[big data bibiji 20210117] how to associate hive big table with small table

Big data is fun 2021-01-21 19:56:53
big data bibiji associate hive


stay Hive Tuning inside , We often encounter a very small table and a large table Join, How to optimize .

It's time to MAPJOIN.

principle

When a large table and one or more small tables do JOIN when , Best use MAPJOIN, The performance is better than ordinary JOIN Much faster . in addition ,MAPJOIN It can also solve the problem of data skew .MAPJOIN The basic principle of : In a small amount of data ,SQL All the small tables specified by the user will be loaded into the execution JOIN In the memory of the operating program , So as to speed up JOIN Execution speed of .

Examples :

select /*+MAPJOIN(b)*/ a.a1,a.a2,b.b2 from tablea a JOIN tableb b ON a.a1=b.b1

Cache multiple small tables :

select /*+MAPJOIN(b,c)*/ a.a1,a.a2,b.b2 from tablea a JOIN tableb b ON a.a1=b.b1 JOIN tbalec c on a.a1=c.c1

mapjoin Of join It happened in map Stage ,join Of join It happened in reduce Stage ,mapjoin Can improve efficiency .

Usage mode

stay Hive0.11 front , You have to use MAPJOIN To start the optimization operation visually , Because it needs to load the small table into memory, so pay attention to the size of the small table .

SELECT /*+ MAPJOIN(smalltable)*/ .key,value
FROM smalltable JOIN bigtable ON smalltable.key = bigtable.key

stay Hive0.11 after ,Hive The optimization is started by default , That is to say, it is not used when it needs to be displayed MAPJOIN Mark , It will trigger the optimization operation when necessary, which will be normal JOIN convert to MapJoin, You can set the trigger time of the optimization through the following two properties

hive.auto.convert.join

The default value is true, Automatic account opening MAPJOIN Optimize .

hive.mapjoin.smalltable.filesize

The default value is 2500000(25M), Determine the size of the table using the optimization by configuring this property , If the size of the table is less than this value, it will be loaded into memory .

Be careful : Use the default way to start the optimization. If the default name appears BUG( such as MAPJOIN It doesn't work ), Set the following two properties to fase Manual use MAPJOIN Tag to start the optimization .

hive.auto.convert.join=false( Turn off auto MAPJOIN Conversion operation )
hive.ignore.mapjoin.hint=false( Don't ignore MAPJOIN Mark )

Method 2 is not supported for the following queries (MAPJOIN Mark ) To start the optimized

select /*+MAPJOIN(smallTableTwo)*/ idOne, idTwo, value FROM
( select /*+MAPJOIN(smallTableOne)*/ idOne, idTwo, value FROM
bigTable JOIN smallTableOne on (bigTable.idOne = smallTableOne.idOne)
) firstjoin
JOIN
smallTableTwo ON (firstjoin.idTwo = smallTableTwo.idTwo)

however , If you use method one, you don't have MAPJOIN Tag, the above query statement will be treated as two MJ perform , further , If you know the size of the table in advance, it can be loaded into memory , You can use the following attributes to separate the two MJ Merge into one MJ.

hive.auto.convert.join.noconditionaltask:Hive Based on the size of the input file, normal JOIN convert to MapJoin, And whether or not multiple MJ Merge into one
hive.auto.convert.join.noconditionaltask.size: Multiple MJ Merge into one MJ when , The total size of the table must be less than this value , meanwhile hive.auto.convert.join.noconditionaltask It has to be for true

MAPJOIN matters needing attention

When a large table and one or more small tables do JOIN when , Best use MAPJOIN, The performance is better than ordinary JOIN Much faster . in addition ,MAPJOIN It can also solve the problem of data skew .MAPJOIN The basic principle of : In a small amount of data ,SQL All the small tables specified by the user will be loaded into the execution JOIN In the memory of the operating program , So as to speed up JOIN Execution speed of . Use MAPJOIN when , We need to pay attention to :

* LEFT OUTER JOIN The left table must be large ;
* RIGHT OUTER JOIN The right table must be a large table ;
* INNER JOIN Both left and right tables can be used as large tables ;
* FULL OUTER JOIN Out of commission MAPJOIN;
* MAPJOIN Support small table as subquery ;
* Use MAPJOIN When you need to refer to a small table or subquery , Need to refer to alias ;
* stay MAPJOIN in , You can use unequal connection or use OR Join multiple conditions ;
* at present ODPS stay MAPJOIN At most... Is supported in 6 Zhang xiaobiao , Otherwise, report grammatical errors ;
* If you use MAPJOIN, Then the total memory occupied by all small tables must not exceed 512M( The amount of decompressed logical data ).

MAPJOIN Decision logic

 At the same time meet the following 2 Conditions :
1) Join Stage max(join instance The elapsed time ) > 10 minute && max( join instance The elapsed time ) > 2 * avg( join instance The elapsed time )
2) Participate in join The minimum table data size of is less than 100M ( The amount of logical data before decompression )

MAPJOIN Memory custom settings

set odps.sql.mapjoin.memory.max=512 Set up mapjoin Maximum memory of time table , Default 512, Company M,[128,2048] Adjust between

Case study

This example is more comprehensive , It involves data skewing , It's also about when “ Watch ” Not very young (>512M) How to use it mapjoin.

select * from log a
left outer join users b
on a.user_id = b.user_id;

Log table (log) Generally speaking, there are many records , But user tables (users) It's not small ,600W+ The record of , hold users Distribute to all map It's also a big expense , and map join I don't support such a small watch . If you use ordinary join, We will encounter the problem of data skew .

 select /*+mapjoin(b)*/
*
from log a
left outer join (
select /*+mapjoin(c)*/
d.*
from ( select distinct user_id from log ) c
join users d
on c.user_id = d.user_id
) b
on a.user_id = b.user_id;

The premise scenario for this solution is : Daily members uv Not too much , namely log In the table count(distinct user_id) Not too big .

