Jungwook Song

In today’s fast-paced world of real-time data processing, Apache Kafka has become essential for managing massive streams of information. A key performance metric is consumer lag—the number of messages waiting unprocessed in a consumer group. At first glance, rising lag appears to signal that consumers are falling behind. Yet, this metric alone can be misleading. Imagine a busy restaurant where orders pile up on the counter. It might be tempting to blame the chefs, but delays could also stem from late ingredient deliveries or a malfunctioning oven. Similarly, spikes in consumer lag might not indicate a failing consumer at all; they can result from external factors like sluggish downstream systems, temporary bottlenecks in external services, or sudden surges in data volume. This presentation challenges the conventional reliance on consumer lag as the sole indicator of performance. We will explore how integrating additional metrics—such as message ingestion rates, processing throughput, and the health of interconnected services—provides a more holistic view of your Kafka ecosystem. Through real-world case studies and practical insights, you’ll learn to diagnose issues more accurately…