Hive入门教程

Hive 安装

相比起很多教程先介绍概念，我喜欢先动手装上，然后用例子来介绍概念。我们先来安装一下Hive

先确认是否已经安装了对应的yum源，如果没有照这个教程里面写的安装cdh的yum源http://blog.csdn.net/nsrainbow/article/details/36629339

Hive是什么

Hive 提供了一个让大家可以使用sql去查询数据的途径。但是最好不要拿Hive进行实时的查询。因为Hive的实现原理是把sql语句转化为多个Map Reduce任务所以Hive非常慢，官方文档说Hive 适用于高延时性的场景而且很费资源。

举个简单的例子，可以像这样去查询

hive> select * from h_employee;
OK
1   1   peter
2   2   paul
Time taken: 9.289 seconds, Fetched: 2 row(s)

这个h_employee不一定是一个数据库表

metastore

Hive 中建立的表都叫metastore表。这些表并不真实的存储数据，而是定义真实数据跟hive之间的映射，就像传统数据库中表的meta信息，所以叫做metastore。实际存储的时候可以定义的存储模式有四种：

内部表（默认）分区表桶表外部表举个例子，这是一个简历内部表的语句

CREATE TABLE worker(id INT, name STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '54';

这个语句的意思是建立一个worker的内部表，内部表是默认的类型，所以不用写存储的模式。并且使用逗号作为分隔符存储

建表语句支持的类型

基本数据类型
tinyint / smalint / int /bigint
float / double
boolean
string

复杂数据类型
Array/Map/Struct

没有date /datetime

建完的表存在哪里呢？

在 /user/hive/warehouse 里面，可以通过hdfs来查看建完的表位置

$ hdfs dfs -ls /user/hive/warehouse
Found 11 items
drwxrwxrwt   - root     supergroup          0 2014-12-02 14:42 /user/hive/warehouse/h_employee
drwxrwxrwt   - root     supergroup          0 2014-12-02 14:42 /user/hive/warehouse/h_employee2
drwxrwxrwt   - wlsuser  supergroup          0 2014-12-04 17:21 /user/hive/warehouse/h_employee_export
drwxrwxrwt   - root     supergroup          0 2014-08-18 09:20 /user/hive/warehouse/h_http_access_logs
drwxrwxrwt   - root     supergroup          0 2014-06-30 10:15 /user/hive/warehouse/hbase_apache_access_log
drwxrwxrwt   - username supergroup          0 2014-06-27 17:48 /user/hive/warehouse/hbase_table_1
drwxrwxrwt   - username supergroup          0 2014-06-30 09:21 /user/hive/warehouse/hbase_table_2
drwxrwxrwt   - username supergroup          0 2014-06-30 09:43 /user/hive/warehouse/hive_apache_accesslog
drwxrwxrwt   - root     supergroup          0 2014-12-02 15:12 /user/hive/warehouse/hive_employee

一个文件夹对应一个metastore表

Hive 各种类型表使用

CREATE TABLE workers( id INT, name STRING)  
ROW FORMAT DELIMITED FIELDS TERMINATED BY '54';

通过这样的语句就建立了一个内部表叫 workers，并且分隔符是逗号， 54 是ASCII 码
我们可以通过 show tables; 来看看有多少表，其实hive的很多语句是模仿mysql的，当你们不知道语句的时候，把mysql的语句拿来基本可以用。除了limit比较怪，这个后面会说

hive> show tables;
OK
h_employee
h_employee2
h_employee_export
h_http_access_logs
hive_employee
workers
Time taken: 0.371 seconds, Fetched: 6 row(s)

　　建立完后，我们试着插入几条数据。这边要告诉大家Hive不支持单句插入的语句，必须批量，所以不要指望能用insert into workers values (1,'jack') 这样的语句插入数据。hive支持的插入数据的方式有两种：从文件读取数据从别的表读出数据插入(insert from select) 这里我采用从文件读数据进来。先建立一个叫 worker.csv的文件

$ cat workers.csv
1,jack
2,terry
3,michael

用LOAD DATA 导入到Hive的表中

hive> LOAD DATA LOCAL INPATH '/home/alex/workers.csv' INTO TABLE workers;
Copying data from file:/home/alex/workers.csv
Copying file: file:/home/alex/workers.csv
Loading data to table default.workers
Table default.workers stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 25, raw_data_size: 0]
OK
Time taken: 0.655 seconds

