Apache Kafka is one of today’s most commonly used event streaming platforms. While using the Kafka platform, quite often, we run into a scenario where we have to process a large number of events/messages that are placed on a broker. Traditional approaches, where a consumer is listening to a topic and then processes these message within the consumer itself, can become a performance bottleneck if the number of messages being placed on the topic is high. In such cases, the rate at which a consumer can process messages will be very low, as there are a large number of messages getting placed on the topic. A potential solution that can be applied in such a scenario is to offload message processing to the worker threads in a thread pool.
In this section, we will take a look into how a Kafka consumer can offload its work to a thread pool. We will leverage Java’s ExecutorService framework to create a thread pool.
This approach primarily involves two steps. The first step is to create a KafkaConsumer that can read messages from a topic. Once the messages are read, they are delivered to a threadpool for further processing. The second step is to create worker threads that perform further processing of each message.
Step 1, Kafka Consumer Implementation: Here, we read the messages from a topic and dispatch the messages to a thread pool created using ThreadPoolExecutorService.
Step 2, Worker Thread(Message/Record Handler) Implementation: Here, we perform further processing of the messages.
The final step is to create a KafkaConsumer (KafkaProcessor) and specify the number of worker threads through the
This approach might not be needed/suitable for all scenarios. You have to carefully evaluate the best approach to be used with your Kafka consumer implementation.