ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
Developer tools · #apachekafka
Learn how to use Apache Kafka Log Compaction to manage stateful data, handle deletions with tombstones, and avoid the pitfalls of infinite log growth.
Learn how Apache Kafka Log Compaction manages stateful data by retaining the latest value for each key, reducing storage overhead, and implementing tombstones for deletions.
Decide between idempotent and transactional delivery in Kafka. Compare latency, operational complexity, and configurations for financial event processing.
Learn how to use Apache Kafka Log Compaction to transform topics into durable key-value stores for efficient application state recovery and management.
Learn how to select the right Kafka producer 'acks' setting to balance throughput and durability, and why 'acks=all' requires 'min.insync.replicas' to be effective.
Optimizing a Kafka deployment for low-traffic workloads requires balancing infrastructure costs against operational stability. When reducing the broker footprint to minimize CPU and memory usage, certain configuration defaults may lead to unnecessary resource consumption. Specifically, the allocation of num.network.threads and num.io.threads is typically tun
An upgrade needs a compatibility check, a tested release and a recovery path. Which changes deserve particular attention before the new version reaches production?
Apache Kafka's idempotent producer uses a Producer ID (PID) and sequence numbers to prevent duplicate messages during retry cycles. This mechanism ensures that if a broker fails to acknowledge a write but the message was actually persisted, subsequent retries are discarded by the broker. The deduplication state is tied to the lifecycle of the producer sessio
Recovery needs to recreate the working service and its required data after a machine or process is lost. Which artifacts and state need protection, and how should the restore be checked?