Java JavaStreamingContext.awaitTermination方法代码示例

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) {

        SparkConf conf = new SparkConf()
                .setAppName("kafka-sandbox")
                .setMaster("local[*]");
        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(2000));

        Set<String> topics = Collections.singleton("mytopic");
        Map<String, String> kafkaParams = new HashMap<>();
        kafkaParams.put("metadata.broker.list", "localhost:9092");

        JavaPairInputDStream<String, String> directKafkaStream = KafkaUtils.createDirectStream(ssc,
                String.class, String.class, StringDecoder.class, StringDecoder.class, kafkaParams, topics);

        directKafkaStream.foreachRDD(rdd -> {
            System.out.println("--- New RDD with " + rdd.partitions().size()
                    + " partitions and " + rdd.count() + " records");
            rdd.foreach(record -> System.out.println(record._2));
        });

        ssc.start();
        ssc.awaitTermination();
    }
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
private void run(CompositeConfiguration conf) {
    // Spark conf
    SparkConf sparkConf = new SparkConf().setAppName("TwitterSparkCrawler").setMaster(conf.getString("spark.master"))
            .set("spark.serializer", conf.getString("spark.serializer"));
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(conf.getLong("stream.duration")));

    // Twitter4J
    // IMPORTANT: put keys in twitter4J.properties
    Configuration twitterConf = ConfigurationContext.getInstance();
    Authorization twitterAuth = AuthorizationFactory.getInstance(twitterConf);

    // Create twitter stream
    String[] filters = { "#Car" };
    TwitterUtils.createStream(jssc, twitterAuth, filters).print();
    // Start the computation
    jssc.start();
    jssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
	System.setProperty("hadoop.home.dir", "E:\\hadoop");

	final String ip = "10.0.75.1";
	final int port = Integer.parseInt("9000");
	final String checkpointDirectory = "E:\\hadoop\\checkpoint";
	// Function to create JavaStreamingContext without any output operations
	// (used to detect the new context)
	Function0<JavaStreamingContext> createContextFunc = new Function0<JavaStreamingContext>() {
		@Override
		public JavaStreamingContext call() {
			return createContext(ip, port, checkpointDirectory);
		}
	};

	JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(checkpointDirectory, createContextFunc);
	ssc.start();
	ssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
	Flags.setFromCommandLineArgs(THE_OPTIONS, args);

	// 初始化Spark Conf.
	SparkConf conf = new SparkConf().setAppName("A SECTONG Application: Apache Log Analysis with Spark");
	JavaSparkContext sc = new JavaSparkContext(conf);
	JavaStreamingContext jssc = new JavaStreamingContext(sc, Flags.getInstance().getSlideInterval());
	SQLContext sqlContext = new SQLContext(sc);

	// 初始化参数
	HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(Flags.getInstance().getKafka_topic().split(",")));
	HashMap<String, String> kafkaParams = new HashMap<String, String>();
	kafkaParams.put("metadata.broker.list", Flags.getInstance().getKafka_broker());

	// 从Kafka Stream获取数据
	JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(jssc, String.class, String.class,
			StringDecoder.class, StringDecoder.class, kafkaParams, topicsSet);

	JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
		private static final long serialVersionUID = 5266880065425088203L;

		public String call(Tuple2<String, String> tuple2) {
			return tuple2._2();
		}
	});

	JavaDStream<ApacheAccessLog> accessLogsDStream = lines.flatMap(line -> {
		List<ApacheAccessLog> list = new ArrayList<>();
		try {
			// 映射每一行
			list.add(ApacheAccessLog.parseFromLogLine(line));
			return list;
		} catch (RuntimeException e) {
			return list;
		}
	}).cache();

	accessLogsDStream.foreachRDD(rdd -> {

		// rdd to DataFrame
		DataFrame df = sqlContext.createDataFrame(rdd, ApacheAccessLog.class);
		// 写入Parquet文件
		df.write().partitionBy("ipAddress", "method", "responseCode").mode(SaveMode.Append).parquet(Flags.getInstance().getParquetFile());

		return null;
	});

	// 启动Streaming服务器
	jssc.start(); // 启动计算
	jssc.awaitTermination(); // 等待终止
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) 
{
 SparkConf conf = new SparkConf();
 conf.setAppName("Wordcount Background");
 conf.setMaster("local");
  
 
 JavaStreamingContext ssc = new JavaStreamingContext(conf, Durations.seconds(15));
 
