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WeAreDevelopers LIVE • Nov 30, 2020

How to Benchmark Your Apache Kafka

Kirill Kulikov

Why does setting acks=1 sacrifice durability without actually improving end-to-end latency? Uncover the true configuration trade-offs needed to benchmark and optimize your Apache Kafka clusters.

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#1 about 2 min

Introduction to Apache Kafka benchmarking and performance analysis

Why benchmarking is critical for understanding cluster capacity and optimizing resource utilization.

#2 about 4 min

Reviewing core Apache Kafka architecture and distributed fundamentals

An overview of events, topics, partitions, consumer groups, and the append-only log.

#3 about 2 min

Understanding throughput and latency trade-offs for varied workloads

How workload requirements dictate the service goals when configuring an Apache Kafka cluster.

#4 about 2 min

Measuring end-to-end latency components across the message lifecycle

Breaking down processing time across producing, publishing, replication, and fetching stages.

#5 about 4 min

Tuning producer and consumer configurations for low latency

Adjusting batch size, linger times, acks, and fetch sizes to ensure rapid message delivery.

#6 about 3 min

Reducing end-to-end latency using the sticky partitioner strategy

How the sticky partition strategy improves batching efficiency for non-keyed records to decrease wait times.

#7 about 7 min

Scaling topic partitions and configurations to maximize throughput

Maximizing data transmission rates through partition scaling, batch optimization, and selection of compression algorithms.

#8 about 2 min

Defining objectives and methodologies for systematic load testing

Exploring stress, spike, and soak testing methodologies to evaluate system behavioral limits under heavy use.

#9 about 4 min

Best practices for environment setup and executing load tests

Ensuring accurate results through dedicated test environments, JVM warm-up phases, and interpreting percentile metrics.

#10 about 3 min

Tracking observability metrics to ensure healthy cluster performance

Monitoring hardware usage, system state, and structural JMX metrics to maintain overall cluster health during operation.

#11 about 3 min

Using native command-line tools for synthetic performance testing

Utilizing built-in producer and consumer CLI scripts to generate specific computational loads and measure throughput accurately.

#12 about 2 min

Executing workloads and injecting system faults with Trogdor

Leveraging Apache Kafka's built-in framework to run complex benchmarks and simulate resilient system failures.

#13 about 2 min

Conducting advanced load testing workflows with Apache JMeter

Using the Pepper-Box plugin to finely control request threads, message payload sizes, and construct detailed web dashboards.

Matching moments

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