Latest Performance Articles

Kafka performance depends on how producers batch records, how partitions distribute work, how brokers use disk and network, and how fast consumer groups can process data. Scaling Kafka successfully requires identifying which layer is saturated instead of assuming that more brokers or more partitions

Caching improves latency and reduces pressure on databases, APIs, storage systems, and expensive computations by keeping frequently needed data closer to where it is consumed. In production, however, adding a cache also creates another copy of data that can become stale, inconsistent, overloaded, or

Caching significantly reduces database load, but poorly designed caches can become bottlenecks themselves. Two of the most common production problems are cache stampedes , where many requests simultaneously rebuild an expired value, and hot keys , where a single cache entry receives a disproportiona