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.

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/

from their docs:

> PgDog does not detect primary failure and will not call pg_promote(). It is expected that the databases are managed externally by another tool, like Patroni or AWS RDS, which handle replica promotion.