Examples with TimeWindow org.apache.flink.streaming.api.windowing.windows.TimeWindow used on opensource projects

Example 16 with TimeWindow

use of org.apache.flink.streaming.api.windowing.windows.TimeWindow in project flink by apache.

the class WindowOperatorContractTest method testMergeWindowsIsCalled.

@Test
public void testMergeWindowsIsCalled() throws Exception {
    MergingWindowAssigner<Integer, TimeWindow> mockAssigner = mockMergingAssigner();
    Trigger<Integer, TimeWindow> mockTrigger = mockTrigger();
    InternalWindowFunction<Iterable<Integer>, Void, Integer, TimeWindow> mockWindowFunction = mockWindowFunction();
    KeyedOneInputStreamOperatorTestHarness<Integer, Integer, Void> testHarness = createWindowOperator(mockAssigner, mockTrigger, 0L, intListDescriptor, mockWindowFunction);
    testHarness.open();
    when(mockAssigner.assignWindows(anyInt(), anyLong(), anyAssignerContext())).thenReturn(Arrays.asList(new TimeWindow(2, 4), new TimeWindow(0, 2)));
    assertEquals(0, testHarness.getOutput().size());
    assertEquals(0, testHarness.numKeyedStateEntries());
    testHarness.processElement(new StreamRecord<>(0, 0L));
    verify(mockAssigner).mergeWindows(eq(Lists.newArrayList(new TimeWindow(2, 4))), anyMergeCallback());
    verify(mockAssigner).mergeWindows(eq(Lists.newArrayList(new TimeWindow(2, 4), new TimeWindow(0, 2))), anyMergeCallback());
    verify(mockAssigner, times(2)).mergeWindows(anyCollection(), anyMergeCallback());
}

Example 17 with TimeWindow

use of org.apache.flink.streaming.api.windowing.windows.TimeWindow in project flink by apache.

the class WindowOperatorMigrationTest method testRestoreApplyEventTimeWindowsFromFlink11.

@Test
@SuppressWarnings("unchecked")
public void testRestoreApplyEventTimeWindowsFromFlink11() throws Exception {
    final int WINDOW_SIZE = 3;
    TypeInformation<Tuple2<String, Integer>> inputType = TypeInfoParser.parse("Tuple2<String, Integer>");
    ListStateDescriptor<Tuple2<String, Integer>> stateDesc = new ListStateDescriptor<>("window-contents", inputType.createSerializer(new ExecutionConfig()));
    WindowOperator<String, Tuple2<String, Integer>, Iterable<Tuple2<String, Integer>>, Tuple2<String, Integer>, TimeWindow> operator = new WindowOperator<>(TumblingEventTimeWindows.of(Time.of(WINDOW_SIZE, TimeUnit.SECONDS)), new TimeWindow.Serializer(), new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()), stateDesc, new InternalIterableWindowFunction<>(new RichSumReducer<TimeWindow>()), EventTimeTrigger.create(), 0, null);
    ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
    /*

		operator.setInputType(TypeInfoParser.<Tuple2<String, Integer>>parse("Tuple2<String, Integer>"), new ExecutionConfig());

		OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness =
				new OneInputStreamOperatorTestHarness<>(operator);

		testHarness.configureForKeyedStream(new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);

		testHarness.setup();
		testHarness.open();

		// add elements out-of-order
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 3999));
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 3000));

		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 20));
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 0));
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 999));

		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1998));
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1999));
		testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1000));

		testHarness.processWatermark(new Watermark(999));
		expectedOutput.add(new Watermark(999));
		TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(), new Tuple2ResultSortComparator());

		testHarness.processWatermark(new Watermark(1999));
		expectedOutput.add(new Watermark(1999));
		TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(), new Tuple2ResultSortComparator());

		// do snapshot and save to file
		StreamTaskState snapshot = testHarness.snapshot(0L, 0L);
		testHarness.snaphotToFile(snapshot, "src/test/resources/win-op-migration-test-apply-event-time-flink1.1-snapshot");
		testHarness.close();

