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Testing Kafka Applications with Deterministic Replay

Testing event-driven architectures is notoriously difficult. Synchronizing asynchronous events in JUnit often leads to Thread.sleep() and flaky tests.

BitDive solves this by treating Kafka Messages as just another input/output stream to be recorded and deterministically replayed, providing full observability into your event-driven flows.

The Challenge with Kafka Testing​

1. EmbeddedKafka is Heavy​

Starting an embedded broker for every test suite is slow and resource-intensive.

2. Asynchronous Flakiness​

Asserting that "Message B arrived after Message A" usually involves polling loops (awaitility), which inevitably lead to flakes in CI.

3. Schema Drift​

Your test expects JSON Format A, but the producer has updated to Format B. Mocks don't catch this.

How BitDive Virtualizes Kafka​

BitDive intercepts the Consumer and Producer interfaces within the JVM.

For Consumers (Input)​

BitDive "injects" the recorded record directly into your @KafkaListener method.

  • No Broker Needed: The test runs without a running Kafka instance.
  • Instant Execution: The message is processed immediately in the test thread.

For Producers (Output)​

BitDive captures the ProducerRecord your code sends.

  • ** deterministic Assertion**: You can assert that exactly 3 specific messages were sent, locally, without checking a real topic.

Code Example​

class OrderProcessingReplayTest extends ReplayTestBase {

    @Override
    protected List<ReplayTestConfiguration> getTestConfigurations() {
        // Replay a specific Kafka message processing scenario
        return ReplayTestUtils.fromKafkaConsumerConfigFile(
            Arrays.asList("order-processing-event-id")
        );
    }
}

Why it's better than MockConsumer​

MockConsumer requires you to manually construct ConsumerRecords. BitDive uses real runtime data from your staging or production environment, ensuring your tests verify actual behavior, not just your understanding of the schema.

Next Steps​

See Full Cycle Testing for enterprise event patterns.

Last updated on by turmyshevd