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Apache Hive

The Apache HiveTM data warehouse software facilitates querying and managing large datasets residing in distributed storage. Built on top of Apache HadoopTM , it provides

  • Tools to enable easy data extract/transform/load (ETL)
  • A mechanism to impose structure on a variety of data formats
  • Access to files stored either directly in Apache HDFSTM or in other data storage systems such as Apache HBaseTM
  • Query execution via MapReduce

Hive defines a simple SQL-like query language, called QL, that enables users familiar with SQL to query the data. At the same time, this language also allows programmers who are familiar with the MapReduce framework to be able to plug in their custom mappers and reducers to perform more sophisticated analysis that may not be supported by the built-in capabilities of the language. QL can also be extended with custom scalar functions (UDF's), aggregations (UDAF's), and table functions (UDTF's).

Hive does not mandate read or written data be in the "Hive format"---there is no such thing. Hive works equally well on Thrift, control delimited, or your specialized data formats. Please see File Format and SerDe in the Developer Guide for details.

Hive is not designed for OLTP workloads and does not offer real-time queries or row-level updates. It is best used for batch jobs over large sets of append-only data (like web logs). What Hive values most are scalability (scale out with more machines added dynamically to the Hadoop cluster), extensibility (with MapReduce framework and UDF/UDAF/UDTF), fault-tolerance, and loose-coupling with its input formats.

General information about Hive

User Documentation

Administrator Documentation

Resources for Contributors

For more information, please see the official Hive website.

Apache Hive, Apache Hadoop, Apache HBase, Apache HDFS, Apache, the Apache feather logo, and the Apache Hive project logo are trademarks of The Apache Software Foundation.

 
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