Failures are inevitable in distributed systems. We often come across unreliable networks, botched-up downstream systems, and rogue message payloads, forcing our applications to detect and handle failures as gracefully as possible.

After accepting a message, Kafka durably stores it in its infrastructure, allowing consumers to process it at their will. After that, the consumer must be responsible for processing the message reliably and efficiently handling failures.

This talk discusses several error-handling patterns you can implement in Kafka consumer applications. We will explore different approaches to handling transient and non-transient errors and highlight the use of dead letter topics in Kafka for message reprocessing. Finally, we will walk through a Spring Kafka application code to showcase blocking and non-blocking message retry scenarios.