Python Caching Libraries

This project is a disk-backed key-value store and persistent data structure library for Python. It provides a mechanism for persisting mappings, sets, and queues to the local filesystem to bypass memory limitations and cache expensive function results across threads and processes. The system serves as a cross-process synchronization tool, offering distributed locks, semaphores, and barriers to coordinate shared resource access. It implements advanced caching strategies such as probabilistic stampede prevention, sharded data partitioning to increase throughput, and least-recently-used eviction

Extensible memoizing collections and decorators

Automatic caching and invalidation for Django models through the ORM.

Cache and Session Library

A Python wrapper around the libmemcached interface from TangentOrg.

johnny cache django caching framework

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

Reselect is a state management optimization tool and memoization utility designed to minimize redundant calculations and UI re-renders. It functions as a library for creating memoized selectors that derive specific data from a state store. The library implements input-based memoization and recursive selector composition to create a hierarchy of derived data dependencies. It ensures reference-stable result caching and provides custom equality checking to determine when a cache should be invalidated. The project covers capabilities for derived data computation, frontend performance optimizatio

Cinder is a high-performance Python runtime implementation based on CPython. It is designed as an execution environment optimized for large-scale distributed systems and cloud environments. The project integrates a distributed memory cache and an asynchronous memory layer to manage data across multiple network nodes. It also provides a native C extension framework for developing high-performance compiled modules that link directly into the interpreter memory space. The system covers capabilities for asynchronous data retrieval, large-scale execution, and the integration of embedded scripting

Memcached is a high-performance, distributed, in-memory key-value storage and request routing engine. It functions as a volatile data store designed to accelerate dynamic applications by caching objects in RAM, thereby reducing backend database load and providing sub-millisecond response times. The system utilizes a specialized architecture that organizes memory into fixed-size slabs to minimize fragmentation and maximize throughput for high-concurrency workloads. The project distinguishes itself through a multi-threaded, lock-friendly design that scales across CPU cores and supports complex