Hadoop2.2.0+Hive0.13+MySQL5.1集成安装

古城微笑少年丶 2022-04-13 11:25 119阅读 0赞

\[b\]\[color=olive\]\[size=large\]本次散仙安装的Hive是Hive最新版本中的稳定版本，是基于Hadoop2.2.0，以前有写过，如何在hadoop1.x下面安装Hive0.8，本次Hive的版本是Hive0.13，可以直接在Hive官网上下载二进制包，无须进行源码编译。Hive需要依赖底层的Hadoop环境，所以在安装Hive前，请确保你的hadoop集群环境已经可以正常工作。  
Hive0.13稳定版本的下载地址  
\[url\]http://apache.fayea.com/apache-mirror/hive/stable/\[/url\]  
关于Hadoop2.2.0分布式集群的搭建  
\[url\]http://qindongliang1922.iteye.com/blog/2078423\[/url\]  
MySQL的安装  
\[url\]http://qindongliang1922.iteye.com/blog/1987199\[/url\]  
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\[b\]\[color=green\]\[size=large\] 下载具体看下安装的步骤和过程:  
\[table\]  
|1|序号|描述  
|2|Hadoop2.2.0集群的搭建|底层依赖环境  
|3|下载Hive0.13的bin包，并解压|Hive包  
|4|配置HIVE\_HOME环境变量|环境变量所需  
|5|配置hive-env.sh|涉及hadoop的目录，和hive的conf目录  
|6|配置hive-site.xml|配置hive属性和集成MySQL存储元数据  
|7|启动bin/hive服务|测试启动hive  
|8|建库，建表,测试hive|测试hive是否正常工作  
|9|退出Hive客户端|执行命令exit  
|10|工程师一枚|开工  
|11|拷贝mysql的jdbc包到hive的lib目录下|元数据存储为MySQL  
|12|hadoop技术交流群|376932160  
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首先，先执行如下4个命令，把Hive自带的模板文件，变为Hive实际所需的文件：  
cp hive-default.xml.template hive-site.xml  
cp hive-env.sh.template hive-env.sh  
cp hive-exec-log4j.properties.template hive-exec-log4j.properties  
cp hive-log4j.properties.template hive-log4j.properties  
  
  
Hive环境变量的设置:  
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export PATH=.:$PATH
    
    export JAVA_HOME="/usr/local/jdk"
    export CLASSPATH=.:$CLASSPATH:$JAVA_HOME/lib:$JAVA_HOME/jre/lib
    export PATH=$PATH:$JAVA_HOME/bin
    
    export HADOOP_HOME=/home/search/hadoop
    export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop/
    export CLASSPATH=.:$CLASSPATH:$HADOOP_HOME/lib
    export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
    
    export ANT_HOME=/usr/local/ant
    export CLASSPATH=$CLASSPATH:$ANT_HOME/lib
    export PATH=$PATH:$ANT_HOME/bin
    
    export MAVEN_HOME="/usr/local/maven"
    export CLASSPATH=$CLASSPATH:$MAVEN_HOME/lib
    export PATH=$PATH:$MAVEN_HOME/bin
    
    
    export HIVE_HOME=/home/search/hive
    export CLASSPATH=$CLASSPATH:$HIVE_HOME/lib
    export PATH=$PATH:$HIVE_HOME/bin:$HIVE_HOME/conf

\[b\]\[color=green\]\[size=large\]下面是Hive-env.sh里面的内容：\[/size\]\[/color\]\[/b\]

# 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.
    
    # Set Hive and Hadoop environment variables here. These variables can be used
    # to control the execution of Hive. It should be used by admins to configure
    # the Hive installation (so that users do not have to set environment variables
    # or set command line parameters to get correct behavior).
    #
    # The hive service being invoked (CLI/HWI etc.) is available via the environment
    # variable SERVICE
    
