wangmeng created HIVE-7296: ------------------------------ Summary: 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 . I am familiar with hive and hadoop , and I have implemented an efficient storage format based on hive.( https://github.com/sjtufighter/----Data---Storage--). So, is there anything I can do ? Many Thanks. -- This message was sent by Atlassian JIRA (v6.2#6252)