Spark Streaming教程

 

废话不说，先来个示例，有个感性认识再介绍。

这个示例来自spark自带的example，基本步骤如下：

（1）使用以下命令输入流消息：


（2）在一个新的终端中运行NetworkWordCount，统计上面的词语数量并输出：


（3）在第一步创建的输入流程中敲入一些内容，在第二步创建的终端中会看到统计结果，如：

第一个终端输入的内容：


第二个端口的输出


简单解释一下，上面的示例通过手工敲入内容，并传给spark streaming统计单词数量，然后将结果打印出来。

附上代码：

package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
*
* Usage: NetworkWordCount <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.NetworkWordCount localhost 9999`
*/
object NetworkWordCount {
def main(args: Array[String]) {
if (args.length < 2) {
System.err.println("Usage: NetworkWordCount <hostname> <port>")
System.exit(1)
}

StreamingExamples.setStreamingLogLevels()

// Create the context with a 1 second batch size
val sparkConf = new SparkConf().setAppName("NetworkWordCount")
val ssc = new StreamingContext(sparkConf, Seconds(1))

// Create a socket stream 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.
val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}

（一）构建自己的项目

本示例使用java+maven来构建一个wordcount

1、创建项目，在pom.xml添加如下的依赖关系

<dependency>

<groupId>org.slf4j</groupId>

<artifactId>slf4j-api</artifactId>

<version>1.7.0</version>

</dependency>

<dependency>

<groupId>org.slf4j</groupId>

<artifactId>slf4j-log4j12</artifactId>

<version>1.7.0</version>

</dependency>

<dependency>

<groupId>log4j</groupId>

<artifactId>log4j</artifactId>

<version>1.2.17</version>

</dependency>

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-core_2.10</artifactId>

<version>1.4.0</version>

</dependency>

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-streaming_2.10</artifactId>

<version>1.4.0</version>

</dependency>

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-streaming-kafka_2.10</artifactId>

<version>1.4.0</version>

</dependency>

 

<dependency>

<groupId>org.apache.kafka</groupId>

<artifactId>kafka_2.10</artifactId>

<version>0.8.2.1</version>

</dependency>

2、写代码，此部分代码使用了官方的代码：

package com.netease.gdc.kafkaStreaming;

import java.util.Map;
import java.util.HashMap;
import java.util.regex.Pattern;

import scala.Tuple2;
import com.google.common.collect.Lists;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;

/**
* Consumes messages from one or more topics in Kafka and does wordcount.
*
* Usage: JavaKafkaWordCount
* is a list of one or more zookeeper servers that make quorum
* is the name of kafka consumer group
* is a list of one or more kafka topics to consume from
*is the number of threads the kafka consumer should use
*
* To run this example:
*   `$ bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
*    zoo03 my-consumer-group topic1,topic2 1`
*/

public final class JavaKafkaWordCount {
private static final Pattern SPACE = Pattern.compile(" ");

private JavaKafkaWordCount() {
}

public static void main(String[] args) {
if (args.length < 4) {
System.err.println("Usage: JavaKafkaWordCount

");
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 topicMap = new HashMap();
String[] topics = args[2].split(",");
for (String topic: topics) {
topicMap.put(topic, numThreads);
}

JavaPairReceiverInputDStream messages =
KafkaUtils.createStream(jssc, args[0], args[1], topicMap);

JavaDStream lines = messages.map(new Function<tuple2, String>() {
@Override
public String call(Tuple2 tuple2) {
return tuple2._2();
}
});

JavaDStream words = lines.flatMap(new FlatMapFunction() {
@Override
public Iterable call(String x) {
return Lists.newArrayList(SPACE.split(x));
}
});

JavaPairDStream wordCounts = words.mapToPair(
new PairFunction() {
@Override
public Tuple2 call(String s) {
return new Tuple2(s, 1);
}
}).reduceByKey(new Function2() {
@Override
public Integer call(Integer i1, Integer i2) {
return i1 + i2;
}
});

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

3、上传到服务器中然后编译

mvn clean package

4、提交job到spark中

/home/hadoop/spark/bin/spark-submit --jars ../mylib/metrics-core-2.2.0.jar,../mylib/zkclient-0.3.jar,../mylib/spark-streaming-kafka_2.10-1.4.0.jar,../mylib/kafka-clients-0.8.2.1.jar,../mylib/kafka_2.10-0.8.2.1.jar  --class com.netease.gdc.kafkaStreaming.JavaKafkaWordCount --master spark://192.168.165.102:7077  target/kafkaStreaming-0.0.1-SNAPSHOT.jar 192.168.172.111:2181/kafka my-consumer-group test 3

当然，前提是kafka集群已经正常运行，且存在test这个topic

5、验证

打开一个console producer，输入内容，然后观察wordcount的结果。

结果形式如下：

(hi,1)