State Management in Apache Flink
Summary: In-flight, lightweight snapshots ensure consistent state with minimal disruption to Flink processing. Explicit state declarations enable partitioning, asynchronous storage commits, and robust recovery across config changes, with HA and external state queries. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Paris Carbone
- 2. Seif Haridi
- 3. Stephan Ewen
- 4. Stefan Richter
- 5. Gyula Fóra
- 6. Kostas Tzoumas
Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 37 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.0007522744 |
| 314 | MillWheel: Fault-Tolerant Stream Processing at Internet Scale | 2013 | VLDB | 0.00028084774 |
| 538 | The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing | 2015 | VLDB | 0.00020678804 |
| 591 | TelegraphCQ: Continuous Dataflow Processing | 2003 | SIGMOD | 0.00019569071 |
| 1,226 | Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management | 2013 | SIGMOD | 0.00013180799 |
| 1,340 | Scalable Distributed Stream Processing | 2003 | CIDR | 0.00012489223 |
| 5,263 | Consistent Regions: Guaranteed Tuple Processing in IBM Streams | 2016 | VLDB | 5.5976361e-05 |
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