Storage-Engines

Layered diagram of a copy-on-write B-tree showing page versions branching from a stable root.

Architecting Copy-on-Write B-Trees: High-Performance Snapshot Strategies for Production Databases

A practical deep dive into copy-on-write B-tree design: page versioning, root promotion, MVCC snapshots, and the GC strategies that keep production storage engines fast under write pressure.

Stylized illustration of a bloom filter grid and an LSM-tree SSTable stack.

Implementing Bloom Filters in LSM-Tree Storage Engines for High-Throughput Key Lookups

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.

Abstract diagram of an LSM-tree with sorted runs cascading across levels.

Architecting RocksDB Storage Engines: Selecting Between Tiered and Leveled Compaction for Production Workloads

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.

Layered diagram of memtable flushing into sorted SSTables being compacted.

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.

Diagram comparing LSM tree layers with B‑tree nodes.

Why Log Structured Merge Trees Outperform B‑Trees for Writes

LSM trees batch writes into immutable files, avoiding costly in‑place updates that B‑trees require. This post explains the mechanics behind their superior write performance.