Apache Pinot: Difference between revisions

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{{Infobox software

| name = Apache Pinot

| logo = [[File:Pinot LogoApache_Pinot_Crimson_logotype,_2023.svgpng|frameless|alt=Pinot Logo]]

| screenshot =

| caption =

| author = {{ubl|Kishore Gopalakrishna|Xiang Fu}}

| developer = Apache Pinot

| latest release version = 01.112.0

| latest release date = {{Start date and age|df=yes|20222024|0908|0121}}

| repo = [https://gitboxgithub.com/apache.org/repos/asf?p=pinot.git Pinot repository]

| programming language = [[Java (programming language)|Java]]

| operating system = [[Cross-platform]]

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}}

'''Apache Pinot''' is a [[Column-oriented DBMS|column-oriented]], [[open-source software|open-source]], [[Distributed database|distributed]] [[data store]] written in [[Java (programming language)|Java]]. Pinot is designed to execute [[Online analytical processing|OLAP]] queries with low latency.<ref>{{cite journalbook |last1=Cui |first1=Tingting |last2=Peng |first2=Lijun |last3=Pardoe |first3=David |last4=Liu |first4=Kun |last5=Agarwal |first5=Deepak |last6=Kumar |first6=Deepak |title=Proceedings of the ADKDD'17 |chapter=Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn |journal=Proceedings of the ADKDD'17 |series=Adkdd'17 |date=14 August 2017 |pages=1–7 |doi=10.1145/3124749.3124759 |chapter-url=https://dl.acm.org/doi/abs/10.1145/3124749.3124759 |publisher=Association for Computing Machinery|isbn=9781450351942 |s2cid=12327343 }}</ref><ref>{{cite book |last1=Rosa |first1=Marcello La |title=ADVANCED INFORMATION SYSTEMS ENGINEERING: 33rd International Conference |date=2021 |publisher=Springer Nature |isbn=978-3-030-79382-1 |url=https://books.google.com/books?id=Q7k0EAAAQBAJ&dq=Apache+Pinot+-wikipedia&pg=PA384 |language=en}}</ref><ref>{{cite book |last1=Chin |first1=Francis Y. L. |last2=Chen |first2=C. L. Philip |last3=Khan |first3=Latifur |last4=Lee |first4=Kisung |last5=Zhang |first5=Liang-Jie |title=Big Data – BigData 2018: 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June 25–30, 2018, Proceedings |date=20 June 2018 |publisher=Springer |isbn=978-3-319-94301-5 |page=153 |url=https://books.google.com/books?id=eSVhDwAAQBAJ |language=en}}</ref><ref>{{cite book |last1=Im |first1=Jean-François |last2=Gopalakrishna |first2=Kishore |last3=Subramaniam |first3=Subbu |last4=Shrivastava |first4=Mayank |last5=Tumbde |first5=Adwait |last6=Jiang |first6=Xiaotian |last7=Dai |first7=Jennifer |last8=Lee |first8=Seunghyun |last9=Pawar |first9=Neha |last10=Li |first10=Jialiang |last11=Aringunram |first11=Ravi |title=Proceedings of the 2018 International Conference on Management of Data |chapter=Pinot: Realtime OLAP for 530 Million Users |series=Sigmod '18 |date=2018-05-27 |pages=583–594 |doi=10.1145/3183713.3190661 |url=https://dl.acm.org/doi/10.1145/3183713.3190661#d13801648e1 |publisher=Association for Computing Machinery|isbn=9781450347037 |s2cid=44083085 }}</ref><ref>{{cite web |title=The Apache Software Foundation Announces Apache® Pinot™ as a Top-Level Project |url=https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces76 |website=blogs.apache.org|date=2 August 2021 }}</ref> It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion.<ref>{{cite arXiv |last1=Rogers |first1=Ryan |last2=Subramaniam |first2=Subbu |last3=Peng |first3=Sean |last4=Durfee |first4=David |last5=Lee |first5=Seunghyun |last6=Kancha |first6=Santosh Kumar |last7=Sahay |first7=Shraddha |last8=Ahammad |first8=Parvez |title=LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale |date=16 November 2020 |class=cs.CR |eprint=2002.05839}}</ref><ref>{{cite journalbook |last1=Javadi |first1=Seyyed Ahmad |last2=Gupta |first2=Harsh |last3=Manhas |first3=Robin |last4=Sahu |first4=Shweta |last5=Gandhi |first5=Anshul |title=2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) |chapter=EASY: Efficient Segment Assignment Strategy for Reducing Tail Latencies in Pinot |journal=2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) |date=July 2018 |pages=1432–1437 |doi=10.1109/ICDCS.2018.00144 |isbn=978-1-5386-6871-9 |s2cid=21659844 |url=https://ieeexplore.ieee.org/document/8416407}}</ref><ref name="pinot-joins-apache-foundation">Pawar, Neha. [https://engineering.linkedin.com/blog/2019/03/pinot-joins-apache-incubator "Pinot Joins Apache Incubator"] {{Webarchive|url=https://web.archive.org/web/20190402090136/https://engineering.linkedin.com/blog/2019/03/pinot-joins-apache-incubator |date=2019-04-02 }}, ''LinkedIn Engineering'', 01 April 2019</ref> The name Pinot comes from the [[Pinot grape]] vines that are pressed into liquid that is used to produce a variety of different wines. The founders of the database chose the name as a metaphor for analyzing vast quantities of data from a variety of different file formats or streaming data sources.<ref name="open-sourcing-pinot">{{cite web |last1=Gopalakrishna |first1=Kishore |title=Open Sourcing Pinot: Scaling the Wall of Real-Time Analytics |url=https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics |website=engineering.linkedin.com |publisher=LinkedIn |accessdate=3 September 2020 |archiveurl=https://web.archive.org/web/20150910081445/http://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics |archivedate=10 September 2015 |language=en}}</ref>

Pinot was first created at [[LinkedIn]] after the engineering staff determined that there were no off the shelf solutions that met the social networking site's requirements like predictable low latency, data freshness in seconds, fault tolerance and scalability.<ref name="open-sourcing-pinot" /><ref>{{cite news |last1=Yegulalp |first1=Serdar |title=LinkedIn fills another SQL-on-Hadoop niche |url=https://www.infoworld.com/article/2934506/linkedins-pinot-fills-another-sql-on-hadoop-niche.html |work=InfoWorld |date=2015-06-11 |language=en}}</ref> Pinot is used in production by technology companies such as [[Uber]],<ref>{{cite journalbook |last1=Fu |first1=Yupeng |last2=Soman |first2=Chinmay |title=Real-time Data Infrastructure at Uber |journal=Proceedings of the 2021 International Conference on Management of Data |chapter=Real-time Data Infrastructure at Uber |series=Sigmod/Pods '21 |date=9 June 2021 |pages=2503–2516 |doi=10.1145/3448016.3457552 |chapter-url=https://dl.acm.org/doi/abs/10.1145/3448016.3457552 |publisher=Association for Computing Machinery|arxiv=2104.00087 |isbn=9781450383431 |s2cid=232478317 }}</ref> [[Microsoft]],<ref name="pinot-joins-apache-foundation" /> and [[Factual]].

== History ==

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[[Category:Structured storage]]

[[Category:Free database management systems]]

[[Category:Free software programmed in Java (programming language)]]

[[Category:Database engines]]

[[Category:Big data products]]