Hive內部函式簡介及查詢語法
1.Hive內建函式：
在Hive中 系統給我們內建了很多函式 具體參考官方地址
- 看下官網給我們的介紹:
SHOW FUNCTIONS; --檢視所有內建函式
DESCRIBE FUNCTION <function_name>; --檢視某個函式的描述
DESCRIBE FUNCTION EXTENDED <function_name>; --檢視某個函式的具體使用方法
hive> DESCRIBE FUNCTION case;
OK
CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f
Time taken: 0.006 seconds, Fetched: 1 row(s)
hive> DESCRIBE FUNCTION EXTENDED case;
OK
CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f
Example:
SELECT
CASE deptno
WHEN 1 THEN Engineering
WHEN 2 THEN Finance
ELSE admin
END,
CASE zone
WHEN 7 THEN Americas
ELSE Asia-Pac
END
FROM emp_details
Time taken: 0.008 seconds, Fetched: 13 row(s)
# DESCRIBE 可簡寫為desc
hive> desc FUNCTION EXTENDED case;
OK
CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f
Example:
SELECT
CASE deptno
WHEN 1 THEN Engineering
WHEN 2 THEN Finance
ELSE admin
END,
CASE zone
WHEN 7 THEN Americas
ELSE Asia-Pac
END
FROM emp_details
Time taken: 0.009 seconds, Fetched: 13 row(s)
下面我們瞭解下常用函式的使用方法：
# 為了方便測試 我們建立常用的dual表
hive> create table dual(x string);
OK
Time taken: 0.11 seconds
hive> insert into table dual values('');
Query ID = hadoop_20180702100505_f0566585-06b2-4c53-910a-b6a58791fc2d
Total jobs = 3
Launching Job 1 out of 3
...
OK
Time taken: 29.535 seconds
hive> select * from dual;
OK
Time taken: 0.147 seconds, Fetched: 1 row(s)
# 測試當前時間
hive> select current_date from dual;
OK
2018-07-02
Time taken: 0.111 seconds, Fetched: 1 row(s)
# 測試當前時間戳
hive> select current_timestamp from dual;
OK
2018-07-02 15:03:28.919
Time taken: 0.117 seconds, Fetched: 1 row(s)
# 測試substr函式 用於擷取字串
hive> desc function extended substr;
OK
substr(str, pos[, len]) - returns the substring of str that starts at pos and is of length len orsubstr(bin, pos[, len]) - returns the slice of byte array that starts at pos and is of length len
Synonyms: substring
pos is a 1-based index. If pos<0 the starting position is determined by counting backwards from the end of str.
Example:
> SELECT substr('Facebook', 5) FROM src LIMIT 1;
'book'
> SELECT substr('Facebook', -5) FROM src LIMIT 1;
'ebook'
> SELECT substr('Facebook', 5, 1) FROM src LIMIT 1;
'b'
Time taken: 0.016 seconds, Fetched: 10 row(s)
hive> SELECT substr('helloworld',-5) FROM dual;
OK
world
Time taken: 0.171 seconds, Fetched: 1 row(s)
hive> SELECT substr('helloworld',5) FROM dual;
OK
oworld
Time taken: 0.12 seconds, Fetched: 1 row(s)
hive> SELECT substr('helloworld',5,3) FROM dual;
OK
owo
Time taken: 0.142 seconds, Fetched: 1 row(s)
# 測試函式concat 用於將字元連線起來
hive> desc function extended concat_ws;
OK
concat_ws(separator, [string | array(string)]+) - returns the concatenation of the strings separated by the separator.
Example:
> SELECT concat_ws('.', 'www', array('facebook', 'com')) FROM src LIMIT 1;
'www.facebook.com'
Time taken: 0.019 seconds, Fetched: 4 row(s)
hive> select concat_ws(".","192","168","199","151") from dual;
OK
192.168.199.151
Time taken: 0.152 seconds, Fetched: 1 row(s)
# 測試函式split 用於拆分
hive> desc function extended split;
OK
split(str, regex) - Splits str around occurances that match regex
Example:
> SELECT split('oneAtwoBthreeC', '[ABC]') FROM src LIMIT 1;
["one", "two", "three"]
Time taken: 0.021 seconds, Fetched: 4 row(s)
hive> select split("192.168.199.151","\\.") from dual;
OK
["192","168","199","151"]
Time taken: 0.169 seconds, Fetched: 1 row(s)
2.Hive查詢語法：
- 簡單select語法：
# 簡單select語法
hive> select * from emp where deptno=10;
OK
7782 CLARK MANAGER 7839 1981-6-9 2450.0 NULL 10
7839 KING PRESIDENT NULL 1981-11-17 5000.0 NULL 10
7934 MILLER CLERK 7782 1982-1-23 1300.0 NULL 10
Time taken: 0.899 seconds, Fetched: 3 row(s)
hive> select * from emp where empno <= 7800;
OK
7369 SMITH CLERK 7902 1980-12-17 800.0 NULL 20
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30
7566 JONES MANAGER 7839 1981-4-2 2975.0 NULL 20
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30
7698 BLAKE MANAGER 7839 1981-5-1 2850.0 NULL 30
7782 CLARK MANAGER 7839 1981-6-9 2450.0 NULL 10
7788 SCOTT ANALYST 7566 1987-4-19 3000.0 NULL 20
Time taken: 0.277 seconds, Fetched: 8 row(s)
hive> select * from emp where salary between 1000 and 1500;
OK
