用Tez替换MR作为Hive的计算引擎

用Tez替换MR作为Hive的计算引擎

一、安装包准备
（1）下载tez的依赖包：http://tez.apache.org2）
（2）解压安装包apache-tez-0.9.1-bin.tar.gz

二、在Hive中配置Tez

（1）进入到Hive的配置目录：/opt/module/hive/conf（这是我的路径，根据自己的安装路径去找）

（2）在hive-env.sh文件中添加tez环境变量配置和依赖包环境变量配置
vim hive-env.sh添加如下配置

 # Set HADOOP_HOME to point to a specific hadoop install directory
 export HADOOP_HOME=/opt/module/hadoop-2.7.2 # Hive Configuration Directory can be controlled by:
 export HIVE_CONF_DIR=/opt/module/hive/conf
  # Folder containing extra libraries required for hive compilation/execution can be controlled by:
  export TEZ_HOME=/opt/module/tez-0.9.1    #是你的tez的解压目录
  export TEZ_JARS=""
  for jar in `ls $TEZ_HOME |grep jar`; 
  do    export TEZ_JARS=$TEZ_JARS:$TEZ_HOME/$jar
  done
  for jar in `ls $TEZ_HOME/lib`; 
  do    export TEZ_JARS=$TEZ_JARS:$TEZ_HOME/lib/$jar
  done
  export HIVE_AUX_JARS_PATH=/opt/module/hadoop-2.7.2/share/hadoop/common/hadoop-lzo-0.4.20.jar$TEZ_JARS

（3）在hive-site.xml文件中添加如下配置，更改hive计算引擎

    hive.execution.engine
     tez

三、 配置Tez

（1）在Hive的/opt/module/hive/conf（安装目录）下面创建一个tez-site.xml文件

[hadoop@hadoop102 conf]$ vim tez-site.xml

添加如下内容

	tez.lib.uris
	${fs.defaultFS}/tez/tez-0.9.1,${fs.defaultFS}/tez/tez-0.9.1/lib
 

	tez.lib.uris.classpath
	${fs.defaultFS}/tez/tez-0.9.1,${fs.defaultFS}/tez/tez-0.9.1/lib

     
	tez.use.cluster.hadoop-libs    
	 true    
	  tez.history.logging.service.class     
	  org.apache.tez.dag.history.logging.ats.ATSHistoryLoggingService

（2）上传Tez到集群

1）将/opt/module/tez-0.9.1上传到HDFS的/tez路径

[hadoop@hadoop102 conf]$ hadoop fs -mkdir /tez
[hadoop@hadoop102 conf]$ hadoop fs -put /opt/module/tez-0.9.1/ /tez
[hadoop@hadoop102 conf]$ hadoop fs -ls /tez/tez/tez-0.9.1

（3）测试
1）启动Hive
[hadoop@hadoop102 hive]$ bin/hive

2）创建LZO表
hive (default)> create table student(id int,name string);

3）向表中插入数据
hive (default)> insert into student values(1,“zhangsan”);

4）如果没有报错就表示成功了
hive (default)> select * from student;
1 zhangsan

（4）小结
1）运行Tez时检查到用过多内存而被NodeManager杀死进程问题：

Caused by: org.apache.tez.dag.api.SessionNotRunning: TezSession has already shutdown. Application application_1546781144082_0005 failed 2 times due to AM Container for appattempt_1546781144082_0005_000002 exited with exitCode: -103For more detailed output, check application tracking page:http://hadoop103:8088/cluster/app/application_1546781144082_0005Then, click on links to logs of each attempt.Diagnostics: Container [pid=11116,containerID=container_1546781144082_0005_02_000001] is running beyond virtual memory limits. Current usage: 216.3 MB of 1 GB physical memory used; 2.6 GB of 2.1 GB virtual memory used. Killing container.

这种问题是从机上运行的Container试图使用过多的内存，而被NodeManager kill掉了。

[摘录] The NodeManager is killing your container. It sounds like you are trying to use hadoop streaming which is running as a child process of the map-reduce task. The NodeManager monitors the entire process tree of the task and if it eats up more memory than the maximum set in mapreduce.map.memory.mb or mapreduce.reduce.memory.mb respectively, we would expect the Nodemanager to kill the task, otherwise your task is stealing memory belonging to other containers, which you don’t want.

解决方法：方案一：或者是关掉虚拟内存检查。
推荐选这个，修改yarn-site.xml

	yarn.nodemanager.vmem-check-enabled
	false

方案二：mapred-site.xml中设置Map和Reduce任务的内存配置如下：(value中实际配置的内存需要根据自己机器内存大小及应用情况进行修改)

　　mapreduce.map.memory.mb　　
　　1536

　　mapreduce.map.java.opts　　
　　-Xmx1024M

　
	　mapreduce.reduce.memory.mb　　
	　3072


　　mapreduce.reduce.java.opts　　
　　-Xmx2560M