Cascading (software)

Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License. Commercial support is available from Driven, Inc.[4]

Cascading
Stable release
3.3.0 / March 24, 2018 (2018-03-24)[1]
Preview release
4.0-wip-120 / March 27, 2021 (2021-03-27)[2]
Repositorygithub.com/Cascading/cascading
Written inJava
LicenseApache License v2[3]
Websitewww.cascading.org

Cascading was originally authored by Chris Wensel, who later founded Concurrent, Inc, which has been re-branded as Driven.[5] Cascading is being actively developed by the community and a number of add-on modules are available.[6]

Architecture

To use Cascading, Apache Hadoop must also be installed, and the Hadoop job .jar must contain the Cascading .jars. Cascading consists of a data processing API, integration API, process planner and process scheduler.

Cascading leverages the scalability of Hadoop but abstracts standard data processing operations away from underlying map and reduce tasks.[7] Developers use Cascading to create a .jar file that describes the required processes. It follows a ‘source-pipe-sink’ paradigm, where data is captured from sources, follows reusable ‘pipes’ that perform data analysis processes, where the results are stored in output files or ‘sinks’. Pipes are created independent from the data they will process. Once tied to data sources and sinks, it is called a ‘flow’. These flows can be grouped into a ‘cascade’, and the process scheduler will ensure a given flow does not execute until all its dependencies are satisfied. Pipes and flows can be reused and reordered to support different business needs.[8]

Developers write the code in a JVM-based language and do not need to learn MapReduce. The resulting program can be regression tested and integrated with external applications like any other Java application.[9]

Cascading is most often used for ad targeting, log file analysis, bioinformatics, machine learning, predictive analytics, web content mining, and extract, transform and load (ETL) applications.[5]

Uses of Cascading

Cascading was cited as one of the top five most powerful Hadoop projects by SD Times in 2011,[10] as a major open source project relevant to bioinformatics[11] and is included in Hadoop: A Definitive Guide, by Tom White.[12] The project has also been cited in presentations, conference proceedings and Hadoop user group meetings as a useful tool for working with Hadoop[13][14][15][16] and with Apache Spark[17]

  • MultiTool on Amazon Web Services was developed using Cascading.[18]
  • LogAnalyzer for Amazon CloudFront was developed using Cascading.[19]
  • BackType[20] - social analytics platform
  • Etsy[7] - marketplace
  • FlightCaster[21] - predicting flight delays
  • Ion Flux[22] - analyzing DNA sequence data
  • RapLeaf[23] - personalization and recommendation systems
  • Razorfish[24] - digital advertising

Domain-Specific Languages Built on Cascading
  • PyCascading[25] - by Twitter, available on GitHub
  • Cascading.jruby[26] - developed by Gregoire Marabout, available on GitHub
  • Cascalog[27] - authored by Nathan Marz, available on GitHub
  • Scalding[28] - A Scala API for Cascading. Makes it easier to transition Cascading/Scalding code to Spark. By Twitter, available on GitHub

References
  1. "Releases · Cascading/cascading". github.com. Retrieved 2021-03-29.
  2. "Releases · cwensel/cascading". github.com. Retrieved 2021-03-29.
  3. "cascading/LICENSE.txt at 3.3 · Cascading/cascading". github.com. Retrieved 2021-03-29.
  4. "Cascading and Driven | Support". Driven.
  5. "Integrate.io - One Platform To Support Your Entire Data Journey". Integrate.io.
  6. "Cascading modules". Archived from the original on 2011-08-11. Retrieved 2011-08-22.
  7. Blog post by Etsy describing their use of Cascading with Hadoop
  8. "Cascading User Guide" (PDF). Archived from the original (PDF) on February 6, 2011.
  9. "Hadoop Application Performance Management - DRIVEN's Features". Driven.
  10. Handy, Alex (1 June 2011). "The top five most powerful Hadoop projects". SD Times. Retrieved 26 October 2013.
  11. Taylor, Ronald (21 December 2010). "An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics". BioMed Central. Springer Science+Business Media. Retrieved 26 October 2013.
  12. White, Tom (September 24, 2010). Hadoop: The Definitive Guide. "O'Reilly Media, Inc.". ISBN 9781449396893 – via Google Books.
  13. "Getting Started on Hadoop". www.slideshare.net.
  14. "Julio Guijarro, Steve Loughran and Paolo Castagna, "Hadoop and beyond," HP Labs, Bristol UK, 2008" (PDF). Archived from the original (PDF) on 2011-10-01. Retrieved 2011-08-22.
  15. "Flightcaster Presentation Hadoop". www.slideshare.net.
  16. "NoSQL, Hadoop, Cascading June 2010". www.slideshare.net.
  17. "Using Cascading to Build Data-centric Applications on Spark". Spark Summit 2014. 2014-05-07. Retrieved 2016-03-25.
  18. "Cascading.Multitool on AWS".
  19. "AWS Articles". Amazon Web Services, Inc.
  20. BackType blog Archived August 25, 2011, at the Wayback Machine
  21. "FlightCaster".
  22. "Ion Flux". Archived from the original on October 23, 2011.
  23. RapLeaf Blog Archived February 1, 2011, at the Wayback Machine
  24. "Razorfish Case Study". Amazon Web Services, Inc.
  25. "PyCascading is no longer maintained". GitHub. 17 September 2021.
  26. "Cascading.JRuby". August 8, 2018 – via GitHub.
  27. "Cascalog". June 23, 2023 – via GitHub.
  28. "Scalding". June 22, 2023 – via GitHub.
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