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.
LSM-Tree
Architecting Log-Structured Merge Trees for Write-Intensive Distributed Databases
How modern distributed databases turn the write-amplification problem into a throughput advantage, and what it costs you on the read path.
Optimizing RocksDB LSM-Tree Compaction: Strategies for Write Amplification and Storage Efficiency
How RocksDB’s compaction strategies trade off write amplification, read amplification, and space amplification — and how to tune them for real workloads.
Architecting Log-Structured Merge Trees for Write-Intensive Distributed Databases
LSM trees are the backbone of modern write-intensive databases like Cassandra, RocksDB, and ScyllaDB. This post walks through the architecture, compaction strategies, and tuning decisions that separate a healthy LSM deployment from a write stall.
Optimizing LSM-Tree Read Performance with Bloom Filters: Architecture, Tuning, and Production Implementation
Bloom filters can turn costly disk scans into fast key checks. This post walks through the underlying design, practical tuning, and a production rollout in a Kafka‑RocksDB pipeline.