Hive的基本用法 | 易学教程

一、创建表

hive建表的时候默认的分割符是'\001'，若在建表的时候没有指明分隔符，load文件的时候文件的分隔符需要是'\001'；

若文件分隔符不是'001'，程序不会报错，但表查询的结果会全部为'null'；

1、建表的时候指定分隔符：

create table pokes(foo int,bar string) row format delimited fields terminated by '\t' lines terminated by '\n' stored as textfile;

load data local inpath '/root/pokes.txt' into table pokes;

2、替换分隔符

待导入的文件的分隔符与表的分隔符不一致，或者hive导出文件的分隔符需要替换：

hive建表的时候虽然可以指定分隔符，不过用insert overwrite local directory这种方式导出文件时，字段的分隔符会被默认

设置为\001，一般都需要将字段分隔符转换为其它字符，可以使用如下命令

sed -e 's/\x01/\t/g' file

二、DDL操作

创建表
hive> CREATE TABLE pokes (foo INT, bar STRING);
创建表并创建索引字段ds
hive> CREATE TABLE invites (foo INT, bar STRING) PARTITIONED BY (ds STRING);
显示所有表
hive> SHOW TABLES;
按正条件（正则表达式）显示表，
hive> SHOW TABLES '.*s';
表添加一列
hive> ALTER TABLE pokes ADD COLUMNS (new_col INT);
添加一列并增加列字段注释
hive> ALTER TABLE invites ADD COLUMNS (new_col2 INT COMMENT 'a comment');
更改表名
hive> ALTER TABLE events RENAME TO 3koobecaf;
删除列
hive> DROP TABLE pokes;
元数据存储
将文件中的数据加载到表中
hive> LOAD DATA LOCAL INPATH './examples/files/kv1.txt' OVERWRITE INTO TABLE pokes;
加载本地数据，同时给定分区信息
hive> LOAD DATA LOCAL INPATH './examples/files/kv2.txt' OVERWRITE INTO TABLE invites PARTITION (ds='2008-08-15');
加载DFS数据，同时给定分区信息
hive> LOAD DATA INPATH '/user/myname/kv2.txt' OVERWRITE INTO TABLE invites PARTITION (ds='2008-08-15');
The above command will load data from an HDFS file/directory to the table. Note that loading data from HDFS will result in moving the file/directory. As a result, the operation is almost instantaneous.
SQL 操作
按先件查询
hive> SELECT a.foo FROM invites a WHERE a.ds='<DATE>';
将查询数据输出至目录
hive> INSERT OVERWRITE DIRECTORY '/tmp/hdfs_out' SELECT a.* FROM invites a WHERE a.ds='<DATE>';
将查询结果输出至本地目录
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/local_out' SELECT a.* FROM pokes a;
选择所有列到本地目录
hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a;
hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a WHERE a.key < 100;
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/reg_3' SELECT a.* FROM events a;
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_4' select a.invites, a.pokes FROM profiles a;
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT COUNT(1) FROM invites a WHERE a.ds='<DATE>';
hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT a.foo, a.bar FROM invites a;
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/sum' SELECT SUM(a.pc) FROM pc1 a;
将一个表的统计结果插入另一个表中
hive> FROM invites a INSERT OVERWRITE TABLE events SELECT a.bar, count(1) WHERE a.foo > 0 GROUP BY a.bar;
hive> INSERT OVERWRITE TABLE events SELECT a.bar, count(1) FROM invites a WHERE a.foo > 0 GROUP BY a.bar;
JOIN
hive> FROM pokes t1 JOIN invites t2 ON (t1.bar = t2.bar) INSERT OVERWRITE TABLE events SELECT t1.bar, t1.foo, t2.foo;
将多表数据插入到同一表中
FROM src
INSERT OVERWRITE TABLE dest1 SELECT src.* WHERE src.key < 100
INSERT OVERWRITE TABLE dest2 SELECT src.key, src.value WHERE src.key >= 100 and src.key < 200
INSERT OVERWRITE TABLE dest3 PARTITION(ds='2008-04-08', hr='12') SELECT src.key WHERE src.key >= 200 and src.key < 300
INSERT OVERWRITE LOCAL DIRECTORY '/tmp/dest4.out' SELECT src.value WHERE src.key >= 300;
将文件流直接插入文件
hive> FROM invites a INSERT OVERWRITE TABLE events SELECT TRANSFORM(a.foo, a.bar) AS (oof, rab) USING '/bin/cat' WHERE a.ds > '2008-08-09';
This streams the data in the map phase through the script /bin/cat (like hadoop streaming). Similarly - streaming can be used on the reduce side (please see the Hive Tutorial or examples)

三、复杂类型的数据表，这里列之间以'\t'分割，数组元素之间以','分割

#数据文件内容如下

1 huangfengxiao beijing,shanghai,tianjin,hangzhou

2 linan changchu,chengdu,wuhan

hive> create table complex(name string,work_locations array)

> ROW FORMAT DELIMITED

> FIELDS TERMINATED BY '\t'

> COLLECTION ITEMS TERMINATED BY ',';

hive> describe complex;

name string

work_locations array

hive> LOAD DATA LOCAL INPATH '/home/hadoop/hfxdoc/complex.txt' OVERWRITE INTO TABLE complex

hive> select * from complex;

huangfengxiao ["beijing","shanghai","tianjin","hangzhou"]

linan ["changchu","chengdu","wuhan"]

