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Hive的安装以及基本的shell操作以及基本模型的介绍

2016-01-16 20:39 471 查看
HIve是数据仓库,运行在HDFS之上。

1、HIve的基本数据模型

表,外部表,分区,桶

(1)表:表的元数据存储在关系型数据库中(MySQL),实际的数据存储在HDFS中。

当我们创建表的时候,会在MYSQl中创建元数据,在HDFS中创建Hive的根目录,所有的表都在这个目录在创建相应的文件夹。

当我们删除表的时候,元数据以及HDFS中的数据也会被删除。

(2)外部表:元数据创建在关系型数据库中,但是实际的数据,不会迁移到指定的hive目录中。在删除外部表的时候,只删除元数据,不删除实际的数据

(3)分区,就是在常见表的时候,根据一些其它的特性,对表中的一些数据建立分区,这个特性不是表中的字段。有利于加快查找的速度,但是这个分区查找的时候

需要人为的填写相应的值,因此需要借助etl工具。

(4)桶,就是根据表中的某一列进行hash的划分,根据key值将不同的数据分散到不同的桶中,也是有助于加快查找的速度。

以下是Hive的安装以及基本语法的介绍

Hive的安装

Hive的基本使用:CRUD

Hive交互式模式

数据导入

数据导出

Hive查询HiveQL

Hive视图

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.xml

<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://c1:3306/hive_metadata?createDatabaseIfNotExist=true</value>
<description>JDBC connect string for a JDBC metastore</description>
</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.ConnectionUserName</name>
<value>hive</value>
<description>username to use against metastore database</description>
</property>

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

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


修改hive-log4j.properties
#log4j.appender.EventCounter=org.apache.hadoop.metrics.jvm.EventCounter
log4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter


设置环境变量
~ sudo vi /etc/environment

PATH="/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/jdk16
ANT_HOME=/home/cos/toolkit/ant184
MAVEN_HOME=/home/cos/toolkit/maven3

HADOOP_HOME=/home/cos/toolkit/hadoop-1.0.3
HIVE_HOME=/home/cos/toolkit/hive-0.9.0

CLASSPATH=/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/lib
mysql-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 shell
Logging initialized using configuration in file:/root/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/root/hive_job_log_root_201211141845_1864939641.txt

hive> show tables
OK


查询MySQL数据库中的元数据
~ mysql -uroot -p
mysql> use hive_metadata;
Database changed

mysql> 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 shell
Logging initialized using configuration in file:/home/cos/toolkit/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/cos/hive_job_log_cos_201307160003_95040367.txt
hive>


新建表
#创建数据(文本以tab分隔)
~ vi /home/cos/demo/t_hive.txt

16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34

#创建新表
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
OK
Time 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.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time taken: 0.397 seconds


查看表和数据
#查看表
hive> show tables;
OK
t_hive
Time taken: 0.099 seconds

#正则匹配表名
hive>show tables '*t*';
OK
t_hive
Time taken: 0.065 seconds

#查看表数据
hive> select * from t_hive;
OK
16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34
Time taken: 0.264 seconds

#查看表结构
hive> desc t_hive;
OK
a       int
b       int
c       int
Time taken: 0.1 seconds


修改表
#增加一个字段
hive> ALTER TABLE t_hive ADD COLUMNS (new_col String);
OK
Time taken: 0.186 seconds
hive> desc t_hive;
OK
a       int
b       int
c       int
new_col string
Time taken: 0.086 seconds

#重命令表名
~ ALTER TABLE t_hive RENAME TO t_hadoop;
OK
Time taken: 0.45 seconds
hive> show tables;
OK
t_hadoop
Time taken: 0.07 seconds


删除表
hive> DROP TABLE t_hadoop;
OK
Time taken: 0.767 seconds

hive> show tables;
OK
Time 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 <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


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.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time 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.txt

16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34


从HDFS加载数据
创建表t_hive2
hive> 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_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
OK
Time taken: 0.325 seconds

