Apache Hive-2.3.0 快速搭建与使用

2017/09/21 hive

Hive 简介

Hive 是一个基于 hadoop 的开源数据仓库工具,用于存储和处理海量结构化数据。它把海量数据存储于 hadoop 文件系统,而不是数据库,但提供了一套类数据库的数据存储和处理机制,并采用 HQL (类 SQL )语言对这些数据进行自动化管理和处理。我们可以把 Hive 中海量结构化数据看成一个个的表,而实际上这些数据是分布式存储在 HDFS 中的。 Hive 经过对语句进行解析和转换,最终生成一系列基于 hadoop 的 map/reduce 任务,通过执行这些任务完成数据处理。

Hive 诞生于 facebook 的日志分析需求,面对海量的结构化数据, Hive 以较低的成本完成了以往需要大规模数据库才能完成的任务,并且学习门槛相对较低,应用开发灵活而高效。

Hive 自 2009.4.29 发布第一个官方稳定版 0.3.0 至今,不过一年的时间,正在慢慢完善,网上能找到的相关资料相当少,尤其中文资料更少,本文结合业务对 Hive 的应用做了一些探索,并把这些经验做一个总结,所谓前车之鉴,希望读者能少走一些弯路。

准备工作

环境

JDK:1.8  
Hadoop Release:2.7.4  
centos:7.3  

node1(master)  主机: 192.168.252.121  
node2(slave1)  从机: 192.168.252.122  
node3(slave2)  从机: 192.168.252.123  

node4(mysql)   从机: 192.168.252.124  

依赖环境

安装Apache Hive前提是要先安装hadoop集群,并且hive只需要在hadoop的namenode节点集群里安装即可(需要在有的namenode上安装),可以不在datanode节点的机器上安装。还需要说明的是,虽然修改配置文件并不需要把hadoop运行起来,但是本文中用到了hadoop的hdfs命令,在执行这些命令时你必须确保hadoop是正在运行着的,而且启动hive的前提也需要hadoop在正常运行着,所以建议先把hadoop集群启动起来。

安装MySQL 用于存储 Hive 的元数据(也可以用 Hive 自带的嵌入式数据库 Derby,但是 Hive 的生产环境一般不用 Derby),这里只需要安装 MySQL 单机版即可,如果想保证高可用的化,也可以部署 MySQL 主从模式;

Hadoop

Hadoop-2.7.4 集群快速搭建

MySQL 随意任选其一

CentOs7.3 安装 MySQL 5.7.19 二进制版本

搭建 MySQL 5.7.19 主从复制,以及复制实现细节分析

安装

下载解压

su hadoop
cd /home/hadoop/
wget https://mirrors.tuna.tsinghua.edu.cn/apache/hive/hive-2.3.0/apache-hive-2.3.0-bin.tar.gz
tar -zxvf apache-hive-2.3.0-bin.tar.gz
mv apache-hive-2.3.0-bin hive-2.3.0

环境变量

如果是对所有的用户都生效就修改vi /etc/profile 文件 如果只针对当前用户生效就修改 vi ~/.bahsrc 文件

sudo vi /etc/profile
#hive
export PATH=${HIVE_HOME}/bin:$PATH
export HIVE_HOME=/home/hadoop/hive-2.3.0/

使环境变量生效,运行 source /etc/profile使/etc/profile文件生效

Hive 配置 Hadoop HDFS

复制 hive-site.xml

cd /home/hadoop/hive-2.3.0/conf
cp hive-default.xml.template hive-site.xml

新建 hdfs 目录

使用 hadoop 新建 hdfs 目录,因为在 hive-site.xml 中有默认如下配置:

<property>
    <name>hive.metastore.warehouse.dir</name>
    <value>/user/hive/warehouse</value>
    <description>location of default database for the warehouse</description>
  </property>
  <property>

