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| <document> |
| <properties> |
| <title>Hadoop Hive</title> |
| <author email="hive-user@hadoop.apache.org">Hadoop Hive Documentation Team</author> |
| </properties> |
| <body> |
| <section name="What is Hive?" href="WhatisHive?"> |
| <p>Hive is a data warehouse infrastructure built on top of Hadoop. It provides tools to enable easy data ETL, a mechanism to put structures on the data, and the capability to querying and analysis of large data sets stored in Hadoop files. 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 fromwork 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.</p> |
| |
| <p> |
| 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 Developer Guide for details. </p> |
| </section> |
| <section name="What Hive is NOT" href="WhatHiveIsNot?"> |
| <p>Hive is based on Hadoop, which is a batch processing system. As a result, Hive 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. In contrast to the systems such as Oracle where analysis is run 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, Hive queries response times for even the smallest jobs can be of the order of several minutes. However for larger jobs (e.g., jobs processing terabytes of data) in general they may run into hours.</p> |
| |
| <p>In summary, low latency performance is not the top-priority of Hive's design principles. 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.</p> |
| </section> |
| </body> |
| </document> |
