Database Caching Proxy Servers

ProxySQL is a database proxy designed to route and balance traffic between applications and MySQL or PostgreSQL servers. It provides database high availability by monitoring backend server health and automatically rerouting traffic to healthy nodes during failover events to prevent downtime. The system integrates with generative AI models by proxying requests through a protocol handler, creating a bridge that allows large language models to access structured data. The project includes capabilities for traffic management through automatic failover routing and cluster topology discovery. It optimizes performance using multiplexing-based connection pooling and parsed-query result caching. Observability is provided through real-time query logging, anomaly detection, and a time-series metric subsystem. Security is managed via transport layer security encryption, IP-based access control, and LDAP authentication. Operational settings are managed through a SQL-based runtime configuration interface and file-based bootstrapping.

This project is a native Windows port of the Redis in-memory key-value store. It provides a high-speed database that maintains datasets in RAM while using periodic snapshots to disk for data persistence. The implementation includes a background system service wrapper, allowing the data store to be installed as a Windows service that starts automatically upon computer boot. It utilizes native Windows compilation to enable execution directly on the host operating system. The system covers in-memory data caching and local data persistence, ensuring that information is recovered after a process restart or system stop. It also handles operating system specific path resolution for storing data snapshots in valid local folders.

PostgREST is a standalone server process that automatically transforms a relational database schema into a fully functional RESTful API. By querying system catalogs at startup, it maps tables, views, and stored procedures into standardized web endpoints, allowing developers to build backend services by focusing exclusively on database design. The service functions as a declarative engine that translates HTTP requests and query parameters directly into native SQL operations. It maintains stateless request processing and integrates connection pooling to manage high-frequency interactions efficiently. Security is enforced by delegating authentication and authorization to the database engine, which maps request tokens to specific database roles and applies row-level security policies to ensure consistent data access. This approach enables rapid prototyping by eliminating the need for custom application code to handle standard data operations. The system acts as a relational data gateway, ensuring that all interactions adhere to the integrity constraints and permissions defined within the underlying database.

Dragonfly is a high-performance, multi-model in-memory data store designed to serve as a drop-in replacement for existing database infrastructures. By utilizing a multi-threaded, shared-nothing architecture and a fiber-based concurrency model, it maximizes CPU utilization and minimizes latency for read and write operations. The system supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, and JSON documents, while maintaining full compatibility with standard industry wire protocols and client libraries. What distinguishes Dragonfly is its focus on efficiency and scalability through advanced memory management and request processing. It employs a lock-free, cache-friendly hash table structure and zero-copy serialization to reduce overhead during high-throughput operations. For durability, the system utilizes asynchronous, snapshot-based persistence that captures the state of the dataset without blocking active requests. Furthermore, it provides built-in support for horizontal scaling and cluster management, allowing for the distribution of large datasets across multiple nodes to ensure high availability. Beyond core storage, the platform includes a comprehensive suite of operational and analytical capabilities. It features integrated support for geospatial data management, real-time message brokering via publish-subscribe patterns, and full-text search. To handle massive datasets efficiently, the engine incorporates probabilistic data structures for cardinality estimation, frequency tracking, and membership testing. These features are complemented by robust administrative tools, including access control, request rate limiting, and detailed server monitoring.