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Computer Science > Networking and Internet Architecture

arXiv:2205.09415 (cs)
[Submitted on 19 May 2022]

Title:On Efficiently Partitioning a Topic in Apache Kafka

Authors:Theofanis P. Raptis, Andrea Passarella
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    Abstract: Apache Kafka addresses the general problem of
    delivering extreme high volume event data to diverse consumers
    via a publish-subscribe messaging system. It uses partitions to
    scale a topic across many brokers for producers to write data in
    parallel, and also to facilitate parallel reading of consumers.
    Even though Apache Kafka provides some out of the box
    optimizations, it does not strictly define how each topic shall
    be efficiently distributed into partitions. The well-formulated
    fine-tuning that is needed in order to improve an Apache Kafka
    cluster performance is still an open research problem. In this
    paper, we first model the Apache Kafka topic partitioning process
    for a given topic. Then, given the set of brokers, constraints
    and application requirements on throughput, OS load, replication
    latency and unavailability, we formulate the optimization problem
    of finding how many partitions are needed and show that it is
    computationally intractable, being an integer program.
    Furthermore, we propose two simple, yet efficient heuristics to
    solve the problem: the first tries to minimize and the second to
    maximize the number of brokers used in the cluster. Finally, we
    evaluate its performance via large-scale simulations, considering
    as benchmarks some Apache Kafka cluster configuration
    recommendations provided by Microsoft and Confluent. We
    demonstrate that, unlike the recommendations, the proposed
    heuristics respect the hard constraints on replication latency
    and perform better w.r.t. unavailability time and OS load, using
    the system resources in a more prudent way.

          This work has been submitted to the IEEE for possible
          publication. Copyright may be transferred without notice,
Comments: after which this version may no longer be accessible. This
          work was funded by the European Union's Horizon 2020
          research and innovation programme MARVEL under grant
          agreement No 957337
Subjects: Networking and Internet Architecture (cs.NI); Distributed,
          Parallel, and Cluster Computing (cs.DC)
Cite as:  arXiv:2205.09415 [cs.NI]
          (or arXiv:2205.09415v1 [cs.NI] for this version)
          https://doi.org/10.48550/arXiv.2205.09415
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Theofanis Raptis [view email]
[v1] Thu, 19 May 2022 09:30:04 UTC (379 KB)
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