 
 JavaDStream<String> lines = ssc.textFileStream("/home/rahul/DATASET");
 JavaDStream<String> words = lines.flatMap(WORDS_EXTRACTOR);
 JavaPairDStream<String, Integer> pairs = words.mapToPair(WORDS_MAPPER);
 JavaPairDStream<String, Integer> counter = pairs.reduceByKey(WORDS_REDUCER);
 
 counter.print();
 
 ssc.start();
 
 ssc.awaitTermination();
 

 /*JavaRDD<String> file = context.textFile("/home/rahul/Desktop/palestine.txt");
 JavaRDD<String> words = file.flatMap(WORDS_EXTRACTOR);
 JavaPairRDD<String, Integer> pairs = words.mapToPair(WORDS_MAPPER);
 JavaPairRDD<String, Integer> counter = pairs.reduceByKey(WORDS_REDUCER);
 counter.saveAsTextFile("/home/rahul/Desktop/wc"); 
 context.close();*/
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public void run() throws IOException {
  SparkConf conf = new SparkConf();
  conf.setAppName(getAppName());
  conf.set(SPARK_SERIALIZER, ORG_APACHE_SPARK_SERIALIZER_KRYO_SERIALIZER);
  JavaSparkUtil.packProjectJars(conf);
  setupSparkConf(conf);

  JavaStreamingContext ssc = new JavaStreamingContext(conf, getDuration());
  List<JavaDStream<T>> streamsList = getStreamsList(ssc);

  // Union all the streams if there is more than 1 stream
  JavaDStream<T> streams = unionStreams(ssc, streamsList);

  JavaPairDStream<String, RowMutation> pairDStream = streams.mapToPair(new PairFunction<T, String, RowMutation>() {
    public Tuple2<String, RowMutation> call(T t) {
      RowMutation rowMutation = convert(t);
      return new Tuple2<String, RowMutation>(rowMutation.getRowId(), rowMutation);
    }
  });

  pairDStream.foreachRDD(getFunction());

  ssc.start();
  ssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws InterruptedException {

        String messagingServiceHost = System.getenv("MESSAGING_SERVICE_HOST");
        if (messagingServiceHost != null) {
            host = messagingServiceHost;
        }
        LOG.info("host = {}", host);
        String messagingServicePort = System.getenv("MESSAGING_SERVICE_PORT");
        if (messagingServicePort != null) {
            port = Integer.valueOf(messagingServicePort);
        }
        LOG.info("port = {}", port);

        JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(CHECKPOINT_DIR, AMQPTemperature::createStreamingContext);

        ssc.start();
        ssc.awaitTermination();
    }
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws InterruptedException {

        // getting AMQP messaging service connection information
        String messagingServiceHost = System.getenv("MESSAGING_SERVICE_HOST");
        if (messagingServiceHost != null) {
            host = messagingServiceHost;
        }
        String messagingServicePort = System.getenv("MESSAGING_SERVICE_PORT");
        if (messagingServicePort != null) {
            port = Integer.valueOf(messagingServicePort);
        }
        log.info("AMQP messaging service hostname {}:{}", host, port);

        // getting credentials for authentication
        username = System.getenv("SPARK_DRIVER_USERNAME");
        password = System.getenv("SPARK_DRIVER_PASSWORD");
        log.info("Credentials {}/{}", username, password);

        JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(CHECKPOINT_DIR, TemperatureAnalyzer::createStreamingContext);

        ssc.start();
        ssc.awaitTermination();
    }
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
/**
 * This will start the spark stream that is reading from the MQTT queue
 *
 * @param broker     - MQTT broker url
 * @param topic      - MQTT topic name
 * @param numSeconds - Number of seconds between batch size
 */
public void processMQTT(final String broker, final String topic, final int numSeconds) {

    LOG.info("************ SparkStreamingMQTTOutside.processMQTT start");

    // Create the spark application and set the name to MQTT
    SparkConf sparkConf = new SparkConf().setAppName("MQTT");

    // Create the spark streaming context with a 'numSeconds' second batch size
    jssc = new JavaStreamingContext(sparkConf, Durations.seconds(numSeconds));
    jssc.checkpoint(checkpointDirectory);

    LOG.info("************ SparkStreamingMQTTOutside.processMQTT about to read the MQTTUtils.createStream");
    //2. MQTTUtils to collect MQTT messages
    JavaReceiverInputDStream<String> messages = MQTTUtils.createStream(jssc, broker, topic);

    LOG.info("************ SparkStreamingMQTTOutside.processMQTT about to do foreachRDD");
    //process the messages on the queue and save them to the database
    messages.foreachRDD(new SaveRDD());

