spark streaming tutorial

本篇咱們介紹一些spark流式計算的基礎概念，並實現一個例子加以說明。

 

spark streaming

spark streaming 是以spark爲核心的流式處理框架，內部經過批處理的方式對數據加以加工。

 

 工做方式：

 生態：

 

Dstream:a DStream is represented as a sequence of RDDs.

 

map和flatmap

任何數據在spark中都表示爲RDD。map和flatmap是RDD提供的兩個函數。map能夠添加本身的映射邏輯將數據進行轉變，flatmap和map的區別就是它的輸出可能不止一個RDD。

 

map()

 

flatmap()

 

Simple example would be applying a flatMap to Strings and using split function to return words to new RDD.

Welcome to TutorialKart
Learn Apache Spark
Learn to work with RDD

 

import java.util.Arrays;
 
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
 
public class RDDflatMapExample {
 
    public static void main(String[] args) {
        // configure spark
        SparkConf sparkConf = new SparkConf().setAppName("Read Text to RDD")
                                        .setMaster("local[2]").set("spark.executor.memory","2g");
        // start a spark context
        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        
        // provide path to input text file
        String path = "data/rdd/input/sample.txt";
        
        // read text file to RDD
        JavaRDD<String> lines = sc.textFile(path);
        
        // flatMap each line to words in the line
        JavaRDD<String> words = lines.flatMap(s -> Arrays.asList(s.split(" ")).iterator()); 
        
        // collect RDD for printing
        for(String word:words.collect()){
            System.out.println(word);
        }
    }
 
}

　　

17/11/29 12:33:59 INFO DAGScheduler: ResultStage 0 (collect at RDDflatMapExample.java:26) finished in 0.513 s
17/11/29 12:33:59 INFO DAGScheduler: Job 0 finished: collect at RDDflatMapExample.java:26, took 0.793858 s
Welcome
to
TutorialKart
Learn
Apache
Spark
Learn
to
work
with
RDD
17/11/29 12:33:59 INFO SparkContext: Invoking stop() from shutdown hook

最後可使用 ~$ spark-submit 將jar包提交查看效果

 

Spark streaming Example

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.streaming;

import java.util.Arrays;
import java.util.regex.Pattern;

import scala.Tuple2;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

/**
 * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
 *
 * Usage: JavaNetworkWordCount <hostname> <port>
 * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
 *
 * To run this on your local machine, you need to first run a Netcat server
 *    `$ nc -lk 9999`
 * and then run the example
 *    `$ bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
 */
public final class JavaNetworkWordCount {
  private static final Pattern SPACE = Pattern.compile(" ");

  public static void main(String[] args) throws Exception {
    if (args.length < 2) {
      System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
      System.exit(1);
    }

    StreamingExamples.setStreamingLogLevels();

    // Create the context with a 1 second batch size
    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));

    // Create a JavaReceiverInputDStream on target ip:port and count the
    // words in input stream of \n delimited text (eg. generated by 'nc')
    // Note that no duplication in storage level only for running locally.
    // Replication necessary in distributed scenario for fault tolerance.
    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
            args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER);
    JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1))
        .reduceByKey((i1, i2) -> i1 + i2);

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

　　

 

reference：

https://www.tutorialkart.com/apache-spark/spark-rdd-flatmap/

https://spark.apache.org/docs/2.2.0/streaming-programming-guide.html#discretized-streams-dstreams