Time taken: 0.125 seconds

hive> select name, work_locations[0] from complex;

MapReduce Total cumulative CPU time: 790 msec

Ended Job = job_201301211420_0012

MapReduce Jobs Launched:

Job 0: Map: 1 Cumulative CPU: 0.79 sec HDFS Read: 296 HDFS Write: 37 SUCCESS

Total MapReduce CPU Time Spent: 790 msec

huangfengxiao beijing

linan changchu

Time taken: 20.703 seconds

四、如何分区

表class(teacher sting,student string,age int)

Mis li huangfengxiao 20

Mis li lijie 21

Mis li dongdong 21

Mis li liqiang 21

Mis li hemeng 21

Mr xu dingding 19

Mr xu wangqiang 19

Mr xu lidong 19

Mr xu hexing 19

如果我们将这个班级成员的数据按teacher来分区

create table classmem(student string,age int) partitioned by(teacher string)

分区文件

classmem_Misli.txt

huangfengxiao 20

lijie 21

dongdong 21

liqiang 21

hemeng 21

classmem_MrXu.txt

dingding 19

wangqiang 19

lidong 19

hexing 19

LOAD DATA LOCAL INPATH '/home/hadoop/hfxdoc/classmem_Misli.txt' INTO TABLE classmem partition (teacher = 'Mis.li')

LOAD DATA LOCAL INPATH '/home/hadoop/hfxdoc/classmem_MrXu.txt' INTO TABLE classmem partition (teacher = 'Mis.Xu')

#分区列被默认到最后一列

hive> select * from classmem where teacher = 'Mr.Xu';

dingding 19 NULL Mr.Xu

wangqiang 19 NULL Mr.Xu

lidong 19 NULL Mr.Xu

hexing 19 NULL Mr.Xu

Time taken: 0.196 seconds

#直接从分区检索，加速；如果where子句的条件不是分区列，那么，这个sql将被编译成mapreduce程序，延时很大。

#所以，我们建立分区，是为了一些常用的筛选查询字段而用的。

五、桶的使用？更高效！可取样！主要用于大数据集的取样

桶的原理是对一个表（或者分区）进行切片，选择被切片的字段，设定桶的个数，用字段与个数的hash值进行入桶。

比如bucket.txt数据文件内容如下：

id name age

1 huang 11

2 li 11

3 xu 12

4 zhong 14

5 hu 15

6 liqiang 17

7 zhonghua 19

如果我们想将这个数据表切成3个桶，切片字段为id

那么用id字段hash后，3个桶的内容如下：

桶id hash 3 =0

3 xu 12

6 liqiang 17

桶id hash 3 =1

1 huang 11

4 zhong 14

7 zhonghua 19

桶id hash 3 =2

2 li 11

5 hu 15

这个过程的创建表语句如下：

create table bucketmem (id int,name string,age int) CLUSTERED BY (id) sorted by (id asc) into 3 buckets

ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

LOAD DATA LOCAL INPATH '/home/hadoop/hfxdoc/bucketmem.txt' INTO TABLE bucketmem;

select * from bucketmem tablesample(bucket 1 out of 4)

六、实际示例

创建一个表

CREATE TABLE u_data (

userid INT,

movieid INT,

rating INT,

unixtime STRING)

ROW FORMAT DELIMITED

FIELDS TERMINATED BY '\t'

STORED AS TEXTFILE;

下载示例数据文件，并解压缩

wget http://www.grouplens.org/system/files/ml-data.tar__0.gz

tar xvzf ml-data.tar__0.gz

加载数据到表中

LOAD DATA LOCAL INPATH 'ml-data/u.data'

OVERWRITE INTO TABLE u_data;

统计数据总量

SELECT COUNT(1) FROM u_data;

现在做一些复杂的数据分析

创建一个 weekday_mapper.py: 文件，作为数据按周进行分割

import sys

import datetime

for line in sys.stdin:

line = line.strip()

userid, movieid, rating, unixtime = line.split('\t')

生成数据的周信息

weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()

print '\t'.join([userid, movieid, rating, str(weekday)])

使用映射脚本

//创建表，按分割符分割行中的字段值

CREATE TABLE u_data_new (

userid INT,

movieid INT,

rating INT,

weekday INT)

ROW FORMAT DELIMITED

FIELDS TERMINATED BY '\t';

//将python文件加载到系统

add FILE weekday_mapper.py;

将数据按周进行分割

INSERT OVERWRITE TABLE u_data_new

SELECT

TRANSFORM (userid, movieid, rating, unixtime)

USING 'python weekday_mapper.py'

AS (userid, movieid, rating, weekday)

FROM u_data;

SELECT weekday, COUNT(1)

FROM u_data_new

GROUP BY weekday;

处理Apache Weblog 数据

将WEB日志先用正则表达式进行组合，再按需要的条件进行组合输入到表中

add jar ../build/contrib/hive_contrib.jar;

CREATE TABLE apachelog (

host STRING,

identity STRING,

user STRING,

time STRING,

request STRING,

status STRING,

size STRING,

referer STRING,

agent STRING)

ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.RegexSerDe'

WITH SERDEPROPERTIES (

"input.regex" = "([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?",

"output.format.string" = "%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s"

)

STORED AS TEXTFILE;

来源：https://www.cnblogs.com/jingyunyb/p/3401303.html

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