danielealbano/cachegrand

Health v2 · maintenance only

Adoption not part of the score

1004 stars · 35 forks observed · 2026-10-21

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-07, confidence 0.85

cachegrand is a high-performance key-value store and data ingestion, processing, and serving platform written in C and optimized for modern hardware using io_uring. It speaks Redis, Memcache, Kafka, and HTTP(s) protocols, includes its own database engine, and supports WebAssembly for server-side data processing.

Use cases

• fast key-value store handling millions of operations per second with sub-millisecond latency
• redis-compatible cache server optimized for modern multi-core hardware
• drop-in memcached alternative written in c
• ingest streaming data from kafka and serve it over redis or http
• run custom server-side data processing with webassembly
• low-latency caching layer for high-traffic applications on linux

When to choose

• You need a Redis-protocol-compatible key-value store tuned for modern hardware and very low latency on Linux
• You want an integrated pipeline that ingests via Kafka or Redis, processes data with WebAssembly, and serves results over Redis, Memcache, HTTP(s), or Kafka without stitching together multiple tools
• You are benchmarking next-generation cache stores that leverage io_uring for maximum throughput

When to avoid

• You need the full Redis feature set or guaranteed protocol completeness - only part of the Redis protocol is implemented and features are still in development
• You need a battle-tested store with a mature ecosystem, wide tooling support, and production track record like Redis or Memcached
• You need to run on platforms other than Linux, since the project targets Linux with io_uring

Facets

application · maturity experimental

caching database http-server streaming wasm databases backend performance self-hosted cloud key-value-store redis-compatible memcached-compatible kafka io-uring low-latency high-performance webassembly data-ingestion real-time data-engineering linux

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markdown · JSON · MCP: product_card(name="danielealbano/cachegrand")