注意不要少了那个 LOCAL ， LOAD DATA LOCAL INPATH 跟 LOAD DATA INPATH 的区别是一个是从你本地磁盘上找源文件，一个是从hdfs上找文件如果加上OVERWRITE可以再导入之前先清空表，比如 LOAD DATA LOCAL INPATH '/home/alex/workers.csv' OVERWRITE INTO TABLE workers; 查询一下数据

hive> select * from workers;
OK
1   jack
2   terry
3   michael
Time taken: 0.177 seconds, Fetched: 3 row(s)

我们去看下导入后在hive内部表是怎么存的

# hdfs dfs -ls /user/hive/warehouse/workers/
Found 1 items
-rwxrwxrwt   2 root supergroup         25 2014-12-08 15:23 /user/hive/warehouse/workers/workers.csv

原来就是原封不动的把文件拷贝进去啊！就是这么土！我们可以试验再放一个文件 workers2.txt （我故意把扩展名换一个，其实hive是不看扩展名的）

# cat workers2.txt 
4,peter
5,kate
6,ted

导入

hive> LOAD DATA LOCAL INPATH '/home/alex/workers2.txt' INTO TABLE workers;
Copying data from file:/home/alex/workers2.txt
Copying file: file:/home/alex/workers2.txt
Loading data to table default.workers
Table default.workers stats: [num_partitions: 0, num_files: 2, num_rows: 0, total_size: 46, raw_data_size: 0]
OK
Time taken: 0.79 seconds

去看下文件的存储结构

# hdfs dfs -ls /user/hive/warehouse/workers/
Found 2 items
-rwxrwxrwt   2 root supergroup         25 2014-12-08 15:23 /user/hive/warehouse/workers/workers.csv
-rwxrwxrwt   2 root supergroup         21 2014-12-08 15:29 /user/hive/warehouse/workers/workers2.txt

多出来一个workers2.txt 再用sql查询下

hive> select * from workers;
OK
1   jack
2   terry
3   michael
4   peter
5   kate
6   ted
Time taken: 0.144 seconds, Fetched: 6 row(s)

分区表

分区表是用来加速查询的，比如你的数据非常多，但是你的应用场景是基于这些数据做日报表，那你就可以根据日进行分区，当你要做2014-05-05的报表的时候只需要加载2014-05-05这一天的数据就行了。我们来创建一个分区表来看下

create table partition_employee(id int, name string) 
partitioned by(daytime string) 
row format delimited fields TERMINATED BY '54';

可以看到分区的属性，并不是任何一个列我们先建立2个测试数据文件，分别对应两天的数据

# cat 2014-05-05
22,kitty
33,lily
# cat 2014-05-06
14,sami
45,micky

导入到分区表里面

hive> LOAD DATA LOCAL INPATH '/home/alex/2014-05-05' INTO TABLE partition_employee partition(daytime='2014-05-05');
Copying data from file:/home/alex/2014-05-05
Copying file: file:/home/alex/2014-05-05
Loading data to table default.partition_employee partition (daytime=2014-05-05)
Partition default.partition_employee{daytime=2014-05-05} stats: [num_files: 1, num_rows: 0, total_size: 21, raw_data_size: 0]
Table default.partition_employee stats: [num_partitions: 1, num_files: 1, num_rows: 0, total_size: 21, raw_data_size: 0]
OK
Time taken: 1.154 seconds
hive> LOAD DATA LOCAL INPATH '/home/alex/2014-05-06' INTO TABLE partition_employee partition(daytime='2014-05-06');
Copying data from file:/home/alex/2014-05-06
Copying file: file:/home/alex/2014-05-06
Loading data to table default.partition_employee partition (daytime=2014-05-06)
Partition default.partition_employee{daytime=2014-05-06} stats: [num_files: 1, num_rows: 0, total_size: 21, raw_data_size: 0]
Table default.partition_employee stats: [num_partitions: 2, num_files: 2, num_rows: 0, total_size: 42, raw_data_size: 0]
OK
Time taken: 0.763 seconds

导入的时候通过 partition 来指定分区。
查询的时候通过指定分区来查询

hive> select * from partition_employee where daytime='2014-05-05';
OK
22  kitty   2014-05-05
33  lily    2014-05-05
Time taken: 0.173 seconds, Fetched: 2 row(s)

我的查询语句并没有什么特别的语法，hive 会自动判断你的where语句中是否包含分区的字段。而且可以使用大于小于等运算符

hive> select * from partition_employee where daytime>='2014-05-05';
OK
22  kitty   2014-05-05
33  lily    2014-05-05
14  sami    2014-05-06
45  mick'   2014-05-06
Time taken: 0.273 seconds, Fetched: 4 row(s)

