Java DataStream類代碼示例

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
public static void main(String[] args) throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();

    Properties properties = new Properties();
    properties.load(new FileInputStream("src/main/resources/application.properties"));

    Properties mqttProperties = new Properties();

    // client id = a:<Organization_ID>:<App_Id>
    mqttProperties.setProperty(MQTTSource.CLIENT_ID,
            String.format("a:%s:%s",
                    properties.getProperty("Org_ID"),
                    properties.getProperty("App_Id")));

    // mqtt server url = tcp://<Org_ID>.messaging.internetofthings.ibmcloud.com:1883
    mqttProperties.setProperty(MQTTSource.URL,
            String.format("tcp://%s.messaging.internetofthings.ibmcloud.com:1883",
                    properties.getProperty("Org_ID")));

    // topic = iot-2/type/<Device_Type>/id/<Device_ID>/evt/<Event_Id>/fmt/json
    mqttProperties.setProperty(MQTTSource.TOPIC,
            String.format("iot-2/type/%s/id/%s/evt/%s/fmt/json",
                    properties.getProperty("Device_Type"),
                    properties.getProperty("Device_ID"),
                    properties.getProperty("EVENT_ID")));

    mqttProperties.setProperty(MQTTSource.USERNAME, properties.getProperty("API_Key"));
    mqttProperties.setProperty(MQTTSource.PASSWORD, properties.getProperty("APP_Authentication_Token"));


    MQTTSource mqttSource = new MQTTSource(mqttProperties);
    DataStreamSource<String> tempratureDataSource = env.addSource(mqttSource);
    DataStream<String> stream = tempratureDataSource.map((MapFunction<String, String>) s -> s);
    stream.print();

    env.execute("Temperature Analysis");
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
@Test(expected = InvalidTypesException.class)
public void testUnboundedPojoSourceButReturnInvalidTupleType() throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<Event> input = env.addSource(new RandomEventSource(5).closeDelay(1500));

    DataStream<Tuple5<Long, Integer, String, Double, Long>> output = SiddhiCEP
        .define("inputStream", input, "id", "name", "price", "timestamp")
        .cql("from inputStream select timestamp, id, name, price insert into  outputStream")
        .returns("outputStream");

    DataStream<Long> following = output.map(new MapFunction<Tuple5<Long, Integer, String, Double, Long>, Long>() {
        @Override
        public Long map(Tuple5<Long, Integer, String, Double, Long> value) throws Exception {
            return value.f0;
        }
    });

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

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
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.datastream.DataStream; //導入依賴的package包/類
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.datastream.DataStream; //導入依賴的package包/類
public static void main(String[] args) throws Exception {
	Properties properties = new Properties();
	properties.setProperty("bootstrap.servers", "localhost:9092");
	properties.setProperty("zookeeper.connect", "localhost:2181");
	properties.setProperty("group.id", "test");
	properties.setProperty("auto.offset.reset", "latest");
	FlinkKafkaConsumer08<DeviceEvent> flinkKafkaConsumer08 = new FlinkKafkaConsumer08<>("device-data",
			new DeviceSchema(), properties);

	StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
	DataStream<DeviceEvent> messageStream = env.addSource(flinkKafkaConsumer08);
	
	Map<String, String> config = new HashMap<>();
	config.put("cluster.name", "my-application");
	// This instructs the sink to emit after every element, otherwise they would be buffered
	config.put("bulk.flush.max.actions", "1");

	List<InetSocketAddress> transportAddresses = new ArrayList<>();
	transportAddresses.add(new InetSocketAddress(InetAddress.getByName("127.0.0.1"), 9300));

	messageStream.addSink(new ElasticsearchSink<DeviceEvent>(config, transportAddresses, new ESSink()));
	env.execute();
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
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.datastream.DataStream; //導入依賴的package包/類
@SuppressWarnings("Convert2Lambda")
public static void main(String[] args) throws Exception {
    StreamExecutionEnvironment streamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<String> dataStream = streamExecutionEnvironment.readTextFile("file:///tmp/flink-esper-input");
    
    EsperStream<String> esperStream = Esper.pattern(dataStream, "select bytes from String");

    DataStream<String> result = esperStream.select(new EsperSelectFunction<String>() {
        private static final long serialVersionUID = 7093943872082195786L;

        @Override
        public String select(EventBean eventBean) throws Exception {
            return new String((byte[]) eventBean.get("bytes"));
        }
    });

    result.writeAsText("file:///tmp/flink-esper-output");

    streamExecutionEnvironment.execute("Simple Flink Esper Example");
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
/***
 * Setup the kayed stream of a raw stream.
 * 
 * @param env
 * @param streamSource
 * @param parsingConfig
 * @return
 */
private static KeyedStream<Tuple3<String, Long, String>, Tuple> setupKayedRawMessagesStream(
    final StreamExecutionEnvironment env, String parsingConfig) {
  DataStream<Tuple3<String, Long, String>> rawStream =
      env.addSource(
          new FileLinesStreamSource(configs.getStringProp("aisDataSetFilePath"), parsingConfig,true))
          .flatMap(new RawStreamMapper(parsingConfig)).setParallelism(1);

