[Paper] Analysis of Design Patterns and Benchmark Practices in Apache Kafka Event-Streaming Systems

Source: arXiv - 2512.16146v1

Overview

Apache Kafka is now the de‑facto backbone for high‑throughput, low‑latency event streaming in everything from fintech to IoT. While countless teams have built production‑grade pipelines on Kafka, the academic and industry literature on how to design those pipelines—and how to reliably benchmark them—remains scattered. This paper synthesises 42 peer‑reviewed studies (2015‑2025) into a single, actionable taxonomy of Kafka design patterns and a critical review of benchmarking practices.

Key Contributions

• Unified taxonomy of nine recurring Kafka design patterns (e.g., log compaction, CQRS bus, exactly‑once pipelines, CDC, stream‑table joins, saga orchestration, tiered storage, multi‑tenant topics, event‑sourcing replay).
• Co‑usage analysis that shows which patterns tend to appear together in real‑world deployments and which are domain‑specific.
• Benchmark‑practice audit covering TPCx‑Kafka, Yahoo Streaming Benchmark, and custom workloads, exposing gaps in configuration disclosure and reproducibility.
• Pattern‑benchmark matrix linking each design pattern to the most suitable benchmark suite and key performance indicators (throughput, latency, durability, resource utilisation).
• Decision‑making heuristics (flowcharts & checklists) to help architects pick patterns and benchmark setups that match their SLAs and operational constraints.

Methodology

1. Systematic literature review – The authors applied PRISMA‑style screening to identify 42 peer‑reviewed papers that explicitly discuss Kafka architecture or performance evaluation.
2. Pattern extraction – Using open coding, recurring architectural solutions were grouped, resulting in nine high‑level patterns. Frequency counts and co‑occurrence matrices were generated to reveal common pattern bundles.
3. Benchmark audit – Each study’s evaluation methodology was examined for:
   • (a) benchmark suite used,
   • (b) workload description,
   • (c) hardware/software configuration,
   • (d) reproducibility artefacts (scripts, Docker images, etc.).
4. Synthesis – Findings were distilled into a two‑dimensional matrix (patterns × benchmark suites) and distilled into practical heuristics for engineers.

The approach is deliberately non‑technical: it relies on qualitative coding and simple statistical summaries rather than deep‑learning or formal verification, making the results easy to digest for practitioners.

Results & Findings

• Pattern popularity: Log compaction (78 % of papers) and exactly‑once pipelines (65 %) dominate, while tiered storage and multi‑tenant topics appear in <30 % of studies, reflecting newer Kafka features.
• Co‑usage trends: CQRS bus frequently pairs with saga orchestration (42 % of co‑occurrences), suggesting a common “micro‑service command‑event” style. Event‑sourcing replay often couples with stream‑table joins for audit‑trail reconstruction.
• Benchmark inconsistencies: Over 60 % of papers omitted critical configuration details (e.g., replication factor, segment size), and only 18 % released reproducible artefacts. This hampers cross‑paper performance comparison.
• Performance insights: Exactly‑once pipelines incur a 15‑30 % latency penalty compared with at‑least‑once, but provide deterministic state for financial use‑cases. Tiered storage can reduce storage costs by up to 40 % with minimal impact on hot‑topic latency when properly tuned.
• Domain mapping: Real‑time analytics workloads gravitate toward stream‑table joins and CQRS; industrial telemetry leans on multi‑tenant topics and tiered storage; fintech prefers exactly‑once pipelines and saga orchestration.

Practical Implications

• Architecture selection: Engineers can now reference a concise checklist to decide whether to adopt, say, a saga‑orchestrated workflow versus a simple CQRS bus, based on latency tolerance and fault‑tolerance needs.
• Benchmarking roadmap: The pattern‑benchmark matrix tells teams which benchmark suite (TPCx‑Kafka for throughput‑focused workloads, Yahoo Streaming for end‑to‑end latency) best validates their chosen pattern, reducing trial‑and‑error.
• Operational cost optimisation: Tiered storage guidelines help cloud‑native teams shift cold data to cheaper object stores without breaking consumer guarantees.
• Reproducibility standards: By highlighting the current gaps, the paper nudges vendors and open‑source contributors to publish Docker‑Compose or Helm charts alongside performance papers, enabling “bench‑as‑code” pipelines in CI/CD.
• Risk mitigation: Understanding co‑usage patterns helps avoid anti‑patterns (e.g., coupling exactly‑once pipelines with aggressive compaction settings that can cause log‑segment churn).

Limitations & Future Work

• Scope of literature: The review only includes peer‑reviewed articles; many industry white‑papers and internal case studies were excluded, possibly missing emerging patterns.
• Benchmark diversity: While TPCx‑Kafka and Yahoo benchmarks are widely used, they may not capture niche workloads such as ultra‑low‑latency market‑data feeds; custom benchmarks remain under‑documented.
• Dynamic environments: The taxonomy is static; it does not yet address how patterns evolve under autoscaling or serverless deployments.
• Future directions: The authors propose extending the study with a living online repository of benchmark artefacts, incorporating real‑time telemetry from production Kafka clusters, and exploring pattern‑aware auto‑tuning tools.

Authors

• Muzeeb Mohammad

Paper Information

• arXiv ID: 2512.16146v1
• Categories: cs.SE
• Published: December 18, 2025
• PDF: Download PDF