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

Example 6 with ReducingStateDescriptor

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

the class AllWindowTranslationTest method testReduceWithCustomTrigger.

@Test
@SuppressWarnings("rawtypes")
public void testReduceWithCustomTrigger() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DummyReducer reducer = new DummyReducer();
    DataStream<Tuple2<String, Integer>> window1 = source.windowAll(SlidingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS))).trigger(CountTrigger.of(1)).reduce(reducer);
    OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>> transform = (OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>>) window1.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 CountTrigger);
    Assert.assertTrue(winOperator.getWindowAssigner() instanceof SlidingEventTimeWindows);
    Assert.assertTrue(winOperator.getStateDescriptor() instanceof ReducingStateDescriptor);
    processElementAndEnsureOutput(winOperator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}

Example 7 with ReducingStateDescriptor

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

the class AllWindowTranslationTest method testApplyWithPreReducerEventTime.

/**
 * Test for the deprecated .apply(Reducer, WindowFunction).
 */
@Test
@SuppressWarnings("rawtypes")
public void testApplyWithPreReducerEventTime() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DummyReducer reducer = new DummyReducer();
    DataStream<Tuple3<String, String, Integer>> window = source.windowAll(TumblingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS))).apply(reducer, new AllWindowFunction<Tuple2<String, Integer>, Tuple3<String, String, Integer>, TimeWindow>() {

        private static final long serialVersionUID = 1L;

        @Override
        public void apply(TimeWindow window, Iterable<Tuple2<String, Integer>> values, Collector<Tuple3<String, String, Integer>> out) throws Exception {
            for (Tuple2<String, Integer> in : values) {
                out.collect(new Tuple3<>(in.f0, in.f0, in.f1));
            }
        }
    });
    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 ReducingStateDescriptor);
    processElementAndEnsureOutput(operator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}

Example 8 with ReducingStateDescriptor

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

the class AllWindowTranslationTest method testReduceWithWindowFunctionEventTime.

@Test
@SuppressWarnings("rawtypes")
public void testReduceWithWindowFunctionEventTime() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
    DummyReducer reducer = new DummyReducer();
    DataStream<Tuple3<String, String, Integer>> window = source.windowAll(TumblingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS))).reduce(reducer, new AllWindowFunction<Tuple2<String, Integer>, Tuple3<String, String, Integer>, TimeWindow>() {

        private static final long serialVersionUID = 1L;

        @Override
        public void apply(TimeWindow window, Iterable<Tuple2<String, Integer>> values, Collector<Tuple3<String, String, Integer>> out) throws Exception {
            for (Tuple2<String, Integer> in : values) {
                out.collect(new Tuple3<>(in.f0, in.f0, in.f1));
            }
        }
    });
    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 ReducingStateDescriptor);
    processElementAndEnsureOutput(operator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}

Example 9 with ReducingStateDescriptor

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

the class WindowOperatorTest method testReduceSessionWindowsWithProcessFunction.

