HIVE基本语法使用

set hive.cli.print.header=true;

创建一张表

CREATE TABLE page_view(viewTime INT, userid BIGINT,
     page_url STRING, referrer_url STRING,
     ip STRING COMMENT 'IP Address of the User')
     #添加描述
 COMMENT 'This is the page view table'
 #添加分区
 PARTITIONED BY(dt STRING, country STRING)
 #制定文件的分隔符
 ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
 #
STORED AS SEQUENCEFILE; 

TEXTFILE
以小文件形式储存

//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;

load数据，加载到hive的目录中去

//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;

加载数据，以链接的方式来创建

//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 是干嘛的，跟Oder by 有什么关系
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;

分桶

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分区

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把数据写到hdfs中

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 数组的使用

create table tab_array(a array<int>,b array)
row format delimited
fields terminated by '\t'
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的使用

create table tab_map(name string,info map)
row format delimited
fields terminated by '\t'
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结构体

create table tab_struct(name string,info structint,tel:string,addr:string>)
row format delimited
fields terminated by '\t'
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;

怎么在shell语句中执行，可以用于批处理

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自定义函数的使用

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;