Hive的安装及使用方法

这篇文章主要介绍" Hive的安装及使用方法"，在日常操作中，相信很多人在 Hive的安装及使用方法问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答" Hive的安装及使用方法"的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

前言

Hive是Hadoop一个程序接口，Hive让数据分析人员快速上手，Hive使用了类SQL的语法，Hive让JAVA的世界变得简单而轻巧，Hive让Hadoop普及到了程序员以外的人。

从Hive开始，让分析师们也能玩转大数据。

目录

1. Hive的安装

2. Hive的基本使用:CRUD

3. Hive交互式模式

4. 数据导入

5. 数据导出

6. Hive查询HiveQL

7. Hive视图

8. Hive分区表

1. Hive的安装

系统环境
装好hadoop的环境后，我们可以把Hive装在namenode机器上(c1)。
hadoop的环境，请参考：让Hadoop跑在云端系列文章，RHadoop实践系列之一:Hadoop环境搭建

下载: hive-0.9.0.tar.gz
解压到： /home/cos/toolkit/hive-0.9.0

hive配置

~ cd /home/cos/toolkit/hive-0.9.0~ cp hive-default.xml.template hive-site.xml~ cp hive-log4j.properties.template hive-log4j.properties

修改hive-site.xml配置文件
把Hive的元数据存储到MySQL中

~ vi conf/hive-site.xmljavax.jdo.option.ConnectionURLjdbc:mysql://c1:3306/hive_metadata?createDatabaseIfNotExist=trueJDBC connect string for a JDBC metastorejavax.jdo.option.ConnectionDriverNamecom.mysql.jdbc.DriverDriver class name for a JDBC metastorejavax.jdo.option.ConnectionUserNamehiveusername to use against metastore databasejavax.jdo.option.ConnectionPasswordhivepassword to use against metastore databasehive.metastore.warehouse.dir/user/hive/warehouselocation of default database for the warehouse

修改hive-log4j.properties

#log4j.appender.EventCounter=org.apache.hadoop.metrics.jvm.EventCounterlog4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter

设置环境变量

~ sudo vi /etc/environmentPATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/home/cos/toolkit/ant184/bin:/home/cos/toolkit/jdk16/bin:/home/cos/toolkit/maven3/bin:/home/cos/toolkit/hadoop-1.0.3/bin:/home/cos/toolkit/hive-0.9.0/bin"JAVA_HOME=/home/cos/toolkit/jdk16ANT_HOME=/home/cos/toolkit/ant184MAVEN_HOME=/home/cos/toolkit/maven3HADOOP_HOME=/home/cos/toolkit/hadoop-1.0.3HIVE_HOME=/home/cos/toolkit/hive-0.9.0CLASSPATH=/home/cos/toolkit/jdk16/lib/dt.jar:/home/cos/toolkit/jdk16/lib/tools.jar

在hdfs上面，创建目录

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

在MySQL中创建数据库

create database hive_metadata;grant all on hive_metadata.* to hive@'%' identified by 'hive';grant all on hive_metadata.* to hive@localhost identified by 'hive';ALTER DATABASE hive_metadata CHARACTER SET latin1;

手动上传mysql的jdbc库到hive/lib

~ ls /home/cos/toolkit/hive-0.9.0/libmysql-connector-java-5.1.22-bin.jar

启动hive

#启动metastore服务~ bin/hive --service metastore &Starting Hive Metastore Server#启动hiveserver服务~ bin/hive --service hiveserver &Starting Hive Thrift Server#启动hive客户端~ bin/hive shellLogging initialized using configuration in file:/root/hive-0.9.0/conf/hive-log4j.propertiesHive history file=/tmp/root/hive_job_log_root_201211141845_1864939641.txthive> show tablesOK

