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: