[mildbyte]

Another difference is that this solution uses parquet_fdw, which handles fast scans through Parquet files and filter pushdown via row group pruning, but doesn't vectorize the groupby / join operations above the table scan in the query tree (so you're still using the row-by-row PG query executor in the end).

pg_analytics uses DataFusion (dedicated analytical query engine) to run the entire query, which can achieve orders of magnitude speedups over vanilla PG with indexes on analytical benchmarks like TPC-H. We use the same approach at EDB for our Postgres Lakehouse (I'm part of the team that works on it).

[wanderinglight]

This is definitely something I intend to fix.

My initial intent was to use duckdb for fast vectored query execution but I wasn't able to create a planner / execution hook that uses duckdb internally. Will definitely checkout pg_analytics / Datafusion to see if the same can be integrated here as well. Thanks for the pointers.

[mildbyte]

Have you seen duckdb_fdw (https://github.com/alitrack/duckdb_fdw)? IIRC it's built based on sqlite_fdw, but points the outbound queries to DuckDB instead of SQLite, and it does handle running aggregations inside of DuckDB. Could be useful.

[wanderinglight]

This is great, thank so much! I'll see if I can I can integrate this and how it compares to parquet_fdw.