    
    # Hive Client memory usage can be an issue if a large number of clients
    # are running at the same time. The flags below have been useful in 
    # reducing memory usage:
    #
    # if [ "$SERVICE" = "cli" ]; then
    #   if [ -z "$DEBUG" ]; then
    #     export HADOOP_OPTS="$HADOOP_OPTS -XX:NewRatio=12 -Xms10m -XX:MaxHeapFreeRatio=40 -XX:MinHeapFreeRatio=15 -XX:+UseParNewGC -XX:-UseGCOverheadLimit"
    #   else
    #     export HADOOP_OPTS="$HADOOP_OPTS -XX:NewRatio=12 -Xms10m -XX:MaxHeapFreeRatio=40 -XX:MinHeapFreeRatio=15 -XX:-UseGCOverheadLimit"
    #   fi
    # fi
    
    # The heap size of the jvm stared by hive shell script can be controlled via:
    #
    # export HADOOP_HEAPSIZE=1024
    #
    # Larger heap size may be required when running queries over large number of files or partitions. 
    # By default hive shell scripts use a heap size of 256 (MB).  Larger heap size would also be 
    # appropriate for hive server (hwi etc).
    
    
    # Set HADOOP_HOME to point to a specific hadoop install directory
     HADOOP_HOME=/home/search/hadoop
    
    # Hive Configuration Directory can be controlled by:
     export HIVE_CONF_DIR=/home/search/hive/conf
    
    # Folder containing extra ibraries required for hive compilation/execution can be controlled by:
    # export HIVE_AUX_JARS_PATH=

\[b\]\[color=olive\]\[size=large\]hive-site.xml里面的配置如下：\[/size\]\[/color\]\[/b\]

<configuration>
     <property>
    
      <name>javax.jdo.option.ConnectionURL</name>
      <value>jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true</value>
    </property>
    
    <property>
      <name>javax.jdo.option.ConnectionUserName</name>
      <value>root</value>
    </property>
    
    <property>
      <name>javax.jdo.option.ConnectionDriverName</name>
      <value>com.mysql.jdbc.Driver</value>
      <description>Driver class name for a JDBC metastore</description>
    </property>
    
    <property>
      <name>javax.jdo.option.ConnectionPassword</name>
      <value>qin</value>
    </property>
    
    <property>
      <name>hive.metastore.warehouse.dir</name>
      <value>hdfs://h1:9000//user/hive/warehouse</value>
    </property>
    
    <property>
      <name>hive.exec.scratchdir</name>
      <value>/tmp</value>
    </property>
    <property>
      <name>mapred.child.java.opts</name>
      <value>-Xmx4096m</value>
    </property>
    
    <property>
    <name>hive.querylog.location</name>
    <value>/home/search/hive/logs</value>
    </property>
    
    <property>
    <name>hive.metastore.local</name>
    <value>true</value>
    </property>
    </configuration>

\[b\]\[color=olive\]\[size=large\]在HDFS上，新建hive的数据存储目录，以及MapReduce执行过程，生成的临时文件目录，执行命令如下，并赋值权限:\[/size\]\[/color\]\[/b\]

hadoop fs -mkidr /tmp
    hadoop fs -mkidr /user/hive/warehouse
    hadoop fs -chmod g+w /tmp
    hadoop fs -chmod g+w /user/hive/warehouse

\[b\]\[color=green\]\[size=large\]启动hive：  
执行命令:bin/hive,启动信息如下：  
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[search@h1 hive]$ bin/hive
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.min.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.min.split.size.per.node is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize.per.node
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.input.dir.recursive is deprecated. Instead, use mapreduce.input.fileinputformat.input.dir.recursive
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.min.split.size.per.rack is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize.per.rack
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.max.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.maxsize
    14/07/30 04:18:08 INFO Configuration.deprecation: mapred.committer.job.setup.cleanup.needed is deprecated. Instead, use mapreduce.job.committer.setup.cleanup.needed
    14/07/30 04:18:09 WARN conf.HiveConf: DEPRECATED: Configuration property hive.metastore.local no longer has any effect. Make sure to provide a valid value for hive.metastore.uris if you are connecting to a remote metastore.
    
