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From "wangmeng (JIRA)" <>
Subject [jira] [Created] (HIVE-7296) big data approximate processing at a very low cost based on hive sql
Date Thu, 26 Jun 2014 04:49:24 GMT
wangmeng created HIVE-7296:

             Summary: big data approximate processing  at a very  low cost  based on hive
                 Key: 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

Now , based  on some theoretical analysis materials ,I want to  do some for these new features
so much .

I am familiar with hive and  hadoop , and  I have implemented an efficient  storage format
based on hive.(

So, is there anything I can do ?  Many Thanks.

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