hive的常用语法

hive的常用语法：建表，导入数据

包括：内表，外表，分区表，追加数据

insert overwrite table tab_ip like..

partitioned by 分区，分区是作为一个字段可以直接查询

shell方式：hive -S -e 'select * from wk110 t_order';

set hive.cli.print.header=true;

CREATE TABLE page_view(viewTime INT, userid BIGINT,

     page_url STRING, referrer_url STRING,

     ip STRING COMMENT 'IP Address of the User')

 COMMENT 'This is the page view table'

 PARTITIOED BYN(dt STRING, country STRING)

 ROW FORMAT DELIMITED

   FIELDS TERMINATED BY '01'

STORED AS SEQUENCEFILE;   TEXTFILE

//sequencefile

create table tab_ip_seq(id int,name string,ip string,country string) 

    row format delimited

    fields terminated by ','

    stored as sequencefile;

insert overwrite table tab_ip_seq select * from tab_ext;

//create & load

create table tab_ip(id int,name string,ip string,country string) 

    row format delimited

    fields terminated by ','

    stored as textfile;

load data local inpath '/home/hadoop/ip.txt' into table tab_ext;

//external

CREATE EXTERNAL TABLE tab_ip_ext(id int, name string,

     ip STRING,

     country STRING)

 ROW FORMAT DELIMITED FIELDS TERMINATED BY ','

 STORED AS TEXTFILE

 LOCATION '/external/hive';

 

// CTAS  用于创建一些临时表存储中间结果

CREATE TABLE tab_ip_ctas

   AS

SELECT id new_id, name new_name, ip new_ip,country new_country

FROM tab_ip_ext

SORT BY new_id;

//insert from select   用于向临时表中追加中间结果数据

create table tab_ip_like like tab_ip;

insert overwrite table tab_ip_like

    select * from tab_ip;

//CLUSTER <--相对高级一点，你可以放在有精力的时候才去学习>

create table tab_ip_cluster(id int,name string,ip string,country string)

clustered by(id) into 3 buckets;

load data local inpath '/home/hadoop/ip.txt' overwrite into table tab_ip_cluster;

set hive.enforce.bucketing=true;

insert into table tab_ip_cluster select * from tab_ip;

select * from tab_ip_cluster tablesample(bucket 2 out of 3 on id); 

//PARTITION

create table tab_ip_part(id int,name string,ip string,country string) 

    partitioned by (part_flag string)

    row format delimited fields terminated by ',';

    

load data local inpath '/home/hadoop/ip.txt' overwrite into table tab_ip_part

     partition(part_flag='part1');

    

    

load data local inpath '/home/hadoop/ip_part2.txt' overwrite into table tab_ip_part

     partition(part_flag='part2');

select * from tab_ip_part;

select * from tab_ip_part  where part_flag='part2';

select count(*) from tab_ip_part  where part_flag='part2';

alter table tab_ip change id id_alter string;

ALTER TABLE tab_cts ADD PARTITION (partCol = 'dt') location '/external/hive/dt';

show partitions tab_ip_part;

   

//write to hdfs

insert overwrite local directory '/home/hadoop/hivetemp/test.txt' select * from tab_ip_part where part_flag='part1';    

insert overwrite directory '/hiveout.txt' select * from tab_ip_part where part_flag='part1';

//array 

create table tab_array(a array<int>,b array<string>)

row format delimited

fields terminated by ' '

collection items terminated by ',';

示例数据

tobenbrone,laihama,woshishui     13866987898,13287654321

abc,iloveyou,itcast     13866987898,13287654321

select a[0] from tab_array;

select * from tab_array where array_contains(b,'word');

insert into table tab_array select array(0),array(name,ip) from tab_ext t; 

//map

create table tab_map(name string,info map<string,string>)

row format delimited

fields terminated by ' '

collection items terminated by ';'

map keys terminated by ':';

示例数据：

fengjie age:18;size:36A;addr:usa

furong    age:28;size:39C;addr:beijing;weight:180KG

load data local inpath '/home/hadoop/hivetemp/tab_map.txt' overwrite into table tab_map;

insert into table tab_map select name,map('name',name,'ip',ip) from tab_ext; 

//struct

create table tab_struct(name string,info struct<age:int,tel:string,addr:string>)

row format delimited

fields terminated by ' '

collection items terminated by ','

load data local inpath '/home/hadoop/hivetemp/tab_st.txt' overwrite into table tab_struct;

insert into table tab_struct select name,named_struct('age',id,'tel',name,'addr',country) from tab_ext;

//cli shell

hive -S -e 'select country,count(*) from tab_ext' > /home/hadoop/hivetemp/e.txt  

有了这种执行机制，就使得我们可以利用脚本语言（bash shell,python）进行hql语句的批量执行

select * from tab_ext sort by id desc limit 5;

select a.ip,b.book from tab_ext a join tab_ip_book b on(a.name=b.name);

//UDF(是在写hive jar时候必须继承的类，UDF只能实现一进一出的操作，如果需要实现多进一出，则需要实现UDAF)

select if(id=1,first,no-first),name from tab_ext;

hive>add jar /home/hadoop/myudf.jar;

hive>CREATE TEMPORARY FUNCTION my_lower AS 'org.dht.Lower';

select my_upper(name) from tab_ext;  

来自为知笔记(Wiz)