Java DataStream.filter方法代碼示例

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

		ParameterTool params = ParameterTool.fromArgs(args);
		final String nycTaxiRidesPath = params.getRequired("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
		// ===============================================================================
		DataStream<TaxiRide> filteredRides = rides
				// filter out rides that do not start or stop in NYC
				.filter(new NYCFilter());

		// print the filtered stream
		filteredRides.print();

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

import org.apache.flink.streaming.api.datastream.DataStream; //導入方法依賴的package包/類
public DataStream<TaxiRide> execute(DataStream<TaxiRide> rides) throws Exception {
	DataStream<TaxiRide> filteredRidesByNYC = rides
			.filter(new NewYorkTaxiFilter());

	return filteredRidesByNYC;
}
 

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

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

		// -------------------------------------------------------------------------------
		//   Clean the ride events and write them to Kafka (topic: CLEANSED_RIDES_TOPIC)
		// -------------------------------------------------------------------------------

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

		// ===============================================================================
		//   1. remember to set the auto watermark interval in the environment config
		// ===============================================================================

		// start the data generator
		DataStream<TaxiRide> rawRidesFromFile = env.addSource(
				new TaxiRideSource(nycTaxiRidesPath, MAX_EVENT_DELAY, SERVING_SPEED_FACTOR));

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

		// ===============================================================================
		//   2. write the cleansed events to the Kafka topic CLEANSED_RIDES_TOPIC;
		//      the Kafka server location is at LOCAL_KAFKA_BROKER
		// ===============================================================================

		// -------------------------------------------------------------------------------
		//   Consume the cleansed ride events from Kafka and calculate popular places
		// -------------------------------------------------------------------------------

		// configure the Kafka consumer
		Properties kafkaProps = new Properties();
		kafkaProps.setProperty("bootstrap.servers", LOCAL_KAFKA_BROKER);
		kafkaProps.setProperty("group.id", KAFKA_GROUP_ID);
		// always read the Kafka topic from the start
		kafkaProps.setProperty("auto.offset.reset", "earliest");

		// ===============================================================================
		//   3. replace "env.fromElements(new TaxiRide())" with a FlinkKafkaConsumer09
		//      that reads from the topic CLEANSED_RIDES_TOPIC.
		//   4. remember to assign watermarks to the events read from Kafka by calling
		//      "assignTimestampsAndWatermarks". The events will at most be out-of-order
		//      by MAX_EVENT_DELAY, so you can simply use a default
		//      BoundedOutOfOrdernessTimestampExtractor for this.
		// ===============================================================================

		// create a TaxiRide data stream
		DataStream<TaxiRide> ridesFromKafka = env.fromElements(new TaxiRide());

		// find popular places
		DataStream<Tuple5<Float, Float, Long, Boolean, Integer>> popularPlaces = ridesFromKafka
				// match ride to grid cell and event type (start or end)
				.map(new GridCellMatcher())
				// partition by cell id and event type
				.keyBy(0, 1)
				// build sliding window
				.timeWindow(Time.minutes(15), Time.minutes(5))
				// count ride events in window
				.apply(new RideCounter())
				// filter by popularity threshold
				.filter(new FilterFunction<Tuple4<Integer, Long, Boolean, Integer>>() {
					@Override
					public boolean filter(Tuple4<Integer, Long, Boolean, Integer> count) throws Exception {
						return count.f3 >= POPULAR_THRESHOLD;
					}
				})
				// map grid cell to coordinates
				.map(new GridToCoordinates());

		popularPlaces.print();

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

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

            // Checking input parameters
            final ParameterTool params = ParameterTool.fromArgs(args);

            // set up the execution environment
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

            // make parameters available in the web interface
            env.getConfig().setGlobalJobParameters(params);

            env.setParallelism(params.getInt("parallelism", 1));

            DataStream<String> streamSource = env.fromElements(TwitterExampleData.TEXTS);

            DataStream<Tuple2<Long, String>> tweets = streamSource
                    .flatMap(new TwitterFilterFunction());

            DataStream<Tuple2<Long, String>> filtered = tweets.filter(
                    tweet -> tweet != null
            );

            DataStream<Tuple2<Long, String>> tweetsFiltered = filtered
                    .flatMap(new TextFilterFunction());

            tweetsFiltered = tweetsFiltered
                    .flatMap(new StemmingFunction());

            DataStream<Tuple3<Long, String, Float>> positiveTweets =
                    tweetsFiltered.flatMap(new PositiveScoreFunction());

            DataStream<Tuple3<Long, String, Float>> negativeTweets =
                    tweetsFiltered.flatMap(new NegativeScoreFunction());

            DataStream<Tuple4<Long, String, Float, Float>> scoredTweets = positiveTweets
                    .join(negativeTweets)
                    .onWindow(10, TimeUnit.SECONDS)
                    .where(0,1)
                    .equalTo(0,1)
                    .with(new JoinFunction<Tuple3<Long, String, Float>, Tuple3<Long, String, Float>, Tuple4<Long, String, Float, Float>>() {
                        @Override
                        public Tuple4<Long, String, Float, Float> join(Tuple3<Long, String, Float> positive, Tuple3<Long, String, Float> negative) throws Exception {
                            return new Tuple4<>(positive.f0, positive.f1, positive.f2,negative.f2);
                        }
                    });

            DataStream<Tuple5<Long, String, Float, Float, String>> result =
                    scoredTweets.flatMap(new ScoreTweetsFunction());

            result.print();

            result.writeAsText("file:///home/veith/erad2016-streamprocessing/results/teste-flink");

            env.execute("Twitter Streaming Example");
        }