What typical difficulties arise when a microservice that reads data from Kafka fails?

When a microservice that reads data from Kafka fails, the following typical issues may occur:

• Message loss: if the correct acknowledgment (commit) of offsets is not configured, the service may skip or reprocess messages upon restart.

• Reprocessing (duplication): improper management of offsets can lead to processing the same messages multiple times, which may cause data inconsistency.

• Hanging or blocking: if the service hangs or crashes during processing, resource locking or delays in processing new messages may occur.

• Load balancing issues: failure of one microservice instance can cause Kafka partition rebalancing, leading to temporary unavailability or overload of other instances.

• Serialization/deserialization issues: if data has changed and the service is not updated, errors may occur when reading messages.

• Accumulation of unsent messages: if the service cannot keep up with processing messages, the Kafka queue may grow, leading to delays and possible overflow.

• Monitoring and alerting: without proper monitoring, it is difficult to quickly detect and fix failures.

To minimize problems, it is important to:

• Use the correct offset acknowledgment mode (e.g., commit after successful processing).
• Process messages idempotently.
• Configure automatic rebalancing and monitoring.
• Ensure resilience to deserialization errors.

Example of processing with offset commit after successful processing in Java Kafka Consumer:

consumer.subscribe(Collections.singletonList("topic"));
try {
    while (true) {
        ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
        for (ConsumerRecord<String, String> record : records) {
            // process message
        }
        consumer.commitSync(); // acknowledge processing
    }
} catch (Exception e) {
    // log error
} finally {
    consumer.close();
}