ZiggyCreatures/FusionCache
FusionCache
FusionCache is a .NET caching framework that manages local and distributed data storage. It functions as a hybrid cache implementation, combining fast in-memory storage with a distributed second-level cache to reduce latency and remote data fetches.
The framework includes a cache stampede protector that ensures only one request fetches missing data while concurrent calls wait. It synchronizes data across multiple application nodes using a backplane and provides an observability-driven approach with built-in metrics, structured logging, and telemetry.
The system covers a broad range of caching strategies, including adaptive expiration, tag-based group invalidation, and eager background refreshing. It provides high availability features such as circuit breakers for distributed resource protection, fail-safe fallbacks for unavailable data sources, and object cloning to prevent internal store mutation.
Performance and system health are monitored via OpenTelemetry integration.
Features
- Multi-level Caching - Combines a fast local memory store with a distributed second-level cache to minimize latency and remote fetches.
- Stampede Mitigation - Ensures only one request fetches missing data while concurrent calls wait to prevent backend overload.
- Backplane Synchronization - Uses a messaging backplane to notify multiple distributed nodes of cache changes to maintain data consistency.
- Distributed Caching - Keeps data consistent across multiple application nodes by syncing cache changes and managing distributed backplanes.
- Distributed Cache Synchronizers - Uses a backplane to notify multiple application nodes of cache changes for data consistency.
- Distributed Memory Caches - Combines fast local memory storage with a distributed second-level cache to reduce latency and remote data fetches.
- Cache Invalidation - Manages complex data dependencies by grouping related entries with tags for simultaneous expiration and removal.
- Entity-Based Cache Tags - Associates multiple cache entries with shared labels to expire related sets of data with a single command.
- Tiered Caching - Implements a tiered caching system combining fast in-memory storage with a distributed second-level cache.
- .NET Caching Frameworks - Provides a high-performance caching library for .NET applications that manages local and distributed data storage.
- Request Collapsing - Blocks concurrent requests for the same missing key so only one operation fetches data from the source.
- Adaptive Caching Strategies - Dynamically adjusts expiration rules and handles null values based on the specific data retrieved from the source.
- Adaptive Expiration - Provides the capability to dynamically adjust cache expiration settings based on the specific values retrieved from the data source.
- Fallback Caching Mechanisms - Provides fail-safe fallbacks that serve expired cache entries when the underlying data source is unavailable.
- High Availability Cache Layers - Prevents system crashes and cache stampedes using fail-safe fallbacks, circuit breakers, and request locking.
- Adaptive Expiration - Implements logic to determine cache lifetimes dynamically based on the actual value retrieved from the data source.
- Circuit Breakers - Implements circuit breakers to stop requests to failing distributed caches or backplanes, preventing system overload.
- Distributed Resource Circuit Breakers - Implements circuit breakers to stop requests to failing distributed caches or backplanes, preventing system overload during outages.
- Background Cache Refresh - Provides non-blocking background updates of cache entries before they expire to eliminate requester latency.
- Cache Performance Metrics - Tracks system health and performance through integrated metrics, structured logs, and telemetry for caching pipelines.
- Observability-Driven Caches - Provides a storage system with built-in metrics, structured logging, and telemetry to monitor cache health and performance.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
