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Cited by 276Advanced Database Systems and QueriesCloud Computing and Resource ManagementDistributed systems and fault tolerance

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Samza: stateful scalable stream processing at LinkedIn

Noghabi, Shadi A., Paramasivam, Kartik, Pan, Yi, Ramesh, Navina, Bringhurst, Jon, Gupta, Indranil, Campbell, Roy H.·2017·Proceedings of the VLDB Endowment
DOI 10.14778/3137765.3137770
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Abstract

Distributed stream processing systems need to support stateful processing, recover quickly from failures to resume such processing, and reprocess an entire data stream quickly. We present Apache Samza, a distributed system for stateful and fault-tolerant stream processing. Samza utilizes a partitioned local state along with a low-overhead background changelog mechanism, allowing it to scale to massive state sizes (hundreds of TB) per application. Recovery from failures is sped up by re-scheduling based on Host Affinity. In addition to processing infinite streams of events, Samza supports processing a finite dataset as a stream, from either a streaming source (e.g., Kafka), a database snapshot (e.g., Databus), or a file system (e.g. HDFS), without having to change the application code (unlike the popular Lambda-based architectures which necessitate maintenance of separate code bases for batch and stream path processing). Samza is currently in use at LinkedIn by hundreds of production applications with more than 10, 000 containers. Samza is an open-source Apache project adopted by many top-tier companies (e.g., LinkedIn, Uber, Netflix, TripAdvisor, etc.). Our experiments show that Samza: a) handles state efficiently, improving latency and throughput by more than 100X compared to using a remote storage; b) provides recovery time independent of state size; c) scales performance linearly with number of containers; and d) supports reprocessing of the data stream quickly and with minimal interference on real-time traffic.

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Metadata

Authors
Noghabi, S. A., Paramasivam, K., Pan, Y., Ramesh, N., Bringhurst, J., Gupta, I., & Campbell, R. H.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • LinkedIn (United States) (US)
ORCID
Venue
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery (ACM)
Published
2017-08
Volume
10
Issue
12
Pages
1634-1645
ISSN
2150-8097 (ISSN-L)
DOI
10.14778/3137765.3137770
OpenAlex
W2753910905
Cited by
276
References
0
Citations by year
2026: 21 · 2025: 23 · 2024: 17 · 2023: 31 · 2022: 22 · 2021: 40 · 2020: 41 · 2019: 46 · 2018: 33 · 2017: 2
FWCI
41.31
Type
journal article
Language
en
Open access
closed
Locations
Proceedings of the VLDB Endowment (publishedVersion)
Funders
University of Illinois at Urbana-Champaign
Topics
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
Keywords
distributed stream processing, fault tolerance, fault recovery, reprocessing, Franz Kafka, HDFS

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