		*/
    OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness = new KeyedOneInputStreamOperatorTestHarness<>(operator, new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);
    testHarness.setup();
    testHarness.initializeStateFromLegacyCheckpoint(getResourceFilename("win-op-migration-test-apply-event-time-flink1.1-snapshot"));
    testHarness.open();
    testHarness.processWatermark(new Watermark(2999));
    expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 3), 2999));
    expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 3), 2999));
    expectedOutput.add(new Watermark(2999));
    testHarness.processWatermark(new Watermark(3999));
    expectedOutput.add(new Watermark(3999));
    testHarness.processWatermark(new Watermark(4999));
    expectedOutput.add(new Watermark(4999));
    testHarness.processWatermark(new Watermark(5999));
    expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 2), 5999));
    expectedOutput.add(new Watermark(5999));
    TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(), new Tuple2ResultSortComparator());
    testHarness.close();
}

Example 18 with TimeWindow

use of org.apache.flink.streaming.api.windowing.windows.TimeWindow in project flink by apache.

the class WindowOperatorTest method testCleanupTimeOverflow.

@Test
public void testCleanupTimeOverflow() throws Exception {
    final int WINDOW_SIZE = 1000;
    final long LATENESS = 2000;
    TypeInformation<Tuple2<String, Integer>> inputType = TypeInfoParser.parse("Tuple2<String, Integer>");
    ReducingStateDescriptor<Tuple2<String, Integer>> stateDesc = new ReducingStateDescriptor<>("window-contents", new SumReducer(), inputType.createSerializer(new ExecutionConfig()));
    TumblingEventTimeWindows windowAssigner = TumblingEventTimeWindows.of(Time.milliseconds(WINDOW_SIZE));
    final WindowOperator<String, Tuple2<String, Integer>, Tuple2<String, Integer>, Tuple2<String, Integer>, TimeWindow> operator = new WindowOperator<>(windowAssigner, new TimeWindow.Serializer(), new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()), stateDesc, new InternalSingleValueWindowFunction<>(new PassThroughWindowFunction<String, TimeWindow, Tuple2<String, Integer>>()), EventTimeTrigger.create(), LATENESS, null);
    OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness = new KeyedOneInputStreamOperatorTestHarness<>(operator, new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);
    testHarness.open();
    ConcurrentLinkedQueue<Object> expected = new ConcurrentLinkedQueue<>();
    long timestamp = Long.MAX_VALUE - 1750;
    Collection<TimeWindow> windows = windowAssigner.assignWindows(new Tuple2<>("key2", 1), timestamp, new WindowAssigner.WindowAssignerContext() {

        @Override
        public long getCurrentProcessingTime() {
            return operator.windowAssignerContext.getCurrentProcessingTime();
        }
    });
    TimeWindow window = Iterables.getOnlyElement(windows);
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), timestamp));
    // the garbage collection timer would wrap-around
    Assert.assertTrue(window.maxTimestamp() + LATENESS < window.maxTimestamp());
    // and it would prematurely fire with watermark (Long.MAX_VALUE - 1500)
    Assert.assertTrue(window.maxTimestamp() + LATENESS < Long.MAX_VALUE - 1500);
    // if we don't correctly prevent wrap-around in the garbage collection
    // timers this watermark will clean our window state for the just-added
    // element/window
    testHarness.processWatermark(new Watermark(Long.MAX_VALUE - 1500));
    // this watermark is before the end timestamp of our only window
    Assert.assertTrue(Long.MAX_VALUE - 1500 < window.maxTimestamp());
    Assert.assertTrue(window.maxTimestamp() < Long.MAX_VALUE);
    // push in a watermark that will trigger computation of our window
    testHarness.processWatermark(new Watermark(window.maxTimestamp()));
    expected.add(new Watermark(Long.MAX_VALUE - 1500));
    expected.add(new StreamRecord<>(new Tuple2<>("key2", 1), window.maxTimestamp()));
    expected.add(new Watermark(window.maxTimestamp()));
    TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, testHarness.getOutput(), new Tuple2ResultSortComparator());
    testHarness.close();
}

Example 19 with TimeWindow

use of org.apache.flink.streaming.api.windowing.windows.TimeWindow in project flink by apache.

the class WindowOperatorTest method testSideOutputDueToLatenessSessionZeroLateness.

