Right - this is not about sharding data/datasets. This is for sharding in-memory state that a service might have. The problem of building services at low cost, high scale, low latency and high throughput is common in many environments including our services at Databricks, and Dicer helps with that.

Sounds related to Google Slicer: https://research.google/pubs/slicer-auto-sharding-for-datace...

These show up once you have a certain scale where it is either cost inefficient or the hot spots are very dynamic. They also try to avoid latency by being eventually consistent sidecars instead of proxies.

I’ve seen them used for traffic routing, storage system metadata systems, distributed cache etc

Sharded in-memory caching turns out to be rather useful at scale :)

Some of the key examples highlighted on our blog are Unity Catalog, which is essentially the metadata layer for Databricks, our Query Orchestration Engine, and our distributed remote cache. See the blog post for more!