Java TimeWindow.Serializer方法代码示例

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //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.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testMergingWindows() throws Exception {
	TriggerTestHarness<Object, TimeWindow> testHarness =
			new TriggerTestHarness<>(EventTimeTrigger.create(), new TimeWindow.Serializer());

	assertTrue(EventTimeTrigger.create().canMerge());

	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.mergeWindows(new TimeWindow(0, 4), Lists.newArrayList(new TimeWindow(0, 2), new TimeWindow(2, 4)));

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

	assertEquals(TriggerResult.FIRE, testHarness.advanceWatermark(4, new TimeWindow(0, 4)));

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

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testMergingWindows() throws Exception {
	TriggerTestHarness<Object, TimeWindow> testHarness =
			new TriggerTestHarness<>(ProcessingTimeTrigger.create(), new TimeWindow.Serializer());

	assertTrue(ProcessingTimeTrigger.create().canMerge());

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

	testHarness.mergeWindows(new TimeWindow(0, 4), Lists.newArrayList(new TimeWindow(0, 2), new TimeWindow(2, 4)));

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

	assertEquals(TriggerResult.FIRE, testHarness.advanceProcessingTime(4, new TimeWindow(0, 4)));

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

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //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.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testMergingWindows() throws Exception {
	TriggerTestHarness<Object, TimeWindow> testHarness =
			new TriggerTestHarness<>(CountTrigger.<TimeWindow>of(3), 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(TriggerResult.CONTINUE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(4, 6)));

	// shouldn't have any timers
	assertEquals(0, testHarness.numProcessingTimeTimers());
	assertEquals(0, testHarness.numEventTimeTimers());

	assertEquals(3, testHarness.numStateEntries());
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(0, 2)));
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(2, 4)));
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(4, 6)));

	testHarness.mergeWindows(new TimeWindow(0, 4), Lists.newArrayList(new TimeWindow(0, 2), new TimeWindow(2, 4)));

	assertEquals(2, testHarness.numStateEntries());
	assertEquals(0, testHarness.numStateEntries(new TimeWindow(0, 2)));
	assertEquals(0, testHarness.numStateEntries(new TimeWindow(2, 4)));
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(0, 4)));
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(4, 6)));

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

	assertEquals(1, testHarness.numStateEntries());
	assertEquals(0, testHarness.numStateEntries(new TimeWindow(0, 4)));
	assertEquals(1, testHarness.numStateEntries(new TimeWindow(4, 6)));

	assertEquals(TriggerResult.CONTINUE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(4, 6)));
	assertEquals(TriggerResult.FIRE, testHarness.processElement(new StreamRecord<Object>(1), new TimeWindow(4, 6)));

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

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testCleanupTimeOverflow() throws Exception {
	final int windowSize = 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(windowSize));

	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 /* late data output tag */);

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

	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();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testLateness() throws Exception {
	final int windowSize = 2;
	final long lateness = 500;

	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>>()),
			PurgingTrigger.of(EventTimeTrigger.create()),
			lateness,
			lateOutputTag);

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

	testHarness.open();

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

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 500));
	testHarness.processWatermark(new Watermark(1500));

	expected.add(new Watermark(1500));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1300));
	testHarness.processWatermark(new Watermark(2300));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 2), 1999));
	expected.add(new Watermark(2300));

	// this will not be sideoutput because window.maxTimestamp() + allowedLateness > currentWatermark
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1997));
	testHarness.processWatermark(new Watermark(6000));

	// this is 1 and not 3 because the trigger fires and purges
	expected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 1999));
	expected.add(new Watermark(6000));

	// this will be side output because window.maxTimestamp() + allowedLateness < currentWatermark
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1998));
	testHarness.processWatermark(new Watermark(7000));

	lateExpected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 1998));
	expected.add(new Watermark(7000));

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

	TestHarnessUtil.assertOutputEqualsSorted(
			"SideOutput was not correct.",
			lateExpected,
			(Iterable) testHarness.getSideOutput(lateOutputTag),
			new Tuple2ResultSortComparator());

	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testCleanupTimerWithEmptyListStateForSessionWindows() throws Exception {
	final int gapSize = 3;
	final long lateness = 10;

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

	ListStateDescriptor<Tuple2<String, Integer>> windowStateDesc =
		new ListStateDescriptor<>("window-contents", inputType.createSerializer(new ExecutionConfig()));

	WindowOperator<String, Tuple2<String, Integer>, Iterable<Tuple2<String, Integer>>, Tuple2<String, Integer>, TimeWindow> operator =
		new WindowOperator<>(
			EventTimeSessionWindows.withGap(Time.seconds(gapSize)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			windowStateDesc,
			new InternalIterableWindowFunction<>(new PassThroughFunction()),
			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<>();