    LOG.info("************ SparkStreamingMQTTOutside.processMQTT prior to context.strt");
    // Start the context
    jssc.start();
    jssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
private void start() {
	// Create a local StreamingContext with two working thread and batch interval of
	// 1 second
	SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount");
	JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));

	JavaDStream<String> msgDataStream = jssc.textFileStream(StreamingUtils.getInputDirectory());
	msgDataStream.print();

	jssc.start();
	try {
		jssc.awaitTermination();
	} catch (InterruptedException e) {
		// TODO Auto-generated catch block
		e.printStackTrace();
	}
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
private void start() {
	// Create a local StreamingContext with two working thread and batch interval of
	// 1 second
	SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("Streaming Ingestion File System Text File to Dataframe");
	JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));

	JavaDStream<String> msgDataStream = jssc.textFileStream(StreamingUtils.getInputDirectory());

	msgDataStream.print();
	// Create JavaRDD<Row>
	msgDataStream.foreachRDD(new RowProcessor());	

	jssc.start();
	try {
		jssc.awaitTermination();
	} catch (InterruptedException e) {
		// TODO Auto-generated catch block
		e.printStackTrace();
	}
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) {
    if (args.length < 4) {
        System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
        System.exit(1);
    }

    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
    // Create the context with a 1 second batch size
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
    int numThreads = Integer.parseInt(args[3]);
    Map<String, Integer> topicMap = new HashMap<String, Integer>();
    String[] topics = args[2].split(",");
    for (String topic : topics) {
        topicMap.put(topic, numThreads);
    }
    JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc, args[0], args[1],
            topicMap);
    JavaDStream<String> lines = messages.map(tuple2 -> tuple2._2());
    JavaDStream<String> words = lines.flatMap(x -> Lists.newArrayList(SPACE.split(x)));
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<String, Integer>(s, 1)).reduceByKey(
            (i1, i2) -> i1 + i2);
    wordCounts.print();
    jssc.start();
    jssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
private void run(CompositeConfiguration conf) {
    // Spark conf
    SparkConf sparkConf = new SparkConf().setAppName("TwitterSparkCrawler").setMaster(conf.getString("spark.master"))
            .set("spark.serializer", conf.getString("spark.serializer"))
            .registerKryoClasses(new Class<?>[] { Parameter.class, BatchRequestBuilder.class, BatchRequest.class });
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(conf.getLong("stream.duration")));

    // Create facebook stream
    Parameter typeParam = Parameter.with("type", "event");
    FacebookUtils
            .createStream(jssc, conf.getString("access.token"),
                    new BatchRequestBuilder[] {
                            new BatchRequestBuilder("search").parameters(new Parameter[] { Parameter.with("q", "car"), typeParam }) })
            .print();

    // Start the computation
    jssc.start();
    jssc.awaitTermination();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
   String zkQuorum = args[0];
   String group = args[1];
   SparkConf conf = new SparkConf().setAppName("KafkaInput");
   // Create a StreamingContext with a 1 second batch size
   JavaStreamingContext jssc = new JavaStreamingContext(conf, new Duration(1000));
   Map<String, Integer> topics = new HashMap<String, Integer>();
   topics.put("pandas", 1);
   JavaPairDStream<String, String> input = KafkaUtils.createStream(jssc, zkQuorum, group, topics);
   input.print();
   // start our streaming context and wait for it to "finish"
   jssc.start();
   // Wait for 10 seconds then exit. To run forever call without a timeout
   jssc.awaitTermination(10000);
   // Stop the streaming context
   jssc.stop();
}
 

import org.apache.spark.streaming.api.java.JavaStreamingContext; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
	String master = args[0];
	JavaSparkContext sc = new JavaSparkContext(master, "StreamingLogInput");
   // Create a StreamingContext with a 1 second batch size
   JavaStreamingContext jssc = new JavaStreamingContext(sc, new Duration(1000));
   // Create a DStream from all the input on port 7777
   JavaDStream<String> lines = jssc.socketTextStream("localhost", 7777);
   // Filter our DStream for lines with "error"
   JavaDStream<String> errorLines = lines.filter(new Function<String, Boolean>() {
       public Boolean call(String line) {
         return line.contains("error");
       }});
   // Print out the lines with errors, which causes this DStream to be evaluated
   errorLines.print();
   // start our streaming context and wait for it to "finish"
   jssc.start();
   // Wait for 10 seconds then exit. To run forever call without a timeout
   jssc.awaitTermination(10000);
   // Stop the streaming context
   jssc.stop();
}