Explore how RocksDB’s LSM-tree compaction strategy manages write-heavy workloads by buffering mutations in memory and flushing sorted runs to disk, then merging them through configurable compaction paths with distinct amplification tradeoffs.
LSM trees power high-performance key-value stores like RocksDB. This post breaks down their storage layout, compaction mechanics, and the latency trade-offs that matter for production workloads.
How RocksDB picks compaction strategies, what each one costs you in write amplification and read latency, and the knobs that matter in production.
How RocksDB’s compaction strategies trade off write amplification, read amplification, and space amplification — and how to tune them for real workloads.
A practical comparison of RocksDB’s leveled and tiered compaction, with architecture diagrams, performance numbers, and actionable tuning guidelines for production systems.