[spark]Spark Streaming教程
(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, '\n' 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 \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()
}
}
(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:
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>
最新文章
- 数据挖掘系列(1)关联规则挖掘基本概念与Aprior算法
- springmvc单文件上传
- Heavy Transportation(最短路 + dp)
- Android 使用AIDL调用外部服务
- 使用选择器语法来查找元素 - 你想使用类似于CSS或jQuery的语法来查找和操作元素
- 动态规划+滚动数组 -- POJ 1159 Palindrome
- mysql免安装版配置与使用方法
- 从汇编看c++成员函数指针(二)
- Android WindowManager 小结
- SimpleDateFormat 线程不安全及解决方案
- 2018-2019-2 网络对抗技术 20165220 Exp2 后门原理与实践
- This 关键字的三个用处
- Google API Design Guide (谷歌API设计指南)中文版
- MySQL学习(五)
- babel 7 简单指北
- postman和接口自动化测试
- c+内存管理机制
- poj 3264 Balanced Lineup 题解
- 十八、IntelliJ IDEA 常用快捷键 之 Windows 版
- python3 on macos with vscode