It's not clear from the readme physically where the data is stored, nor where in the storage process the "congestion" is coming from.
I'm surprised there's no range scan. Range scans enable a whole swathe of functionality that make kv stores punch above their weight. I suppose that's more rocksdb/dynamo/bigtable/cassandra than redis/memcached, though.
Yeah, something weird happened with the image rendering; I'll fix that.
> *"It's not clear from the readme physically where the data is stored, nor where in the storage process the 'congestion' is coming from."*
The data is *fully in-memory*, distributed across dynamically growing shards (think of an adaptive hashtable that resizes itself). There’s no external storage layer like RocksDB or disk persistence—this is meant to be *pure cache-speed KV storage.*
Congestion happens when a shard starts getting too many keys relative to the rest of the system. The engine constantly tracks *contention per shard*, and when it crosses a threshold, we trigger an upgrade (new shards added, old ones redistributed). Migration is *zero-downtime*, but at very high write rates, there’s a brief moment where some writes are directed to the old store while the new one warms up.
> *"I'm surprised there's no range scan."*
Yeah, that’s an intentional design choice—this is meant to be a *high-speed cache*, closer to Redis than a full database like RocksDB or BigTable. Range queries would need an ordered structure (e.g., skip lists or B-trees), which add overhead. But I’m definitely considering implementing *prefix scans* (e.g., `SCAN user:*` style queries) since that’d be useful for a lot of real-world use cases.
