hive lateral view 与 explode详解[通俗易懂]

hive lateral view 与 explode详解[通俗易懂]1.explodehivewiki对于expolde的解释如下:explode()takesinanarray(oramap)asaninputandoutputstheelementsofthearray(map)asseparaterows.UDTFscanbeusedintheSELECTexpressionlistandas

大家好,又见面了,我是你们的朋友全栈君。如果您正在找激活码,请点击查看最新教程,关注关注公众号 “全栈程序员社区” 获取激活教程,可能之前旧版本教程已经失效.最新Idea2022.1教程亲测有效,一键激活。

Jetbrains全系列IDE使用 1年只要46元 售后保障 童叟无欺

项目github地址:bitcarmanlee easy-algorithm-interview-and-practice
欢迎大家star,留言,一起学习进步

1.explode

hive wiki对于expolde的解释如下:

explode() takes in an array (or a map) as an input and outputs the elements of the array (map) as separate rows. UDTFs can be used in the SELECT expression list and as a part of LATERAL VIEW.

As an example of using explode() in the SELECT expression list, consider a table named myTable that has a single column (myCol) and two rows:

这里写图片描述

Then running the query:

SELECT explode(myCol) AS myNewCol FROM myTable;

will produce:
这里写图片描述
The usage with Maps is similar:

SELECT explode(myMap) AS (myMapKey, myMapValue) FROM myMapTable;

总结起来一句话:explode就是将hive一行中复杂的array或者map结构拆分成多行。

使用实例:
xxx表中有一个字段mvt为string类型,数据格式如下:

[{“eid”:“38”,“ex”:“affirm_time_Android”,“val”:“1”,“vid”:“31”,“vr”:“var1”},{“eid”:“42”,“ex”:“new_comment_Android”,“val”:“1”,“vid”:“34”,“vr”:“var1”},{“eid”:“40”,“ex”:“new_rpname_Android”,“val”:“1”,“vid”:“1”,“vr”:“var1”},{“eid”:“19”,“ex”:“hotellistlpage_Android”,“val”:“1”,“vid”:“1”,“vr”:“var01”},{“eid”:“29”,“ex”:“bookhotelpage_Android”,“val”:“0”,“vid”:“1”,“vr”:“var01”},{“eid”:“17”,“ex”:“trainMode_Android”,“val”:“1”,“vid”:“1”,“vr”:“mode_Android”},{“eid”:“44”,“ex”:“ihotelList_Android”,“val”:“1”,“vid”:“36”,“vr”:“var1”},{“eid”:“47”,“ex”:“ihotelDetail_Android”,“val”:“0”,“vid”:“38”,“vr”:“var1”}]

用explode小试牛刀一下:

select explode(split(regexp_replace(mvt,'\\[|\\]',''),'\\},\\{')) from ods_mvt_hourly where day=20160710 limit 10;

最后出来的结果如下:
{“eid”:“38”,“ex”:“affirm_time_Android”,“val”:“1”,“vid”:“31”,“vr”:“var1”
“eid”:“42”,“ex”:“new_comment_Android”,“val”:“1”,“vid”:“34”,“vr”:“var1”
“eid”:“40”,“ex”:“new_rpname_Android”,“val”:“1”,“vid”:“1”,“vr”:“var1”
“eid”:“19”,“ex”:“hotellistlpage_Android”,“val”:“1”,“vid”:“1”,“vr”:“var01”
“eid”:“29”,“ex”:“bookhotelpage_Android”,“val”:“0”,“vid”:“1”,“vr”:“var01”
“eid”:“17”,“ex”:“trainMode_Android”,“val”:“1”,“vid”:“1”,“vr”:“mode_Android”
“eid”:“44”,“ex”:“ihotelList_Android”,“val”:“1”,“vid”:“36”,“vr”:“var1”
“eid”:“47”,“ex”:“ihotelDetail_Android”,“val”:“0”,“vid”:“38”,“vr”:“var1”}
{“eid”:“38”,“ex”:“affirm_time_Android”,“val”:“1”,“vid”:“31”,“vr”:“var1”
“eid”:“42”,“ex”:“new_comment_Android”,“val”:“1”,“vid”:“34”,“vr”:“var1”

2.lateral view

hive wiki 上的解释如下:

Lateral View Syntax

lateralView: LATERAL VIEW udtf(expression) tableAlias AS columnAlias (’,’ columnAlias)*
fromClause: FROM baseTable (lateralView)*

Description

Lateral view is used in conjunction with user-defined table generating functions such as explode(). As mentioned in Built-in Table-Generating Functions, a UDTF generates zero or more output rows for each input row. A lateral view first applies the UDTF to each row of base table and then joins resulting output rows to the input rows to form a virtual table having the supplied table alias.