#查看数据
hive> select * from t_hive2;
OK
16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34
Time 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 = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0002, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0002 Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 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 sec
2013-07-16 10:32:49,050 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:50,068 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:51,082 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:52,093 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:53,102 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:54,112 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.03 sec
MapReduce Total cumulative CPU time: 1 seconds 30 msec
Ended Job = job_201307131407_0002
Ended 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-10000
Loading data to table default.t_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
Table 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_hive2
MapReduce Jobs Launched:
Job 0: Map: 1   Cumulative CPU: 1.03 sec   HDFS Read: 273 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 30 msec
OK
Time taken: 23.227 seconds

hive> select * from t_hive2;
OK
16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34
Time 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 = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0003, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0003 Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 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 sec
2013-07-16 10:36:55,657 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:56,666 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:57,673 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:58,683 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:59,691 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.13 sec
MapReduce Total cumulative CPU time: 1 seconds 130 msec
Ended Job = job_201307131407_0003
Ended 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-10001
Moving data to: hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
Table 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-10000
MapReduce Jobs Launched:
Job 0: Map: 1   Cumulative CPU: 1.13 sec   HDFS Read: 272 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 130 msec
OK
Time taken: 20.13 seconds

hive> select * from t_hive;
OK
16      2       3
61      12      13
41      2       31
17      21      3
71      2       31
1       12      34
11      2       34
Time 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;
OK
Time 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_hive
Found 1 items
-rw-r--r--   1 cos supergroup         56 2013-07-16 10:41 /t_hive/000000_0

~ hadoop fs -cat /t_hive/000000_0
1623
611213
41231
17213
71231
11234
11234


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 = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0005, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0005 Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job  -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 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 sec
2013-07-16 10:46:31,833 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:32,844 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:33,856 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:34,865 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:35,873 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:36,884 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 0.87 sec
MapReduce Total cumulative CPU time: 870 msec
Ended Job = job_201307131407_0005
Copying data to local directory /tmp/t_hive
Copying data to local directory /tmp/t_hive
7 Rows loaded to /tmp/t_hive
MapReduce Jobs Launched:
Job 0: Map: 1   Cumulative CPU: 0.87 sec   HDFS Read: 271 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 870 msec
OK
Time taken: 23.369 seconds

#查看本地操作系统
hive> ! cat /tmp/t_hive/000000_0;
hive> 1623
611213
41231
17213
71231
11234
11234


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      13
21      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       12
11      2       11      2
41      2       41      2
61      12      61      12
71      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 BY
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c
16.0    2       3
56.0    2       62
11.0    2       34
61.0    12      13
1.0     12      34
17.0    21      3

#HAVING
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c HAVING sum(c)>30
56.0    2       62
11.0    2       34
1.0     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      2
71      2
1       12
11      2


删除视图
hive> DROP VIEW IF EXISTS v_hive;
OK
Time 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.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45

~ vi /home/cos/demo/t_hft_20130628.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45


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
#20130627
hive> 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.csv
Copying file: file:/home/cos/demo/t_hft_20130627.csv
Loading data to table default.t_hft partition (tradedate=20130627)

#20130628
hive> 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.csv
Copying file: file:/home/cos/demo/t_hft_20130628.csv
Loading data to table default.t_hft partition (tradedate=20130628)


查看分区表
hive> SHOW PARTITIONS t_hft;
tradedate=20130627
tradedate=20130628
Time taken: 0.082 seconds


查询数据
hive> select * from t_hft where securityid='000001';
000001  092023  9.76    20130627
000001  092008  9.7     20130627
000001  092059  9.45    20130627
000001  092023  9.76    20130628
000001  092008  9.7     20130628
000001  092059  9.45    20130628

hive> select * from t_hft where tradedate=20130627 and PreClosePx<9;
000002  091947  8.99    20130627
000005  091514  2.2     20130627
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