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30
7844 TURNER SALESMAN 7698 1981-9-8 1500.0 0.0 30
7876 ADAMS CLERK 7788 1987-5-23 1100.0 NULL 20
7934 MILLER CLERK 7782 1982-1-23 1300.0 NULL 10
Time taken: 0.187 seconds, Fetched: 5 row(s)
hive> select * from emp limit 5;
OK
7369 SMITH CLERK 7902 1980-12-17 800.0 NULL 20
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30
7566 JONES MANAGER 7839 1981-4-2 2975.0 NULL 20
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30
Time taken: 0.154 seconds, Fetched: 5 row(s)
hive> select * from emp where empno in(7566,7499);
OK
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30
7566 JONES MANAGER 7839 1981-4-2 2975.0 NULL 20
Time taken: 0.153 seconds, Fetched: 2 row(s)
hive> select * from emp where comm is not null;
OK
7499 ALLEN SALESMAN 7698 1981-2-20 1600.0 300.0 30
7521 WARD SALESMAN 7698 1981-2-22 1250.0 500.0 30
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30
7844 TURNER SALESMAN 7698 1981-9-8 1500.0 0.0 30
Time taken: 0.291 seconds, Fetched: 4 row(s)
- 聚合函式及分組函式：
# 聚合函式及分組函式
# max/min/count/sum/avg 特點：多進一出，進來很多條記錄出去只有一條記錄
# 查詢部門編號為10的有多少條記錄
hive> select count(1) from emp where deptno=10;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
3
Time taken: 38.951 seconds, Fetched: 1 row(s)
# 求最大工資，最小工資，平均工資，工資的和
hive> select max(salary),min(salary),avg(salary),sum(salary) from emp;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
5000.0 800.0 2073.214285714286 29025.0
Time taken: 23.748 seconds, Fetched: 1 row(s)
# 分組函式 group by
# 求部門的平均工資
# 注：select中出現的欄位，如果沒有出現在組函式/聚合函式中，必須出現在group by裡面，否則就會產生報錯
hive> select deptno,avg(salary) from emp group by deptno;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
10 2916.6666666666665
20 2175.0
30 1566.6666666666667
Time taken: 36.502 seconds, Fetched: 3 row(s)
# 求每個部門(deptno)、工作崗位(job)的最高工資(salary)
hive> select deptno,job,max(salary) from emp group by deptno,job;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
10 CLERK 1300.0
10 MANAGER 2450.0
10 PRESIDENT 5000.0
20 ANALYST 3000.0
20 CLERK 1100.0
20 MANAGER 2975.0
30 CLERK 950.0
30 MANAGER 2850.0
30 SALESMAN 1600.0
Time taken: 36.096 seconds, Fetched: 9 row(s)
# 查詢平均工資大於2000的部門（使用having子句限定分組查詢）
hive> select deptno,avg(salary) from emp group by deptno having avg(salary) >2000;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
10 2916.6666666666665
20 2175.0
Time taken: 24.71 seconds, Fetched: 2 row(s)
# case when then end（不會跑mr）
hive> select ename, salary,
> case
> when salary > 1 and salary <= 1000 then 'LOWER'
> when salary > 1000 and salary <= 2000 then 'MIDDLE'
> when salary > 2000 and salary <= 4000 then 'HIGH'
> ELSE 'HIGHEST'
> end
> from emp;
OK
SMITH 800.0 LOWER
ALLEN 1600.0 MIDDLE
WARD 1250.0 MIDDLE
JONES 2975.0 HIGH
MARTIN 1250.0 MIDDLE
BLAKE 2850.0 HIGH
CLARK 2450.0 HIGH
SCOTT 3000.0 HIGH
KING 5000.0 HIGHEST
TURNER 1500.0 MIDDLE
ADAMS 1100.0 MIDDLE
JAMES 950.0 LOWER
FORD 3000.0 HIGH
MILLER 1300.0 MIDDLE
Time taken: 0.096 seconds, Fetched: 14 row(s)
- 多表join查詢：
# 建立測試表
hive> create table a(
> id int, name string
> ) row format delimited fields terminated by '\t';
OK
Time taken: 0.311 seconds
hive> create table b(
> id int, age int
> ) row format delimited fields terminated by '\t';
OK
Time taken: 0.142 seconds
# insert或load資料 最後表資料如下
hive> select * from a;
OK
1 zhangsan
2 lisi
3 wangwu
hive> select * from b;
OK
1 20
2 30
4 40
Time taken: 0.2 seconds, Fetched: 3 row(s)
# 內連線 inner join = join 僅列出表1和表2符合連線條件的資料
hive> select a.id,a.name,b.age from a join b on a.id=b.id;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
1 zhangsan 20
2 lisi 30
Time taken: 24.415 seconds, Fetched: 2 row(s)
# 左外連線（left join） 以左邊的為基準，左邊的資料全部資料全部出現，如果沒有出現就賦null值
hive> select a.id,a.name,b.age from a left join b on a.id=b.id;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
1 zhangsan 20
2 lisi 30
3 wangwu NULL
Time taken: 26.218 seconds, Fetched: 3 row(s)
# 右外連線（right join） 以右表為基準
hive> select a.id,a.name,b.age from a right join b on a.id=b.id;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
1 zhangsan 20
2 lisi 30
NULL NULL 40
Time taken: 24.027 seconds, Fetched: 3 row(s)