  // assign the timestamp of the AIS messages based on their timestamps
  DataStream<Tuple3<String, Long, String>> rawStreamWithTimeStamp =
      rawStream.assignTimestampsAndWatermarks(new RawMessageTimestampAssigner());

  // Construct the keyed stream (i.e., trajectories stream) of the raw messages by grouping them
  // based on the message ID (MMSI for vessels)
  KeyedStream<Tuple3<String, Long, String>, Tuple> kaydAisMessagesStream =
      rawStreamWithTimeStamp.keyBy(0).process(new RawMessagesSorter()).keyBy(0);
  return kaydAisMessagesStream;
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
private static void writeEnrichedStream(DataStream<AisMessage> enrichedAisMessagesStream,
    String parsingConfig, boolean writeOutputStreamToFile, String outputLineDelimiter,
    String outputPath, String outputStreamTopic) throws IOException {

  if (writeOutputStreamToFile) {
    enrichedAisMessagesStream.map(new AisMessagesToCsvMapper(outputLineDelimiter)).writeAsText(
        outputPath, WriteMode.OVERWRITE);
    return;
  }

  // Write to Kafka
  Properties producerProps = AppUtils.getKafkaProducerProperties();

  FlinkKafkaProducer010Configuration<AisMessage> myProducerConfig =
      FlinkKafkaProducer010.writeToKafkaWithTimestamps(enrichedAisMessagesStream,
          outputStreamTopic, new AisMessageCsvSchema(parsingConfig, outputLineDelimiter),
          producerProps);
  myProducerConfig.setLogFailuresOnly(false);
  myProducerConfig.setFlushOnCheckpoint(true);

}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
public static void main(String[] args) throws Exception {
  // create execution environment
  StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

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


  DataStream<String> stream = env
          .addSource(new FlinkKafkaConsumer09<>("flink-demo", new SimpleStringSchema(), properties));

  stream.map(new MapFunction<String, String>() {
    private static final long serialVersionUID = -6867736771747690202L;

    @Override
    public String map(String value) throws Exception {
      return "Stream Value: " + value;
    }
  }).print();

  env.execute();
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
private static DataStreamSink<CustomerImpression>[] setupCustomerSinks(DataStream<CustomerImpression> msgStream) {
  // Split the stream into multiple streams by customer Id
  SplitStream<CustomerImpression> splitStream = msgStream.split(customerOutputSelector());

  // Tie a separate sink to each fork of the split stream
  List<Customer> customers = Customer.getAllCustomers();
  DataStreamSink<CustomerImpression>[] customerSinks = new DataStreamSink[customers.size()];

  int i = 0;
  for (Customer customer : customers) {
    customerSinks[i++] = splitStream
      .select(customerStreamName(customer))
      .addSink(new CustomerSinkFunction(customer));
  }
  return customerSinks;
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
@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.datastream.DataStream; //導入依賴的package包/類
public static void main(String[] args) throws Exception {

		ParameterTool params = ParameterTool.fromArgs(args);
		final String nycTaxiRidesPath = params.get("nycTaxiRidesPath");

		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();

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

		// ===============================================================================
		//   1. clean up `rides`, so that the output stream only contains events
		//      with valid geo coordinates within NYC.
		//   2. print out the result stream to console
		// ===============================================================================

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

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
public static void main(String[] args) throws Exception {
	StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	SiteToSiteClientConfig clientConfig = new SiteToSiteClient.Builder()
			.url("http://localhost:8080/nifi")
			.portName("Data for Flink")
			.requestBatchCount(5)
			.buildConfig();

	SourceFunction<NiFiDataPacket> nifiSource = new NiFiSource(clientConfig);
	DataStream<NiFiDataPacket> streamSource = env.addSource(nifiSource).setParallelism(2);

	DataStream<String> dataStream = streamSource.map(new MapFunction<NiFiDataPacket, String>() {
		@Override
		public String map(NiFiDataPacket value) throws Exception {
			return new String(value.getContent(), Charset.defaultCharset());
		}
	});

	dataStream.print();
	env.execute();
}
 

import org.apache.flink.streaming.api.datastream.DataStream; //導入依賴的package包/類
@Test
public void testInheritOverride() {
	// verify that we can explicitly disable inheritance of the input slot sharing groups

	StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	FilterFunction<Long> dummyFilter = new FilterFunction<Long>() {
		@Override
		public boolean filter(Long value) {
			return false;
		}
	};

	DataStream<Long> src1 = env.generateSequence(1, 10).slotSharingGroup("group-1");
	DataStream<Long> src2 = env.generateSequence(1, 10).slotSharingGroup("group-1");

	// this should not inherit group but be in "default"
	src1.union(src2).filter(dummyFilter).slotSharingGroup("default");
	JobGraph jobGraph = env.getStreamGraph().getJobGraph();

	List<JobVertex> vertices = jobGraph.getVerticesSortedTopologicallyFromSources();

	assertEquals(vertices.get(0).getSlotSharingGroup(), vertices.get(1).getSlotSharingGroup());
	assertNotEquals(vertices.get(0).getSlotSharingGroup(), vertices.get(2).getSlotSharingGroup());
	assertNotEquals(vertices.get(1).getSlotSharingGroup(), vertices.get(2).getSlotSharingGroup());
}