    Logging initialized using configuration in file:/home/search/hive/conf/hive-log4j.properties
    hive>

\[b\]\[color=olive\]\[size=large\]执行，建表命令，并导入数据：  
  
建表:  
create table mytt (name string ,count int) row format delimited fields terminated by '\#' stored as textfile ;  
导入数据:  
LOAD DATA LOCAL INPATH '/home/search/abc1.txt' OVERWRITE INTO TABLE info;  
执行查询命令，并降序输出：\[/size\]\[/color\]\[/b\]

Time taken: 0.837 seconds, Fetched: 5 row(s)
    hive> select * from info limit 5 order by count desc;
    FAILED: ParseException line 1:27 missing EOF at 'order' near '5'
    hive> select * from info   order by count desc  limit 5 ;
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks determined at compile time: 1
    In order to change the average load for a reducer (in bytes):
      set hive.exec.reducers.bytes.per.reducer=<number>
    In order to limit the maximum number of reducers:
      set hive.exec.reducers.max=<number>
    In order to set a constant number of reducers:
      set mapreduce.job.reduces=<number>
    Starting Job = job_1406660797211_0003, Tracking URL = http://h1:8088/proxy/application_1406660797211_0003/
    Kill Command = /home/search/hadoop/bin/hadoop job  -kill job_1406660797211_0003
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
    2014-07-30 04:26:13,538 Stage-1 map = 0%,  reduce = 0%
    2014-07-30 04:26:26,398 Stage-1 map = 67%,  reduce = 0%, Cumulative CPU 5.41 sec
    2014-07-30 04:26:27,461 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 6.64 sec
    2014-07-30 04:26:39,177 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 10.02 sec
    MapReduce Total cumulative CPU time: 10 seconds 20 msec
    Ended Job = job_1406660797211_0003
    MapReduce Jobs Launched: 
    Job 0: Map: 1  Reduce: 1   Cumulative CPU: 10.02 sec   HDFS Read: 143906707 HDFS Write: 85 SUCCESS
    Total MapReduce CPU Time Spent: 10 seconds 20 msec
    OK
    英的国  999999
    中的国  999997
    美的国  999996
    中的国  999993
    英的国  999992
    Time taken: 37.892 seconds, Fetched: 5 row(s)
    hive>

\[b\]\[color=green\]\[size=large\]hive shell一些交互式命令的使用方法:\[/size\]\[/color\]\[/b\]

quit,exit:  退出交互式shell
    reset: 重置配置为默认值
    set <key>=<value> : 修改特定变量的值(如果变量名拼写错误，不会报错)
    set :  输出用户覆盖的hive配置变量
    set -v : 输出所有Hadoop和Hive的配置变量
    add FILE[S] *, add JAR[S] *, add ARCHIVE[S] * : 添加 一个或多个 file, jar, archives到分布式缓存
    list FILE[S], list JAR[S], list ARCHIVE[S] : 输出已经添加到分布式缓存的资源。
    list FILE[S] *, list JAR[S] *,list ARCHIVE[S] * : 检查给定的资源是否添加到分布式缓存
    delete FILE[S] *,delete JAR[S] *,delete ARCHIVE[S] * : 从分布式缓存删除指定的资源
    ! <command> :  从Hive shell执行一个shell命令
    dfs <dfs command> :  从Hive shell执行一个dfs命令
    <query string> : 执行一个Hive 查询，然后输出结果到标准输出
    source FILE <filepath>:  在CLI里执行一个hive脚本文件

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以debug模式启动： hive -hiveconf hive.root.logger=DEBUG,console  
  
至此，我们的Hive，已经安装成功，并可以正常运行。  
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