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Twitter Heron: Stream Processing at Scale

Published: 27 May 2015 Publication History

Abstract

Storm has long served as the main platform for real-time analytics at Twitter. However, as the scale of data being processed in real-time at Twitter has increased, along with an increase in the diversity and the number of use cases, many limitations of Storm have become apparent. We need a system that scales better, has better debug-ability, has better performance, and is easier to manage -- all while working in a shared cluster infrastructure. We considered various alternatives to meet these needs, and in the end concluded that we needed to build a new real-time stream data processing system. This paper presents the design and implementation of this new system, called Heron. Heron is now the de facto stream data processing engine inside Twitter, and in this paper we also share our experiences from running Heron in production. In this paper, we also provide empirical evidence demonstrating the efficiency and scalability of Heron.

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  • (2024)Incremental Sliding Window Connectivity over Streaming GraphsProceedings of the VLDB Endowment10.14778/3675034.367504017:10(2473-2486)Online publication date: 1-Jun-2024
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cover image ACM Conferences
SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
May 2015
2110 pages
ISBN:9781450327589
DOI:10.1145/2723372
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 ACM 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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Publication History

Published: 27 May 2015

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Author Tags

  1. real-time data processing.
  2. stream data processing systems

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SIGMOD/PODS'15
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SIGMOD/PODS'15: International Conference on Management of Data
May 31 - June 4, 2015
Victoria, Melbourne, Australia

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SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all
  • (2024)Incremental Sliding Window Connectivity over Streaming GraphsProceedings of the VLDB Endowment10.14778/3675034.367504017:10(2473-2486)Online publication date: 1-Jun-2024
  • (2024)POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least ResistanceProceedings of the VLDB Endowment10.14778/3648160.364817517:6(1350-1363)Online publication date: 1-Feb-2024
  • (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
  • (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
  • (2024)TensAIR: Real-Time Training of Neural Networks from Data-streamsProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647762(73-82)Online publication date: 26-Jan-2024
  • (2024)Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration OptimizationProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645048(142-153)Online publication date: 7-May-2024
  • (2024)Evaluating Stream Processing AutoscalersProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666036(110-122)Online publication date: 24-Jun-2024
  • (2024)Safe Shared State in Dataflow SystemsProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666029(30-41)Online publication date: 24-Jun-2024
  • (2024)Snatch: Online Streaming Analytics at the Network EdgeProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3629577(349-369)Online publication date: 22-Apr-2024
  • (2024)Bayesian-Driven Automated Scaling in Stream Computing With Multiple QoS TargetsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339983435:7(1251-1267)Online publication date: Jul-2024
  • Show More Cited By

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