State Management in Apache Flink: Consistent Stateful Distributed Stream Processing
Summary: Apache Flink introduces asynchronous, pipelined in-flight snapshots for lightweight, consistent distributed state capture without halting stream processing. Explicit state enables partitioning, transparent persistence, fault recovery, reconfiguration, versioning, external queries, and output commits with low overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Paris Carbone (Royal Institute of Technology)
- 2. Seif Haridi (Royal Institute of Technology)
- 3. Stephan Ewen (data Artisans)
- 4. Stefan Richter (data Artisans)
- 5. Gyula Fóra (King Digital Entertainment Limited)
- 6. Kostas Tzoumas (data Artisans)
BibTeX Citation
@article{carbone_vldb17,
title = {{State Management in Apache Flink: Consistent Stateful Distributed Stream Processing}},
author = {Carbone, Paris and Haridi, Seif and Ewen, Stephan and Richter, Stefan and Fóra, Gyula and Tzoumas, Kostas},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {12},
pages = {1718--1729},
doi = {10.14778/3137765.3137777},
url = {https://doi.org/10.14778/3137765.3137777},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 32 of 32 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 22 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00055938421 |
| 218 | MillWheel: Fault-Tolerant Stream Processing at Internet Scale | 2013 | VLDB | 0.00024379041 |
| 325 | The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing | 2015 | VLDB | 0.0002095522 |
| 511 | TelegraphCQ: Continuous Dataflow Processing | 2003 | SIGMOD | 0.00017058115 |
| 970 | Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management | 2013 | SIGMOD | 0.00012776109 |
| 1,101 | Scalable Distributed Stream Processing | 2003 | CIDR | 0.00011999722 |
| 4,310 | Consistent Regions: Guaranteed Tuple Processing in IBM Streams | 2016 | VLDB | 6.6700421e-05 |
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Semantically Similar Papers
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|---|---|---|---|---|
| 1 | 6,601 | AStream: Ad-hoc Shared Stream Processing | 2019 | SIGMOD |
| 2 | 11,978 | Klink: Progress-Aware Scheduling for Streaming Data Systems | 2021 | SIGMOD |
| 3 | 9,831 | Optimistic Recovery for Iterative Dataflows in Action | 2015 | SIGMOD |
| 4 | 1,771 | Samza: Stateful Scalable Stream Processing at LinkedIn | 2017 | VLDB |
| 5 | 6,626 | Watermarks in Stream Processing Systems: Semantics and Comparative Analysis of Apache Flink and Google Cloud Dataflow | 2021 | VLDB |
| 6 | 11,445 | How Reliable Are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems | 2025 | VLDB |
| 7 | 4,453 | Consistency and Completeness: Rethinking Distributed Stream Processing in Apache Kafka | 2021 | SIGMOD |
| 8 | 970 | Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management | 2013 | SIGMOD |
| 9 | 10,871 | Fugue: Online Elasticity for Distributed Stateful Stream Processing | 2026 | VLDB |
| 10 | 9,656 | Disaggregated State Management in Apache Flink 2.0 | 2025 | VLDB |