Kafka enables the movement of large volumes of streaming data between systems quickly, securely, and efficiently. For this reason, it has emerged as a powerful tool in our big data era where data velocity and security are more important than ever.

Understanding and optimizing your use of Kafka throughput metrics is an essential component to successfully supporting use cases built on top of Kafka, such as real time Kafka streams and streaming analytics frameworks. Throughput pertains to the amount of data that can be moved between systems or applications within a given timeframe. It’s widely used to gauge the performance of RAM, hard drives, network connections, and the Internet. With Kafka, throughput pertains to simply the speed at which messages move from point to point.

Identifying Throughput Metrics

The performance of various components contributes to the overall throughput inside a Kafka cluster: How fast can producers produce content? How are brokers handling the movement of messages? How fast are consumers consuming the messages? All of these factors impact throughput. Measuring these components and their performance helps you form a baseline set of numbers. Kafka JMX metrics are foundational to your ability to determine whether your Kafka cluster is operating optimally.

Once the JMX metrics are being captured, it is up to the cluster owner and/or architect to consume the metrics, from visualizing those metrics to creating charts and ultimately gleaning insights. This process of capture, analyze, and gain actionable insights is next to impossible to tackle manually. Solutions are required to automate as much of the process as possible while not representing their own steep learning curve and context switch for the Kafka platform owners.

How Pepperdata Supercharges Your Kafka Throughput Metrics

Kafka provides you with JMX metrics, but they represent only a subset of the data you need to evaluate and maintain the health and performance of the platform. Big questions to ask are: Are JMX metrics alone enough to do the job? (Spoiler: No). Can you synthesize those metrics into fuel for crucial decisions? Can you use those Kafka throughput metrics to improve your cluster’s performance and avoid being surprised by performance problems? How will you correlate the JMX-based performance data with performance metrics from the platform hardware and applications built on top of Kafka? 

This is where Pepperdata Streaming Spotlight comes in. It takes those JMX metrics, correlates them with the critical hardware and application metrics, and serves up the insights you need to maintain a high-performance platform. It also supports alerting to move you into a proactive mode preventing those surprise outages that no one likes. Finally, it stores all of this performance data long term so you can perform trend analysis and capacity planning against it.

With Pepperdata, managing and improving your Kafka environment’s throughput is quick and easy. You quickly recognize your cluster’s issues and understand its requirements. You can make the necessary adjustments to your cluster’s configurations, resources, producers, brokers, and consumers.

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