Apache Impala

Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop.[2] Impala has been described as the open-source equivalent of Google F1, which inspired its development in 2012.[3]

Apache Impala
Developer(s)Apache Software Foundation
Initial releaseApril 28, 2013 (2013-04-28)
Stable release
3.3.0 / August 22, 2019 (2019-08-22)[1]
RepositoryImpala Repository
Written inC++, Java
Operating systemCross-platform
TypeRelational Hadoop-analytics
LicenseApache License 2.0
Websiteimpala.apache.org

Description

Apache Impala is a query engine that runs on Apache Hadoop. The project was announced in October 2012 with a public beta test distribution[4][5] and became generally available in May 2013.[6]

Impala brings scalable parallel database technology to Hadoop, enabling users to issue low-latency SQL queries to data stored in HDFS and Apache HBase without requiring data movement or transformation. Impala is integrated with Hadoop to use the same file and data formats, metadata, security and resource management frameworks used by MapReduce, Apache Hive, Apache Pig and other Hadoop software.

Impala is promoted for analysts and data scientists to perform analytics on data stored in Hadoop via SQL or business intelligence tools. The result is that large-scale data processing (via MapReduce) and interactive queries can be done on the same system using the same data and metadata – removing the need to migrate data sets into specialized systems and/or proprietary formats simply to perform analysis.

Features include:

In early 2013, a column-oriented file format called Parquet was announced for architectures including Impala.[7] In December 2013, Amazon Web Services announced support for Impala.[8] In early 2014, MapR added support for Impala.[9] In 2015, another format called Kudu was announced, which Cloudera proposed to donate to the Apache Software Foundation along with Impala.[10] Impala graduated to an Apache Top-Level Project (TLP) on 28 November 2017.[11]

gollark: With parallelization it could be reduced to *one* hour!
gollark: Will anyone *notice* if computing their autocrafting job takes a few hours?
gollark: So that doesn't actually help at all with the more significant problem of dealing with the fact that this makes there be a lot of different crafting paths to try!
gollark: They're stored in a `Vec<Item>`.
gollark: That's pretty much something I already did!!!!!!!!!!!!

See also
  • Apache Drill — similar open source project inspired by Dremel
  • Dremel — similar tool from Google
  • Presto — open source SQL query engine created by Facebook and supported by Teradata.

References
  1. "3.3.0 release". Retrieved 23 August 2019.
  2. "Apache Impala". Retrieved 15 September 2017.
  3. Cade Metz (October 24, 2012). "Man Busts Out of Google, Rebuilds Top-Secret Query Machine". Wired Magazine. Retrieved October 10, 2016.
  4. Larry Digna (October 24, 2012). "Cloudera aims to bring real-time queries to Hadoop, big data". Between the lines blog. ZDNet. Retrieved January 20, 2014.
  5. Andrew Brust (October 25, 2012). "Cloudera's Impala brings Hadoop to SQL and BI". ZDNet. Retrieved January 20, 2014.
  6. Marcel Kornacker, Justin Erickson (May 1, 2013). "Cloudera Impala 1.0: It's Here, It's Real, It's Already the Standard for SQL on Hadoop". Archived from the original on April 13, 2014. Retrieved April 10, 2014.
  7. "Parquet: Columnar Storage for Hadoop". Project web site. 2013. Retrieved January 20, 2014.
  8. "Announcing Support for Impala with Amazon Elastic MapReduce". Amazon.com. December 12, 2013. Retrieved January 20, 2014.
  9. "Impala for MapR". MapR.com. February 2, 2014. Retrieved April 10, 2014.
  10. David Ramel (November 18, 2015). "Cloudera to Donate Impala and Kudu Big Data Projects to Apache". Application Development Trends. Retrieved October 10, 2016.
  11. "The Apache Software Foundation Announces Apache® Impala™ as a Top-Level Project". November 28, 2017. Retrieved November 30, 2017.
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