Java Time类代码示例

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
@Test
public void testMergeConsecutiveWindows() {
	MergingWindowAssigner.MergeCallback callback = mock(MergingWindowAssigner.MergeCallback.class);

	EventTimeSessionWindows assigner = EventTimeSessionWindows.withGap(Time.milliseconds(5000));

	assigner.mergeWindows(
			Lists.newArrayList(
					new TimeWindow(0, 1),
					new TimeWindow(1, 2),
					new TimeWindow(2, 3),
					new TimeWindow(4, 5),
					new TimeWindow(5, 6)),
			callback);

	verify(callback, times(1)).merge(
			(Collection<TimeWindow>) argThat(containsInAnyOrder(new TimeWindow(0, 1), new TimeWindow(1, 2), new TimeWindow(2, 3))),
			eq(new TimeWindow(0, 3)));

	verify(callback, times(1)).merge(
			(Collection<TimeWindow>) argThat(containsInAnyOrder(new TimeWindow(4, 5), new TimeWindow(5, 6))),
			eq(new TimeWindow(4, 6)));

	verify(callback, times(2)).merge(anyCollection(), Matchers.anyObject());
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    // set up the execution environment
    final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    Properties props = new Properties();
    props.setProperty(TwitterSource.CONSUMER_KEY, "...");
    props.setProperty(TwitterSource.CONSUMER_SECRET, "...");
    props.setProperty(TwitterSource.TOKEN, "...");
    props.setProperty(TwitterSource.TOKEN_SECRET, "...");

    env.addSource(new TwitterSource(props))
        .flatMap(new ExtractHashTags())
        .keyBy(0)
        .timeWindow(Time.seconds(30))
        .sum(1)
        .filter(new FilterHashTags())
        .timeWindowAll(Time.seconds(30))
        .apply(new GetTopHashTag())
        .print();

    env.execute();
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
	StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	Properties properties = new Properties();
	properties.setProperty("bootstrap.servers", "localhost:9092");
	properties.setProperty("group.id", "test");

	DataStream<TemperatureEvent> inputEventStream = env.addSource(
			new FlinkKafkaConsumer09<TemperatureEvent>("test", new EventDeserializationSchema(), properties));

	Pattern<TemperatureEvent, ?> warningPattern = Pattern.<TemperatureEvent> begin("first")
			.subtype(TemperatureEvent.class).where(new FilterFunction<TemperatureEvent>() {
				private static final long serialVersionUID = 1L;

				public boolean filter(TemperatureEvent value) {
					if (value.getTemperature() >= 26.0) {
						return true;
					}
					return false;
				}
			}).within(Time.seconds(10));

	DataStream<Alert> patternStream = CEP.pattern(inputEventStream, warningPattern)
			.select(new PatternSelectFunction<TemperatureEvent, Alert>() {
				private static final long serialVersionUID = 1L;

				public Alert select(Map<String, TemperatureEvent> event) throws Exception {

					return new Alert("Temperature Rise Detected:" + event.get("first").getTemperature()
							+ " on machine name:" + event.get("first").getMachineName());
				}

			});

	patternStream.print();
	env.execute("CEP on Temperature Sensor");
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public static void main(String args[]) throws Exception {
    ParameterTool tool = ParameterTool.fromArgs(args);

    Properties p = new Properties();
    p.setProperty(TwitterSource.CONSUMER_KEY, tool.getRequired("consumer.key"));
    p.setProperty(TwitterSource.CONSUMER_SECRET, tool.getRequired("consumer.secret"));
    p.setProperty(TwitterSource.TOKEN, tool.getRequired("token"));
    p.setProperty(TwitterSource.TOKEN_SECRET, tool.getRequired("token.secret"));

    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);

    DataStream<String> streamSource = env.addSource(new TwitterSource(p));

    // Every 10 minutes, most trending of last 1 hour of tweets.
    SlidingEventTimeWindows windowSpec = SlidingEventTimeWindows.of(Time.hours(1), Time.minutes(10));

    streamSource.flatMap(hashTagExtraction())
            .keyBy(0)
            .sum(1)
            .windowAll(windowSpec)
            .maxBy(1)
            .writeAsText("file:///Users/abij/projects/tryouts/flink-streaming-xke/hot-hashtags.log", OVERWRITE);

    env.execute("Twitter Stream");
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public static void main(String... args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<WikipediaEditEvent> edits = env.addSource(new WikipediaEditsSource());

        edits
            .timeWindowAll(Time.minutes(1))
            .apply(new AllWindowFunction<WikipediaEditEvent, Tuple3<Date, Long, Long>, TimeWindow>() {
                @Override
                public void apply(TimeWindow timeWindow, Iterable<WikipediaEditEvent> iterable, Collector<Tuple3<Date, Long, Long>> collector) throws Exception {
                    long count = 0;
                    long bytesChanged = 0;

                    for (WikipediaEditEvent event : iterable) {
                        count++;
                        bytesChanged += event.getByteDiff();
                    }

                    collector.collect(new Tuple3<>(new Date(timeWindow.getEnd()), count, bytesChanged));
                }
            })
            .print();


        env.execute();
    }
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public static void main(String[] args) throws Exception {

		final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

		DataStream<Tuple2<String,Integer>> counts = env
				// read stream of words from socket
				.socketTextStream("localhost", 1234)
				// split up the lines into tuple: (word, 1)
				.flatMap(new LineSplitter())
				// use the “word” as key
				.keyBy(0)
				// compute counts every 10 seconds
				.timeWindow(Time.seconds(10))
				// sum up the values
				.sum(1);

		// print result to console
		counts.print();
		// execute program
		env.execute("Socket Word Count Example");
	}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
/**
 * .reduce() does not support RichReduceFunction, since the reduce function is used internally
 * in a {@code ReducingState}.
 */
@Test(expected = UnsupportedOperationException.class)
public void testReduceWithRichReducerFails() throws Exception {
	StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	DataStream<Tuple2<String, Integer>> source = env.fromElements(Tuple2.of("hello", 1), Tuple2.of("hello", 2));
	env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);

	source
		.keyBy(0)
		.window(SlidingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS)))
		.reduce(new RichReduceFunction<Tuple2<String, Integer>>() {

			@Override
			public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1,
				Tuple2<String, Integer> value2) throws Exception {
				return null;
			}
		});

	fail("exception was not thrown");
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public DataStream<Tuple3<K, AnomalyResult, RV>> getAnomalySteam(DataStream<V> ds, KeySelector<V, K> keySelector , KeySelector<V,Double> valueSelector, PayloadFold<V, RV> valueFold, Time window) {

        KeyedStream<V, K> keyedInput = ds
                .keyBy(keySelector);

        TypeInformation<Object> foldResultType = TypeExtractor.getUnaryOperatorReturnType(valueFold,
                PayloadFold.class,
                false,
                false,
                ds.getType(),
                "PayloadFold",
                false);

         TypeInformation<Tuple3<K,Tuple4<Double,Double,Long,Long>,RV>> resultType = (TypeInformation) new TupleTypeInfo<>(Tuple3.class,
                new TypeInformation[] {keyedInput.getKeyType(), new TupleTypeInfo(Tuple4.class,
                        BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO,BasicTypeInfo.LONG_TYPE_INFO), foldResultType});

        Tuple3<K,Tuple4<Double,Double,Long,Long>, RV> init= new Tuple3<>(null,new Tuple4<>(0d,0d,0l,0l), valueFold.getInit());
        KeyedStream<Tuple3<K,Tuple4<Double,Double,Long,Long>,RV>, Tuple> kPreStream = keyedInput
                .timeWindow(window)
                .apply(init, new ExtCountSumFold<>(keySelector, valueSelector, valueFold, resultType), new ExtWindowTimeExtractor(resultType))
                .keyBy(0);

        return kPreStream.flatMap(afm);
    }
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public DataStream<Tuple3<K, AnomalyResult, RV>> getAnomalySteam(DataStream<V> ds, KeySelector<V, K> keySelector, KeySelector<V,Double> valueSelector, PayloadFold<V, RV> valueFold, Time window) {

        KeyedStream<V, K> keyedInput = ds
                .keyBy(keySelector);

        TypeInformation<Object> foldResultType = TypeExtractor.getUnaryOperatorReturnType(valueFold,
                PayloadFold.class,
                false,
                false,
                ds.getType(),
                "PayloadFold",
                false);


        TypeInformation<Tuple3<K,Tuple4<Double,Double,Long,Long>,RV>> resultType = (TypeInformation) new TupleTypeInfo<>(Tuple3.class,
                new TypeInformation[] {keyedInput.getKeyType(), new TupleTypeInfo(Tuple4.class,
                        BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO,BasicTypeInfo.LONG_TYPE_INFO), foldResultType});

        Tuple3<K,Tuple4<Double,Double,Long,Long>, RV> init= new Tuple3<>(null,new Tuple4<>(0d,0d,0l,0l), valueFold.getInit());
        KeyedStream<Tuple3<K,Tuple4<Double,Double,Long,Long>,RV>, Tuple> kPreStream = keyedInput
                .timeWindow(window)
                .apply(init, new ExtCountSumFold<>(keySelector,valueSelector,valueFold, resultType),new ExtWindowTimeExtractor(resultType))
                .keyBy(0);

        return kPreStream.flatMap(afm);
    }
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
public DataStream<Tuple2<K, AnomalyResult>> getAnomalySteam(DataStream<V> ds, KeySelector<V, K> keySelector , Time window) {