@Test
public void testSideOutputDueToLatenessSessionZeroLateness() throws Exception {
    final int GAP_SIZE = 3;
    final long LATENESS = 0;
    TypeInformation<Tuple2<String, Integer>> inputType = TypeInfoParser.parse("Tuple2<String, Integer>");
    ReducingStateDescriptor<Tuple2<String, Integer>> stateDesc = new ReducingStateDescriptor<>("window-contents", new SumReducer(), inputType.createSerializer(new ExecutionConfig()));
    WindowOperator<String, Tuple2<String, Integer>, Tuple2<String, Integer>, Tuple3<String, Long, Long>, TimeWindow> operator = new WindowOperator<>(EventTimeSessionWindows.withGap(Time.seconds(GAP_SIZE)), new TimeWindow.Serializer(), new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()), stateDesc, new InternalSingleValueWindowFunction<>(new ReducedSessionWindowFunction()), EventTimeTrigger.create(), LATENESS, lateOutputTag);
    OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple3<String, Long, Long>> testHarness = new KeyedOneInputStreamOperatorTestHarness<>(operator, new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);
    testHarness.open();
    ConcurrentLinkedQueue<Object> expected = new ConcurrentLinkedQueue<>();
    ConcurrentLinkedQueue<Object> sideExpected = 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));
    // this is sideoutput as late, reuse last timestamp
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 10000));
    sideExpected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 10000));
    testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 14500));
    testHarness.processWatermark(new Watermark(20000));
    expected.add(new StreamRecord<>(new Tuple3<>("key2-1", 14500L, 17500L), 17499));
    expected.add(new Watermark(20000));
    testHarness.processWatermark(new Watermark(100000));
    expected.add(new Watermark(100000));
    ConcurrentLinkedQueue<Object> actual = testHarness.getOutput();
    ConcurrentLinkedQueue<StreamRecord<Tuple2<String, Integer>>> sideActual = testHarness.getSideOutput(lateOutputTag);
    TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, actual, new Tuple2ResultSortComparator());
    TestHarnessUtil.assertOutputEqualsSorted("SideOutput was not correct.", sideExpected, (Iterable) sideActual, new Tuple2ResultSortComparator());
    testHarness.close();
}

Example 20 with TimeWindow

use of org.apache.flink.streaming.api.windowing.windows.TimeWindow in project flink by apache.

the class WindowTranslationTest method testApplyWithEvictor.

@Test
@SuppressWarnings("rawtypes")
public void testApplyWithEvictor() 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(new TupleKeySelector()).window(TumblingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS))).trigger(CountTrigger.of(1)).evictor(TimeEvictor.of(Time.of(100, TimeUnit.MILLISECONDS))).apply(new WindowFunction<Tuple2<String, Integer>, Tuple2<String, Integer>, String, TimeWindow>() {

        private static final long serialVersionUID = 1L;

        @Override
        public void apply(String key, TimeWindow window, Iterable<Tuple2<String, Integer>> values, Collector<Tuple2<String, Integer>> out) throws Exception {
            for (Tuple2<String, Integer> in : values) {
                out.collect(in);
            }
        }
    });
    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 EvictingWindowOperator);
    EvictingWindowOperator<String, Tuple2<String, Integer>, ?, ?> winOperator = (EvictingWindowOperator<String, Tuple2<String, Integer>, ?, ?>) operator;
    Assert.assertTrue(winOperator.getTrigger() instanceof CountTrigger);
    Assert.assertTrue(winOperator.getEvictor() instanceof TimeEvictor);
    Assert.assertTrue(winOperator.getWindowAssigner() instanceof TumblingEventTimeWindows);
    Assert.assertTrue(winOperator.getStateDescriptor() instanceof ListStateDescriptor);
    processElementAndEnsureOutput(winOperator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}