// FEATURE: Check if key is tiered (exists on disk, not in RAM) if (o == nullptr && isKeyTiered(c->db, c->key))
KeyDB is designed to be a drop-in replacement for Redis. This means you can switch from Redis to KeyDB without changing your application code. Single-threaded Multithreaded Throughput Very High (5-10x) Latency Lower & More Consistent Active-Active No (requires CRDT plugins) Yes (Built-in) Compatibility Full Redis Compatibility Key Performance Benefits:
KeyDB is not a science experiment—it’s a pragmatic engineering fork that applies decades of multi-threading knowledge to the Redis architecture. For teams running Redis at scale, KeyDB can triple throughput without rewriting a line of application code. However, test your module dependencies and cluster failover patterns first.
Utilizing KeyDB's speed to handle rapid-fire pub/sub or list-based queues. keydb eng
KeyDB can use disk storage (SSD/NVMe) as an extension of RAM.
As the demands on modern, high-performance applications increase, the need for faster, more efficient in-memory data structures has become paramount. While Redis has long been the industry standard, has emerged as a formidable, high-performance fork that addresses the bottleneck of single-threaded architecture.
Processing and storing high-frequency data streams. // FEATURE: Check if key is tiered (exists
In practice, this yields:
For many engineering teams, the decision to use KeyDB comes down to cost and complexity. By getting more performance out of a single large VM, teams can reduce the number of shards required in a cluster. This leads to:
Here is a proposal for a new feature: .
You heavily rely on complex data types (like nested hashes or sorted sets) with high churn rates. Choose the if:
To maximize throughput on the Flash engine, optimize memory allocation between the KeyDB front-end and the RocksDB back-end:
Introduction to KeyDB | KeyDB - The Faster Redis Alternative For teams running Redis at scale, KeyDB can
KeyDB can directly backup its dataset to AWS S3, simplifying disaster recovery and data portability.
# Use 4 worker threads (should match your CPU core count) server-threads 4