@Test
@SuppressWarnings("unchecked")
public void testReduceSessionWindowsWithProcessFunction() throws Exception {
    closeCalled.set(0);
    final int sessionSize = 3;
    ReducingStateDescriptor<Tuple2<String, Integer>> stateDesc = new ReducingStateDescriptor<>("window-contents", new SumReducer(), STRING_INT_TUPLE.createSerializer(new ExecutionConfig()));
    WindowOperator<String, Tuple2<String, Integer>, Tuple2<String, Integer>, Tuple3<String, Long, Long>, TimeWindow> operator = new WindowOperator<>(EventTimeSessionWindows.withGap(Time.seconds(sessionSize)), new TimeWindow.Serializer(), new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()), stateDesc, new InternalSingleValueProcessWindowFunction<>(new ReducedProcessSessionWindowFunction()), EventTimeTrigger.create(), 0, null);
    OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple3<String, Long, Long>> testHarness = createTestHarness(operator);
    ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
    testHarness.open();
    // add elements out-of-order
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 0));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 2), 1000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 3), 2500));
    // do a snapshot, close and restore again
    OperatorSubtaskState snapshot = testHarness.snapshot(0L, 0L);
    testHarness.close();
    testHarness = createTestHarness(operator);
    testHarness.setup();
    testHarness.initializeState(snapshot);
    testHarness.open();
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 10));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 2), 1000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 3), 2500));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 4), 5501));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 5), 6000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 5), 6000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 6), 6050));
    testHarness.processWatermark(new Watermark(12000));
    expectedOutput.add(new StreamRecord<>(new Tuple3<>("key1-6", 10L, 5500L), 5499));
    expectedOutput.add(new StreamRecord<>(new Tuple3<>("key2-6", 0L, 5500L), 5499));
    expectedOutput.add(new StreamRecord<>(new Tuple3<>("key2-20", 5501L, 9050L), 9049));
    expectedOutput.add(new Watermark(12000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 10), 15000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 20), 15000));
    testHarness.processWatermark(new Watermark(17999));
    expectedOutput.add(new StreamRecord<>(new Tuple3<>("key2-30", 15000L, 18000L), 17999));
    expectedOutput.add(new Watermark(17999));
    TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(), new Tuple3ResultSortComparator());
    testHarness.close();
}

Example 10 with ReducingStateDescriptor

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

the class WindowOperatorTest method testDropDueToLatenessSessionWithLatenessPurgingTrigger.

@Test
public void testDropDueToLatenessSessionWithLatenessPurgingTrigger() throws Exception {
    // this has the same output as testSideOutputDueToLatenessSessionZeroLateness() because
    // the allowed lateness is too small to make a difference
    final int gapSize = 3;
    final long lateness = 10;
    ReducingStateDescriptor<Tuple2<String, Integer>> stateDesc = new ReducingStateDescriptor<>("window-contents", new SumReducer(), STRING_INT_TUPLE.createSerializer(new ExecutionConfig()));
    WindowOperator<String, Tuple2<String, Integer>, Tuple2<String, Integer>, Tuple3<String, Long, Long>, TimeWindow> operator = new WindowOperator<>(EventTimeSessionWindows.withGap(Time.seconds(gapSize)), new TimeWindow.Serializer(), new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()), stateDesc, new InternalSingleValueWindowFunction<>(new ReducedSessionWindowFunction()), PurgingTrigger.of(EventTimeTrigger.create()), lateness, lateOutputTag);
    OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple3<String, Long, Long>> testHarness = createTestHarness(operator);
    testHarness.open();
    ConcurrentLinkedQueue<Object> expected = new ConcurrentLinkedQueue<>();
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1000));
    testHarness.processWatermark(new Watermark(1999));
    expected.add(new Watermark(1999));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 2000));
    testHarness.processWatermark(new Watermark(4998));
    expected.add(new Watermark(4998));
    // this will not be dropped because the session we're adding two has maxTimestamp
    // after the current watermark
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 4500));
    // new session
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 8500));
    testHarness.processWatermark(new Watermark(7400));
    expected.add(new Watermark(7400));
    // this will merge the two sessions into one
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 7000));
    testHarness.processWatermark(new Watermark(11501));
    expected.add(new StreamRecord<>(new Tuple3<>("key2-5", 1000L, 11500L), 11499));
    expected.add(new Watermark(11501));
    // new session
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 11600));
    testHarness.processWatermark(new Watermark(14600));
    expected.add(new StreamRecord<>(new Tuple3<>("key2-1", 11600L, 14600L), 14599));
    expected.add(new Watermark(14600));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 10000));
    expected.add(new StreamRecord<>(new Tuple3<>("key2-1", 10000L, 14600L), 14599));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 14500));
    testHarness.processWatermark(new Watermark(20000));
    expected.add(new StreamRecord<>(new Tuple3<>("key2-1", 10000L, 17500L), 17499));
    expected.add(new Watermark(20000));
    testHarness.processWatermark(new Watermark(100000));
    expected.add(new Watermark(100000));
    ConcurrentLinkedQueue<Object> actual = testHarness.getOutput();
    TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, actual, new Tuple3ResultSortComparator());
    testHarness.close();
}