查询MySQL数据库中的元数据

~ mysql -uroot -pmysql> use hive_metadata;Database changedmysql> show tables;+-------------------------+| Tables_in_hive_metadata |+-------------------------+| BUCKETING_COLS          || CDS                     || COLUMNS_V2              || DATABASE_PARAMS         || DBS                     || IDXS                    || INDEX_PARAMS            || PARTITIONS              || PARTITION_KEYS          || PARTITION_KEY_VALS      || PARTITION_PARAMS        || PART_COL_PRIVS          || PART_PRIVS              || SDS                     || SD_PARAMS               || SEQUENCE_TABLE          || SERDES                  || SERDE_PARAMS            || SORT_COLS               || TABLE_PARAMS            || TBLS                    || TBL_COL_PRIVS           || TBL_PRIVS               |+-------------------------+23 rows in set (0.00 sec)

Hive已经成功安装，下面是hive的使用攻略。

2. Hive的基本使用

1. 进入hive控制台

~ cd /home/cos/toolkit/hive-0.9.0~ bin/hive shellLogging initialized using configuration in file:/home/cos/toolkit/hive-0.9.0/conf/hive-log4j.propertiesHive history file=/tmp/cos/hive_job_log_cos_201307160003_95040367.txthive>

新建表

#创建数据(文本以tab分隔)~ vi /home/cos/demo/t_hive.txt16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34#创建新表hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';OKTime taken: 0.489 seconds#导入数据t_hive.txt到t_hive表hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;Copying data from file:/home/cos/demo/t_hive.txtCopying file: file:/home/cos/demo/t_hive.txtLoading data to table default.t_hiveDeleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hiveOKTime taken: 0.397 seconds

查看表和数据

#查看表 hive> show tables;OKt_hiveTime taken: 0.099 seconds#正则匹配表名hive>show tables '*t*';OKt_hiveTime taken: 0.065 seconds#查看表数据hive> select * from t_hive;OK16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34Time taken: 0.264 seconds#查看表结构hive> desc t_hive;OKa       intb       intc       intTime taken: 0.1 seconds

修改表

#增加一个字段hive> ALTER TABLE t_hive ADD COLUMNS (new_col String);OKTime taken: 0.186 secondshive> desc t_hive;OKa       intb       intc       intnew_col stringTime taken: 0.086 seconds#重命令表名~ ALTER TABLE t_hive RENAME TO t_hadoop;OKTime taken: 0.45 secondshive> show tables;OKt_hadoopTime taken: 0.07 seconds

删除表

hive> DROP TABLE t_hadoop;OKTime taken: 0.767 secondshive> show tables;OKTime taken: 0.064 seconds

3. Hive交互式模式

• quit,exit: 退出交互式shell

• reset: 重置配置为默认值

• set = : 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

• ! :

• dfs : 从Hive shell执行一个dfs命令

• : 执行一个Hive 查询，然后输出结果到标准输出

• source FILE : 在CLI里执行一个hive脚本文件

4. 数据导入

还以刚才的t_hive为例。

#创建表结构hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

从操作本地文件系统加载数据(LOCAL)

hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;Copying data from file:/home/cos/demo/t_hive.txtCopying file: file:/home/cos/demo/t_hive.txtLoading data to table default.t_hiveDeleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hiveOKTime taken: 0.612 seconds#在HDFS中查找刚刚导入的数据~ hadoop fs -cat /user/hive/warehouse/t_hive/t_hive.txt16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34

从HDFS加载数据

创建表t_hive2hive> CREATE TABLE t_hive2 (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';#从HDFS加载数据hive> LOAD DATA INPATH '/user/hive/warehouse/t_hive/t_hive.txt' OVERWRITE INTO TABLE t_hive2;Loading data to table default.t_hive2Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2OKTime taken: 0.325 seconds#查看数据hive> select * from t_hive2;OK16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34Time taken: 0.287 seconds

