（一）官方入门示例
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：
$ nc -lk 9999
（2）在一个新的终端中运行NetworkWordCount，统计上面的词语数量并输出：
$ bin/run-example streaming.NetworkWordCount localhost 9999
（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：
hello world again
第二个端口的输出
-------------------------------------------
Time: 1436758706000 ms
-------------------------------------------
(again,1)
(hello,1)
(world,1)
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, '
' delimited text received from the network every second.
*
* Usage: NetworkWordCount
* and 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 ")
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
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()
}
}
（二）Spark Streaming kafka示例
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() {
@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://192.168.16.102:7077 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)
（三）基本步骤
本部分介绍创建一个spark streaming应用的基本步骤
1、构建依赖关系，以maven为例，需要在pom.xml中添加以下内容
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>1.4.0</version>
</dependency>
如果需要使用其它数据源，则还需要将相应的依赖关系放入pom.xml。
如使用kafka作为数据源：
<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>
当然，spark的核心包也要包含：
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.10</artifactId> <version>1.4.0</version> </dependency>