Spark集群部署

一、简介

Spark是UC Berkeley AMPLab开发的类MapRed计算框架。MapRed框架适用于batch job，但是由于它自身的框架限制，第一，pull-based heartbeat作业调度。第二，shuffle中间结果全部落地disk，导致了高延迟，启动开销很大。
而Spark是为迭代式，交互式计算所生的。第一，它采用了actor model的akka作为通讯框架。第二，它使用了RDD分布式内存，操作之间的数据不需要dump到磁盘上，而是通过RDD Partition分布在各个节点内存中，极大的提高了数据间的流转，同时RDD之间维护了血统关系，一旦RDD fail掉了，能通过父RDD自动重建，保证了fault tolerance。
而且在Spark之上有丰富的应用，比如Shark，Spark Streaming，MLBase。我们在生产环境中已经使用Shark来作为Hive的一种补充，它共享了hive 的metastore，serde，使用方式也和hive几乎一样，如果data input size不是很大的情况下，相同语句确实比hive会快很多。

二、安装部署

1. 下载安装配置Scala

   [root@master ~]# wget https://downloads.lightbend.com/scala/2.12.2/scala-2.12.2.tgz
   [root@master spark]# tar xvf scala-2.12.2.tgz -C /usr/local/program/scala/
   #在etc/profile中增加环境变量SCALA_HOME，并使之生效：
   vim /etc/profile
   export SCALA_HOME=/usr/local/program/scala/scala-2.12.2
   export PATH=$PATH:$SCALA_HOME/bin
   [root@master spark]# . /etc/profile
   

2. 下载安装配置Spark

   #因为我现有的hadoop是2.7.1版本，故...
   [root@master spark]# wget https://d3kbcqa49mib13.cloudfront.net/spark-2.1.1-bin-hadoop2.7.tgz
   [root@master spark]# tar xvf spark-2.1.1-bin-hadoop2.7.tgz -C /usr/local/program/spark/
   #在etc/profile中增加环境变量SPARK_HOME，并使之生效：
   export SPARK_HOME=/usr/local/program/spark/spark-2.1.1-bin-hadoop2.7
   export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin
   [root@master spark]# . /etc/profile
   #在m1上配置Spark，修改spark-env.sh配置文件
   #进入spark的conf目录
   [root@master spark]# cd /usr/local/program/spark/spark-2.1.1-bin-hadoop2.7/conf/
   [root@master conf]# cp spark-env.sh.template spark-env.sh
   [root@master conf]# cat spark-env.sh
   export SCALA_HOME=/usr/local/program/scala/scala-2.12.2
   export HADOOP_HOME=/home/hadoop/hadoop-2.7.3
   export JAVA_HOME=/usr/lib/jvm/java-1.7.0-openjdk-1.7.0.79.x86_64
   export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop
   export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
   export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
   #export SPARK_JAR=/usr/local/program/spark/
   export SPARK_MASTER_IP=master
   #修改conf/slaves文件，将计算节点的主机名添加到该文件，一行一个
   # 这里应该包含master，将master也同时作为一个计算节点
   [root@master conf]# cat slaves
   slave01
   slave02
   slave03
   slave04
   

3. 配置ssh免密码登陆

4. 复制到集群节点

   [root@master conf]# scp /etc/profile slave01:/etc/
   [root@master conf]# scp -r /usr/local/program/spark/spark-2.1.1-bin-hadoop2.7 slave02:/usr/local/program/spark/
   [root@master conf]# scp -r /usr/local/program/scala/scala-2.12.2/ slave02:/usr/local/program/scala/
   

5. 启动master和slaves

   [root@master conf]# start-master.sh 
   [root@master conf]# start-slaves.sh 
   

6. 通过web端口访问spark

   http://master:8080
   

三、 运行简单的example

1. 单机运行

   #计算圆周率
   [root@master spark-2.1.1-bin-hadoop2.7]# ./bin/run-example SparkPi 10
   Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
   17/06/05 19:19:00 INFO SparkContext: Running Spark version 2.1.1
   17/06/05 19:19:00 WARN SparkContext: Support for Java 7 is deprecated as of Spark 2.0.0
   17/06/05 19:19:00 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
   17/06/05 19:19:00 INFO SecurityManager: Changing view acls to: root
   17/06/05 19:19:00 INFO SecurityManager: Changing modify acls to: root
   17/06/05 19:19:00 INFO SecurityManager: Changing view acls groups to: 
   17/06/05 19:19:00 INFO SecurityManager: Changing modify acls groups to: 
   17/06/05 19:19:00 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(root); groups with view permissions: Set(); users  with modify permissions: Set(root); groups with modify permissions: Set()
   ...
   17/06/05 19:19:02 INFO DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 0.761265 s
   Pi is roughly 3.143967143967144
   
   

2. spark-shell的简单使用

   [root@master spark-2.1.1-bin-hadoop2.7]# spark-shell
   scala> val s=sc.textFile("hdfs://master:9000/user/hadoop/test/Temperature.txt")
   s: org.apache.spark.rdd.RDD[String] = hdfs://master:9000/user/hadoop/test/Temperature.txt MapPartitionsRDD[3] at textFile at <console>:24
   
   scala> s.count
   res1: Long = 11
   [hadoop@slave02 ~]$ hdfs dfs -cat test/Temperature.txt
   2015,1,24
   2015,3,56
   2015,1,3
   2015,2,-43
   2015,4,5
   2015,3,46
   2014,2,64
   2015,1,4
   2015,1,21
   2015,2,35
   2015,2,0
   
   

3. 集群提交作业

   [hadoop@master ~]$ spark-submit --class org.apache.spark.examples.SparkPi --master yarn --deploy-mode cluster --driver-memory 4g --executor-memory 2g --executor-cores 1  /usr/local/program/spark/spark-2.1.1-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.1.1.jar 100
   17/06/06 16:03:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
   17/06/06 16:03:22 INFO client.RMProxy: Connecting to ResourceManager at master/10.10.18.229:8032
   17/06/06 16:03:22 INFO yarn.Client: Requesting a new application from cluster with 4 NodeManagers
   17/06/06 16:03:22 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
   ...
   17/06/06 16:04:33 INFO yarn.Client: Application report for application_1494595290830_0061 (state: RUNNING)
   17/06/06 16:04:34 INFO yarn.Client: Application report for application_1494595290830_0061 (state: RUNNING)
   17/06/06 16:04:35 INFO yarn.Client: Application report for application_1494595290830_0061 (state: RUNNING)
   17/06/06 16:04:36 INFO yarn.Client: Application report for application_1494595290830_0061 (state: FINISHED)
   17/06/06 16:04:36 INFO yarn.Client: 
        client token: N/A
        diagnostics: N/A
        ApplicationMaster host: 10.10.19.232
        ApplicationMaster RPC port: 0
        queue: default
        start time: 1496736259866
        final status: SUCCEEDED
        tracking URL: http://master:8088/proxy/application_1494595290830_0061/
        user: hadoop
   17/06/06 16:04:36 INFO util.ShutdownHookManager: Shutdown hook called
   17/06/06 16:04:36 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-6039cb14-8084-404e-b970-633dff4dd086