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Watermarks in stream processing systems: semantics and comparative analysis of Apache Flink and Google cloud dataflow
Open accessAbstract
Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of watermarks , a key tool for reasoning about temporal completeness in infinite streams. First, we describe what watermarks are and why they are important, highlighting how they address a suite of stream processing needs that are poorly served by eventually-consistent approaches: • Computing a single correct answer, as in notifications. • Reasoning about a lack of data, as in dip detection. • Performing non-incremental processing over temporal subsets of an infinite stream, as in statistical anomaly detection with cubic spline models. • Safely and punctually garbage collecting obsolete inputs and intermediate state. • Surfacing a reliable signal of overall pipeline health . Second, we describe, evaluate, and compare the semantically equivalent, but starkly different, watermark implementations in two modern stream processing engines: Apache Flink and Google Cloud Dataflow.
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Apache Flink offers one of the most elaborated dataflow models, providing precise control of time and state.
“It offers one of the most elaborated dataflow models, providing precise control of time and state (Carbone et al., 2015(Carbone et al., , 2017;;Akidau et al., 2021).”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Akidau et al., 2021
Section 5.2 describes Flink as a dataflow processor with stateful operators and event-time processing; Section 5.2.3 describes timer APIs whose callbacks can modify state. These passages support control over time and state, but the paper does not establish that Flink has one of the most elaborated dataflow models. (LLM, conf=0.83)
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Large-scale cluster management at Google with Borg
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015) · Proceedings of the Tenth European Conference on Computer Systems · doi:10.1145/2741948.2741964 · Cited by 1,468
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Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
Henning, S., & Hasselbring, W. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111879 · Cited by 85
Metadata
- Authors
- Akidau, T., Begoli, E., Chernyak, S., Hueske, F., Knight, K., Knowles, K., Mills, D., & Sotolongo, D.
- Affiliations
- Snowflake Inc. (United States) (US)
- Oak Ridge National Laboratory (US)
- Google (United States) (US)
- Ververica GmbH, Berlin, Germany
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2021-07
- Volume
- 14
- Issue
- 12
- Pages
- 3135-3147
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W3197174867
- Cited by
- 56
- References
- 1
- Citations by year
- 2026: 17 · 2025: 15 · 2024: 6 · 2023: 9 · 2022: 8 · 2021: 1
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Proceedings of the VLDB Endowment (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (AC05-00OR22725, DE--AC05--00OR22725, DE-AC05?, 00OR22725)
- Battelle (DE-AC05- 00OR22725, DE-AC05)
- UT-Battelle (DE-AC05, DEAC05-00OR22725, AC05-00OR22725)
- Topics
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- watermarks, stream processing, infinite streams, Apache Flink, garbage collection, anomaly detection, data completeness, stream processing systems
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