Integer>, Tuple2<String, Integer>> operator1 = transform1.getOperator();
    Assert.assertTrue(operator1 instanceof WindowOperator);
    WindowOperator winOperator1 = (WindowOperator) operator1;
    Assert.assertTrue(winOperator1.getTrigger() instanceof EventTimeTrigger);
    Assert.assertTrue(winOperator1.getWindowAssigner() instanceof SlidingEventTimeWindows);
    Assert.assertTrue(winOperator1.getStateDescriptor() instanceof FoldingStateDescriptor);
}

Example 17 with FoldingStateDescriptor

use of org.apache.flink.api.common.state.FoldingStateDescriptor in project flink by apache.

the class AllWindowedStream method fold.

/**
	 * Applies the given window function to each window. The window function is called for each
	 * evaluation of the window for each key individually. The output of the window function is
	 * interpreted as a regular non-windowed stream.
	 *
	 * <p>
	 * Arriving data is incrementally aggregated using the given fold function.
	 *
	 * @param initialValue The initial value of the fold.
	 * @param foldFunction The fold function that is used for incremental aggregation.
	 * @param function The process window function.
	 * @param foldAccumulatorType Type information for the result type of the fold function
	 * @param resultType Type information for the result type of the window function
	 * @return The data stream that is the result of applying the window function to the window.
	 */
@PublicEvolving
public <ACC, R> SingleOutputStreamOperator<R> fold(ACC initialValue, FoldFunction<T, ACC> foldFunction, ProcessAllWindowFunction<ACC, R, W> function, TypeInformation<ACC> foldAccumulatorType, TypeInformation<R> resultType) {
    if (foldFunction instanceof RichFunction) {
        throw new UnsupportedOperationException("FoldFunction of fold can not be a RichFunction.");
    }
    if (windowAssigner instanceof MergingWindowAssigner) {
        throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");
    }
    //clean the closures
    function = input.getExecutionEnvironment().clean(function);
    foldFunction = input.getExecutionEnvironment().clean(foldFunction);
    String callLocation = Utils.getCallLocationName();
    String udfName = "AllWindowedStream." + callLocation;
    String opName;
    KeySelector<T, Byte> keySel = input.getKeySelector();
    OneInputStreamOperator<T, R> operator;
    if (evictor != null) {
        @SuppressWarnings({ "unchecked", "rawtypes" }) TypeSerializer<StreamRecord<T>> streamRecordSerializer = (TypeSerializer<StreamRecord<T>>) new StreamElementSerializer(input.getType().createSerializer(getExecutionEnvironment().getConfig()));
        ListStateDescriptor<StreamRecord<T>> stateDesc = new ListStateDescriptor<>("window-contents", streamRecordSerializer);
        opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + evictor + ", " + udfName + ")";
        operator = new EvictingWindowOperator<>(windowAssigner, windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()), keySel, input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()), stateDesc, new InternalIterableProcessAllWindowFunction<>(new FoldApplyProcessAllWindowFunction<>(initialValue, foldFunction, function, foldAccumulatorType)), trigger, evictor, allowedLateness, lateDataOutputTag);
    } else {
        FoldingStateDescriptor<T, ACC> stateDesc = new FoldingStateDescriptor<>("window-contents", initialValue, foldFunction, foldAccumulatorType.createSerializer(getExecutionEnvironment().getConfig()));
        opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";
        operator = new WindowOperator<>(windowAssigner, windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()), keySel, input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()), stateDesc, new InternalSingleValueProcessAllWindowFunction<>(function), trigger, allowedLateness, lateDataOutputTag);
    }
    return input.transform(opName, resultType, operator).forceNonParallel();
}

Example 18 with FoldingStateDescriptor

use of org.apache.flink.api.common.state.FoldingStateDescriptor in project flink by apache.

the class AllWindowedStream method apply.

