r7n

We obsessed over optimizations and pushing the apis to the limits of how we could pack it.

So much so, we re-wrote the DynamoSDK to squeeze out more optimizations so we could be the same cost even though we were a layer in front of dynamo. We used key encoding and other various technique as well as managed capacity (on demand vs reserved) to transparently optimize workloads for price. In our experience we saw dramatic gains vs just vanilla SDK usage.

If you're curious, here was the marketing website, but we're now part of Databricks: https://stately.cloud/

Agreed, my critique was about how the article frames scalability. I've yet to see an OLTP problem that can't live in something like Dynamo. KV can model anything if you put in the work, the question is how much modeling discipline you trade for the scale, and in my experience the up front work is always worth it. Most of the time operational issues are swept under the rug and not consider tech debt.

Take for example AuroraDB: the sheer engineering it took to make SQL do scalable OLTP at all tells you how much that flexibility actually costs to keep.

I've extensively used Dynamo (internally at Amazon and externally) and even founded a DB startup with it at it's core. Boiling down scalability of Postgres vs Dynamo as it's written in blog is a bit terse. Dynamo scales writes horizontally with the keyspace, forever. Postgres simply can't, and no number of layers between the machines and the developer changes that. Sharding, pooling, Citus are all layered on top of an engine where a given row's writes still land on one primary.

We did exactly this when designing StatleyDB. We realized there are a set of metadata fields[0] that almost everyone needs when modeling data. Our approach takes this a step further in that we always track these fields transparently so if you forget to add them to your schema initially for any reason, that's okay, you can always add them later!

[0] https://docs.stately.cloud/schema/fields/#metadata-fields