Java TimeCharacteristic类代码示例

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    final String input = "C:\\dev\\github\\clojured-taxi-rides\\resources\\datasets\\nycTaxiRides.gz";

    final int maxEventDelay = 60;       // events are out of order by max 60 seconds
    final int servingSpeedFactor = 600; // events of 10 minutes are served in 1 second

    // set up streaming execution environment
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

    // start the data generator
    DataStream<TaxiRide> rides = env.addSource(
            new TaxiRideSource(input, maxEventDelay, servingSpeedFactor));

    DataStream<TaxiRide> filteredRides = rides
            // filter out rides that do not start or stop in NYC
            .filter(new NYCFilter());

    // print the filtered stream
    //filteredRides.print();
    filteredRides.writeAsText("file:\\\\C:\\Users\\ht\\rides_java.txt");

    // run the cleansing pipeline
    env.execute("Taxi Ride Cleansing");
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
public static void main(String[] args) throws IOException {
  StreamExecutionEnvironment execEnv = StreamExecutionEnvironment.createLocalEnvironment();
  StreamTableEnvironment env = StreamTableEnvironment.getTableEnvironment(execEnv);
  execEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
  CompilationResult res = new CompilationResult();

  try {
    JobDescriptor job = getJobConf(System.in);
    res.jobGraph(new JobCompiler(env, job).getJobGraph());
  } catch (Throwable e) {
    res.remoteThrowable(e);
  }

  try (OutputStream out = chooseOutputStream(args)) {
    out.write(res.serialize());
  }
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    final String input = "C:\\dev\\github\\clojured-taxi-rides\\resources\\datasets\\nycTaxiRides.gz";

    final int maxEventDelay = 60;       // events are out of order by max 60 seconds
    final int servingSpeedFactor = 600; // events of 10 minute are served in 1 second

    // set up streaming execution environment
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

    // start the data generator
    DataStream<TaxiRide> rides = env.addSource(new TaxiRideSource(input, maxEventDelay, servingSpeedFactor));

    DataStream<TaxiRide> filteredRides = rides
            // filter out rides that do not start or stop in NYC
            .filter(new NYCFilter());

    // write the filtered data to a Kafka sink
    filteredRides.addSink(new FlinkKafkaProducer09<>(
            LOCAL_KAFKA_BROKER,
            CLEANSED_RIDES_TOPIC,
            new TaxiRideSchema()));

    // run the cleansing pipeline
    env.execute("Taxi Ride Cleansing");
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testFoldEventTime() 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)))
			.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 EventTimeTrigger);
	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.TimeCharacteristic; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
	// parse arguments
	ParameterTool params = ParameterTool.fromPropertiesFile(args[0]);

	// create streaming environment
	final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	// enable event time processing
	env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

	// enable fault-tolerance
	env.enableCheckpointing(1000);

	// enable restarts
	env.setRestartStrategy(RestartStrategies.fixedDelayRestart(50, 500L));

	env.setStateBackend(new FsStateBackend("file:///home/robert/flink-workdir/flink-streaming-etl/state-backend"));

	// run each operator separately
	env.disableOperatorChaining();

	// get data from Kafka
	Properties kParams = params.getProperties();
	kParams.setProperty("group.id", UUID.randomUUID().toString());
	DataStream<ObjectNode> inputStream = env.addSource(new FlinkKafkaConsumer09<>(params.getRequired("topic"), new JSONDeserializationSchema(), kParams)).name("Kafka 0.9 Source")
		.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<ObjectNode>(Time.minutes(1L)) {
			@Override
			public long extractTimestamp(ObjectNode jsonNodes) {
				return jsonNodes.get("timestamp_ms").asLong();
			}
		}).name("Timestamp extractor");

	// filter out records without lang field
	DataStream<ObjectNode> tweetsWithLang = inputStream.filter(jsonNode -> jsonNode.has("user") && jsonNode.get("user").has("lang")).name("Filter records without 'lang' field");

	// select only lang = "en" tweets
	DataStream<ObjectNode> englishTweets = tweetsWithLang.filter(jsonNode -> jsonNode.get("user").get("lang").asText().equals("en")).name("Select 'lang'=en tweets");

	// write to file system
	RollingSink<ObjectNode> rollingSink = new RollingSink<>(params.get("sinkPath", "/home/robert/flink-workdir/flink-streaming-etl/rolling-sink"));
	rollingSink.setBucketer(new DateTimeBucketer("yyyy-MM-dd-HH-mm")); // do a bucket for each minute
	englishTweets.addSink(rollingSink).name("Rolling FileSystem Sink");

	// build aggregates (count per language) using window (10 seconds tumbling):
	DataStream<Tuple3<Long, String, Long>> languageCounts = tweetsWithLang.keyBy(jsonNode -> jsonNode.get("user").get("lang").asText())
		.timeWindow(Time.seconds(10))
		.apply(new Tuple3<>(0L, "", 0L), new JsonFoldCounter(), new CountEmitter()).name("Count per Langauage (10 seconds tumbling)");

	// write window aggregate to ElasticSearch
	List<InetSocketAddress> transportNodes = ImmutableList.of(new InetSocketAddress(InetAddress.getByName("localhost"), 9300));
	ElasticsearchSink<Tuple3<Long, String, Long>> elasticsearchSink = new ElasticsearchSink<>(params.toMap(), transportNodes, new ESRequest());

	languageCounts.addSink(elasticsearchSink).name("ElasticSearch2 Sink");

	// word-count on the tweet stream
	DataStream<Tuple2<Date, List<Tuple2<String, Long>>>> topWordCount = tweetsWithLang
		// get text from tweets
		.map(tweet -> tweet.get("text").asText()).name("Get text from Tweets")
		// split text into (word, 1) tuples
		.flatMap(new FlatMapFunction<String, Tuple2<String, Long>>() {
			@Override
			public void flatMap(String s, Collector<Tuple2<String, Long>> collector) throws Exception {
				String[] splits = s.split(" ");
				for (String sp : splits) {
					collector.collect(new Tuple2<>(sp, 1L));
				}
			}
		}).name("Tokenize words")
		// group by word
		.keyBy(0)
		// build 1 min windows, compute every 10 seconds --> count word frequency
		.timeWindow(Time.minutes(1L), Time.seconds(10L)).apply(new WordCountingWindow()).name("Count word frequency (1 min, 10 sec sliding window)")
		// build top n every 10 seconds
		.timeWindowAll(Time.seconds(10L)).apply(new TopNWords(10)).name("TopN Window (10s)");

	// write top Ns to Kafka topic
	topWordCount.addSink(new FlinkKafkaProducer09<>(params.getRequired("wc-topic"), new ListSerSchema(), params.getProperties())).name("Write topN to Kafka");

	env.execute("Streaming ETL");

}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testReduceProcessingTime() 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>> window1 = source
			.windowAll(SlidingProcessingTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS)))
			.reduce(new DummyReducer());

	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 ProcessingTimeTrigger);
	Assert.assertTrue(winOperator.getWindowAssigner() instanceof SlidingProcessingTimeWindows);
	Assert.assertTrue(winOperator.getStateDescriptor() instanceof ReducingStateDescriptor);

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

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
public void testUnboundedPojoStreamAndReturnMap() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(1);
    env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);
    DataStream<Event> input = env.addSource(new RandomEventSource(5));

    DataStream<Map<String, Object>> output = SiddhiCEP
        .define("inputStream", input, "id", "name", "price", "timestamp")
        .cql("from inputStream select timestamp, id, name, price insert into  outputStream")
        .returnAsMap("outputStream");

    String resultPath = tempFolder.newFile().toURI().toString();
    output.writeAsText(resultPath, FileSystem.WriteMode.OVERWRITE);
    env.execute();
    assertEquals(5, getLineCount(resultPath));
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testReduceEventTime() 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(SlidingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS)))
			.reduce(new DummyReducer());

	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 EventTimeTrigger);
	Assert.assertTrue(winOperator.getWindowAssigner() instanceof SlidingEventTimeWindows);
	Assert.assertTrue(winOperator.getStateDescriptor() instanceof ReducingStateDescriptor);

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

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testFoldWithProcessAllWindowFunctionEventTime() 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>> window = source
			.windowAll(TumblingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS)))
			.fold(new Tuple3<>("", "", 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 EventTimeTrigger);
	Assert.assertTrue(winOperator.getWindowAssigner() instanceof TumblingEventTimeWindows);
	Assert.assertTrue(winOperator.getStateDescriptor() instanceof FoldingStateDescriptor);

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

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
/**
 * Test restoring from an empty state taken using Flink 1.2, when some partitions could be
 * found for topics.
 */
@Test
public void testRestoreFromEmptyStateWithPartitions() throws Exception {
	final List<KafkaTopicPartition> partitions = new ArrayList<>(PARTITION_STATE.keySet());

	final DummyFlinkKafkaConsumer<String> consumerFunction = new DummyFlinkKafkaConsumer<>(partitions);

	StreamSource<String, DummyFlinkKafkaConsumer<String>> consumerOperator =
			new StreamSource<>(consumerFunction);

	final AbstractStreamOperatorTestHarness<String> testHarness =
			new AbstractStreamOperatorTestHarness<>(consumerOperator, 1, 1, 0);

	testHarness.setTimeCharacteristic(TimeCharacteristic.ProcessingTime);

	testHarness.setup();
	// restore state from binary snapshot file
	testHarness.initializeState(
			OperatorSnapshotUtil.readStateHandle(
					OperatorSnapshotUtil.getResourceFilename("kafka-consumer-migration-test-flink1.2-empty-state-snapshot")));
	testHarness.open();

	// the expected state in "kafka-consumer-migration-test-flink1.2-empty-state-snapshot";
	// since the state is empty, the consumer should reflect on the startup mode to determine start offsets.
	final HashMap<KafkaTopicPartition, Long> expectedSubscribedPartitionsWithStartOffsets = new HashMap<>();
	for (KafkaTopicPartition partition : PARTITION_STATE.keySet()) {
		expectedSubscribedPartitionsWithStartOffsets.put(partition, KafkaTopicPartitionStateSentinel.GROUP_OFFSET);
	}

	// assert that there are partitions and is identical to expected list
	assertTrue(consumerFunction.getSubscribedPartitionsToStartOffsets() != null);
	assertTrue(!consumerFunction.getSubscribedPartitionsToStartOffsets().isEmpty());
	Assert.assertEquals(expectedSubscribedPartitionsWithStartOffsets, consumerFunction.getSubscribedPartitionsToStartOffsets());

	assertTrue(consumerFunction.getRestoredState() == null);

	consumerOperator.close();
	consumerOperator.cancel();
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testMergingWindowsWithEvictor() 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
			.windowAll(EventTimeSessionWindows.withGap(Time.seconds(5)))
			.evictor(CountEvictor.of(5))
			.process(new TestProcessAllWindowFunction());

	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 EventTimeTrigger);
	Assert.assertTrue(winOperator.getWindowAssigner() instanceof EventTimeSessionWindows);
	Assert.assertTrue(winOperator.getStateDescriptor() instanceof ListStateDescriptor);

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

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testReduceEventTimeWindows() 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(0)
			.timeWindow(Time.of(1000, TimeUnit.MILLISECONDS), Time.of(100, TimeUnit.MILLISECONDS))
			.reduce(new DummyReducer());

	OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>> transform1 = (OneInputTransformation<Tuple2<String, Integer>, Tuple2<String, Integer>>) window1.getTransformation();
	OneInputStreamOperator<Tuple2<String, Integer>, Tuple2<String, Integer>> operator1 = transform1.getOperator();
	Assert.assertTrue(operator1 instanceof WindowOperator);
	WindowOperator winOperator1 = (WindowOperator) operator1;
	Assert.assertTrue(winOperator1.getTrigger() instanceof EventTimeTrigger);
	Assert.assertTrue(winOperator1.getWindowAssigner() instanceof SlidingEventTimeWindows);
	Assert.assertTrue(winOperator1.getStateDescriptor() instanceof ReducingStateDescriptor);
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
/**
 * .fold() does not support RichFoldFunction, since the fold function is used internally
 * in a {@code FoldingState}.
 */
@Test(expected = UnsupportedOperationException.class)
public void testFoldWithRichFolderFails() 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
			.windowAll(SlidingEventTimeWindows.of(Time.of(1, TimeUnit.SECONDS), Time.of(100, TimeUnit.MILLISECONDS)))
			.fold(new Tuple2<>("", 0), new RichFoldFunction<Tuple2<String, Integer>, Tuple2<String, Integer>>() {
				private static final long serialVersionUID = -6448847205314995812L;

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

	fail("exception was not thrown");
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
public void testStateBackendClosingOnFailure() throws Exception {
	Configuration taskManagerConfig = new Configuration();
	taskManagerConfig.setString(CheckpointingOptions.STATE_BACKEND, MockStateBackend.class.getName());

	StreamConfig cfg = new StreamConfig(new Configuration());
	cfg.setOperatorID(new OperatorID(4711L, 42L));
	cfg.setStreamOperator(new StreamSource<>(new MockSourceFunction()));
	cfg.setTimeCharacteristic(TimeCharacteristic.ProcessingTime);

	Task task = createTask(StateBackendTestSource.class, cfg, taskManagerConfig);

	StateBackendTestSource.fail = true;
	task.startTaskThread();

	// wait for clean termination
	task.getExecutingThread().join();

	// ensure that the state backends are closed
	verify(StateBackendTestSource.operatorStateBackend).close();
	verify(StateBackendTestSource.keyedStateBackend).close();

	assertEquals(ExecutionState.FAILED, task.getExecutionState());
}
 

import org.apache.flink.streaming.api.TimeCharacteristic; //导入依赖的package包/类
@Test
@SuppressWarnings("rawtypes")
public void testReduceWithCustomTrigger() 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));

	DummyReducer reducer = new DummyReducer();

	DataStream<Tuple2<String, Integer>> window1 = source
			.keyBy(0)
			.window(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));
}