Compacted topics in Apache Kafka provide a way to efficiently store and manage large amounts of data by allowing for the asynchronous deletion of older records that are no longer needed. This feature is particularly useful for representing the state of resources within a topic, where more recent records represent a more recent state of that resource. Compacted topics offer several benefits over regular topics, including reduced disk usage and faster consumer restart times. However, implementing compacted topics requires careful consideration of memory usage and can be complex due to the need to balance deduplication with metadata storage costs. Apache Kafka's implementation of compacted topics uses a two-pass algorithm that reduces memory requirements by storing hashes instead of keys, while WarpStream's implementation also uses a similar approach but tackles additional challenges related to object storage.