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.
LSM-Tree
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.
Optimizing Bloom Filters in LSM-Trees: Performance Tuning, Probabilistic Structures, and Production-Ready Implementation Strategies
Bloom filters are the de‑facto guard against unnecessary disk reads in LSM‑tree databases. This post shows concrete tuning knobs, architectural patterns, and code snippets to make them production‑grade.