我们去看看存储的结构

# hdfs dfs -ls /user/hive/warehouse/partition_employee
Found 2 items
drwxrwxrwt   - root supergroup          0 2014-12-08 15:57 /user/hive/warehouse/partition_employee/daytime=2014-05-05
drwxrwxrwt   - root supergroup          0 2014-12-08 15:57 /user/hive/warehouse/partition_employee/daytime=2014-05-06

我们试试二维的分区表

create table p_student(id int, name string) 
partitioned by(daytime string,country string) 
row format delimited fields TERMINATED BY '54';

查入一些数据

# cat 2014-09-09-CN 
1,tammy
2,eric
# cat 2014-09-10-CN 
3,paul
4,jolly
# cat 2014-09-10-EN 
44,ivan
66,billy

导入hive

hive> LOAD DATA LOCAL INPATH '/home/alex/2014-09-09-CN' INTO TABLE p_student partition(daytime='2014-09-09',country='CN');
Copying data from file:/home/alex/2014-09-09-CN
Copying file: file:/home/alex/2014-09-09-CN
Loading data to table default.p_student partition (daytime=2014-09-09, country=CN)
Partition default.p_student{daytime=2014-09-09, country=CN} stats: [num_files: 1, num_rows: 0, total_size: 19, raw_data_size: 0]
Table default.p_student stats: [num_partitions: 1, num_files: 1, num_rows: 0, total_size: 19, raw_data_size: 0]
OK
Time taken: 0.736 seconds
hive> LOAD DATA LOCAL INPATH '/home/alex/2014-09-10-CN' INTO TABLE p_student partition(daytime='2014-09-10',country='CN');
Copying data from file:/home/alex/2014-09-10-CN
Copying file: file:/home/alex/2014-09-10-CN
Loading data to table default.p_student partition (daytime=2014-09-10, country=CN)
Partition default.p_student{daytime=2014-09-10, country=CN} stats: [num_files: 1, num_rows: 0, total_size: 19, raw_data_size: 0]
Table default.p_student stats: [num_partitions: 2, num_files: 2, num_rows: 0, total_size: 38, raw_data_size: 0]
OK
Time taken: 0.691 seconds
hive> LOAD DATA LOCAL INPATH '/home/alex/2014-09-10-EN' INTO TABLE p_student partition(daytime='2014-09-10',country='EN');
Copying data from file:/home/alex/2014-09-10-EN
Copying file: file:/home/alex/2014-09-10-EN
Loading data to table default.p_student partition (daytime=2014-09-10, country=EN)
Partition default.p_student{daytime=2014-09-10, country=EN} stats: [num_files: 1, num_rows: 0, total_size: 21, raw_data_size: 0]
Table default.p_student stats: [num_partitions: 3, num_files: 3, num_rows: 0, total_size: 59, raw_data_size: 0]
OK
Time taken: 0.622 seconds

看看存储结构

# hdfs dfs -ls /user/hive/warehouse/p_student
Found 2 items
drwxr-xr-x   - root supergroup          0 2014-12-08 16:10 /user/hive/warehouse/p_student/daytime=2014-09-09
drwxr-xr-x   - root supergroup          0 2014-12-08 16:10 /user/hive/warehouse/p_student/daytime=2014-09-10
# hdfs dfs -ls /user/hive/warehouse/p_student/daytime=2014-09-09
Found 1 items
drwxr-xr-x   - root supergroup          0 2014-12-08 16:10 /user/hive/warehouse/p_student/daytime=2014-09-09/country=CN

查询一下数据

hive> select * from p_student;
OK
1   tammy   2014-09-09  CN
2   eric    2014-09-09  CN
3   paul    2014-09-10  CN
4   jolly   2014-09-10  CN
44  ivan    2014-09-10  EN
66  billy   2014-09-10  EN
Time taken: 0.228 seconds, Fetched: 6 row(s)

hive> select * from p_student where daytime='2014-09-10' and country='EN';
OK
44  ivan    2014-09-10  EN
66  billy   2014-09-10  EN
Time taken: 0.224 seconds, Fetched: 2 row(s)

桶表

桶表是根据某个字段的hash值，来将数据扔到不同的“桶”里面。外国人有个习惯，就是分类东西的时候摆几个桶，上面贴不同的标签，所以他们取名的时候把这种表形象的取名为桶表。桶表表专门用于采样分析
下面这个例子是官网教程直接拷贝下来的，因为分区表跟桶表是可以同时使用的，所以这个例子中同时使用了分区跟桶两种特性

