An in-depth look at implementing LSM-Trie caching in RocksDB, covering memtable hierarchies, tiered compaction strategies, and production-proven techniques for maximizing write throughput.
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.
A deep dive into combining Apache Pulsar’s messaging backbone with RocksDB’s embedded state engine to achieve exactly-once semantics in stateful stream processing.
Bloom filters are the unsung hero of LSM-tree reads: a few bits per key let engines like RocksDB skip 99% of disk seeks. Here’s the math, the tradeoffs, and what to tune in production.
Compaction strategy is the single most consequential knob in a RocksDB deployment. This post walks through how tiered (universal) and leveled compaction actually work, what they cost at write and read time, and how to pick one based on your workload shape.