Apache Cassandra is a distributed, wide-column NoSQL database designed for high availability, horizontal scalability, and fault tolerance. The codebase implements a peer-to-peer cluster architecture with consistent hashing for data placement and tunable replication. The system provides the Cassandra Query Language (CQL) as its primary interface, with a native binary protocol for client communication. Key subsystems include the storage engine (SSTables, memtables, commit log), distributed consensus protocols (legacy Paxos and the newer Accord transactional protocol), cluster metadata management (TCM replacing gossip), and secondary indexing (SASI and Storage-Attached Index SAI). The codebase also includes operational tooling (nodetool CLI, cqlsh shell, stress testing tool, sstable utilities) and extensive testing infrastructure (unit, distributed in-JVM, deterministic simulator, property-based, and microbenchmarks). The architecture follows a modular design with clear separation of concerns: transport layer (Netty-based NIO), query processing (CQL parser, statement execution, result set building), storage (commit log for durability, memtables for writes, SSTables for reads, compaction strategies), and distributed coordination (gossip for failure detection, streaming for bootstrap/repair, hinted handoff for availability). The newer Accord protocol introduces a transactional layer on top of the existing storage engine, enabling ACID transactions across partitions. The codebase is predominantly Java with a Python-based CQL shell (cqlsh) and extensive shell-based build and CI automation. Key entry points include the Cassandra server daemon (started via bin/cassandra), the nodetool CLI (bin/nodetool) for administration, cqlsh (bin/cqlsh) for interactive CQL, and various sstable utilities (sstableloader, sstablescrub, etc.). External dependencies include Netty for networking, Dropwizard Metrics for instrumentation, Picocli for CLI parsing, and various Java libraries for compression, encryption, and serialization. The build system uses Ant with Maven dependency resolution, and the project includes Dockerfiles for containerized builds and testing. The repository also contains formal verification artifacts (TLA+ models) and a deterministic simulation framework for testing distributed correctness, reflecting a strong emphasis on reliability.
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.