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 '\001'
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 '\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
create table tab_map(name string,info map<string,string>)
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
create table tab_struct(name string,info struct<age:int,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;
//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;