/**
	 * Applies the given window function to each window. The window function is called for each
	 * evaluation of the window for each key individually. The output of the window function is
	 * interpreted as a regular non-windowed stream.
	 *
	 * <p>
	 * Arriving data is incrementally aggregated using the given fold function.
	 *
	 * @param initialValue The initial value of the fold.
	 * @param foldFunction The fold function that is used for incremental aggregation.
	 * @param function The window function.
	 * @param resultType Type information for the result type of the window function
	 * @return The data stream that is the result of applying the window function to the window.
	 *
	 * @deprecated Use {@link #fold(Object, FoldFunction, AllWindowFunction, TypeInformation, TypeInformation)} instead.
	 */
@Deprecated
public <R> SingleOutputStreamOperator<R> apply(R initialValue, FoldFunction<T, R> foldFunction, AllWindowFunction<R, R, W> function, TypeInformation<R> resultType) {
    if (foldFunction instanceof RichFunction) {
        throw new UnsupportedOperationException("ReduceFunction of apply can not be a RichFunction.");
    }
    if (windowAssigner instanceof MergingWindowAssigner) {
        throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");
    }
    //clean the closures
    function = input.getExecutionEnvironment().clean(function);
    foldFunction = input.getExecutionEnvironment().clean(foldFunction);
    String callLocation = Utils.getCallLocationName();
    String udfName = "AllWindowedStream." + callLocation;
    String opName;
    KeySelector<T, Byte> keySel = input.getKeySelector();
    OneInputStreamOperator<T, R> operator;
    if (evictor != null) {
        @SuppressWarnings({ "unchecked", "rawtypes" }) TypeSerializer<StreamRecord<T>> streamRecordSerializer = (TypeSerializer<StreamRecord<T>>) new StreamElementSerializer(input.getType().createSerializer(getExecutionEnvironment().getConfig()));
        ListStateDescriptor<StreamRecord<T>> stateDesc = new ListStateDescriptor<>("window-contents", streamRecordSerializer);
        opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + evictor + ", " + udfName + ")";
        operator = new EvictingWindowOperator<>(windowAssigner, windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()), keySel, input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()), stateDesc, new InternalIterableAllWindowFunction<>(new FoldApplyAllWindowFunction<>(initialValue, foldFunction, function, resultType)), trigger, evictor, allowedLateness, lateDataOutputTag);
    } else {
        FoldingStateDescriptor<T, R> stateDesc = new FoldingStateDescriptor<>("window-contents", initialValue, foldFunction, resultType.createSerializer(getExecutionEnvironment().getConfig()));
        opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";
        operator = new WindowOperator<>(windowAssigner, windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()), keySel, input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()), stateDesc, new InternalSingleValueAllWindowFunction<>(function), trigger, allowedLateness, lateDataOutputTag);
    }
    return input.transform(opName, resultType, operator).forceNonParallel();
}

Example 19 with FoldingStateDescriptor

use of org.apache.flink.api.common.state.FoldingStateDescriptor in project flink by apache.

the class AllWindowTranslationTest method testFoldWithProcessAllWindowFunctionProcessingTime.

@Test
@SuppressWarnings("rawtypes")
public void testFoldWithProcessAllWindowFunctionProcessingTime() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DataStream<Tuple2<String, Integer>> window = source.windowAll(TumblingProcessingTimeWindows.of(Time.of(1, TimeUnit.SECONDS))).fold(new Tuple3<>("", "empty", 0), new DummyFolder(), new ProcessAllWindowFunction<Tuple3<String, String, Integer>, Tuple2<String, Integer>, TimeWindow>() {

        private static final long serialVersionUID = 1L;

        @Override
        public void process(Context ctx, Iterable<Tuple3<String, String, Integer>> values, Collector<Tuple2<String, Integer>> out) throws Exception {
            for (Tuple3<String, String, Integer> in : values) {
                out.collect(new Tuple2<>(in.f0, in.f2));
            }
        }
    });
    OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>> transform = (OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>>) window.getTransformation();
    OneInputStreamOperator<Tuple2<String, Integer>, Tuple2<String, Integer>> operator = transform.getOperator();
    Assert.assertTrue(operator instanceof WindowOperator);
    WindowOperator<String, Tuple2<String, Integer>, ?, ?, ?> winOperator = (WindowOperator<String, Tuple2<String, Integer>, ?, ?, ?>) operator;
    Assert.assertTrue(winOperator.getTrigger() instanceof ProcessingTimeTrigger);
    Assert.assertTrue(winOperator.getWindowAssigner() instanceof TumblingProcessingTimeWindows);
    Assert.assertTrue(winOperator.getStateDescriptor() instanceof FoldingStateDescriptor);
    processElementAndEnsureOutput(winOperator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}

Example 20 with FoldingStateDescriptor

use of org.apache.flink.api.common.state.FoldingStateDescriptor in project flink by apache.

the class AllWindowTranslationTest method testApplyWithPreFolderEventTime.

@Test
@SuppressWarnings("rawtypes")
public void testApplyWithPreFolderEventTime() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DataStream<Tuple3<String, String, Integer>> window = source.windowAll(TumblingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS))).apply(new Tuple3<>("", "", 0), new DummyFolder(), new AllWindowFunction<Tuple3<String, String, Integer>, Tuple3<String, String, Integer>, TimeWindow>() {

        private static final long serialVersionUID = 1L;

        @Override
        public void apply(TimeWindow window, Iterable<Tuple3<String, String, Integer>> values, Collector<Tuple3<String, String, Integer>> out) throws Exception {
            for (Tuple3<String, String, Integer> in : values) {
                out.collect(new Tuple3<>(in.f0, in.f1, in.f2));
            }
        }
    });
    OneInputTransformation<Tuple2<String, Integer>, Tuple3<String, String, Integer>> transform = (OneInputTransformation<Tuple2<String, Integer>, Tuple3<String, String, Integer>>) window.getTransformation();
    OneInputStreamOperator<Tuple2<String, Integer>, Tuple3<String, String, Integer>> operator = transform.getOperator();
    Assert.assertTrue(operator instanceof WindowOperator);
    WindowOperator<String, Tuple2<String, Integer>, ?, ?, ?> winOperator = (WindowOperator<String, Tuple2<String, Integer>, ?, ?, ?>) operator;
    Assert.assertTrue(winOperator.getTrigger() instanceof EventTimeTrigger);
    Assert.assertTrue(winOperator.getWindowAssigner() instanceof TumblingEventTimeWindows);
    Assert.assertTrue(winOperator.getStateDescriptor() instanceof FoldingStateDescriptor);
    processElementAndEnsureOutput(winOperator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}