This article walks through building a database storage engine from a simple shell script to a full distributed key‑value system, covering in‑memory indexing, SSTable creation, LSM‑Tree architecture with compaction, replication strategies, and sharding techniques for scaling across multiple machines.
This article explains the Log-Structured Merge Tree (LSM-Tree) architecture, describing its immutable storage design, the roles of WAL, MemTable, ImmuTable, and SSTable, and detailing the write workflow, compaction process, and the associated read, space, and write amplification challenges.
This article explains the fundamentals of LSM‑Tree storage engines, introduces SSTable concepts, describes how to build and maintain ordered log files, and provides a complete Java implementation of a tiny key‑value store with code examples and design analysis.
This article explains CB‑SQL's two backup approaches—logical (using DUMP and IMPORT) and physical (using BACKUP and RESTORE)—detailing their mechanisms, supported formats, storage options, performance characteristics, and how they ensure reliable data recovery for large‑scale distributed databases.
LevelDB, Google's high-performance key‑value store built on LSM trees, uses an in‑memory skip‑list, immutable memtables, and sstable files organized in multi‑level compaction, offering interfaces for creation, reads, writes, snapshots, and new features like fuzzy search and JSON storage, all explained with diagrams.