CREATE TABLE b_student(id INT, name STRING)
PARTITIONED BY(dt STRING, country STRING)
CLUSTERED BY(id) SORTED BY(name) INTO 4 BUCKETS
row format delimited 
    fields TERMINATED BY '54';

意思是根据userid来进行计算hash值，用viewTIme来排序存储做数据跟导入的过程我就不在赘述了，这是导入后的数据

hive> select * from b_student;
OK
1   tammy   2014-09-09  CN
2   eric    2014-09-09  CN
3   paul    2014-09-10  CN
4   jolly   2014-09-10  CN
34  allen   2014-09-11  EN
Time taken: 0.727 seconds, Fetched: 5 row(s)

从4个桶中采样抽取一个桶的数据

hive> select * from b_student tablesample(bucket 1 out of 4 on id);
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_1406097234796_0041, Tracking URL = http://hadoop01:8088/proxy/application_1406097234796_0041/
Kill Command = /usr/lib/hadoop/bin/hadoop job  -kill job_1406097234796_0041
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2014-12-08 17:35:56,995 Stage-1 map = 0%,  reduce = 0%
2014-12-08 17:36:06,783 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.9 sec
2014-12-08 17:36:07,845 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.9 sec
MapReduce Total cumulative CPU time: 2 seconds 900 msec
Ended Job = job_1406097234796_0041
MapReduce Jobs Launched: 
Job 0: Map: 1   Cumulative CPU: 2.9 sec   HDFS Read: 482 HDFS Write: 22 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 900 msec
OK
4   jolly   2014-09-10  CN

外部表

外部表就是存储不是由hive来存储的，比如可以依赖Hbase来存储，hive只是做一个映射而已。我用Hbase来举例
先建立一张Hbase表叫 employee

hbase(main):005:0> create 'employee','info' 
0 row(s) in 0.4740 seconds  
   
=> Hbase::Table - employee  
hbase(main):006:0> put 'employee',1,'info:id',1  
0 row(s) in 0.2080 seconds  
   
hbase(main):008:0> scan 'employee' 
ROW                                      COLUMN+CELL                                                                                                             
 1                                       column=info:id, timestamp=1417591291730, value=1                                                                        
1 row(s) in 0.0610 seconds  
   
hbase(main):009:0> put 'employee',1,'info:name','peter' 
0 row(s) in 0.0220 seconds  
   
hbase(main):010:0> scan 'employee' 
ROW                                      COLUMN+CELL                                                                                                             
 1                                       column=info:id, timestamp=1417591291730, value=1                                                                        
 1                                       column=info:name, timestamp=1417591321072, value=peter                                                                  
1 row(s) in 0.0450 seconds  
   
hbase(main):011:0> put 'employee',2,'info:id',2  
0 row(s) in 0.0370 seconds  
   
hbase(main):012:0> put 'employee',2,'info:name','paul' 
0 row(s) in 0.0180 seconds  
   
hbase(main):013:0> scan 'employee' 
ROW                                      COLUMN+CELL                                                                                                             
 1                                       column=info:id, timestamp=1417591291730, value=1                                                                        
 1                                       column=info:name, timestamp=1417591321072, value=peter                                                                  
 2                                       column=info:id, timestamp=1417591500179, value=2                                                                        
 2                                       column=info:name, timestamp=1417591512075, value=paul                                                                   
2 row(s) in 0.0440 seconds

建立外部表进行映射

hive> CREATE EXTERNAL TABLE h_employee(key int, id int, name string)   
    > STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' 
    > WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, info:id,info:name")  
    > TBLPROPERTIES ("hbase.table.name" = "employee");  
OK  
Time taken: 0.324 seconds  
hive> select * from h_employee;  
OK  
1   1   peter  
2   2   paul  
Time taken: 1.129 seconds, Fetched: 2 row(s)

查询语法

具体语法可以参考官方手册https://cwiki.apache.org/confluence/display/Hive/Tutorial 我只说几个比较奇怪的点

显示条数

展示x条数据，用的还是limit，比如

hive> select * from h_employee limit 1
    > ;
OK
1   1   peter
Time taken: 0.284 seconds, Fetched: 1 row(s)

但是不支持起点，比如offset

（转自：http://www.2cto.com/database/201412/359250.html ）