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Hazelcast jet: low-latency stream processing at the 99.99 th percentile
Open accessAbstract
Jet is an open source, high performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and provide exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents the main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.
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Hazelcast Jet differs from other frameworks in its execution model, which is based on a concept similar to coroutines and cooperative threads.
“Hazelcast Jet differs from other frameworks in its execution model, which is based on a concept similar to coroutines and cooperative threads (Gencer et al., 2021).”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Gencer et al., 2021
Hazelcast Jet is a stream processing framework built on top of the Hazelcast IMDG distributed, in-memory object store.
“Hazelcast Jet (Gencer et al., 2021) is a stream processing framework built on top of the Hazelcast IMDG distributed, in-memory object store.”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Gencer et al., 2021
Gencer et al. (2021) implemented the Nexmark benchmark for their performance evaluation of Hazelcast Jet.
“Moreover,Gencer et al. (2021)implemented the Nexmark benchmark for their performance evaluation of Hazelcast Jet.”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Gencer et al., 2021
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Cites
A catalog of stream processing optimizations
Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R. (2014) · ACM Computing Surveys · doi:10.1145/2528412 · Cited by 299
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Cited by
Sorted by citations
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
- Gencer, C., Topolnik, M., Ďurina, V., Demirci, E., Kahveci, E. B., Gürbüz, A., Lukáš, O., Bartók, J., Gierlach, G., Hartman, F., Yılmaz, U., Doğan, M., Mandouh, M., Fragkoulis, M., & Katsifodimos, A.
- Affiliations
- Hazelcast Inc.
- Delft University of Technology (NL)
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2021-07
- Volume
- 14
- Issue
- 12
- Pages
- 3110-3121
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W3198546401
- Cited by
- 38
- References
- 1
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 5
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the VLDB Endowment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- distributed stream processing, in-memory data grid, low-latency processing
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