Java EventTimeTrigger.create方法代码示例

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testSlidingEventTimeWindowsApply() throws Exception {
	closeCalled.set(0);

	final int windowSize = 3;
	final int windowSlide = 1;

	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<>(
			SlidingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS), Time.of(windowSlide, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalIterableWindowFunction<>(new RichSumReducer<TimeWindow>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	testSlidingEventTimeWindows(operator);

	// we close once in the rest...
	Assert.assertEquals("Close was not called.", 2, closeCalled.get());
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testTumblingEventTimeWindowsReduce() throws Exception {
	closeCalled.set(0);

	final int windowSize = 3;

	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>, Tuple2<String, Integer>, TimeWindow> operator = new WindowOperator<>(
			TumblingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalSingleValueWindowFunction<>(new PassThroughWindowFunction<String, TimeWindow, Tuple2<String, Integer>>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	testTumblingEventTimeWindows(operator);
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
/**
 * Verify that clear() does not leak across windows.
 */
@Test
public void testClear() throws Exception {
	TriggerTestHarness<Object, TimeWindow> testHarness =
			new TriggerTestHarness<>(EventTimeTrigger.create(), new TimeWindow.Serializer());

	assertEquals(TriggerResult.CONTINUE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(0, 2)));
	assertEquals(TriggerResult.CONTINUE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(2, 4)));

	assertEquals(0, testHarness.numStateEntries());
	assertEquals(0, testHarness.numProcessingTimeTimers());
	assertEquals(2, testHarness.numEventTimeTimers());
	assertEquals(1, testHarness.numEventTimeTimers(new TimeWindow(0, 2)));
	assertEquals(1, testHarness.numEventTimeTimers(new TimeWindow(2, 4)));

	testHarness.clearTriggerState(new TimeWindow(2, 4));

	assertEquals(0, testHarness.numStateEntries());
	assertEquals(0, testHarness.numProcessingTimeTimers());
	assertEquals(1, testHarness.numEventTimeTimers());
	assertEquals(1, testHarness.numEventTimeTimers(new TimeWindow(0, 2)));
	assertEquals(0, testHarness.numEventTimeTimers(new TimeWindow(2, 4)));

	testHarness.clearTriggerState(new TimeWindow(0, 2));

	assertEquals(0, testHarness.numStateEntries());
	assertEquals(0, testHarness.numProcessingTimeTimers());
	assertEquals(0, testHarness.numEventTimeTimers());
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
/**
 * Verify that late elements trigger immediately and also that we don't set a timer
 * for those.
 */
@Test
public void testLateElementTriggersImmediately() throws Exception {
	TriggerTestHarness<Object, TimeWindow> testHarness =
			new TriggerTestHarness<>(EventTimeTrigger.create(), new TimeWindow.Serializer());

	testHarness.advanceWatermark(2);

	assertEquals(TriggerResult.FIRE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(0, 2)));

	assertEquals(0, testHarness.numStateEntries());
	assertEquals(0, testHarness.numProcessingTimeTimers());
	assertEquals(0, testHarness.numEventTimeTimers());
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testSlidingEventTimeWindowsReduce() throws Exception {
	closeCalled.set(0);

	final int windowSize = 3;
	final int windowSlide = 1;

	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>, Tuple2<String, Integer>, TimeWindow> operator = new WindowOperator<>(
			SlidingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS), Time.of(windowSlide, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalSingleValueWindowFunction<>(new PassThroughWindowFunction<String, TimeWindow, Tuple2<String, Integer>>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	testSlidingEventTimeWindows(operator);
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testTumblingEventTimeWindowsApply() throws Exception {
	closeCalled.set(0);

	final int windowSize = 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(windowSize, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalIterableWindowFunction<>(new RichSumReducer<TimeWindow>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	testTumblingEventTimeWindows(operator);

	// we close once in the rest...
	Assert.assertEquals("Close was not called.", 2, closeCalled.get());
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Override
public Trigger<Object, TimeWindow> getDefaultTrigger(StreamExecutionEnvironment env) {
    if (env.getStreamTimeCharacteristic() == TimeCharacteristic.ProcessingTime) {
        return ProcessingTimeTrigger.create();
    } else {
        return EventTimeTrigger.create();
    }
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Override
public Trigger<Object, TimeWindow> getDefaultTrigger(StreamExecutionEnvironment env) {
	return EventTimeTrigger.create();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
public void testRestoreApplyEventTimeWindows() throws Exception {
	final int windowSize = 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(windowSize, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalIterableWindowFunction<>(new RichSumReducer<TimeWindow>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();

	OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness =
			new KeyedOneInputStreamOperatorTestHarness<>(operator, new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);

	testHarness.setup();
	testHarness.initializeState(
			OperatorSnapshotUtil.readStateHandle(
					OperatorSnapshotUtil.getResourceFilename("win-op-migration-test-apply-event-time-flink1.2-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();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
public void testEventTimeTumblingWindows() throws Exception {
	final int WINDOW_SIZE = 2000;

	TypeInformation<Tuple2<String, Integer>> inputType = TypeInfoParser.parse("Tuple2<String, Integer>");

	TumblingEventTimeWindows windowAssigner = TumblingEventTimeWindows.of(Time.milliseconds(WINDOW_SIZE));

	WindowingTestHarness<String, Tuple2<String, Integer>, TimeWindow> testHarness = new WindowingTestHarness<>(
		windowAssigner,
		BasicTypeInfo.STRING_TYPE_INFO,
		inputType,
		new TupleKeySelector(),
		EventTimeTrigger.create(),
		0);

	// normal element
	testHarness.processElement(new Tuple2<>("key2", 1), 1000);
	testHarness.processWatermark(1985);

	testHarness.addExpectedWatermark(1985);

	// this will not be dropped because window.maxTimestamp() + allowedLateness > currentWatermark
	testHarness.processElement(new Tuple2<>("key2", 1), 1980);

	// dropped as late
	testHarness.processElement(new Tuple2<>("key2", 1), 1998);

	testHarness.processElement(new Tuple2<>("key2", 1), 2001);
	testHarness.processWatermark(2999);

	testHarness.addExpectedElement(new Tuple2<>("key2", 1), 1999);
	testHarness.addExpectedElement(new Tuple2<>("key2", 1), 1999);
	testHarness.addExpectedElement(new Tuple2<>("key2", 1), 1999);
	testHarness.addExpectedWatermark(2999);

	testHarness.addExpectedElement(new Tuple2<>("key2", 1), 3999);

	testHarness.processWatermark(3999);
	testHarness.addExpectedWatermark(3999);

	testHarness.compareActualToExpectedOutput("Output is not correct");

	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testTumblingWindowWithApply() throws Exception {
	AtomicInteger closeCalled = new AtomicInteger(0);

	final int windowSize = 4;

	TypeInformation<Tuple2<String, Integer>> inputType = TypeInfoParser.parse("Tuple2<String, Integer>");

	@SuppressWarnings({"unchecked", "rawtypes"})
	TypeSerializer<StreamRecord<Tuple2<String, Integer>>> streamRecordSerializer =
			(TypeSerializer<StreamRecord<Tuple2<String, Integer>>>) new StreamElementSerializer(inputType.createSerializer(new ExecutionConfig()));

	ListStateDescriptor<StreamRecord<Tuple2<String, Integer>>> stateDesc =
			new ListStateDescriptor<>("window-contents", streamRecordSerializer);

	EvictingWindowOperator<String, Tuple2<String, Integer>, Tuple2<String, Integer>, TimeWindow> operator = new EvictingWindowOperator<>(
		TumblingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS)),
		new TimeWindow.Serializer(),
		new TupleKeySelector(),
		BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
		stateDesc,
		new InternalIterableWindowFunction<>(new RichSumReducer<TimeWindow>(closeCalled)),
		EventTimeTrigger.create(),
		CountEvictor.of(windowSize),
		0,
		null /* late data output tag */);

	OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness =
			new KeyedOneInputStreamOperatorTestHarness<>(operator, new TupleKeySelector(), BasicTypeInfo.STRING_TYPE_INFO);

	long initialTime = 0L;

	testHarness.open();

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 10));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 100));

	testHarness.processWatermark(new Watermark(1999));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 1997));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 1998));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 2310)); // not late but more than 4
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 2310));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 2310));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 2310));

	testHarness.processWatermark(new Watermark(3999));											 // now is the evictor

	ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();
	expectedOutput.add(new Watermark(1999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 4), 3999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 2), 3999));
	expectedOutput.add(new Watermark(3999));

	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(),
		new EvictingWindowOperatorTest.ResultSortComparator());
	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
/**
 * Manually run this to write binary snapshot data.
 */
@Ignore
@Test
public void writeReducingEventTimeWindowsSnapshot() throws Exception {
	final int windowSize = 3;

