Tell about working with Kafka: what happens when lag accumulates, what are the risks, and how have you solved them?

Lag in Kafka is the delay of a consumer from the latest offset in a partition. Accumulation of lag means that the consumer is not processing messages as quickly as they arrive.

Risks of lag accumulation:

• Increased data processing delay, which can lead to outdated results.
• Possible overflow of local buffers or disk space.
• In worst cases — data loss if retention is set too short.

How to address:

• Scale consumers (add more instances for parallel processing).
• Optimize message processing (e.g., reduce processing time, use batching).
• Check and increase resources (CPU, memory) for consumers.
• Set up alerts for lag growth to respond timely.
• In some cases, increase retention to allow more time for processing.

In Go, client libraries are often used to monitor lag and automatically balance load. It is important to have metrics and monitoring to quickly identify and respond to lag growth.