This article is from WeChat official account. - Big data is fun (havefun_bigdata)

The source and reprint of the original text are detailed in the text , If there is any infringement , Please contact the yunjia_community@tencent.com Delete .

Original publication time : 2021-01-17

Participation of this paper Tencent cloud media sharing plan , You are welcome to join us , share .

版权声明
本文为[Big data is fun]所创,转载请带上原文链接,感谢
https://javamana.com/2021/01/20210121195549614v.html

  1. 【计算机网络 12(1),尚学堂马士兵Java视频教程
  2. 【程序猿历程,史上最全的Java面试题集锦在这里
  3. 【程序猿历程(1),Javaweb视频教程百度云
  4. Notes on MySQL 45 lectures (1-7)
  5. [computer network 12 (1), Shang Xuetang Ma soldier java video tutorial
  6. The most complete collection of Java interview questions in history is here
  7. [process of program ape (1), JavaWeb video tutorial, baidu cloud
  8. Notes on MySQL 45 lectures (1-7)
  9. 精进 Spring Boot 03:Spring Boot 的配置文件和配置管理,以及用三种方式读取配置文件
  10. Refined spring boot 03: spring boot configuration files and configuration management, and reading configuration files in three ways
  11. 精进 Spring Boot 03:Spring Boot 的配置文件和配置管理,以及用三种方式读取配置文件
  12. Refined spring boot 03: spring boot configuration files and configuration management, and reading configuration files in three ways
  13. 【递归,Java传智播客笔记
  14. [recursion, Java intelligence podcast notes
  15. [adhere to painting for 386 days] the beginning of spring of 24 solar terms
  16. K8S系列第八篇(Service、EndPoints以及高可用kubeadm部署)
  17. K8s Series Part 8 (service, endpoints and high availability kubeadm deployment)
  18. 【重识 HTML (3),350道Java面试真题分享
  19. 【重识 HTML (2),Java并发编程必会的多线程你竟然还不会
  20. 【重识 HTML (1),二本Java小菜鸟4面字节跳动被秒成渣渣
  21. [re recognize HTML (3) and share 350 real Java interview questions
  22. [re recognize HTML (2). Multithreading is a must for Java Concurrent Programming. How dare you not
  23. [re recognize HTML (1), two Java rookies' 4-sided bytes beat and become slag in seconds
  24. 造轮子系列之RPC 1:如何从零开始开发RPC框架
  25. RPC 1: how to develop RPC framework from scratch
  26. 造轮子系列之RPC 1:如何从零开始开发RPC框架
  27. RPC 1: how to develop RPC framework from scratch
  28. 一次性捋清楚吧,对乱糟糟的,Spring事务扩展机制
  29. 一文彻底弄懂如何选择抽象类还是接口,连续四年百度Java岗必问面试题
  30. Redis常用命令
  31. 一双拖鞋引发的血案,狂神说Java系列笔记
  32. 一、mysql基础安装
  33. 一位程序员的独白:尽管我一生坎坷,Java框架面试基础
  34. Clear it all at once. For the messy, spring transaction extension mechanism
  35. A thorough understanding of how to choose abstract classes or interfaces, baidu Java post must ask interview questions for four consecutive years
  36. Redis common commands
  37. A pair of slippers triggered the murder, crazy God said java series notes
  38. 1、 MySQL basic installation
  39. Monologue of a programmer: despite my ups and downs in my life, Java framework is the foundation of interview
  40. 【大厂面试】三面三问Spring循环依赖,请一定要把这篇看完(建议收藏)
  41. 一线互联网企业中,springboot入门项目
  42. 一篇文带你入门SSM框架Spring开发,帮你快速拿Offer
  43. 【面试资料】Java全集、微服务、大数据、数据结构与算法、机器学习知识最全总结,283页pdf
  44. 【leetcode刷题】24.数组中重复的数字——Java版
  45. 【leetcode刷题】23.对称二叉树——Java版
  46. 【leetcode刷题】22.二叉树的中序遍历——Java版
  47. 【leetcode刷题】21.三数之和——Java版
  48. 【leetcode刷题】20.最长回文子串——Java版
  49. 【leetcode刷题】19.回文链表——Java版
  50. 【leetcode刷题】18.反转链表——Java版
  51. 【leetcode刷题】17.相交链表——Java&python版
  52. 【leetcode刷题】16.环形链表——Java版
  53. 【leetcode刷题】15.汉明距离——Java版
  54. 【leetcode刷题】14.找到所有数组中消失的数字——Java版
  55. 【leetcode刷题】13.比特位计数——Java版
  56. oracle控制用户权限命令
  57. 三年Java开发,继阿里,鲁班二期Java架构师
  58. Oracle必须要启动的服务
  59. 万字长文!深入剖析HashMap,Java基础笔试题大全带答案
  60. 一问Kafka就心慌?我却凭着这份,图灵学院vip课程百度云