Yep! The new version of pgrust supports batch based execution and a columnar format. I'm curious how you got δx to perform that well? From what I've seen a columnar layout only gets you part of the way and really good parallelism and really fast hash tables seem to make up a significant portion of why Clickhouse is faster.
pg_mooncake (now effectively abandoned due to being acquired by Databricks, but still up at https://github.com/Mooncake-Labs/pg_mooncake) pulled the DuckDB engine into Postgres wholesale, if I remember right.
pg_lake also uses DuckDB but keeps it external, routing through Postgres and managing Iceberg tables (but not the data itself) there (https://github.com/Snowflake-Labs/pg_lake).
Both of these were neck and neck with ClickHouse last time I tried them.
Yeah, spent a lot of time on parallelism, vectorizing, pipelining, filter push-downs, bloom filters, all the tricks out there. It's really fun to make pretty steady progress on this.