数据仓库 — 09_Hive的安装与配置（linux环境下Hive的安装、Hive集成Tez）大数据

文章目录

1 安装Hive2.3
2 Hive集成引擎Tez

2.1 安装Tez
2.2 集成Tez
2.3 测试
2.4 注意事项

2.4.1 集成tez后，插入数据失败
2.4.2 解决方法

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1 安装Hive2.3

上传apache-hive-2.3.0-bin.tar.gz 到/opt/software 目录下，并解压到/opt/module

tar -zxvf apache-hive-2.3.6-bin.tar.gz -C /opt/module/

修改apache-hive-2.3.6-bin 名称为hive

mv apache-hive-2.3.6-bin hive

将Mysql 的mysql-connector-java-5.1.27-bin.jar 拷贝到/opt/module/hive/lib/

cp /opt/software/mysql-libs/mysql-connector-java-5.1.27/mysql-connector-java-5.1.27-bin.jar /opt/module/hive/lib/

在/opt/module/hive/conf 路径上，创建hive-site.xml 文件

javax.jdo.option.ConnectionURL jdbc:mysql://hadoop102:3306/metastore?createDatabaseIfNotExist=true JDBC connect string for a JDBC metastore javax.jdo.option.ConnectionDriverName com.mysql.jdbc.Driver Driver class name for a JDBC metastore javax.jdo.option.ConnectionUserName root username to use against metastore database javax.jdo.option.ConnectionPassword root password to use against metastore database hive.metastore.warehouse.dir /user/hive/warehouse location of default database for the warehouse hive.cli.print.header true hive.cli.print.current.db true hive.metastore.schema.verification false datanucleus.schema.autoCreateAll true hive.execution.engine tez

启动hive(/opt/module/hive)

bin/hive

2 Hive集成引擎Tez

Tez 是一个Hive 的运行引擎，性能优于MR。

数据仓库 — 09_Hive的安装与配置（linux环境下Hive的安装、Hive集成Tez）

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用Hive 直接编写MR 程序，假设有四个有依赖关系的MR 作业，上图中，绿色是ReduceTask，云状表示写屏蔽，需要将中间结果持久化写到HDFS。
Tez 可以将多个有依赖的作业转换为一个作业，这样只需写一次HDFS，且中间节点较少，从而大大提升作业的计算性能。

2.1 安装Tez

拷贝apache-tez-0.9.1-bin.tar.gz 到hadoop102 的/opt/software 目录
将apache-tez-0.9.1-bin.tar.gz 上传到HDFS 的/tez 目录下。(方便集群节点共享)

hadoop fs -mkdir /tez

hadoop fs -put /opt/software/apache-tez-0.9.1-bin.tar.gz/ /tez

解压缩apache-tez-0.9.1-bin.tar.gz

tar -zxvf apache-tez-0.9.1-bin.tar.gz -C /opt/module

修改名称 (/opt/module)

mv apache-tez-0.9.1-bin/ tez-0.9.1

2.2 集成Tez

进入到Hive 的配置目录：/opt/module/hive/conf
在Hive 的/opt/module/hive/conf下面创建一个tez-site.xml文件

tez.lib.uris ${fs.defaultFS}/tez/apache-tez-0.9.1-bin.tar.gz tez.use.cluster.hadoop-libs true tez.history.logging.service.class org.apache.tez.dag.history.logging.ats.ATSHistoryLoggingService

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

mv hive-env.sh.template hive-env.sh

# 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 donefor jar in `ls $TEZ_HOME/lib`; do export TEZ_JARS=$TEZ_JARS:$TEZ_HOME/lib/$jar doneexport HIVE_AUX_JARS_PATH=/opt/module/hadoop-2.7.2/share/hadoop/common/hadoop-lzo-0.4.20.jar$TEZ_JARS

在hive-site.xml 文件中添加如下配置，更改hive 计算引擎(步骤1.4已经添加)

hive.execution.engine tez

2.3 测试

在/opt/module/hive目录下启动Hive

bin/hive

创建表

create table student( id int, name string);

插入数据(我在这一步报错，解决方法详见2.4注意事项)

insert into student values(1,"ruki");

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查询一下没有报错表示成功了

【数据仓库 — 09_Hive的安装与配置（linux环境下Hive的安装、Hive集成Tez）】

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2.4 注意事项 2.4.1 集成tez后，插入数据失败

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: -103 For more detailed output, check application tracking page:http://hadoop103:8088/cluster/app/application_15467811440 82_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.

2.4.2 解决方法