LSM-Tree

Architecting High-Performance Storage: Inside RocksDB's LSM-Tree Compaction Strategy

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.

Diagram of a Bloom filter in front of an LSM-tree sorted run.

Inside LSM-Tree Bloom Filters: Architecture, Trade-offs, and Production Tuning

A deep dive into how Bloom filters accelerate point reads in LSM-tree engines like RocksDB and Cassandra, with the math, the trade-offs, and the levers you actually have at the tuning knob.

Layered diagram of a key-value store with WAL, memtable, SSTables, and compaction.

Building a Mini LSM-Tree Key-Value Store in Rust: A CV-Grade Systems Project

Build a runnable LSM-tree key-value store with a write-ahead log in Rust, from WAL append to SSTable flush to compaction. The exact project to make your CV read like a database engineer’s.

Stylized illustration of stacked sorted runs merging into a single sorted level.

Inside RocksDB LSM-Tree Compaction: Strategies, Trade-offs, and Production Tuning

How RocksDB picks compaction strategies, what each one costs you in write amplification and read latency, and the knobs that matter in production.

Diagram of an LSM-tree with bloom filters guarding each SSTable.

Implementing Bloom Filters in LSM-Trees: Reducing Read Amplification

A practical deep dive into how bloom filters are integrated into LSM-tree storage engines to slash read amplification, with concrete examples from RocksDB, Cassandra, and ScyllaDB.