[Hadoop Wiki] Update of "Hive" by ZhengShao

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The "Hive" page has been changed by ZhengShao:
http://wiki.apache.org/hadoop/Hive?action=diff&rev1=40&rev2=41

  = What is Hive =
  [[http://hadoop.apache.org/hive/|Hive]] is a data warehouse infrastructure built on top of Hadoop that provides tools to enable easy data summarization, adhoc querying and analysis of large datasets data stored in Hadoop files. It provides a mechanism to put structure on this data and it also provides a simple query language called QL which is based on SQL and which enables users familiar with SQL to query this data. At the same time, this language also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis which may not be supported by the built in capabilities of the language.
  
- Hive does not mandate read or written data be in "hive format" - there is no such thing; Hive works equally well on Thrift, control delimited, or your data format.  Please see File Format and SerDe in Developer Guide for details.
+ Hive does not mandate read or written data be in "hive format" - there is no such thing; Hive works equally well on Thrift, control delimited, or your data format.  Please see File Format and [[http://www.slideshare.net/ragho/hive-user-meeting-august-2009-facebook|SerDe]] in Developer Guide for details.
  
  = What Hive is NOT =
  Hive is based on Hadoop which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems like Oracle where analysis is done on a significantly smaller amount of data but the analysis proceeds much more iteratively with the response times between iterations being less than a few minutes. For Hive queries response times for even the smallest jobs can be of the order of 5-10 minutes and for larger jobs this may even run into hours.