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From "wangmeng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-7296) big data approximate processing at a very low cost based on hive sql
Date Sat, 05 Jul 2014 11:09:33 GMT

    [ https://issues.apache.org/jira/browse/HIVE-7296?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14052834#comment-14052834
] 

wangmeng commented on HIVE-7296:
--------------------------------

yes,I like it.





--

Best      Regards
HomePage:http://wangmeng.us/
Name:    Wang Meng---Data structures and Algorithms,Java,Jvm, Linux, Shell, Distributed
system , Hadoop  Hive , Performancse Optimization and Debug ,Spark/Shark Impala
Major:     Software Engineering --
Degree:  Master
E-mail:   sjtufighter@163.com   sjtufighter@sjtu.edu.cn
Tel:         13141202303(BeiJing)   18818272832(ShangHai)
GitHub:    https://github.com/sjtufighter







> big data approximate processing  at a very  low cost  based on hive sql 
> ------------------------------------------------------------------------
>
>                 Key: HIVE-7296
>                 URL: https://issues.apache.org/jira/browse/HIVE-7296
>             Project: Hive
>          Issue Type: New Feature
>            Reporter: wangmeng
>
> For big data analysis, we often need to do the following query and statistics:
> 1.Cardinality Estimation,   count the number of different elements in the collection,
such as Unique Visitor ,UV)
> Now we can use hive-query:
> Select distinct(id)  from TestTable ;
> 2.Frequency Estimation: estimate number of an element is repeated, such as the site visits
of  a user 。
> Hive query: select  count(1)  from TestTable where name=”wangmeng”
> 3.Heavy Hitters, top-k elements: such as top-100 shops 
> Hive query: select count(1), name  from TestTable  group by name ;  need UDF……
> 4.Range Query: for example, to find out the number of  users between 20 to 30
> Hive query : select  count(1) from TestTable where age>20 and age <30
> 5.Membership Query : for example, whether  the user name is already registered?
> According to the implementation mechanism of hive , it  will cost too large memory space
and a long query time.
> However ,in many cases, we do not need very accurate results and a small error can be
tolerated. In such case  , we can use  approximate processing  to greatly improve the time
and space efficiency.
> Now , based  on some theoretical analysis materials ,I want to  do some for these new
features so much if possible. 
> So, is there anything I can do ?  Many Thanks.



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