LiteDB is an embedded NoSQL document database library for .NET. In production it is not a server but an in-process database engine that persists BSON documents to a single file with ACID transactions, WAL/checkpointing, MVCC snapshot isolation, B-tree and vector indexes, optional AES-256 encryption, and multi-process shared-file coordination. The public API is built around `LiteDatabase`, `LiteCollection<T>`, and `LiteRepository`, with `BsonMapper` for POCO mapping and both LINQ and SQL-like `Execute` query paths. Internally, `LiteEngine` implements page-based storage, crash recovery, compaction/rebuild, system collections, and an aggressive query optimizer that rewrites predicates, selects indexes, and supports vector similarity search. The repository also ships a CLI shell, a repro runner, stress tools, and a substantial fuzzing harness used to prove durability and recovery correctness under injected faults and power loss. The architecture is a layered monolith: a client/API layer (`LiteDatabase`, mapper, SQL parser, LINQ expression translation), a query execution layer with index selection and predicate optimization, and a storage layer with buffered page cache, WAL recovery, transaction registry, and shared-mutex coordination. Write flows go from POCO through `BsonMapper` into `BsonDocument`, into transaction snapshots and the WAL, then onto data/index pages; reads flow from index seeks/scans through BSON expression evaluation into materialized documents. The core library has no third-party runtime dependencies, relying only on the .NET base class library; supplementary tools add BenchmarkDotNet, Spectre.Console, and Gemini embedding APIs. The code is actively maintained, with a very large regression suite keyed to GitHub issues, continuous fuzzing, and compatibility tooling across file format versions.
redb is a high-performance, ACID-compliant embedded key-value store library written in Rust. It provides a B-tree-based storage engine with copy-on-write semantics, MVCC concurrency control, and support for typed tables, multimaps, savepoints (ephemeral and persistent), compaction, and crash recovery. The library exposes a clean public API centered around `Database`, `TableDefinition`, `WriteTransaction`, `ReadTransaction`, and `ReadableTable` traits, with optional derive macros for user-defined key/value types. It is designed for single-writer, multiple-reader access and supports multi-process concurrency via byte-range file locking (experimental). The codebase is extensively tested with integration tests, fuzzing, and benchmarks comparing against LMDB, RocksDB, SQLite, and fjall. Architecturally, redb uses a copy-on-write B+tree for all tables (data, system, freed pages) with a buddy allocator for page management and a region-based file layout. The page store includes a striped LRU read cache, write buffer, and dynamic cache partitioning. Commit strategies range from non-durable (no fsync) to 1PC+C (default) and 2PC (secure). Crash recovery relies on XXH3-128 checksums and optional quick-repair via persistent allocator state. The library is `no_std`-compatible with feature-gated std-dependent parts, and includes a custom spin-lock synchronization module for embedded targets. Key entry points include the `Database::builder()` and `Database::open()` methods, `TableDefinition::new()` for schema definition, and transaction methods like `WriteTransaction::commit()` and `ReadTransaction::open_table()`. The `StorageBackend` trait allows pluggable storage (file, in-memory, custom) and is used extensively in tests for fault injection and crash simulation. External dependencies are minimal (standard library, optional chrono/uuid), with benchmark crates depending on heed, rocksdb, fjall, and rusqlite for comparison. The repository demonstrates strong engineering craftsmanship with clean abstractions, careful handling of edge cases (max keys, large values, corruption), and a well-documented design document (`docs/design.md`). The codebase is actively maintained with a stable file format and backward compatibility guarantees.
FalkorDB is a graph database implemented as a Redis module, written in Rust. It supports the Cypher query language and uses sparse matrices and GraphBLAS/LAGraph for efficient graph operations. The system features MVCC for concurrency control, a batch-oriented query execution engine, and pluggable indexing (including native FalkorDB index for range, full-text, and vector search). Key architectural layers include the Redis module interface, query parser/planner, runtime operators, and storage backend. The codebase demonstrates strong modularity and includes several reusable patterns such as copy-on-write for MVCC, a storage backend seam, thread-local string interning, batched result emission, and a pipeline of query optimization passes.
This repository contains a technical book titled 'The Art of Problem-Solving in Software Engineering: How to Make MySQL Better'. It is structured as a collection of Markdown files covering MySQL internals, performance optimization, and problem-solving methodologies. The book does not contain executable code or data processing scripts; instead, it provides theoretical explanations, case studies, and references to MySQL versions 8.0.x and 5.7. Topics include transaction throttling, latch sharding, hybrid data structures, Group Replication enhancements, and performance testing techniques using tools like BenchmarkSQL and SysBench. The repository serves as an educational resource for database administrators, performance engineers, and MySQL contributors seeking to understand and resolve MySQL scalability and performance issues. It emphasizes analytical approaches over trial-and-error, drawing from computer science fundamentals such as queueing theory, distributed systems, and algorithmic optimization. While the book references benchmark results and visualizations (stored as images in the media/ directory), it does not include the raw data, scripts, or tools needed to reproduce those results. As a documentation-focused repository, it lacks traditional software engineering components like APIs, CLI tools, or reusable libraries. Its value lies in the consolidated knowledge presented across chapters, which readers can apply to MySQL tuning and troubleshooting scenarios. The content appears well-organized and researched, but the absence of runnable code or data assets limits its direct utility as a software artifact.