（四）Spark集群搭建-Java&Python版Spark

Spark集群搭建

视频教程

1、优酷

2、YouTube

安装scala环境

下载地址http://www.scala-lang.org/download/

上传scala-2.10.5.tgz到master和slave机器的hadoop用户installer目录下

两台机器都要做

[hadoop@master installer]$ ls

hadoop2  hadoop-2.6.0.tar.gz  scala-2.10.5.tgz

解压

[hadoop@master installer]$ tar -zxvf scala-2.10.5.tgz

[hadoop@master installer]$ mv scala-2.10.5 scala

[hadoop@master installer]$ cd scala

[hadoop@master scala]$ pwd

/home/hadoop/installer/scala

配置环境变量：

[hadoop@master ~]$ vim .bashrc

# .bashrc

# Source global definitions

if [ -f /etc/bashrc ]; then

. /etc/bashrc

fi

# User specific aliases and functions

export JAVA_HOME=/usr/java/jdk1.7.0_79

export HADOOP_HOME=/home/hadoop/installer/hadoop2

export SCALA_HOME=/home/hadoop/installer/scala

export HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_HOME}/lib/native

export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"

export CLASSPATH=$CLASSPATH:$HADOOP_HOME/lib:$JAVA_HOME/lib:$SCALA_HOME/lib

export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$SCALA_HOME/bin

[hadoop@master ~]$ . .bashrc

安装python

安装gcc

[root@master ~]# mkdir /RHEL5U4

[root@master ~]# mount /dev/cdrom /media/

[root@master media]# cp -r * /RHEL5U4/

[root@master ~]vim /etc/yum.repos.d/iso.repo

[rhel-Server]

Name=5u4_Server

Baseurl=file:///RHEL5U4/Server

Enable=1

Gpgcheck=0

Gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-redhat-release

yum clean all

yum install gcc

Python安装

[root@master installer]# tar -zxvf Python-2.7.12

上传zlib-1.2.8.tar.gz

替换/root/installer/Python-2.7.12/Modules的zlib

[root@master Python-2.7.12]# ./configure --prefix=/usr/local/python27

[root@master Python-2.7.12]# make

[root@master Python-2.7.12]# make install

[root@master Python-2.7.12]# mv /usr/bin/python /usr/bin/python_old

[root@master Python-2.7.12]# ln -s /usr/local/python27/bin/python /usr/bin/

[root@master Python-2.7.12]# python

Python 2.7.12 (default, Nov  7 2016, 21:42:16)

[GCC 4.1.2 20080704 (Red Hat 4.1.2-46)] on linux2

Type "help", "copyright", "credits" or "license" for more information.

>>>

安装spark环境

下载地址http://spark.apache.org/downloads.html

上传spark-2.0.0-bin-hadoop2.6.tgz到master的hadoop用户installer目录下

解压缩

[hadoop@master installer]$ tar -zxvf spark-2.0.0-bin-hadoop2.6.tgz

[hadoop@master installer]$ mv spark-2.0.0-bin-hadoop2.6 spark2

[hadoop@master installer]$ cd spark2/

[hadoop@master spark2]$ ls

bin  conf  data  examples  jars  LICENSE  licenses  NOTICE  python  R  README.md  RELEASE  sbin  yarn

[hadoop@master spark2]$ pwd

/home/hadoop/installer/spark2

[hadoop@master ~]$ vim .bashrc

# .bashrc

# Source global definitions

if [ -f /etc/bashrc ]; then

. /etc/bashrc

fi

# User specific aliases and functions

export JAVA_HOME=/usr/java/jdk1.7.0_79

export HADOOP_HOME=/home/hadoop/installer/hadoop2

export SCALA_HOME=/home/hadoop/installer/scala

export SPARK_HOME=/home/hadoop/installer/spark2

export HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_HOME}/lib/native

export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"

export CLASSPATH=$CLASSPATH:$HADOOP_HOME/lib:$JAVA_HOME/lib:$SCALA_HOME/lib

export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$SCALA_HOME/bin:$SPARK_HOME/bin:$SPARK_HOME/sbin

[hadoop@master ~]$ . .bashrc

[hadoop@master ~]$ scp .bashrc slave:~

.bashrc                                                                                            100%  621     0.6KB/s   00:00

在slave机器上执行

[hadoop@slave ~]$ . .bashrc

配置spark

[hadoop@master conf]$ cp spark-env.sh.template spark-env.sh

[hadoop@slave conf]$ vim spark-env.sh

#!/usr/bin/env bash

#

# 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.

#

export JAVA_HOME=/usr/java/jdk1.7.0_79

export SCALA_HOME=/home/hadoop/installer/scala

export SPARK_MASTER_HOST=master

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

export SPARK_EXECUTOR_MEMORY=600M

export SPARK_DRIVER_MEMORY=600M

[hadoop@slave conf]$ vim slaves

master

slave

[hadoop@master installer]$ scp -r spark2 slave:~/installer/

启动spark集群

[hadoop@master ~]$ start-master.sh

[hadoop@master ~]$ start-slaves.sh

[hadoop@master ~]$ jps

17769 ResourceManager

20192 Master

20275 Worker

17443 NameNode

20521 Jps

17631 SecondaryNameNode

[hadoop@slave ~]$ jps

13297 DataNode

15367 Worker

13408 NodeManager

16245 Jps

Spark wordcount

[hadoop@master ~]$ spark-shell

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel).

16/11/04 11:05:07 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

16/11/04 11:05:09 WARN spark.SparkContext: Use an existing SparkContext, some configuration may not take effect.

Spark context Web UI available at http://192.168.3.100:4040

Spark context available as 'sc' (master = local[*], app id = local-1478228709028).

Spark session available as 'spark'.

Welcome to

____              __

/ __/__  ___ _____/ /__

_\ \/ _ \/ _ `/ __/  '_/

/___/ .__/\_,_/_/ /_/\_\   version 2.0.0

/_/

Using Scala version 2.11.8 (Java HotSpot(TM) Client VM, Java 1.7.0_79)

Type in expressions to have them evaluated.

Type :help for more information.

scala> val file = sc.textFile("hdfs://master:9000/data/wordcount")

16/11/04 11:05:14 WARN util.SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes

file: org.apache.spark.rdd.RDD[String] = hdfs://master:9000/data/input/wordcount MapPartitionsRDD[1] at textFile at <console>:24

scala> val count=file.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)

count: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:26

scala> count.collect()

res0: Array[(String, Int)] = Array((package,1), (this,1), (Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version),1), (Because,1), (Python,2), (cluster.,1), (its,1), ([run,1), (general,2), (have,1), (pre-built,1), (YARN,,1), (locally,2), (changed,1), (locally.,1), (sc.parallelize(1,1), (only,1), (Configuration,1), (This,2), (basic,1), (first,1), (learning,,1), ([Eclipse](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-Eclipse),1), (documentation,3), (graph,1), (Hive,2), (several,1), (["Specifying,1), ("yarn",1), (page](http://spark.apache.org/documentation.html),1), ([params]`.,1), ([project,2), (prefer,1), (SparkPi,2), (<http://spark.apache.org/>,1), (engine,1), (version,1), (file,1), (documentation...

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