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Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark

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Matei ZahariaAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 601 - 613

https://doi.org/10.1145/3183713.3190664

Published: 27 May 2018 Publication History

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Abstract

With the ubiquity of real-time data, organizations need streaming systems that are scalable, easy to use, and easy to integrate into business applications. Structured Streaming is a new high-level streaming API in Apache Spark based on our experience with Spark Streaming. Structured Streaming differs from other recent streaming APIs, such as Google Dataflow, in two main ways. First, it is a purely declarative API based on automatically incrementalizing a static relational query (expressed using SQL or DataFrames), in contrast to APIs that ask the user to build a DAG of physical operators. Second, Structured Streaming aims to support end-to-end real-time applications that integrate streaming with batch and interactive analysis. We found that this integration was often a key challenge in practice. Structured Streaming achieves high performance via Spark SQL's code generation engine and can outperform Apache Flink by up to 2x and Apache Kafka Streams by 90x. It also offers rich operational features such as rollbacks, code updates, and mixed streaming/batch execution. We describe the system's design and use cases from several hundred production deployments on Databricks, the largest of which process over 1 PB of data per month.

References

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Harby AZulkernine F(2025)Data Lakehouse: A survey and experimental studyInformation Systems10.1016/j.is.2024.102460127(102460)Online publication date: Jan-2025
https://doi.org/10.1016/j.is.2024.102460
Rigas STzouveli PKollias S(2024)An End-to-End Deep Learning Framework for Fault Detection in Marine MachinerySensors10.3390/s2416531024:16(5310)Online publication date: 16-Aug-2024
https://doi.org/10.3390/s24165310
Saravanan SBalasubramanian U(2024)An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT NetworksBig Data Mining and Analytics10.26599/BDMA.2023.90200277:2(500-511)Online publication date: Jun-2024
https://doi.org/10.26599/BDMA.2023.9020027
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Index Terms

Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data streams
    2. Database management system engines
      1. Stream management

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Information

Published In

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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SIGMOD/PODS '18

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SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

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SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Harby AZulkernine F(2025)Data Lakehouse: A survey and experimental studyInformation Systems10.1016/j.is.2024.102460127(102460)Online publication date: Jan-2025
https://doi.org/10.1016/j.is.2024.102460
Rigas STzouveli PKollias S(2024)An End-to-End Deep Learning Framework for Fault Detection in Marine MachinerySensors10.3390/s2416531024:16(5310)Online publication date: 16-Aug-2024
https://doi.org/10.3390/s24165310
Saravanan SBalasubramanian U(2024)An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT NetworksBig Data Mining and Analytics10.26599/BDMA.2023.90200277:2(500-511)Online publication date: Jun-2024
https://doi.org/10.26599/BDMA.2023.9020027
Gracia-Tinedo RJunqueira FKaitchuck T(2024)"Back to the Byte": Towards Byte-oriented Semantics for Streaming StorageProceedings of the 25th International Middleware Conference Industrial Track10.1145/3700824.3701099(43-49)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3700824.3701099
Kozar ADel Monte BZeuch SMarkl V(2024)Fault Tolerance Placement in the Internet of ThingsProceedings of the ACM on Management of Data10.1145/36549412:3(1-29)Online publication date: 30-May-2024
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Jindal ABeedkar KSingh VMohammed JSingla TGupta AChoudhary K(2024)Reactive Dataflow for Inflight Error Handling in ML WorkflowsProceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning10.1145/3650203.3663333(51-61)Online publication date: 9-Jun-2024
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Santhanam KRaghavan DRahman MVenkatesh TKunjal NThaker PLevis PZaharia M(2024)ALTOProceedings of the 4th Workshop on Machine Learning and Systems10.1145/3642970.3655844(117-125)Online publication date: 22-Apr-2024
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Saxena SGhatak SKolla RMukherjee DChakraborty TBaeza-Yates RBonchi F(2024)DPHGNN: A Dual Perspective Hypergraph Neural NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672047(2548-2559)Online publication date: 25-Aug-2024
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Henning SVogel ALeichtfried MErtl ORabiser RBalsamo SKnottenbelt WAbad CShang W(2024)ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing FrameworksProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645036(2-13)Online publication date: 7-May-2024
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Vogel AHenning SPerez-Wohlfeil EErtl ORabiser R(2024)A Comprehensive Benchmarking Analysis of Fault Recovery in Stream Processing FrameworksProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666040(171-182)Online publication date: 24-Jun-2024
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