Keydb Eng

: Fewer instances result in lower monthly cloud bills.

Beyond its parallel execution model, KeyDB offers several open-source optimizations designed to reduce computing footprints and simplify horizontal scaling: 1. MVCC Non-Blocking Database Engine

: When a Redis instance hits a performance ceiling on a single core, KeyDB allows you to scale up by upgrading to a larger multi-core VM instance. keydb eng

emerged in 2019 as a high-performance fork of Redis, designed specifically to address these hardware limitations through a multithreaded engine. The Multithreaded Advantage

Redis’s single-threaded model uses a global lock implicitly—there is no concurrency. KeyDB introduces a based on key hashing. : Fewer instances result in lower monthly cloud bills

Redis uses jemalloc, a fine-tuned allocator for multi-threaded workloads, but Redis itself is single-threaded. KeyDB operates in a truly concurrent environment, exposing allocator contention.

To take full advantage of KeyDB’s multithreading, specify the number of threads when launching the server: emerged in 2019 as a high-performance fork of

storage-mode flash storage-path /path/to/ssd maxmemory 10GB cache-memtables 2GB

If you’d like to see a performance benchmark comparison for a specific use case (e.g., caching vs. database), let me know! AI responses may include mistakes. Learn more Share public link

In the world of high‑performance databases, few names are as well known as Redis. But as modern applications demand ever‑higher throughput and lower latency, the traditional single‑threaded architecture of Redis has become a bottleneck on today’s multi‑core servers. was created to solve that problem. A high‑performance fork of Redis, KeyDB is built from the ground up to fully leverage modern hardware while remaining a drop‑in replacement for existing Redis deployments.

In the world of in-memory databases, Redis has long been the gold standard for caching, real-time analytics, and message brokering. However, as applications scale, the single-threaded nature of Redis becomes a bottleneck—not due to compute, but due to I/O and context switching overhead.