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'''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 OLAP queries with low latency.<ref>{{cite journal |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=Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn |journal=Proceedings of the ADKDD'17 |date=14 August 2017 |pages=1–7 |doi=10.1145/3124749.3124759 |url=https://dl.acm.org/doi/abs/10.1145/3124749.3124759 |publisher=Association for Computing Machinery}}</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://www.google.com/books/edition/ADVANCED_INFORMATION_SYSTEMS_ENGINEERING/Q7k0EAAAQBAJ?hl=en&gbpv=1&dq=Apache+Pinot+-wikipedia&pg=PA384&printsec=frontcover |language=en}}</ref><ref>{{cite book |last1=Koch |first1=Chris |title=Introduction to Information Technology |date=14 November 2018 |publisher=Scientific e-Resources |isbn=978-1-83947-240-4 |url=https://www.google.com/books/edition/Introduction_to_Information_Technology/9eLEDwAAQBAJ?hl=en&gbpv=1&dq=Pinot+(data+store)+-wikipedia&pg=PA130&printsec=frontcover |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=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>
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
== History ==
Pinot was started as an internal project at LinkedIn in 2013 to power a variety of user-facing and business-facing products. The first analytics product at LinkedIn to use Pinot was a redesign of the social networking site's feature that allows members to see who has viewed their profile in real-time. The project was open-sourced in June 2015 under an Apache 2.0 license and was donated to the Apache Software Foundation by LinkedIn in June 2019.<ref name="open-sourcing-pinot" /><ref name="pinot-joins-apache-foundation" />
== Architecture ==
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== Features ==
Pinot shares similar features with comparable OLAP datastores, such as [[Apache Druid]]<ref>{{cite book |last1=Ordonez |first1=Carlos |last2=Song |first2=Il-Yeol |last3=Anderst-Kotsis |first3=Gabriele |last4=Tjoa |first4=A. Min |last5=Khalil |first5=Ismail |title=Big Data Analytics and Knowledge Discovery: 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings |date=2 October 2019 |publisher=Springer |isbn=978-3-030-27520-4 |page=170 |url=https://www.google.com/books/edition/Big_Data_Analytics_and_Knowledge_Discove/sf-pDwAAQBAJ?hl=en&gbpv=1&dq=Pinot+(data+store)+-wikipedia&pg=PA170&printsec=frontcover |language=en}}</ref><ref>{{cite book |last1=Uttamchandani |first1=Sandeep |title=The Self-Service Data Roadmap |date=10 September 2020 |publisher="O'Reilly Media, Inc." |isbn=978-1-4920-7520-2 |url=https://www.google.com/books/edition/The_Self_Service_Data_Roadmap/pEn8DwAAQBAJ?hl=en&gbpv=1&dq=Pinot+(data+store)+-wikipedia&pg=PT72&printsec=frontcover |language=en}}</ref>. Like Druid, Pinot is a column-oriented database with various compression schemes such as [[Run-length encoding|Run Length]] and [[Variable-length encoding|Fixed Bit Length]]. Pinot supports pluggable [[Database index|indexing technologies]] - Sorted Index, [[Bitmap Index]], [[Inverted index|Inverted Index]], Star-Tree Index, and Range Index, which are what primarily differentiates Pinot from other OLAP datastores.
Pinot supports near real-time ingestion from streams such as [[Apache Kafka|Kafka]], [[AWS]] Kinesis and [[Batch processing|batch]] ingestion from sources such as [[Hadoop]], [[Amazon S3|S3]], [[Microsoft Azure|Azure]], [[Google Cloud Storage|GCS]]. Like mostly, all other [[Online analytical processing|OLAP]] datastores and [[data warehousing]] solutions, Pinot supports a [[SQL]]-like query language that supports selection, aggregation, filtering, group by, order by, distinct queries on data.
== See also ==
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