【转载】【hive】——Hive sql语法详解
转载自:https://www.cnblogs.com/zhengrunjian/p/4545915.html
Hive 是基于Hadoop 构建的一套数据仓库分析系统,它提供了丰富的SQL查询方式来分析存储在Hadoop 分布式文件系统中的数据,可以将结构
化的数据文件映射为一张数据库表,并提供完整的SQL查询功能,可以将SQL语句转换为MapReduce任务进行运行,通过自己的SQL 去查询分析需
要的内容,这套SQL 简称Hive SQL,使不熟悉mapreduce 的用户很方便的利用SQL 语言查询,汇总,分析数据。而mapreduce开发人员可以把
己写的mapper 和reducer 作为插件来支持Hive 做更复杂的数据分析。
它与关系型数据库的SQL 略有不同,但支持了绝大多数的语句如DDL、DML 以及常见的聚合函数、连接查询、条件查询。HIVE不适合用于联机
online)事务处理,也不提供实时查询功能。它最适合应用在基于大量不可变数据的批处理作业。
HIVE的特点:可伸缩(在Hadoop的集群上动态的添加设备),可扩展,容错,输入格式的松散耦合。
Hive 的官方文档中对查询语言有了很详细的描述,请参考:http://wiki.apache.org/hadoop/Hive/LanguageManual ,本文的内容大部分翻译自该页面,期间加入了一些在使用过程中需要注意到的事项。
1. DDL 操作
建表:
[(col_name data_type [COMMENT col_comment], ...)]
[COMMENT table_comment]
[PARTITIONED BY (col_name data_type [COMMENT col_comment], ...)]
[CLUSTERED BY (col_name, col_name, ...)
[SORTED BY (col_name [ASC|DESC], ...)] INTO num_buckets BUCKETS]
[ROW FORMAT row_format]
[STORED AS file_format]
[LOCATION hdfs_path]
创建简单表:
hive> CREATE TABLE pokes (foo INT, bar STRING);
创建外部表:
建分区表
建Bucket表
创建表并创建索引字段ds
hive> CREATE TABLE invites (foo INT, bar STRING) PARTITIONED BY (ds STRING);
复制一个空表
例子
create table user_info (user_id int, cid string, ckid string, username string)
row format delimited
fields terminated by '\t'
lines terminated by '\n';
导入数据表的数据格式是:字段之间是tab键分割,行之间是断行。
及要我们的文件内容格式:
100636 100890 c5c86f4cddc15eb7 yyyvybtvt
100612 100865 97cc70d411c18b6f gyvcycy
100078 100087 ecd6026a15ffddf5 qa000100
显示所有表:
hive> SHOW TABLES;
按正条件(正则表达式)显示表,
hive> SHOW TABLES '.*s';
修改表结构
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;
增加、删除分区
重命名表
修改列的名字、类型、位置、注释:
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');
增加/更新列
增加表的元数据信息
改变表文件格式与组织
创建/删除视图
2. DML 操作:元数据存储
hive不支持用insert语句一条一条的进行插入操作,也不支持update操作。数据是以load的方式加载到建立好的表中。数据一旦导入就不可以修改。
向数据表内加载文件
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
例如:加载本地数据,同时给定分区信息:
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.
OVERWRITE
将查询结果插入Hive表
3. DQL 操作:数据查询SQL
3.1 基本的Select 操作
SELECT * FROM test SORT BY amount DESC LIMIT 5
例如
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)
3.2 基于Partition的查询
3.3 Join
table_reference JOIN table_factor [join_condition]
| table_reference {LEFT|RIGHT|FULL} [OUTER] JOIN table_reference join_condition
| table_reference LEFT SEMI JOIN table_reference join_condition
table_reference:
table_factor
| join_table
table_factor:
tbl_name [alias]
| table_subquery alias
| ( table_references )
join_condition:
ON equality_expression ( AND equality_expression )*
equality_expression:
expression = expression
ON (a.id = b.id AND a.department = b.department)
ON (a.key = b.key1) JOIN c ON (c.key = b.key2)
WHERE a.ds='2010-07-07' AND b.ds='2010-07-07‘
ON (c.key=d.key AND d.ds='2009-07-07' AND c.ds='2009-07-07')
FROM a
WHERE a.key in
(SELECT b.key
FROM B);
FROM a LEFT SEMI JOIN b on (a.key = b.key)
4. 从SQL到HiveQL应转变的习惯
4、Hive不支持将数据插入现有的表或分区中,
仅支持覆盖重写整个表,示例如下:
- INSERT OVERWRITE TABLE t1
- SELECT * FROM t2;
4、hive不支持INSERT INTO, UPDATE, DELETE操作
这样的话,就不要很复杂的锁机制来读写数据。
INSERT INTO syntax is only available starting in version 0.8。INSERT INTO就是在表或分区中追加数据。
5、hive支持嵌入mapreduce程序,来处理复杂的逻辑
如:
- FROM (
- MAP doctext USING 'python wc_mapper.py' AS (word, cnt)
- FROM docs
- CLUSTER BY word
- ) a
- REDUCE word, cnt USING 'python wc_reduce.py';
--doctext: 是输入
--word, cnt: 是map程序的输出
--CLUSTER BY: 将wordhash后,又作为reduce程序的输入
并且map程序、reduce程序可以单独使用,如:
- FROM (
- FROM session_table
- SELECT sessionid, tstamp, data
- DISTRIBUTE BY sessionid SORT BY tstamp
- ) a
- REDUCE sessionid, tstamp, data USING 'session_reducer.sh';
--DISTRIBUTE BY: 用于给reduce程序分配行数据
6、hive支持将转换后的数据直接写入不同的表,还能写入分区、hdfs和本地目录。
这样能免除多次扫描输入表的开销。
- FROM t1
- INSERT OVERWRITE TABLE t2
- SELECT t3.c2, count(1)
- FROM t3
- WHERE t3.c1 <= 20
- GROUP BY t3.c2
- INSERT OVERWRITE DIRECTORY '/output_dir'
- SELECT t3.c2, avg(t3.c1)
- FROM t3
- WHERE t3.c1 > 20 AND t3.c1 <= 30
- GROUP BY t3.c2
- INSERT OVERWRITE LOCAL DIRECTORY '/home/dir'
- SELECT t3.c2, sum(t3.c1)
- FROM t3
- WHERE t3.c1 > 30
- GROUP BY t3.c2;
5. 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;