        KeyedStream<V, K> keyedInput = ds
                .keyBy(keySelector);


        TypeInformation<Tuple2<K,Tuple4<Double,Double,Long,Long>>> resultType = (TypeInformation) new TupleTypeInfo<>(Tuple2.class,
                new TypeInformation[] {keyedInput.getKeyType(), new TupleTypeInfo(Tuple4.class,
                        BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.DOUBLE_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO,BasicTypeInfo.LONG_TYPE_INFO)});

        Tuple2<K,Tuple4<Double,Double,Long,Long>> init= new Tuple2<>(null,new Tuple4<>(0d,0d,0l,0l));
        KeyedStream<Tuple2<K,Tuple4<Double,Double,Long,Long>>, Tuple> kPreStream = keyedInput
                .timeWindow(window)
                .apply(init, new CountWindFold<>(keySelector, window, resultType),new WindowTimeExtractor(resultType))
                .keyBy(0);

        return kPreStream.flatMap(afm);
    }
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
@Test
public void testReduceWindowAllState() throws Exception {
	final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
	env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);
	env.registerTypeWithKryoSerializer(File.class, JavaSerializer.class);

	DataStream<File> src = env.fromElements(new File("/"));

	SingleOutputStreamOperator<?> result = src
			.timeWindowAll(Time.milliseconds(1000))
			.reduce(new ReduceFunction<File>() {

				@Override
				public File reduce(File value1, File value2) {
					return null;
				}
			});

	validateStateDescriptorConfigured(result);
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testFoldWithCustomTrigger() 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>> window1 = source
			.keyBy(0)
			.window(SlidingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS)))
			.trigger(CountTrigger.of(1))
			.fold(new Tuple3<>("", "", 1), new DummyFolder());

	OneInputTransformation<Tuple2<String, Integer>, Tuple3<String, String, Integer>> transform =
			(OneInputTransformation<Tuple2<String, Integer>, Tuple3<String, String, Integer>>) window1.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 CountTrigger);
	Assert.assertTrue(winOperator.getWindowAssigner() instanceof SlidingEventTimeWindows);
	Assert.assertTrue(winOperator.getStateDescriptor() instanceof FoldingStateDescriptor);

	processElementAndEnsureOutput(winOperator, winOperator.getKeySelector(), BasicTypeInfo.STRING_TYPE_INFO, new Tuple2<>("hello", 1));
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
@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));
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
/**
 * Test adding a new window that is identical to an existing window. This should not cause
 * a merge.
 */
@Test
public void testAddingIdenticalWindows() throws Exception {
	@SuppressWarnings("unchecked")
	ListState<Tuple2<TimeWindow, TimeWindow>> mockState = mock(ListState.class);

	MergingWindowSet<TimeWindow> windowSet = new MergingWindowSet<>(EventTimeSessionWindows.withGap(Time.milliseconds(3)), mockState);

	TestingMergeFunction mergeFunction = new TestingMergeFunction();

	mergeFunction.reset();
	assertEquals(new TimeWindow(1, 2), windowSet.addWindow(new TimeWindow(1, 2), mergeFunction));
	assertFalse(mergeFunction.hasMerged());
	assertEquals(new TimeWindow(1, 2), windowSet.getStateWindow(new TimeWindow(1, 2)));

	mergeFunction.reset();
	assertEquals(new TimeWindow(1, 2), windowSet.addWindow(new TimeWindow(1, 2), mergeFunction));
	assertFalse(mergeFunction.hasMerged());
	assertEquals(new TimeWindow(1, 2), windowSet.getStateWindow(new TimeWindow(1, 2)));
}
 

import org.apache.flink.streaming.api.windowing.time.Time; //导入依赖的package包/类
@Test
public void testWindowAssignmentWithOffset() {
	WindowAssigner.WindowAssignerContext mockContext =
			mock(WindowAssigner.WindowAssignerContext.class);

	TumblingProcessingTimeWindows assigner = TumblingProcessingTimeWindows.of(Time.milliseconds(5000), Time.milliseconds(100));

	when(mockContext.getCurrentProcessingTime()).thenReturn(100L);
	assertThat(assigner.assignWindows("String", Long.MIN_VALUE, mockContext), contains(timeWindow(100, 5100)));

	when(mockContext.getCurrentProcessingTime()).thenReturn(5099L);
	assertThat(assigner.assignWindows("String", Long.MIN_VALUE, mockContext), contains(timeWindow(100, 5100)));

	when(mockContext.getCurrentProcessingTime()).thenReturn(5100L);
	assertThat(assigner.assignWindows("String", Long.MIN_VALUE, mockContext), contains(timeWindow(5100, 10100)));
}