Apache Phoenix

Apache Phoenix is an open source, massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing store. Phoenix provides a JDBC driver that hides the intricacies of the noSQL store enabling users to create, delete, and alter SQL tables, views, indexes, and sequences; insert and delete rows singly and in bulk; and query data through SQL.[1] Phoenix compiles queries and other statements into native noSQL store APIs rather than using MapReduce enabling the building of low latency applications on top of noSQL stores.[2]

Apache Phoenix
Developer(s)Apache Software Foundation
Initial releaseApril 15, 2014 (2014-04-15)
Stable release
4.x4.15.0 / 20 December 2019 (2019-12-20)
5.x5.0.0 / 4 July 2018 (2018-07-04)
RepositoryPhoenix Repository
Written inJava, SQL
Operating systemCross-platform
TypeSQL database
LicenseApache License 2.0
Websitephoenix.apache.org

History

Phoenix began as an internal project by the company salesforce.com out of a need to support a higher level, well understood, SQL language. It was originally open-sourced on GitHub[3] on 28 Jan 2014 and became a top-level Apache project on 22 May 2014.[4] Apache Phoenix is included in the Hortonworks distribution for HDP 2.1 and above,[5] is available as part of Cloudera labs,[6] and is part of the Hadoop ecosystem.[7]

See also

References
  1. James Taylor. "Apache Phoenix Transforming HBase into a SQL database", HadoopSummit Archived 10 October 2016 at the Wayback Machine, 4 June 2014.
  2. Istvan Szegedi. "Apache Phoenix – an SQL Driver for HBase", BigHadoop, 17 May 2014.
  3. Abel Avram. "Phoenix: Running SQL Queries on Apache HBase", InfoQ, 31 January 2013.
  4. Adam Seligman. "Apache Phoenix: A small step for big data", Salesforce.com Developer, 28 May 2014.
  5. Hortonworks. "Chapter 7. Installing Phoenix", Hortonworks, 2 July 2014.
  6. Srikanth Srungarapu. "Apache Phoenix Joins Cloudera Labs" Archived 11 July 2015 at the Wayback Machine, Cloudera, 6 May 2015.
  7. Serdar Yegulalp. "10 ways to query Hadoop with SQL", "", 16 September 2014.
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