Storm_(event_processor)

Apache Storm

Apache Storm

Open-source distributed stream processing

Apache Storm is a distributed stream processing computation framework written predominantly in the Clojure programming language. Originally created by Nathan Marz[2] and team at BackType,[3] the project was open sourced after being acquired by Twitter.[4] It uses custom created "spouts" and "bolts" to define information sources and manipulations to allow batch, distributed processing of streaming data. The initial release was on 17 September 2011.[5]

Quick Facts Developer(s), Stable release ...

A Storm application is designed as a "topology" in the shape of a directed acyclic graph (DAG) with spouts and bolts acting as the graph vertices. Edges on the graph are named streams and direct data from one node to another. Together, the topology acts as a data transformation pipeline. At a superficial level the general topology structure is similar to a MapReduce job, with the main difference being that data is processed in real time as opposed to in individual batches. Additionally, Storm topologies run indefinitely until killed, while a MapReduce job DAG must eventually end.[6]

Storm became an Apache Top-Level Project in September 2014[7] and was previously in incubation since September 2013.[8][9]

Development

Apache Storm is developed under the Apache License, making it available to most companies to use.[10] Git is used for version control and Atlassian JIRA for issue tracking, under the Apache Incubator program.

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Apache Storm architecture

The Apache Storm cluster comprises following critical components:

  • Nodes- There are two types of nodes: Master Nodes and Worker Nodes. A Master Node executes a daemon Nimbus which assigns tasks to machines and monitors their performances. On the other hand, a Worker Node runs the daemon called Supervisor which assigns the tasks to other worker nodes and operates them as per the need. As Storm cannot monitor the state and health of cluster, it deploys ZooKeeper to solve this issue which connects Nimbus with the Supervisors.
  • Components- Storm has three critical components: Topology, Stream, and Spout. Topology is a network made of Stream and Spout. Stream is an unbounded pipeline of tuples and Spout is the source of the data streams which converts the data into the tuple of streams and sends to the bolts to be processed.[12]

Peer platforms

Storm is but one of dozens of stream processing engines, for a more complete list see Stream processing. Twitter announced Heron on June 2, 2015[13] which is API compatible with Storm. There are other comparable streaming data engines such as Spark Streaming and Flink.[14]

See also

References

  1. [1]
    "Apache Storm 2.5.0 Released". Retrieved 4 August 2023.
  2. [2]
    Marz, Nathan. "About Nathan Marz". Nathan Marz. Retrieved 28 March 2013.
  3. [3]
    "BackType Website (defunct)". BackType. Retrieved 28 March 2013.
  4. [4]
    "A Storm is coming: more details and plans for release". Engineering Blog. Twitter Inc. Retrieved 29 July 2015.
  5. [5]
    "Storm Codebase". Github. Retrieved 8 February 2013.
  6. [6]
    "Tutorial - Components of a Storm cluster". Documentation. Apache Storm. Retrieved 29 July 2015.
  7. [7]
    "Apache Storm Graduates to a Top-Level Project".
  8. [8]
    "Storm Project Incubation Status". Apache Software Foundation. Retrieved 29 October 2013.
  9. [9]
    "Storm Proposal". Apache Software Foundation. Retrieved 29 October 2013.
  10. [10]
    "Powered By Storm". Documentation. Apache Storm. Retrieved 29 July 2015.
  11. [11]
    "Apache Storm". storm.apache.org. Retrieved 18 August 2017.
  12. [12]
    "STREAM PROCESSING BIG DATA PROCESSING" (PDF).
  13. [13]
    "Flying faster with Twitter Heron". Engineering Blog. Twitter Inc. Retrieved 3 June 2015.
  14. [14]
    Chintapalli, Sanket; Dagit, Derek; Evans, Bobby; Farivar, Reza; Graves, Thomas; Holderbaugh, Mark; Liu, Zhuo; Nusbaum, Kyle; Patil, Kishorkumar; Peng, Boyang Jerry; Poulosky, Paul (May 2016). "Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming". 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE. pp. 1789–1792. doi:10.1109/IPDPSW.2016.138. ISBN 978-1-5090-3682-0. S2CID 2180634.
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