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;OK1 1 peter2 2 paulTime taken: 9.289 seconds, Fetched: 2 row(s)
这个h_employee不一定是一个表
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 '\054';
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 /bigintfloat / doublebooleanstring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/warehouseFound 11 itemsdrwxrwxrwt - root supergroup 0 2014-12-02 14:42 /user/hive/warehouse/h_employeedrwxrwxrwt - root supergroup 0 2014-12-02 14:42 /user/hive/warehouse/h_employee2drwxrwxrwt - wlsuser supergroup 0 2014-12-04 17:21 /user/hive/warehouse/h_employee_exportdrwxrwxrwt - root supergroup 0 2014-08-18 09:20 /user/hive/warehouse/h_http_access_logsdrwxrwxrwt - root supergroup 0 2014-06-30 10:15 /user/hive/warehouse/hbase_apache_access_logdrwxrwxrwt - username supergroup 0 2014-06-27 17:48 /user/hive/warehouse/hbase_table_1drwxrwxrwt - username supergroup 0 2014-06-30 09:21 /user/hive/warehouse/hbase_table_2drwxrwxrwt - username supergroup 0 2014-06-30 09:43 /user/hive/warehouse/hive_apache_accesslogdrwxrwxrwt - 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 '\054';
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， \054 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;OKh_employeeh_employee2h_employee_exporth_http_access_logshive_employeeworkersTime 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.csv1,jack2,terry3,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.csvCopying file: file:/home/alex/workers.csvLoading data to table default.workersTable default.workers stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 25, raw_data_size: 0]OKTime 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;OK1 jack2 terry3 michaelTime 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,peter5,kate6,ted
导入
hive> LOAD DATA LOCAL INPATH '/home/alex/workers2.txt' INTO TABLE workers;Copying data from file:/home/alex/workers2.txtCopying file: file:/home/alex/workers2.txtLoading data to table default.workersTable default.workers stats: [num_partitions: 0, num_files: 2, num_rows: 0, total_size: 46, raw_data_size: 0]OKTime 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;OK1 jack2 terry3 michael4 peter5 kate6 tedTime 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 '\054';
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 2014-05-0522,kitty33,lily# cat 2014-05-0614,sami45,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/2014-05-05Copying file: file:/home/alex/2014-05-05Loading 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]OKTime taken: 1.154 secondshive> LOAD DATA LOCAL INPATH '/home/alex/2014-05-06' INTO TABLE partition_employee partition(daytime='2014-05-06');Copying data from file:/home/alex/2014-05-06Copying file: file:/home/alex/2014-05-06Loading 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]OKTime 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';OK22 kitty 2014-05-0533 lily 2014-05-05Time 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';OK22 kitty 2014-05-0533 lily 2014-05-0514 sami 2014-05-0645 mick' 2014-05-06Time 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_employeeFound 2 itemsdrwxrwxrwt - root supergroup 0 2014-12-08 15:57 /user/hive/warehouse/partition_employee/daytime=2014-05-05drwxrwxrwt - 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 '\054';
查入一些数据
# cat 2014-09-09-CN 1,tammy2,eric# cat 2014-09-10-CN 3,paul4,jolly# cat 2014-09-10-EN 44,ivan66,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-CNCopying file: file:/home/alex/2014-09-09-CNLoading 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]OKTime taken: 0.736 secondshive> 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-CNCopying file: file:/home/alex/2014-09-10-CNLoading 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]OKTime taken: 0.691 secondshive> 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-ENCopying file: file:/home/alex/2014-09-10-ENLoading 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]OKTime taken: 0.622 seconds 看看存储结构
# hdfs dfs -ls /user/hive/warehouse/p_studentFound 2 itemsdrwxr-xr-x - root supergroup 0 2014-12-08 16:10 /user/hive/warehouse/p_student/daytime=2014-09-09drwxr-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=2014-09-09Found 1 itemsdrwxr-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;OK1 tammy 2014-09-09 CN2 eric 2014-09-09 CN3 paul 2014-09-10 CN4 jolly 2014-09-10 CN44 ivan 2014-09-10 EN66 billy 2014-09-10 ENTime taken: 0.228 seconds, Fetched: 6 row(s)
hive> select * from p_student where daytime='2014-09-10' and country='EN';OK44 ivan 2014-09-10 EN66 billy 2014-09-10 ENTime 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 BUCKETSrow format delimited fields TERMINATED BY '\054';
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;OK1 tammy 2014-09-09 CN2 eric 2014-09-09 CN3 paul 2014-09-10 CN4 jolly 2014-09-10 CN34 allen 2014-09-11 ENTime 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 = 1Launching Job 1 out of 1Number of reduce tasks is set to 0 since there's no reduce operatorStarting Job = job_1406097234796_0041, Tracking URL = http://hadoop01:8088/proxy/application_1406097234796_0041/Kill Command = /usr/lib/hadoop/bin/hadoop job -kill job_1406097234796_0041Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 02014-12-08 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 sec2014-12-08 17:36:07,845 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.9 secMapReduce Total cumulative CPU time: 2 seconds 900 msecEnded Job = job_1406097234796_0041MapReduce Jobs Launched: Job 0: Map: 1 Cumulative CPU: 2.9 sec HDFS Read: 482 HDFS Write: 22 SUCCESSTotal MapReduce CPU Time Spent: 2 seconds 900 msecOK4 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 employeehbase(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 > ;OK1 1 peterTime 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 ）