Apache Pinot Explained

Apache Pinot
Developer:Apache Pinot
Latest Release Version:1.0.0
Repo:Pinot repository
Programming Language:Java
Operating System:Cross-platform
License:Apache License 2.0

Apache Pinot is a column-oriented, open-source, distributed data store written in Java. Pinot is designed to execute OLAP queries with low latency.[1] [2] [3] [4] [5] It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion.[6] [7] [8] 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.[9]

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.[10] Pinot is used in production by technology companies such as Uber,[11] Microsoft, and Factual.

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.

Architecture

Pinot uses Apache Helix for cluster management. Helix is embedded as an agent within the different components and uses Apache ZooKeeper for coordination and maintaining the overall cluster state and health. All Pinot servers and brokers are managed by Helix. Helix is a generic cluster management framework to manage partitions and replicas in a distributed system.

Query management

Queries are received by brokers—which checks the request against the segment-to-server routing table—scattering the request between real-time and offline servers.

Cluster management

Pinot leverages Apache Helix for cluster management. Helix is a cluster management framework to manage replicated, partitioned resources in a distributed system. Helix uses Zookeeper to store cluster state and metadata.

Features

Pinot shares similar features with comparable OLAP datastores, such as Apache Druid.[12] [13] Like Druid, Pinot is a column-oriented database with various compression schemes such as Run Length and Fixed-Bit Length. Pinot supports pluggable indexing technologies - Sorted Index, Bitmap 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 Kafka, AWS Kinesis and batch ingestion from sources such as Hadoop, S3, Azure, GCS. Like most other 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

Notes and References

  1. Book: Cui . Tingting . Peng . Lijun . Pardoe . David . Liu . Kun . Agarwal . Deepak . Kumar . Deepak . Proceedings of the ADKDD'17 . Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn . 14 August 2017 . 1–7 . 10.1145/3124749.3124759 . https://dl.acm.org/doi/abs/10.1145/3124749.3124759 . Association for Computing Machinery. 9781450351942 . 12327343 .
  2. Book: Rosa . Marcello La . ADVANCED INFORMATION SYSTEMS ENGINEERING: 33rd International Conference . 2021 . Springer Nature . 978-3-030-79382-1 . en.
  3. Book: Chin . Francis Y. L. . Chen . C. L. Philip . Khan . Latifur . Lee . Kisung . Zhang . Liang-Jie . 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 . 20 June 2018 . Springer . 978-3-319-94301-5 . 153 . en.
  4. Book: Im . Jean-François . Gopalakrishna . Kishore . Subramaniam . Subbu . Shrivastava . Mayank . Tumbde . Adwait . Jiang . Xiaotian . Dai . Jennifer . Lee . Seunghyun . Pawar . Neha . Li . Jialiang . Aringunram . Ravi . Proceedings of the 2018 International Conference on Management of Data . Pinot: Realtime OLAP for 530 Million Users . Sigmod '18 . 2018-05-27 . 583–594 . 10.1145/3183713.3190661 . Association for Computing Machinery. 9781450347037 . 44083085 .
  5. Web site: The Apache Software Foundation Announces Apache® Pinot™ as a Top-Level Project . blogs.apache.org. 2 August 2021 .
  6. Rogers . Ryan . Subramaniam . Subbu . Peng . Sean . Durfee . David . Lee . Seunghyun . Kancha . Santosh Kumar . Sahay . Shraddha . Ahammad . Parvez . LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale . 16 November 2020 . cs.CR . 2002.05839.
  7. Book: Javadi . Seyyed Ahmad . Gupta . Harsh . Manhas . Robin . Sahu . Shweta . Gandhi . Anshul . 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) . EASY: Efficient Segment Assignment Strategy for Reducing Tail Latencies in Pinot . July 2018 . 1432–1437 . 10.1109/ICDCS.2018.00144 . 978-1-5386-6871-9 . 21659844 . https://ieeexplore.ieee.org/document/8416407.
  8. Pawar, Neha. "Pinot Joins Apache Incubator", LinkedIn Engineering, 01 April 2019
  9. Web site: Gopalakrishna . Kishore . Open Sourcing Pinot: Scaling the Wall of Real-Time Analytics . engineering.linkedin.com . LinkedIn . 3 September 2020 . https://web.archive.org/web/20150910081445/http://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics . 10 September 2015 . en.
  10. News: Yegulalp . Serdar . LinkedIn fills another SQL-on-Hadoop niche . InfoWorld . 2015-06-11 . en.
  11. Book: Fu . Yupeng . Soman . Chinmay . Proceedings of the 2021 International Conference on Management of Data . Real-time Data Infrastructure at Uber . Sigmod/Pods '21 . 9 June 2021 . 2503–2516 . 10.1145/3448016.3457552 . https://dl.acm.org/doi/abs/10.1145/3448016.3457552 . Association for Computing Machinery. 2104.00087 . 9781450383431 . 232478317 .
  12. Book: Ordonez . Carlos . Song . Il-Yeol . Anderst-Kotsis . Gabriele . Tjoa . A. Min . Khalil . Ismail . Big Data Analytics and Knowledge Discovery: 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings . 2 October 2019 . Springer . 978-3-030-27520-4 . 170 . en.
  13. Book: Uttamchandani . Sandeep . The Self-Service Data Roadmap . 10 September 2020 . "O'Reilly Media, Inc." . 978-1-4920-7520-2 . en.

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