声明
- 本文基于Centos6.x + CDH 5.x
Spark是什么
Spark是Apache的顶级项目。项目背景是 Hadoop 的 MapReduce 太挫太慢了，于是有人就做了Spark，目前Spark声称在内存中比Hadoop快100倍，在磁盘上比Hadoop快10倍。
安装Spark
spark有5个组件
- spark-core: spark核心包
- spark-worker: spark-worker用的脚本
- spark-master: spark-master用的脚本
- spark-python: Spark的Python客户端
- spark-history-server: 任务历史服务
开始安装Spark
安装组件包
我挑选host1作为master 和 worker，所以在host1上安装以下包
sudo yum install spark-core spark-master spark-worker spark-python
host2 作为 history-server 和 worker
sudo yum install spark-core spark-worker spark-history-server spark-python
配置Spark
Spark支持两种模式
- 独立模式: 在独立模式， Spark使用一个 Master 服务来运行任务。
- YARN模式: 在YARN模式, YARN ResourceManager 代替了Spark Master。Job还是由NodeManager运行。YARN 模式搭建会比较复杂，但是它支持安全机制，并且跟YARN集群的配合更好。
本教程中使用独立模式
编辑每一台安装了Spark机器上的 /etc/spark/conf/spark-env.sh 修改master所在机器的机器名，在这个教程中就是host1
### ### === IMPORTANT === ### Change the following to specify a real cluster‘s Master host ### export STANDALONE_SPARK_MASTER_HOST=‘host1‘
注意： 包裹host1的符号也要换成单引号
创建Spark History Server需要的hdfs文件夹 /user/spark/applicationHistory/
$ sudo -u hdfs hadoop fs -mkdir /user/spark $ sudo -u hdfs hadoop fs -mkdir /user/spark/applicationHistory $ sudo -u hdfs hadoop fs -chown -R spark:spark /user/spark $ sudo -u hdfs hadoop fs -chmod 1777 /user/spark/applicationHistory
在Spark客户端，在本例中就是host2，创建一份新的配置文件
cp /etc/spark/conf/spark-defaults.conf.template /etc/spark/conf/spark-defaults.conf
把下面这两行增加到/etc/spark/conf/spark-defaults.conf 里面去
spark.eventLog.dir=/user/spark/applicationHistory spark.eventLog.enabled=true
在所有的机器上复制hdfs-site.xml到 /etc/spark/conf 下
cp /etc/hadoop/conf/hdfs-site.xml /etc/spark/conf/
启动Spark
在host1上启动master服务
sudo service spark-master start
在其他节点上启动woker服务，本教程中就是 host1 和 host2
sudo service spark-worker start
在其中一个节点上启动history服务，本教程中用host2启动history
sudo service spark-history-server start
启动顺序
- master
- worker
- history-server
打开浏览器访问 http://host1:18080 可以看到Spark的管理界面
使用Spark
使用 spark-shell 命令进入spark shell
[[email protected] impala]# spark-shell
2015-02-10 09:02:07,059 INFO [main] spark.SecurityManager (Logging.scala:logInfo(59)) - Changing view acls to: root
2015-02-10 09:02:07,069 INFO [main] spark.SecurityManager (Logging.scala:logInfo(59)) - Changing modify acls to: root
2015-02-10 09:02:07,070 INFO [main] spark.SecurityManager (Logging.scala:logInfo(59)) - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
2015-02-10 09:02:07,072 INFO [main] spark.HttpServer (Logging.scala:logInfo(59)) - Starting HTTP Server
2015-02-10 09:02:07,217 INFO [main] server.Server (Server.java:doStart(272)) - jetty-8.y.z-SNAPSHOT
2015-02-10 09:02:07,350 INFO [main] server.AbstractConnector (AbstractConnector.java:doStart(338)) - Started [email protected]:59058
2015-02-10 09:02:07,352 INFO [main] util.Utils (Logging.scala:logInfo(59)) - Successfully started service ‘HTTP class server‘ on port 59058.
Welcome to
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_\ \/ _ \/ _ `/ __/ ‘_/
/___/ .__/\_,_/_/ /_/\_\ version 1.2.0
/_/
Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_25)
...
2015-02-10 09:02:21,572 INFO [main] storage.BlockManagerMaster (Logging.scala:logInfo(59)) - Registered BlockManager
2015-02-10 09:02:22,472 INFO [main] scheduler.EventLoggingListener (Logging.scala:logInfo(59)) - Logging events to file:/user/spark/applicationHistory/local-1423530140986
2015-02-10 09:02:22,672 INFO [main] repl.SparkILoop (Logging.scala:logInfo(59)) - Created spark context..
Spark context available as sc.
scala>
我们来开始玩一下Spark。还是做之前用YARN做的wordcount任务，看看Spark如何完成这项任务。
STEP1
创建测试文本
$ echo "Hello World Bye World" > file0 $ echo "Hello Hadoop Goodbye Hadoop" > file1 $ hdfs dfs -mkdir -p /user/spark/wordcount/input $ hdfs dfs -put file* /user/spark/wordcount/input
STEP2
进入 spark-shell 运行 wordcount任务脚本
val file = sc.textFile("hdfs://mycluster/user/spark/wordcount/input")
val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://mycluster/user/spark/wordcount/output")
这回不用写java代码了，简单了好多。这里用的是Scala语言。
Spark支持 Java, Scale, Python 三种语言，但是对Scala的支持最全，建议开始用java来写，后期还是熟悉下Scala比较好。
STEP3
我们去看下结果，我用Pig看下结果
grunt> ls hdfs://mycluster/user/spark/wordcount/input <dir> hdfs://mycluster/user/spark/wordcount/output <dir> grunt> cd output grunt> ls hdfs://mycluster/user/spark/wordcount/output/_SUCCESS<r 2> 0 hdfs://mycluster/user/spark/wordcount/output/part-00000<r 2> 8 hdfs://mycluster/user/spark/wordcount/output/part-00001<r 2> 10 hdfs://mycluster/user/spark/wordcount/output/part-00002<r 2> 33 grunt> cat part-00000 (Bye,1) grunt> cat part-00001 (World,2) grunt> cat part-00002 (Goodbye,1) (Hello,2) (Hadoop,2)
更深入的学习请看手册Spark Programming Guide ， 另外这个手册写的真不错。
参考资料
时间： 2024-07-28 14:00:54