What is Apache Hive

Apache Hive is a data warehouse infrastructure built on top of Apache 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 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.

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.

What Hive is NOT

Hadoop is a batch processing system and Hadoop jobs tend to have high latency and incur substantial overheads in job submission and scheduling. As a result - latency for Hive queries is generally very high (minutes) even when data sets involved are very small (say a few hundred megabytes). As a result it cannot be compared with systems such as Oracle where analyses are conducted on a significantly smaller amount of data but the analyses proceed much more iteratively with the response times between iterations being less than a few minutes. Hive aims to provide acceptable (but not optimal) latency for interactive data browsing, queries over small data sets or test queries. Hive also does not provide sort of data or query cache to make repeated queries over the same data set faster.

Hive is not designed for online transaction processing and does not offer real-time queries and row level updates. It is best used for batch jobs over large sets of immutable 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.

Information

• General information about Hive
  • Getting Started
  • Presentations and Papers about Hive
  • A List of Sites and Applications Powered by Hive
  • FAQ
  • hive-users mailing list
  • Hive IRC Channel: #hive at irc.freenode.net
  • Information about this Hive wiki
• For users:
  • Hive Tutorial
  • HiveQL Language Manual (Queries, DML, DDL, and CLI)
  • Hive Plug-in Interfaces - User-Defined Functions and SerDes
  • Guide to Hive Operators and Functions
    • Functions for Statistics and Data Mining
  • Hive Web Interface
  • Hive Client (JDBC, ODBC, Thrift, etc)
• For administrators:
  • Installing Hive
  • Configuring Hive
  • Setting up Metastore
  • Setting up Hive Web Interface
  • Setting up Hive Server (JDBC, ODBC, Thrift, etc)
  • Hive on Amazon Web Services
• For developers:
  • How to Contribute
  • Hive Contributors Meetings
  • Hive Developer Guide
  • Plugin Developer Kit
  • Hive Performance
  • Hive Architecture Overview
  • Hive Design Docs
  • Roadmap/call to Add More Features
  • Full-text search over all Hive resources
  • How to Commit
  • How to Release
  • Build status on Jenkins (formerly Hudson)
  • Project Bylaws

For more information, please see the official Hive website.