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DuckDB has been probably my most used tool in 2026 - if you're comfortable with SQL it's incredible at quickly prototyping and slicing / dicing data. I do a lo
by medvezhenok 4mo ago
DuckDB has been probably my most used tool in 2026 - if you're comfortable with SQL it's incredible at quickly prototyping and slicing / dicing data.
I do a lot of experiments with regexes, and if you get used to the RE2 syntax that DuckDB uses, you can see up to 10-100x uplift in terms of speed compared to Postgres on things like regexp_matches(), regexp_extract(), etc (depending on query/table/machine specifics). It has quite powerful scripting with custom Macros, fixes a lot of annoyances of SQL for me compared to Postgres.
I think if you have access to a machine with a lot of RAM / cores and a beefy data set, then it's basically like a RAMdisk version of Snowflake running locally on your machine.
(and of course the fact that it makes it convenient to read CSV/parquet, read/write from S3, etc) - it's a very ergonomic tool.
- jdw64 4mo agoThank you for your kind reply. I should look into it too. In my case, knowing various libraries is directly related to my livelihood. Have a good day.
- JoelJacobson 4mo agoI was curious what the claim "10-100x uplift in terms of speed compared to Postgres on things like regexp_matches()" was about, so I checked, and DuckDB's regexp_matches() is not the same as PostgreSQL's regexp_matches(). DuckDB's version "Returns true if string contains the regexp pattern, false otherwise." [1] while PostgreSQL's "returns a set of text arrays of matching substring(s)" [2]. I think the closest think in PostgreSQL to DuckDB's regexp_matches() is `string ~ pattern` or `regexp_like(string, pattern)`. [1] https://duckdb.org/docs/lts/sql/functions/regular_expressions https://duckdb.org/docs/lts/sql/functions/regular_expression... [2] https://www.postgresql.org/docs/current/functions-matching.html https://www.postgresql.org/docs/current/functions-matching.h...