Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Spark SQL

  • Xiao Li
  • Cheng Lian
  • Shu MoEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_251


SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark, which is a unified engine for distributed data processing (Zaharia et al. 2012). Spark SQL can process, integrate, and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka, and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). The common use cases include ad hoc analysis, logical warehouse, query federation, and ETL processing. It also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning (Meng et al. 2016; Michael et al. 2018), GraphFrame for graph-parallel computation (Dave et al. 2016), and TensorFrames for TensorFlow binding. These libraries and Spark SQL can be seamlessly combined in the same application with holistic optimization by Spark SQL.


Spark is a general purpose big data processing system. It was...

This is a preview of subscription content, log in to check access.


  1. Armbrust M, Xin RS, Lian C, Huai Y, Liu D, Bradley JK, Meng X, Kaftan T, Franklin MJ, Ghodsi A, Zaharia M (2015) Spark SQL: relational data processing in spark. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD’15)Google Scholar
  2. Dave A, Jindal A, Li LE, Xin R, Gonzalez J, Zaharia M (2016) Graphframes: an integrated API for mixing graph and relational queries. In: Proceedings of the 4th international workshop on graph data management experiences and systems (GRADES’16)Google Scholar
  3. Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A (2016) MLlib: machine learning in apache spark. J Mach Learn Res 17(1):34:1–34:7Google Scholar
  4. Michael A, Tathagata D, Joseph T, Burak Y, Shixiong Z, Reynold X, Ali G, Ion S, and Matei Z (2018) Structured Streaming: A declarative API for real-rime applications in apache spark. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD’18). 601–613Google Scholar
  5. Ousterhout K, Canel C, Ratnasamy S, Shenker S (2017) Monotasks: architecting for performance clarity in data analytics frameworks. In: Proceedings of the 26th ACM symposium on operating system principlesGoogle Scholar
  6. Xin RS, Rosen J, Zaharia M, Franklin MJ, Shenker S, Stoica I (2013) Shark: SQL and rich analytics at scale. In: Proceedings of the ACM SIGMOD workshop on the web and databases (SIGMOD’13)Google Scholar
  7. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX symposium on networked systems design & implementation (NSDI’12)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Databricks Inc.San FranciscoUSA