	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>, Tuple2<String, Integer>, TimeWindow> operator = new WindowOperator<>(
			TumblingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalSingleValueWindowFunction<>(new PassThroughWindowFunction<String, TimeWindow, Tuple2<String, Integer>>()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();

	OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple2<String, Integer>> testHarness =
			new KeyedOneInputStreamOperatorTestHarness<>(operator, 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
	OperatorStateHandles snapshot = testHarness.snapshot(0, 0);
	OperatorSnapshotUtil.writeStateHandle(
		snapshot,
		"src/test/resources/win-op-migration-test-reduce-event-time-flink" + flinkGenerateSavepointVersion + "-snapshot");

	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
public void testSideOutputDueToLatenessSliding() throws Exception {
	final int windowSize = 3;
	final int windowSlide = 1;
	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>, Tuple2<String, Integer>, TimeWindow> operator =
		new WindowOperator<>(
			SlidingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS), Time.of(windowSlide, TimeUnit.SECONDS)),
			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,
			lateOutputTag /* late data output tag */);

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

	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 StreamRecord<>(new Tuple2<>("key2", 1), 1999));
	expected.add(new Watermark(1999));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 2000));
	testHarness.processWatermark(new Watermark(3000));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 2), 2999));
	expected.add(new Watermark(3000));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 3001));

	// lateness is set to 0 and window size = 3 sec and slide 1, the following 2 elements (2400)
	// are assigned to windows ending at 2999, 3999, 4999.
	// The 2999 is dropped because it is already late (WM = 2999) but the rest are kept.

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 2400));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 2400));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 3001));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 3900));
	testHarness.processWatermark(new Watermark(6000));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 5), 3999));
	expected.add(new StreamRecord<>(new Tuple2<>("key1", 2), 3999));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 4), 4999));
	expected.add(new StreamRecord<>(new Tuple2<>("key1", 2), 4999));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 5999));
	expected.add(new StreamRecord<>(new Tuple2<>("key1", 2), 5999));

	expected.add(new Watermark(6000));

	// sideoutput element due to lateness
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 3001));
	sideExpected.add(new StreamRecord<>(new Tuple2<>("key1", 1), 3001));

	testHarness.processWatermark(new Watermark(25000));

	expected.add(new Watermark(25000));

	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, testHarness.getOutput(), new Tuple2ResultSortComparator());
	TestHarnessUtil.assertOutputEqualsSorted("SideOutput was not correct.", sideExpected, (Iterable) testHarness.getSideOutput(lateOutputTag), new Tuple2ResultSortComparator());
	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
public void testCleanupTimerWithEmptyReduceStateForTumblingWindows() throws Exception {
	final int windowSize = 2;
	final long lateness = 1;

	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>, Tuple2<String, Integer>, TimeWindow> operator =
		new WindowOperator<>(
			TumblingEventTimeWindows.of(Time.of(windowSize, TimeUnit.SECONDS)),
			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 /* late data output tag */);

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

	testHarness.open();

	ConcurrentLinkedQueue<Object> expected = new ConcurrentLinkedQueue<>();

	// normal element
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1000));
	testHarness.processWatermark(new Watermark(1599));
	testHarness.processWatermark(new Watermark(1999));
	testHarness.processWatermark(new Watermark(2000));
	testHarness.processWatermark(new Watermark(5000));

	expected.add(new Watermark(1599));
	expected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 1999));
	expected.add(new Watermark(1999)); // here it fires and purges
	expected.add(new Watermark(2000)); // here is the cleanup timer
	expected.add(new Watermark(5000));

	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, testHarness.getOutput(), new Tuple2ResultSortComparator());
	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.triggers.EventTimeTrigger; //导入方法依赖的package包/类
@Test
@SuppressWarnings("unchecked")
public void testSessionWindowsWithProcessFunction() throws Exception {
	closeCalled.set(0);

	final int sessionSize = 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>>, 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 InternalIterableProcessWindowFunction<>(new SessionProcessWindowFunction()),
			EventTimeTrigger.create(),
			0,
			null /* late data output tag */);

	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));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), 10));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 2), 1000));

	// do a snapshot, close and restore again
	OperatorStateHandles snapshot = testHarness.snapshot(0L, 0L);

	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expectedOutput, testHarness.getOutput(), new Tuple3ResultSortComparator());

	testHarness.close();

	testHarness = createTestHarness(operator);
	testHarness.setup();
	testHarness.initializeState(snapshot);
	testHarness.open();

	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();
}