Very cool! pg_duckdb itself is missing fully integrated storage - it can query data stored externally (say in S3) in delta/iceberg formats, but it can't write out data in those formats via transactional writes to PG tables (insert\update\deletes). pg_mooncacke is one neat way of solving that problem. It lets you have a columnstore table in Postgres that can do both reads and writes as if it's any other PG table and have the storage format be an open format like delta/iceberg/etc with that data persisted to blob store (like most cloud DWs would do anyways).
Had a similar thought. Azure Postgres has something similar to pg_parquet (pg_azure_storage), but we're looking into replacing it with pg_duckdb assuming the extension continues to mature.
It would be great if the Postgres community could get behind one good opensource extension for the various columnstore data use cases (querying data stored in an open columnstore format - delta, iceberg, etc. being one of them). pg_duckdb seems to have the best chance at being the goto extension for this.
Checksums can detect a torn page, but not always repair them. It's likely a good part of the database page is gone (i.e., an amount of data that matches the disk / file system atomic write unit size is probably missing). Torn page writes are a pretty common scenario too, so databases need to be able to fully recover from them - not just detect them and report a corruption (ie., just pull the power plug from the machine during a heavy write workload and you're likely to get one - it doesn't require a solar ray to flip a bit :) ).
Maybe good to mention torn pages somewhere too? Both MySQL and Postgres jump through some hoops to both detect them and repair them [1][2]. So, even the scenario in the post where fsync is used to harden writes, the database still needs to handle torn pages (or requires using a file system \ storage that guarantees atomic page writes at the page size the database is using as several managed\cloud databases do).
I'm not sure why you're getting downvoted. I think the pressure to appease Gartner usually starts when companies bring in CEOs whose primary background is enterprise sales. They tend to over value magic quadrant positioning (in my view).
Control theory is also used by databases (probably not as often as it should be). It's great for "self tuning" [1], for example tuning the various cache sizes a database has to maximize throughput under changing workload conditions. Its definitely worth spending the time to understand PID controllers if your an engineer working on databases.