Apache Pinot

Last updated
Apache Pinot
Original author(s)
  • Kishore Gopalakrishna
  • Xiang Fu
Developer(s) Apache Pinot
Stable release
1.2.0 / 21 August 2024;5 months ago (2024-08-21)
Repository Pinot repository
Written in Java
Operating system Cross-platform
Type
License Apache License 2.0
Website pinot.apache.org

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]

Contents

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. [9] [10] Pinot is used in production by technology companies such as Uber, [11] Microsoft, [8] 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. [9] [8]

Architecture

Architecture diagram of Apache Pinot Pinot Architecture.png
Architecture diagram of Apache Pinot

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

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References

  1. ↑ Cui, Tingting; Peng, Lijun; Pardoe, David; Liu, Kun; Agarwal, Deepak; Kumar, Deepak (14 August 2017). "Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn". Proceedings of the ADKDD'17. Association for Computing Machinery. pp. 1–7. doi:10.1145/3124749.3124759. ISBN   9781450351942. S2CID   12327343.
  2. ↑ Rosa, Marcello La (2021). ADVANCED INFORMATION SYSTEMS ENGINEERING: 33rd International Conference. Springer Nature. ISBN   978-3-030-79382-1.
  3. ↑ Chin, Francis Y. L.; Chen, C. L. Philip; Khan, Latifur; Lee, Kisung; Zhang, Liang-Jie (20 June 2018). 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. Springer. p. 153. ISBN   978-3-319-94301-5.
  4. ↑ Im, Jean-François; Gopalakrishna, Kishore; Subramaniam, Subbu; Shrivastava, Mayank; Tumbde, Adwait; Jiang, Xiaotian; Dai, Jennifer; Lee, Seunghyun; Pawar, Neha; Li, Jialiang; Aringunram, Ravi (2018-05-27). "Pinot: Realtime OLAP for 530 Million Users". Proceedings of the 2018 International Conference on Management of Data. Sigmod '18. Association for Computing Machinery. pp. 583–594. doi:10.1145/3183713.3190661. ISBN   9781450347037. S2CID   44083085.
  5. ↑ "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 (16 November 2020). "LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale". arXiv: 2002.05839 [cs.CR].
  7. ↑ Javadi, Seyyed Ahmad; Gupta, Harsh; Manhas, Robin; Sahu, Shweta; Gandhi, Anshul (July 2018). "EASY: Efficient Segment Assignment Strategy for Reducing Tail Latencies in Pinot". 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). pp. 1432–1437. doi:10.1109/ICDCS.2018.00144. ISBN   978-1-5386-6871-9. S2CID   21659844.
  8. 1 2 3 Pawar, Neha. "Pinot Joins Apache Incubator" Archived 2019-04-02 at the Wayback Machine , LinkedIn Engineering, 01 April 2019
  9. 1 2 3 Gopalakrishna, Kishore. "Open Sourcing Pinot: Scaling the Wall of Real-Time Analytics". engineering.linkedin.com. LinkedIn. Archived from the original on 10 September 2015. Retrieved 3 September 2020.
  10. ↑ Yegulalp, Serdar (2015-06-11). "LinkedIn fills another SQL-on-Hadoop niche". InfoWorld.
  11. ↑ Fu, Yupeng; Soman, Chinmay (9 June 2021). "Real-time Data Infrastructure at Uber". Proceedings of the 2021 International Conference on Management of Data. Sigmod/Pods '21. Association for Computing Machinery. pp. 2503–2516. arXiv: 2104.00087 . doi:10.1145/3448016.3457552. ISBN   9781450383431. S2CID   232478317.
  12. ↑ Ordonez, Carlos; Song, Il-Yeol; Anderst-Kotsis, Gabriele; Tjoa, A. Min; Khalil, Ismail (2 October 2019). Big Data Analytics and Knowledge Discovery: 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings. Springer. p. 170. ISBN   978-3-030-27520-4.
  13. ↑ Uttamchandani, Sandeep (10 September 2020). The Self-Service Data Roadmap. "O'Reilly Media, Inc.". ISBN   978-1-4920-7520-2.
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