Example

Consider the following base table named pageAds. It has two columns: pageid (name of the page) and adid_list (an array of ads appearing on the page)
这里写图片描述

An example table with two rows:
这里写图片描述

and the user would like to count the total number of times an ad appears across all pages.
A lateral view with explode() can be used to convert adid_list into separate rows using the query:

SELECT pageid, adid
FROM pageAds LATERAL VIEW explode(adid_list) adTable AS adid;

The resulting output will be
这里写图片描述
Then in order to count the number of times a particular ad appears, count/group by can be used:

SELECT adid, count(1)
FROM pageAds LATERAL VIEW explode(adid_list) adTable AS adid
GROUP BY adid;

The resulting output will be
这里写图片描述
lateral view用于和split, explode等UDTF一起使用,它能够将一行数据拆成多行数据,在此基础上可以对拆分后的数据进行聚合。lateral view首先为原始表的每行调用UDTF,UDTF会把一行拆分成一或者多行,lateral view再把结果组合,产生一个支持别名表的虚拟表。

由此可见,lateral view与explode等udtf就是天生好搭档,explode将复杂结构一行拆成多行,然后再用lateral view做各种聚合。

3.实例

还是第一部分的例子,上面我们explode出来以后的数据,不是标准的json格式,我们通过lateral view与explode组合解析出标准的json格式数据:

SELECT ecrd, CASE WHEN instr(mvtstr,'{')=0
    AND instr(mvtstr,'}')=0 THEN concat('{',mvtstr,'}') WHEN instr(mvtstr,'{')=0
    AND instr(mvtstr,'}')>0 THEN concat('{',mvtstr) WHEN instr(mvtstr,'}')=0
    AND instr(mvtstr,'{')>0 THEN concat(mvtstr,'}') ELSE mvtstr END AS mvt
      FROM ods.ods_mvt_hourly LATERAL VIEW explode(split(regexp_replace(mvt,'\\[|\\]',''),'\\},\\{')) addTable AS mvtstr
        WHERE DAY='20160710' and ecrd is not null limit 10

查询出来的结果:
xxx
{“eid”:“38”,“ex”:“affirm_time_Android”,“val”:“1”,“vid”:“31”,“vr”:“var1”}
xxx
{“eid”:“42”,“ex”:“new_comment_Android”,“val”:“1”,“vid”:“34”,“vr”:“var1”}
xxx
{“eid”:“40”,“ex”:“new_rpname_Android”,“val”:“1”,“vid”:“1”,“vr”:“var1”}
xxx
{“eid”:“19”,“ex”:“hotellistlpage_Android”,“val”:“1”,“vid”:“1”,“vr”:“var01”}
xxx
{“eid”:“29”,“ex”:“bookhotelpage_Android”,“val”:“0”,“vid”:“1”,“vr”:“var01”
xxx
{“eid”:“17”,“ex”:“trainMode_Android”,“val”:“1”,“vid”:“1”,“vr”:“mode_Android”}
xxx
{“eid”:“44”,“ex”:“ihotelList_Android”,“val”:“1”,“vid”:“36”,“vr”:“var1”}
xxx
{“eid”:“47”,“ex”:“ihotelDetail_Android”,“val”:“1”,“vid”:“38”,“vr”:“var1”}
xxx
{“eid”:“38”,“ex”:“affirm_time_Android”,“val”:“1”,“vid”:“31”,“vr”:“var1”}
xxx
{“eid”:“42”,“ex”:“new_comment_Android”,“val”:“1”,“vid”:“34”,“vr”:“var1”}

4.Ending

Lateral View通常和UDTF一起出现,为了解决UDTF不允许在select字段的问题。
Multiple Lateral View可以实现类似笛卡尔乘积。
Outer关键字可以把不输出的UDTF的空结果,输出成NULL,防止丢失数据。

参考内容:

1.http://blog.csdn.net/oopsoom/article/details/26001307 lateral view的用法实例
2.https://my.oschina.net/leejun2005/blog/120463 复合函数的用法,比较详细
3.http://blog.csdn.net/zhaoli081223/article/details/46637517 udtf的介绍

版权声明:本文内容由互联网用户自发贡献,该文观点仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 举报,一经查实,本站将立刻删除。

发布者:全栈程序员-用户IM,转载请注明出处:https://javaforall.cn/191008.html原文链接:https://javaforall.cn

【正版授权,激活自己账号】: Jetbrains全家桶Ide使用,1年售后保障,每天仅需1毛

【官方授权 正版激活】: 官方授权 正版激活 支持Jetbrains家族下所有IDE 使用个人JB账号...

(0)
blank

相关推荐

发表回复

您的电子邮箱地址不会被公开。

关注全栈程序员社区公众号