进入 hadoop 安装目录 执行hadoop命令新建/user/hive/warehouse目录,并授权,用于存储文件

cd /home/hadoop/hadoop-2.7.4

bin/hadoop fs -mkdir -p /user/hive/warehouse  
bin/hadoop fs -mkdir -p /user/hive/tmp  
bin/hadoop fs -mkdir -p /user/hive/log  
bin/hadoop fs -chmod -R 777 /user/hive/warehouse  
bin/hadoop fs -chmod -R 777 /user/hive/tmp  
bin/hadoop fs -chmod -R 777 /user/hive/log  

用以下命令检查目录是否创建成功

bin/hadoop fs -ls /user/hive

修改 hive-site.xml

搜索hive.exec.scratchdir,将该name对应的value修改为/user/hive/tmp

<property>  
    <name>hive.exec.scratchdir</name>  
    <value>/user/hive/tmp</value>  
</property>  

搜索hive.querylog.location,将该name对应的value修改为/user/hive/log/hadoop

<property>
	<name>hive.querylog.location</name>
	<value>/user/hive/log/hadoop</value>
	<description>Location of Hive run time structured log file</description>
</property>

搜索javax.jdo.option.connectionURL,将该name对应的value修改为MySQL的地址

<property>
    <name>javax.jdo.option.ConnectionURL</name>
    <value>jdbc:mysql://192.168.252.124:3306/hive?createDatabaseIfNotExist=true</value>
    <description>
      JDBC connect string for a JDBC metastore.
      To use SSL to encrypt/authenticate the connection, provide database-specific SSL flag in the connection URL.
      For example, jdbc:postgresql://myhost/db?ssl=true for postgres database.
    </description>
  </property>

搜索javax.jdo.option.ConnectionDriverName,将该name对应的value修改为MySQL驱动类路径

<property>
	<name>javax.jdo.option.ConnectionDriverName</name>
	<value>com.mysql.jdbc.Driver</value>
	<description>Driver class name for a JDBC metastore</description>
</property>

搜索javax.jdo.option.ConnectionUserName,将对应的value修改为MySQL数据库登录名

<property>
	<name>javax.jdo.option.ConnectionUserName</name>
	<value>root</value>
	<description>Username to use against metastore database</description>
</property>

搜索javax.jdo.option.ConnectionPassword,将对应的value修改为MySQL数据库的登录密码

<property>
	<name>javax.jdo.option.ConnectionPassword</name>
	<value>mima</value>
	<description>password to use against metastore database</description>
</property>

创建 tmp 文件

mkdir /home/hadoop/hive-2.3.0/tmp

并在 hive-site.xml 中修改

{system:java.io.tmpdir} 改成 /home/hadoop/hive-2.3.0/tmp

{system:user.name} 改成 {user.name}

新建 hive-env.sh

cp hive-env.sh.template hive-env.sh

vi hive-env.sh

HADOOP_HOME=/home/hadoop/hadoop-2.7.4/
export HIVE_CONF_DIR=/home/hadoop/hive-2.3.0/conf
export HIVE_AUX_JARS_PATH=/home/hadoop/hive-2.3.0/lib

下载 mysql 驱动包

cd /home/hadoop/hive-2.3.0/lib

wget http://maven.aliyun.com/nexus/service/local/repositories/hongkong-nexus/content/Mysql/mysql-connector-java/5.1.38/mysql-connector-java-5.1.38.jar

初始化 mysql

MySQL数据库进行初始化

首先确保 mysql 中已经创建 hive

cd /home/hadoop/hive-2.3.0/bin
./schematool -initSchema -dbType mysql

如果看到如下,表示初始化成功

Starting metastore schema initialization to 2.3.0
Initialization script hive-schema-2.3.0.mysql.sql
Initialization script completed
schemaTool completed