# 全連線（full join）相當於表1和表2的資料都顯示，如果沒有對應的資料，則顯示Null.
hive> select a.id,a.name,b.age from a full join b on a.id=b.id;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
1 zhangsan 20
2 lisi 30
3 wangwu NULL
NULL NULL 40
Time taken: 32.94 seconds, Fetched: 4 row(s)
# 笛卡爾積（cross join） 沒有連線條件 會針對表1和表2的每條資料做連線
hive> select a.id,a.name,b.age from a cross join b;
Warning: Map Join MAPJOIN[7][bigTable=a] in task 'Stage-3:MAPRED' is a cross product
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
1 zhangsan 20
1 zhangsan 30
1 zhangsan 40
2 lisi 20
2 lisi 30
2 lisi 40
3 wangwu 20
3 wangwu 30
3 wangwu 40
Time taken: 29.825 seconds, Fetched: 9 row(s)
3.利用Hive sql實現wordcount：
# 建立表 載入測試資料
hive> create table hive_wc(sentence string);
OK
Time taken: 0.149 seconds
[hadoop@hadoop000 ~]$ cat hive-wc.txt
hello,world,welcome
hello,welcome
hive> load data local inpath '/home/hadoop/hive-wc.txt' into table hive_wc;
Loading data to table default.hive_wc
Table default.hive_wc stats: [numFiles=1, totalSize=34]
OK
Time taken: 0.729 seconds
hive> select * from hive_wc;
OK
hello,world,welcome
hello,welcome
Time taken: 0.13 seconds, Fetched: 2 row(s)
# 獲取每個單詞 利用split分割
hive> select split(sentence,",") from hive_wc;
OK
["hello","world","welcome"]
["hello","welcome"]
Time taken: 0.163 seconds, Fetched: 2 row(s)
# explode把陣列轉成多行 結合split使用如下
hive> select explode(split(sentence,",")) from hive_wc;
OK
hello
world
welcome
hello
welcome
Time taken: 0.068 seconds, Fetched: 5 row(s)
# 做group by操作 一條語句即可實現wordcount統計
hive> select word, count(1) as c
> from (select explode(split(sentence,",")) as word from hive_wc) t
> group by word ;
Query ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce
Total jobs = 1
...
OK
hello 2
welcome 2
world 1
Time taken: 34.168 seconds, Fetched: 3 row(s)
相關文章
- Hive學習筆記 5 Hive的資料查詢、函式Hive筆記函式
- hive內建函式Hive函式
- 簡單的查詢語法
- 【函式】Oracle TRIM函式語法介紹函式Oracle
- MySQL入門系列：查詢簡介（三）之表示式和函式MySql函式
- ElasticSearch基礎及查詢語法Elasticsearch
- [Mysql 查詢語句]——集合函式MySql函式
- Hive常用函式及自定義函式Hive函式
- jvm 物件查詢語言（OQL）簡介JVM物件
- Hive函式（內建函式+自定義標準函式UDF）Hive函式
- Hive之Hive快捷查詢（避免Mapruduce查詢）Hive
- C語言Math函式庫簡介C語言函式
- 語法:OVER(PARTITION BY)及開窗函式函式
- 3GPP簡介及標準查詢指南
- 如何在函式內部定義函式？函式
- C語言函式手冊：c語言庫函式大全|C語言標準函式庫|c語言常用函式查詢C語言函式
- Hive的基本介紹以及常用函式Hive函式
- Elasticsearch查詢語句語法Elasticsearch
- 好程式設計師大資料學習路線hive內部函式程式設計師大資料Hive函式
- 函式式 JavaScript 簡介函式JavaScript
- 函式式API簡介函式API
- hive函式Hive函式
- JMeter36個內建函式及11個新增函式介紹JMeter函式
- Solr複雜查詢一：函式查詢Solr函式
- Tcl語法簡介
- MongoDB 常用查詢語法MongoDB
- php 函式簡介PHP函式
- SPF 記錄：原理、語法及配置方法簡介
- JavaScript 函式語法JavaScript函式
- 好程式設計師大資料培訓分享之hive常用內部函式程式設計師大資料Hive函式
- [Hive]Hive實現抽樣查詢Hive
- Hive -------- hive常見查詢練習Hive
- Python語法—函式及引數傳遞Python函式
- CPU簡介（2）內部結構簡析
- 簡單批處理內部命令簡介
- TiDB 查詢優化及調優系列（二）TiDB 查詢計劃簡介TiDB優化
- Hive函式大全Hive函式
- Hive FUNCTIONS函式HiveFunction函式