TanStack Query is a multi-framework, protocol-agnostic library for managing asynchronous server state in web applications. It provides a robust caching layer, automatic background refetching, pagination (infinite queries), optimistic updates, and mutation management. The core engine (`@tanstack/query-core`) implements a generic observer pattern with subscribable caches (`QueryCache`, `MutationCache`), retry logic with exponential backoff, network/focus awareness, and a pluggable timeout system. Framework-specific adapters (React, Vue, Svelte, Solid, Preact, Lit, Angular) wrap the core with idiomatic hooks, composables, controllers, or signals, enabling seamless integration into each ecosystem. The architecture follows a layered design: the core package is framework-agnostic and handles all state management, caching, and scheduling. Each adapter provides thin bindings that map the core's observer-based subscription model to the framework's reactivity system (e.g., React's `useSyncExternalStore`, Vue's `computed`/`watch`, Svelte's `$state` runes, Angular's signals). Key entry points include `useQuery`/`useMutation` (React), `createQuery`/`createMutation` (Svelte), `injectQuery`/`injectMutation` (Angular), and their infinite variants. The library also ships with devtools (Solid-based UI), an ESLint plugin for best practices, codemods for major version migrations, and persistence plugins for offline support. Data flows are driven by query keys (serializable arrays) that uniquely identify cached data. Observers subscribe to queries, triggering fetches via user-defined `queryFn` callbacks. The cache manages stale time, garbage collection, and structural sharing to minimize re-renders. Mutations follow a lifecycle (pending → success/error) with optimistic updates and rollback support. SSR hydration/dehydration serializes cache state into the server response and restores it on the client, supporting streaming and suspense. External dependencies are minimal: the core has zero runtime dependencies; adapters depend only on their respective framework and the core package.
Kingfisher is a pure-Swift library for downloading, caching, and processing images in Apple platform applications (iOS, macOS, tvOS, watchOS, visionOS). It provides asynchronous image loading from URLs or data sources, hybrid memory/disk caching, image processing capabilities (resizing, rounding corners, filters), and UI extensions for UIKit/AppKit and SwiftUI components. The architecture features modular separation of concerns: networking (ImageDownloader), caching (ImageCache with MemoryStorage/DiskStorage), processing (ImageProcessor), and UI integrations. Key entry points include UIImageView.kf extensions, KFImage SwiftUI view, and KingfisherManager.shared for programmatic control. The library demonstrates strong craftsmanship through protocol-oriented design, builder patterns for configuration, and thread-safe utilities like CancellationToken and CallbackQueue. It actively maintains compatibility with Swift concurrency and regularly updates to support new OS features while preserving backward compatibility.
SDWebImage is a mature, high-performance asynchronous image loading and caching library for Apple platforms (iOS, macOS, tvOS, watchOS, visionOS). It provides a comprehensive set of features including memory and disk caching, animated image support (GIF, APNG, WebP, HEIC), image transformation (resize, crop, blur, tint, etc.), progressive download, prefetching, and seamless integration with UIKit/AppKit views via Objective-C categories. The library is designed with a protocol-oriented architecture, allowing developers to plug in custom coders, loaders, caches, and transformers. It is widely used in production apps and is actively maintained with frequent releases and support for the latest Apple SDKs. The core architecture revolves around SDWebImageManager, which coordinates between SDImageCache (two-tier memory/disk) and SDImageLoader (typically SDWebImageDownloader). Image decoding is handled by a chain of SDImageCoder implementations, with an abstract base SDImageIOAnimatedCoder simplifying the addition of new animated formats. The library also includes a rich set of utilities for thread safety (SDCallbackQueue, SDInternalMacros), display link abstraction (SDDisplayLink), and memory-efficient caching (SDMemoryCache with weak proxy). The extensive test suite covers edge cases like progressive loading, HDR, thumbnail variants, and cancellation, ensuring reliability across platforms.
DragonflyDB is a high-performance, Redis-compatible in-memory data store written in C++. It implements the Redis wire protocol (RESP) and Memcached protocol, providing drop-in replacement capabilities with significantly higher throughput and lower latency, especially on multi-core machines. The architecture is based on a shared-nothing, multi-threaded design using fibers (boost.fibers) and a custom proactor event loop (epoll/io_uring). Data is partitioned across shards, each with its own thread, and transactions use a VLL-based locking scheme to minimize contention. The system supports replication via a write-ahead journal, cluster mode with slot migration, persistence via RDB snapshots, and tiered storage for offloading cold data to SSD. It also includes a full-text search engine with vector similarity (HNSW), JSON document store, Lua scripting, ACL-based security, and probabilistic data structures (Bloom, Cuckoo, Count-Min Sketch, Top-K). The codebase demonstrates advanced engineering in memory management (custom allocators, SIMD probing, compact object encoding) and concurrency (fiber-aware synchronization, RCU patterns). While primarily an application, it contains research-grade data structures and algorithms, giving it a hybrid character between production service and systems research project.
Valkey is a high-performance in-memory data structure server, forked from Redis under a BSD-3-Clause license. It serves as a key-value store with support for strings, hashes, lists, sets, sorted sets, streams, geospatial indexes, and more. The architecture is event-loop-driven (single-threaded main loop with background I/O threads for persistence, replication, and lazy freeing). It supports clustering (up to 16384 slots), replication (leader-follower with partial resync), persistence (RDB snapshots, AOF logs), Lua scripting (EVAL/FUNCTION), a pluggable module API, TLS, RDMA, and advanced features like client-side caching, hot-key detection, and replication throttling. Key entry points include the `valkey-server` daemon, `valkey-cli` client, `valkey-benchmark` load generator, and `valkey-check-rdb`/`valkey-check-aof` integrity tools. External dependencies include jemalloc (default allocator), Lua 5.1, LZ4, Zstd (optional), OpenSSL (TLS), and RDMA libraries (optional). The codebase is actively maintained with extensive unit and integration tests, and demonstrates high craftsmanship in its lock-free queues, streaming compression, and data structure implementations.
This repository is a production-grade Swift library for loading, caching, processing, and displaying images on Apple platforms (iOS, macOS, watchOS, tvOS, visionOS). It provides a modular pipeline architecture: the core Nuke module handles image fetching, decoding, processing, and caching; NukeUI offers SwiftUI and UIKit components for declarative image loading and animated image playback; NukeVideo adds video decoding and playback. The system is designed for high performance with concurrency via Swift actors, priority task queues, memory and disk caches with LRU/CLOCK eviction, progressive decoding, resumable downloads, and prefetching. Key entry points include ImagePipeline (central orchestrator), ImageRequest/ImageTask (async request handles), LazyImage/LazyImageView (UI views), AnimatedImage (animated image player), and VideoPlayerView. Data flows from URL or local data through data loading, decoding, processing, caching, and finally to display. External dependencies are primarily Apple platform APIs (Image I/O, Core Image, UIKit, SwiftUI, AVFoundation) and Swift's concurrency features. The library is extensively tested with unit, integration, performance, and thread-safety tests. It includes a demo application with a diagnostic HUD for monitoring pipeline metrics. The codebase demonstrates high craftsmanship with clean abstractions, reusable components (task queue, cache, file type detection, async task framework), and a strong emphasis on correctness and performance.
SWR is a React Hooks library for data fetching that implements the stale-while-revalidate cache strategy. It provides a set of hooks (useSWR, useSWRInfinite, useSWRImmutable, useSWRSubscription, useSWRMutation) that enable efficient client-side data fetching with automatic caching, deduplication, revalidation on focus, interval polling, and optimistic updates. The library is designed as a standalone npm package with a modular internal architecture, featuring a middleware system for extensibility, a configurable cache provider, and built-in support for React Suspense and server-side rendering. It is widely used in production and actively maintained by Vercel.
This repository is a monorepo for the Cacheable ecosystem, providing a comprehensive suite of caching libraries for Node.js. It includes packages for in-memory caching (CacheableMemory, NodeCache), file-based caching (FlatCache, FileEntryCache), HTTP request caching (CacheableRequest, CacheableNet), and a multi-tier cache manager (cache-manager) used by NestJS. The core Cacheable class implements layered (L1/L2) caching with support for TTL, events, hooks, tag-based invalidation, non-blocking writes, and distributed synchronization via Qified/Redis. The architecture is modular, with each package designed to be used independently or composed together. The codebase is written in TypeScript with extensive test coverage (vitest), dual CJS/ESM builds, and uses modern tooling (biome, pnpm). It emphasizes production-readiness with features like stampede protection (coalesceAsync), per-store TTLs, and statistics tracking. The repository also includes a benchmark package comparing various caching implementations and a documentation website. While the API surface is large, the code demonstrates solid engineering practices and provides genuinely reusable components that solve recurring caching problems.
Redisson is a feature-rich Java client for Valkey and Redis that extends basic key-value storage with distributed Java collections, objects, and services. It provides synchronous, asynchronous, and reactive APIs for Redis operations, along with integrations for popular frameworks like Spring, Micronaut, Quarkus, and Hibernate. The library implements advanced Redis features including streams, geo-spatial indexing, pub/sub, bloom filters, and distributed computing primitives. Its architecture features a clean separation between core Redis protocol handling, codec-based serialization, and framework-specific adapters, making it a versatile tool for building distributed applications with Redis as a backend.
cacache is a Node.js library implementing a content-addressable cache system. It provides key-based and content-addressable access to cached data with built-in integrity verification using Subresource Integrity (SRI) standards. The system is designed for high concurrency and fault tolerance, protecting against corruption, partial writes, and race conditions. It features a modular architecture separating concerns like index management, content storage, memoization, and verification/garbage collection.
