Queuelib is a Python library providing persistent and in-memory queue data structures. It implements FIFO, LIFO, priority, and round-robin queues with disk-based storage options for durability. The library is dependency-free, using only the Python standard library, and is designed for scenarios requiring reliable queuing such as web crawlers. Architecture consists of modular components: base queue classes in queue.py, priority queue in pqueue.py, and round-robin queue in rrqueue.py. Disk-based queues manage data through chunk files (FIFO) or single files (LIFO) with careful bookkeeping to survive process restarts. The priority and round-robin queues use a factory pattern to decouple queue logic from storage implementation, enabling flexibility.
PowerThreadPool is a high-performance .NET thread pool library designed for managing both synchronous and asynchronous workloads with granular control. It implements a low-contention thread pool using work-stealing priority queues, lock-free synchronization primitives, and sophisticated state management for workers and work items. The library provides features like work dependency tracking, group-based operations, pause/resume/cancel controls, timeout/retry policies, and event-driven monitoring. Architecture centers around the PowerPool class managing Worker instances that execute work from concurrent collections, with async support via custom synchronization contexts. Entry points include QueueWorkItem overloads for submitting work and control methods like Start/Stop/Pause/Resume. The library targets .NET Framework 4.0+ through .NET 5+ and netstandard, demonstrating careful backward compatibility through conditional compilation.
This repository is a comprehensive educational resource that implements a wide range of data structures and algorithms in both JavaScript and TypeScript. It is organized by chapter, mirroring the book 'Learning JavaScript Data Structures and Algorithms' by Loiane Groner. The codebase covers fundamental data structures (arrays, stacks, queues, linked lists, sets, dictionaries, hash tables), trees (binary search trees, AVL trees, red-black trees, Fenwick trees, segment trees), heaps, tries, graphs, and classic algorithms (BFS, DFS, Dijkstra, Floyd-Warshall, Prim, Kruskal, heap sort, etc.). Each implementation is accompanied by Jest tests and many files include both .js and .ts versions. The code is well-structured, uses modern JavaScript/TypeScript features (private fields, generics, arrow functions), and includes JSDoc comments. While primarily educational, the implementations are clean and could serve as reference or be extracted for reuse in other projects. Known minor bugs exist in some hash table collision resolution implementations (e.g., get() always returns undefined in separate chaining, hash function returns NaN in linear probing).