从其他表导入数据

hive> INSERT OVERWRITE TABLE t_hive2 SELECT * FROM t_hive ;Total MapReduce jobs = 2Launching Job 1 out of 2Number of reduce tasks is set to 0 since there's no reduce operatorStarting Job = job_201307131407_0002, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0002Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0002Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 02013-07-16 10:32:41,979 Stage-1 map = 0%,  reduce = 0%2013-07-16 10:32:48,034 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:49,050 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:50,068 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:51,082 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:52,093 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:53,102 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec2013-07-16 10:32:54,112 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.03 secMapReduce Total cumulative CPU time: 1 seconds 30 msecEnded Job = job_201307131407_0002Ended Job = -314818888, job is filtered out (removed at runtime).Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-32-31_323_5732404975764014154/-ext-10000Loading data to table default.t_hive2Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2Table default.t_hive2 stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]7 Rows loaded to t_hive2MapReduce Jobs Launched:Job 0: Map: 1   Cumulative CPU: 1.03 sec   HDFS Read: 273 HDFS Write: 56 SUCCESSTotal MapReduce CPU Time Spent: 1 seconds 30 msecOKTime taken: 23.227 secondshive> select * from t_hive2;OK16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34Time taken: 0.134 seconds

创建表并从其他表导入数据

#删除表hive> DROP TABLE t_hive;#创建表并从其他表导入数据hive> CREATE TABLE t_hive AS SELECT * FROM t_hive2 ;Total MapReduce jobs = 2Launching Job 1 out of 2Number of reduce tasks is set to 0 since there's no reduce operatorStarting Job = job_201307131407_0003, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0003Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0003Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 02013-07-16 10:36:48,612 Stage-1 map = 0%,  reduce = 0%2013-07-16 10:36:54,648 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec2013-07-16 10:36:55,657 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec2013-07-16 10:36:56,666 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec2013-07-16 10:36:57,673 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec2013-07-16 10:36:58,683 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec2013-07-16 10:36:59,691 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.13 secMapReduce Total cumulative CPU time: 1 seconds 130 msecEnded Job = job_201307131407_0003Ended Job = -670956236, job is filtered out (removed at runtime).Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10001Moving data to: hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hiveTable default.t_hive stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]7 Rows loaded to hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10000MapReduce Jobs Launched:Job 0: Map: 1   Cumulative CPU: 1.13 sec   HDFS Read: 272 HDFS Write: 56 SUCCESSTotal MapReduce CPU Time Spent: 1 seconds 130 msecOKTime taken: 20.13 secondshive> select * from t_hive;OK16      2       361      12      1341      2       3117      21      371      2       311       12      3411      2       34Time taken: 0.109 seconds

仅复制表结构不导数据

hive> CREATE TABLE t_hive3 LIKE t_hive;hive> select * from t_hive3;OKTime taken: 0.077 seconds

从MySQL数据库导入数据
我们将在介绍Sqoop时讲。

5. 数据导出

从HDFS复制到HDFS其他位置

~ hadoop fs -cp /user/hive/warehouse/t_hive /~ hadoop fs -ls /t_hiveFound 1 items-rw-r--r--   1 cos supergroup         56 2013-07-16 10:41 /t_hive/000000_0~ hadoop fs -cat /t_hive/000000_016236112134123117213712311123411234

通过Hive导出到本地文件系统

hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/t_hive' SELECT * FROM t_hive;Total MapReduce jobs = 1Launching Job 1 out of 1Number of reduce tasks is set to 0 since there's no reduce operatorStarting Job = job_201307131407_0005, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0005Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0005Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 02013-07-16 10:46:24,774 Stage-1 map = 0%,  reduce = 0%2013-07-16 10:46:30,823 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:31,833 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:32,844 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:33,856 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:34,865 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:35,873 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec2013-07-16 10:46:36,884 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 0.87 secMapReduce Total cumulative CPU time: 870 msecEnded Job = job_201307131407_0005Copying data to local directory /tmp/t_hiveCopying data to local directory /tmp/t_hive7 Rows loaded to /tmp/t_hiveMapReduce Jobs Launched:Job 0: Map: 1   Cumulative CPU: 0.87 sec   HDFS Read: 271 HDFS Write: 56 SUCCESSTotal MapReduce CPU Time Spent: 870 msecOKTime taken: 23.369 seconds#查看本地操作系统hive> ! cat /tmp/t_hive/000000_0;hive> 16236112134123117213712311123411234