Octopool is a production-grade GitHub API relay and caching proxy, deployed as a Cloudflare Worker with a companion Go CLI. It pools multiple GitHub identities (PATs, GitHub App installations) to share API quota, caches responses in D1 and edge cache, and applies string-rewrite policies to protect sensitive data. The CLI acts as a drop-in `gh` shim, routing eligible read commands through the relay before falling back to native `gh`. The system also supports token-free transports (public web pages, raw content, Git smart HTTP) to minimize API consumption. Architecture includes Durable Objects for coordination (pool identity selection, cache-fill ownership, policy revisioning), D1 for persistent cache and audit, and edge cache for low-latency reads. The codebase is dual-language (TypeScript for Worker, Go for CLI) with extensive unit and e2e tests, CI/CD, and a dashboard for monitoring.
This repository is a Redis-compatible in-memory data structure server with significant custom extensions, including vector similarity search via HNSW (vector-sets module), rate limiting (GCRA), key eviction protection (BLESS), and a comprehensive module system. The architecture is a single-threaded event loop (ae) with optional IO threads, supporting a wide range of data structures: strings, hashes, lists, sets, sorted sets, streams, geospatial indexes, HyperLogLog, and bitfields. Persistence is provided via RDB snapshots and AOF logs, with replication and cluster support. The codebase is a fork/derivative of Redis/Valkey, incorporating additional features not present in upstream, such as hash field expiration (ebuckets/estore), per-key metadata (keymeta), and a Fenwick tree-based key selection (kvstore). Key entry points include the redis-server daemon, redis-cli, redis-benchmark, and module loading via the redismodule.h API. External dependencies include jemalloc, hiredis, Lua 5.1, linenoise, tre, xxhash, OpenSSL, and optional ZSTD compression. The system is designed for high throughput and low latency, with extensive use of custom memory management (zmalloc) and data structure optimizations.
BigCache is a high-performance, concurrent, evicting in-memory cache library for Go, designed to store large numbers of entries without impacting garbage collection. It achieves this by storing entries in byte slices and using a `map[uint64]uint64` (hash to index) that contains no pointers, thus avoiding GC scanning. The cache is sharded for concurrency, supports TTL-based eviction, a configurable cleanup goroutine, and optional hard memory limits. It provides a simple key-value API (Set, Get, Delete, Append) along with iteration, statistics, and per-key metadata. The repository also includes an optional standalone HTTP server that exposes a RESTful API for cache operations, making it suitable for deployment as a caching microservice. The codebase is well-tested with unit tests, benchmarks, and fuzz testing, and follows modern Go practices.
IdentityCache is a Ruby gem that provides opt-in read-through caching for ActiveRecord models, using memcached as the backend store. It allows developers to declare cache indexes on model attributes and associations, and automatically handles cache invalidation via after_commit hooks. The library supports embedded and reference-based caching for associations, secondary indexes, attribute caching, and batch fetching. It includes advanced features like fill locks to mitigate thundering herd problems, memoization per request, and deferred expiration. The codebase is well-structured with clear separation of concerns, extensive test coverage, and production use at Shopify.
xmlquery is a Go library for querying XML documents using XPath expressions. It provides functions to parse XML from various sources (string, file, URL, etc.), execute XPath queries, and manipulate the resulting node tree. The library features a caching mechanism for compiled XPath expressions to improve query performance and supports streaming parsing for large XML files to minimize memory usage. The architecture is modular, with separate concerns for parsing, querying, and node manipulation, and it relies on external dependencies for XPath expression handling and LRU caching.
FastCache is a small, high-performance, thread-safe in-memory cache library for .NET 6+. The core is a generic FastCache<TKey, TValue> class built on ConcurrentDictionary, using Environment.TickCount64 for wrap-free expiration timestamps and a shared, lock-throttled background timer to evict expired entries across all cache instances. It provides atomic GetOrAdd with factory arguments to avoid delegate allocations, indexer-based upserts, and an atomic Remove(key, value) operation. The library is distributed as a NuGet package (Jitbit.FastCache) and includes a BenchmarkDotNet project comparing it to MemoryCache and HybridCache, plus a unit test project covering eviction callbacks and cache semantics.
