Kafka Streams is best defined as a client library designed specifically for building applications and microservices. A concise way to think about Kafka streams is to think of it as a messaging service, where data (in the form of messages) is transferred from one application to another, from one location to a different warehouse, within the Kafka cluster. All the input and output data are housed in Apache Kafka Clusters.

To visualize, here’s how data transfer looks like inside the Kafka environment: a data request (a consumer, in Kafka terminology) is created and sent through to the other side. Here, a producer generates the content in response to the request and delivers it through the Kafka architecture directly back to the consumer, who then consumes the information.

Kafka Streams is a highly popular tool for developers, mainly because it can handle millions of requests from consumers to producers and spread them across tens and dozens of servers to ensure fast and continuous transit while retaining accuracy. Conversely, the platform can move large volumes of data from producers to consumers while guaranteeing that the consumers are actually able to consume the data at the speed at which they need to and in the order in which they need to.

But what people really love about Kafka Streams is that there is no pause in the data flow. Real-time data processing at scale is vital for many applications. (Without it, a Facebook or an Uber would be in big trouble.) Kafka streams data non-stop, unlike in a traditional setting where dated legacy applications are used. Kafka Streams enables unhindered data flows within its cluster, ensuring data is transported from one application to another, from consumer to producer, in a back-and-forth cycle with no stops in between. Its built-in redundancy assures no data is lost in transit and arrives at its intended destination with integrity intact.

Best Practices for Optimizing Kafka Streams

So: Kafka Streams is great. The advantages Kafka Streams brings to any application development project squarely justify the platform’s increasing popularity and ubiquity. But to really derive the greatest value from Kafka Streams, you need to apply a few best practices to the underlying Kafka platform:

  • Be Mindful of Speed

There are a number of things you need to track if you want to optimize data streams within the Kafka Streams environment. One of these is speed, as it relates to:

  • Production rates for producers
  • Consumption rates for consumers
  • The health of the Kafka cluster

Speed is a critical component. Kafka Streams can facilitate the movement of data within the cluster efficiently. However, if the messages are moving at a pace that isn’t fast enough for your application, it can mean trouble. Make sure that the data and the messages are moving at the speed at which you need them to move.

  • Make Sure You Have The Resources

To fully optimize Kafka Streams, your architecture must be built with the right amount of resources so that it attains and maintains the necessary data streaming speed for it to achieve its goals. To put simply, you need to address these questions:

  • Are there enough producers?
  • Are there enough brokers in the middle to move the data?
  • Am I consuming the data fast enough?

It is essential that the route of the message is built to meet your application’s requirements and that the throughput is to your satisfaction. It’s about being built right for the job. You wouldn’t want a semi-truck to deliver pizzas. The same principle applies to bits, not just atoms. 

  • Use the Right Holistic Software Tools

If Kafka starts to underperform, it might be the Kafka metrics that are the issue. But it could also be another issue, like a hard drive or some memory that is underperforming. Teams need to be able to troubleshoot as quickly and effectively as possible. 

With the right big data analytics tool, you gain a centralized view of all the hardware, applications, and monitoring metrics in your stack, including metrics from Kafka Streams. What you get is a single unified interface where metrics and messaging are correlated. This allows you to see which application is experiencing issues and how it impacts Kafka and its functions.

In most big data monitoring setups, hardware monitoring is different from big data application monitoring. Add Kafka monitoring to the picture and you have one big disjointed monitoring environment. But with a unified monitoring suite like Pepperdata, you can see and track everything. You enjoy absolute and unparalleled visibility and observability into your big data stack.

Kafka is a rich and complex platform. There is lots more to learn. But these three best practices for Kafka Streams are powerful foundations for setting you up for success. 

Learn how you can now start monitoring Kafka Streams using Pepperdata.

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