How to Quickly Resolve Massive Kafka Message Backlog in Production

In daily development, many teams encounter massive Kafka message backlogs caused by bugs (e.g., not committing offsets) or producer speed exceeding consumer speed.

Step 1: Diagnose and fix bugs. Ensure consumers commit offsets after processing. Example of faulty code that never commits offsets and the corrected version that calls

consumer.commitSync()

.

<code>while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        process(record);
        // missing commit
    }
}
</code>

<code>while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        process(record);
    }
    // commit offset
    consumer.commitSync();
}
</code>

Step 2: Optimize consumer logic. Use multithreading or reduce unnecessary computation to increase throughput. For example, two consumer machines processing 100 messages/s can be tuned to handle 500 messages/s, processing 3.6 million messages per hour.

Step 3: Emergency scaling with a temporary topic. When backlog is urgent, create a temporary topic with many more partitions (e.g., 10×) and forward messages there, allowing fast consumption. After clearing the backlog, revert to the original topology.

Key takeaways:

Set up monitoring and alerts to detect backlog early.

Always check for bugs and optimize consumer code before creating temporary topics.

If messages have expiration, schedule a retry job to reprocess timed‑out messages.