查看 mysql 数据库

/usr/local/mysql/bin/mysql -uroot -p
mysql> show databases;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| hive               |
| mysql              |
| performance_schema |
| sys                |
+--------------------+
5 rows in set (0.00 sec)
mysql> use hive;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Database changed
mysql> show tables;
+---------------------------+
| Tables_in_hive            |
+---------------------------+
| AUX_TABLE                 |
| BUCKETING_COLS            |
| CDS                       |
| COLUMNS_V2                |
| COMPACTION_QUEUE          |
| COMPLETED_COMPACTIONS     |
| COMPLETED_TXN_COMPONENTS  |
| DATABASE_PARAMS           |
| DBS                       |
| DB_PRIVS                  |
| DELEGATION_TOKENS         |
| FUNCS                     |
| FUNC_RU                   |
| GLOBAL_PRIVS              |
| HIVE_LOCKS                |
| IDXS                      |
| INDEX_PARAMS              |
| KEY_CONSTRAINTS           |
| MASTER_KEYS               |
| NEXT_COMPACTION_QUEUE_ID  |
| NEXT_LOCK_ID              |
| NEXT_TXN_ID               |
| NOTIFICATION_LOG          |
| NOTIFICATION_SEQUENCE     |
| NUCLEUS_TABLES            |
| PARTITIONS                |
| PARTITION_EVENTS          |
| PARTITION_KEYS            |
| PARTITION_KEY_VALS        |
| PARTITION_PARAMS          |
| PART_COL_PRIVS            |
| PART_COL_STATS            |
| PART_PRIVS                |
| ROLES                     |
| ROLE_MAP                  |
| SDS                       |
| SD_PARAMS                 |
| SEQUENCE_TABLE            |
| SERDES                    |
| SERDE_PARAMS              |
| SKEWED_COL_NAMES          |
| SKEWED_COL_VALUE_LOC_MAP  |
| SKEWED_STRING_LIST        |
| SKEWED_STRING_LIST_VALUES |
| SKEWED_VALUES             |
| SORT_COLS                 |
| TABLE_PARAMS              |
| TAB_COL_STATS             |
| TBLS                      |
| TBL_COL_PRIVS             |
| TBL_PRIVS                 |
| TXNS                      |
| TXN_COMPONENTS            |
| TYPES                     |
| TYPE_FIELDS               |
| VERSION                   |
| WRITE_SET                 |
+---------------------------+
57 rows in set (0.00 sec)

启动 Hive

简单测试

启动Hive

cd /home/hadoop/hive-2.3.0/bin

./hive

创建 hive 库

hive>  create database ymq;
OK
Time taken: 0.742 seconds

选择库

hive> use ymq;
OK
Time taken: 0.036 seconds

创建表

hive> create table test (mykey string,myval string);
OK
Time taken: 0.569 seconds

插入数据

hive> insert into test values("1","www.ymq.io");

WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = hadoop_20170922011126_abadfa44-8ebe-4ffc-9615-4241707b3c03
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1506006892375_0001, Tracking URL = http://node1:8088/proxy/application_1506006892375_0001/
Kill Command = /home/hadoop/hadoop-2.7.4//bin/hadoop job  -kill job_1506006892375_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2017-09-22 01:12:12,763 Stage-1 map = 0%,  reduce = 0%
2017-09-22 01:12:20,751 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.24 sec
MapReduce Total cumulative CPU time: 1 seconds 240 msec
Ended Job = job_1506006892375_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to directory hdfs://node1:9000/user/hive/warehouse/ymq.db/test/.hive-staging_hive_2017-09-22_01-11-26_242_8022847052615616955-1/-ext-10000
Loading data to table ymq.test
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1   Cumulative CPU: 1.24 sec   HDFS Read: 4056 HDFS Write: 77 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 240 msec
OK
Time taken: 56.642 seconds

查询数据

hive> select * from test;
OK
1	www.ymq.io
Time taken: 0.253 seconds, Fetched: 1 row(s)

页面数据

在界面上查看刚刚写入的hdfs数据

Contact

  • 作者:鹏磊
  • 出处:http://www.ymq.io
  • Email:admin@souyunku.com
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