6. Hive查询HiveQL

注：以下代码将去掉map,reduce的日志输出部分。

普通查询：排序，列别名，嵌套子查询

hive> FROM (    >   SELECT b,c as c2 FROM t_hive    > ) t    > SELECT t.b, t.c2    > WHERE b>2    > LIMIT 2;12      1321      3

连接查询：JOIN

hive> SELECT t1.a,t1.b,t2.a,t2.b    > FROM t_hive t1 JOIN t_hive2 t2 on t1.a=t2.a    > WHERE t1.c>10;1       12      1       1211      2       11      241      2       41      261      12      61      1271      2       71      2

聚合查询1：count, avg

hive> SELECT count(*), avg(a) FROM t_hive;7       31.142857142857142

聚合查询2：count, distinct

hive> SELECT count(DISTINCT b) FROM t_hive;3

聚合查询3：GROUP BY, HAVING

#GROUP BYhive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c16.0    2       356.0    2       6211.0    2       3461.0    12      131.0     12      3417.0    21      3#HAVINGhive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c HAVING sum(c)>3056.0    2       6211.0    2       341.0     12      34

7. Hive视图

Hive视图和数据库视图的概念是一样的，我们还以t_hive为例。

hive> CREATE VIEW v_hive AS SELECT a,b FROM t_hive where c>30;hive> select * from v_hive;41      271      21       1211      2

删除视图

hive> DROP VIEW IF EXISTS v_hive;OKTime taken: 0.495 seconds

8. Hive分区表

分区表是数据库的基本概念，但很多时候数据量不大，我们完全用不到分区表。Hive是一种OLAP数据仓库软件，涉及的数据量是非常大的，所以分区表在这个场景就显得非常重要！！

下面我们重新定义一个数据表结构：t_hft

创建数据

~ vi /home/cos/demo/t_hft_20130627.csv000001,092023,9.76000002,091947,8.99000004,092002,9.79000005,091514,2.2000001,092008,9.70000001,092059,9.45~ vi /home/cos/demo/t_hft_20130628.csv000001,092023,9.76000002,091947,8.99000004,092002,9.79000005,091514,2.2000001,092008,9.70000001,092059,9.45

创建数据表

DROP TABLE IF EXISTS t_hft;CREATE TABLE t_hft(SecurityID STRING,tradeTime STRING,PreClosePx DOUBLE) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

创建分区数据表
根据业务：按天和股票ID进行分区设计

DROP TABLE IF EXISTS t_hft;CREATE TABLE t_hft(SecurityID STRING,tradeTime STRING,PreClosePx DOUBLE) PARTITIONED BY (tradeDate INT)ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

导入数据

#20130627hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130627.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130627);Copying data from file:/home/cos/demo/t_hft_20130627.csvCopying file: file:/home/cos/demo/t_hft_20130627.csvLoading data to table default.t_hft partition (tradedate=20130627)#20130628hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130628.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130628);Copying data from file:/home/cos/demo/t_hft_20130628.csvCopying file: file:/home/cos/demo/t_hft_20130628.csvLoading data to table default.t_hft partition (tradedate=20130628)

查看分区表

hive> SHOW PARTITIONS t_hft;tradedate=20130627tradedate=20130628Time taken: 0.082 seconds

查询数据

hive> select * from t_hft where securityid='000001';000001  092023  9.76    20130627000001  092008  9.7     20130627000001  092059  9.45    20130627000001  092023  9.76    20130628000001  092008  9.7     20130628000001  092059  9.45    20130628hive> select * from t_hft where tradedate=20130627 and PreClosePx<9;000002  091947  8.99    20130627000005  091514  2.2     20130627

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