scala配置
1、下載下傳解壓包
tar -xvf scala-2.10.4.tgz -C /usr/local/
2、包重命名為scala
3、配置環境變量
export SCALA_HOME=/usr/local/scala
export PATH=$PATH:/usr/local/scala/bin
4、執行生效source /etc/profile
##驗證配置
scala -version 得到
Scala code runner version 2.10.4 -- Copyright 2002-2013, LAMP/EPFL
如果得到以上這句話,恭喜你,scala配置成功!
maven配置
tar -xvf apache-maven-3.3.9-bin.tar.gz -C /usr/local/
2、包重命名為maven
3、配置環境變量/etc/profile
export MAVEN_HOME=/usr/local/maven
export PATH=$PATH:/usr/local/maven/bin
export MAVEN_OPTS="-Xms256m -Xmx512m"
mvn -v 得到
Apache Maven 3.3.9 (bb52d8502b132ec0a5a3f4c09453c07478323dc5; 2015-11-11T00:41:47+08:00)
Maven home: /usr/local/maven
Java version: 1.7.0_55, vendor: Oracle Corporation
Java home: /usr/local/jdk/jre
Default locale: en_US, platform encoding: UTF-8
OS name: "linux", version: "2.6.32-642.el6.x86_64", arch: "i386", family: "unix"
安裝編譯spark
1、解壓源碼包:tar -zxvf spark-2.0.2-bin-hadoop2.7.tgz -C /usr/local/
cd /usr/local/
mv spark-2.0.2-bin-hadoop2.7 spark-2.0.2
source /etc/profile
2、複制配置模闆檔案
cd /usr/local/spark-2.0.2/conf
cp spark-env.sh.template spark-env.sh
cp slaves.template slaves
cp spark-defaults.conf.template spark-defaults.conf
主要配置JAVA_HOME、SCALA_HOME、HADOOP_HOME、HADOOP_CONF_DIR、SPARK_MASTER_IP等
vim spark-env.sh
export JAVA_HOME=/usr/local/jdk
export HADOOP_HOME=/usr/local/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SPARK_LAUNCH_WITH_SCALA=0
export SPARK_WORKER_MEMORY=1g
export SPARK_DRIVER_MEMORY=1g
export SPARK_MASTER_IP=192.168.1.114
export SPARK_LIBRARY_PATH=/usr/local/spark-2.0.2/lib
export SPARK_MASTER_WEBUI_PORT=18080
export SPARK_WORKER_DIR=/home/spark
export SPARK_MASTER_PORT=7077
export SPARK_WORKER_PORT=7078
export SPARK_LOG_DIR=/home/spark_log
export SPARK_PID_DIR='/home/spark/run'
slaves(将所有節點都加入,master節點同時也是worker節點)
spark-defaults.conf
spark.master yarn-client
spark.home /root/spark-without-hive
spark.eventLog.enabled true
spark.eventLog.dir hdfs://Goblin01:8020/spark-log
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.executor.memory 1g
spark.driver.memory 1g
spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
spark.master指定Spark運作模式,可以是yarn-client、yarn-cluster...
spark.home指定SPARK_HOME路徑
spark.eventLog.enabled需要設為true
spark.eventLog.dir指定路徑,放在master節點的hdfs中,端口要跟hdfs設定的端口一緻(預設為8020),否則會報錯
spark.executor.memory和spark.driver.memory指定executor和dirver的記憶體,512m或1g,既不能太大也不能太小,因為太小運作不了,太大又會影響其他服務
配置yar-site.xml,跟hdfs-site.xml在同一個路徑下($HADOOP_HOME/etc/hadoop)
ll /usr/local/hadoop/etc/hadoop/yarn-site.xml
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>haproxy:8030</value>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>haproxy:8035</value>
<name>yarn.resourcemanager.admin.address</name>
<value>mycat:8033</value>
<name>yarn.resourcemanager.webapp.address</name>
<value>mycat:8088</value>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
</configuration>
把spark-2.0.2複制到其他節點
啟動start-all.sh
7. 運作
1) 準備一個文本檔案放在/logs/wordcount.log内容為:
2) 運作spark-shell
本文轉自 DBAspace 51CTO部落格,原文連結:http://blog.51cto.com/dbaspace/1875951