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), 1000));
	testHarness.processWatermark(new Watermark(4998));

	expected.add(new StreamRecord<>(new Tuple2<>("key2", 1), 3999));
	expected.add(new Watermark(4998));

	testHarness.processWatermark(new Watermark(14600));
	expected.add(new Watermark(14600));

	ConcurrentLinkedQueue<Object> actual = testHarness.getOutput();
	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, actual, new Tuple2ResultSortComparator());
	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Override
public TypeSerializer<TimeWindow> getWindowSerializer(ExecutionConfig executionConfig) {
	return new TimeWindow.Serializer();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //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-flink1.2-snapshot");

	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testCleanupTimerWithEmptyReduceStateForSessionWindows() throws Exception {

	final int gapSize = 3;
	final long lateness = 10;

	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(gapSize)),
			new TimeWindow.Serializer(),
			new TupleKeySelector(),
			BasicTypeInfo.STRING_TYPE_INFO.createSerializer(new ExecutionConfig()),
			stateDesc,
			new InternalSingleValueWindowFunction<>(new ReducedSessionWindowFunction()),
			EventTimeTrigger.create(),
			lateness,
			null /* late data output tag */);

	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(4998));

	expected.add(new StreamRecord<>(new Tuple3<>("key2-1", 1000L, 4000L), 3999));
	expected.add(new Watermark(4998));

	testHarness.processWatermark(new Watermark(14600));
	expected.add(new Watermark(14600));

	ConcurrentLinkedQueue<Object> actual = testHarness.getOutput();
	TestHarnessUtil.assertOutputEqualsSorted("Output was not correct.", expected, actual, new Tuple2ResultSortComparator());
	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
@Test
public void testRestoreReducingProcessingTimeWindows() 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<>(
			TumblingProcessingTimeWindows.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>>()),
			ProcessingTimeTrigger.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-reduce-processing-time-flink1.2-snapshot")));
	testHarness.open();

	testHarness.setProcessingTime(3020);
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 3)));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 3)));

	testHarness.setProcessingTime(6000);

	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 3), 5999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 4), 5999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key3", 1), 5999));

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

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
/**
 * Tests TimeEvictor evictBefore behavior.
 * @throws Exception
 */
@Test
public void testTimeEvictorEvictBefore() throws Exception {
	AtomicInteger closeCalled = new AtomicInteger(0);
	final int triggerCount = 2;
	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)),
		CountTrigger.of(triggerCount),
		TimeEvictor.of(Time.seconds(2)),
		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;
	ConcurrentLinkedQueue<Object> expectedOutput = new ConcurrentLinkedQueue<>();

	testHarness.open();

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 1000));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 3999));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 20));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 999));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 5999));

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 3500));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 2001));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 1001));

	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 1), 3999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 2), 3999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 3), 3999));

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

	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1), initialTime + 6500));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1), initialTime + 1002));

	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 2), 7999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 3), 3999));

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

	testHarness.close();

	Assert.assertEquals("Close was not called.", 1, closeCalled.get());
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
/**
 * Manually run this to write binary snapshot data.
 */
@Ignore
@Test
public void writeSessionWindowsWithCountTriggerSnapshot() throws Exception {
	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 InternalIterableWindowFunction<>(new SessionWindowFunction()),
			PurgingTrigger.of(CountTrigger.of(4)),
			0,
			null /* late data output tag */);

	OneInputStreamOperatorTestHarness<Tuple2<String, Integer>, Tuple3<String, Long, Long>> 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), 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<>("key2", 4), 3500));

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

	// do snapshot and save to file
	OperatorStateHandles snapshot = testHarness.snapshot(0L, 0L);

	OperatorSnapshotUtil.writeStateHandle(
		snapshot,
		"src/test/resources/win-op-migration-test-session-with-stateful-trigger-flink" + flinkGenerateSavepointVersion + "-snapshot");

	testHarness.close();
}
 

import org.apache.flink.streaming.api.windowing.windows.TimeWindow; //导入方法依赖的package包/类
/**
 * Manually run this to write binary snapshot data.
 */
@Ignore
@Test
public void writeReducingProcessingTimeWindowsSnapshot() 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<>(
			TumblingProcessingTimeWindows.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>>()),
			ProcessingTimeTrigger.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();

	testHarness.setProcessingTime(10);
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1)));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key1", 1)));

	testHarness.setProcessingTime(3010);
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key2", 1)));
	testHarness.processElement(new StreamRecord<>(new Tuple2<>("key3", 1)));

	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key1", 1), 2999));
	expectedOutput.add(new StreamRecord<>(new Tuple2<>("key2", 1), 2999));

	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-processing-time-flink1.2-snapshot");

	testHarness.close();

}