IOb-cache is an open-source, highly configurable cache IP core written in Verilog, designed for FPGA and ASIC integration. It features a pipelined architecture that allows one request per clock cycle (read and write), supports configurable set-associativity (direct-mapped to k-way), multiple replacement policies (LRU, PLRU_MRU, PLRU_TREE), and both write-through and write-back write policies. The front-end can connect via IOb, AXI-Lite, or Wishbone interfaces, while the back-end supports IOb or AXI4. A built-in cache controller provides software-accessible registers for performance monitoring (hit/miss counters), cache invalidation, and write-through buffer status. The project is managed through the Py2hwsw Python framework, which generates the full hardware and software stack, including simulation wrappers, synthesis constraints, and testbench software. The repository includes comprehensive documentation (LaTeX user guide), CI/CD pipelines (simulation, FPGA build, linting), and FuseSoC compatibility for easy reuse. Although primarily a hardware design, the testbench software is written in C and exercises the cache through memory-mapped I/O. The core has been peer-reviewed (published in Algorithms journal) and is integrated into the IOb-SoC system. Current limitations include a TODO note about fixing testbenches and improving documentation. The hardware code is well-structured and modular, with reusable components such as the replacement policy and cache controller blocks.
proxpi is a caching reverse-proxy server for PyPI and any PEP 503-compatible package index. It fronts one or more upstream indices, serves the Python simple repository API to pip clients in both HTML (PEP 691 v1.0) and JSON (PEP 691 v1.1) forms, and performs correct HTTP content negotiation including Accept, Accept-Encoding, and Vary headers. Index responses (project list and per-project file lists) are cached in memory with per-index TTLs; package files are cached on disk with a configurable size cap and least-frequently-used eviction. When a requested file is still downloading in the background, the server emits a 302 redirect to the upstream URL rather than blocking the client. The system is a single-process Starlette ASGI application (proxpi.server:app) designed for multi-threaded serving under Gunicorn/Hypercorn, configured entirely through environment variables (index URLs, TTLs, cache size/dir, connect/read/download timeouts, SSL verification toggle, log level). Its main target is reducing pip install latency and upstream load in CI pipelines. The implementation is a compact three-layer design: server.py handles routing, templating, and compression; _cache.py holds the caching engine (a File abstraction with FileFromHTML/FileFromJSON parsers for the dual-format simple repository API, an _IndexCache with per-package locks and a fast-path that fetches project pages without refreshing the full index, and a _FileCache with background downloads, atomic os.replace writes, and LFU eviction); _server_utils.py provides RFC-correct content-negotiation helpers built on the stdlib email header parser. The code is thread-safe by design (per-key locks, exception-storing worker threads) and memory-efficient, streaming HTML index parsing through lxml.iterparse via a custom file-like response adapter. The test suite is notably strong: parametrized integration tests against mock HTML/JSON indices covering yanked files, core-metadata hashes, gpg-sig attributes, content negotiation edge cases, cache invalidation, and 404 paths, plus end-to-end tests that run real pip downloads against live PyPI. CI runs the matrix across Python 3.8-3.14, lints with black/isort, and publishes multi-arch Docker images with fully hash-pinned dependencies. One caveat: the README is stale, documenting the pre-1.3 Flask implementation, while the code has already migrated to Starlette (listed in CHANGELOG as unreleased), and the Docker image now runs Gunicorn with an ASGI worker.
Methanol is an extensible HTTP client library built on Java's java.net.http package. It provides core HTTP functionality enhanced with features like automatic decompression (gzip, deflate, Brotli), pluggable body adapters for JSON/XML/Protobuf, HTTP caching with multiple backends (memory, disk, Redis), request/response interceptors, retry mechanisms, progress tracking, and Kotlin DSL support. The library follows a modular architecture with separate modules for core functionality, format adapters, extensions, and testing. Key entry points include Methanol.Builder for client creation, MutableRequest for request building, and BodyAdapter for encoding/decoding. Data flows involve request interception, body encoding, HTTP exchange via java.net.http, response interception, body decoding, and optional caching/progress tracking. External dependencies vary by module: core requires only JDK 11+, while adapters depend on libraries like Jackson/Gson/Moshi, extensions on Redis Lettuce or native Brotli, and testing on JUnit5/MockWebServer.
This repository is a production-grade FastAPI boilerplate (named 'Fastro') designed to accelerate building SaaS backends. It provides a complete, modular architecture with three layers: infrastructure (config, caching, rate limiting, auth, logging, database), interfaces (REST API, admin panel), and vertical-slice modules (user, tier, API keys, rate limits). The codebase is fully async, uses SQLAlchemy 2.0 with `MappedAsDataclass`, Pydantic v2, and FastCRUD for generic CRUD operations. Authentication is session-based via the external `crudauth` library, with server-side sessions, CSRF, OAuth (Google wired), and API key support. Caching and rate limiting follow a provider pattern with swappable Redis/Memcached backends. The repo also includes a CLI tool (`bp`) with plugin discovery for commands and features, a multi-stage Dockerfile, Alembic migrations, and extensive documentation (though some docs describe aspirational features not yet implemented). A paid fork (FastroAI) adds payments, email, and frontend, but the free boilerplate is self-contained and functional.