sqlglot VS duckdb

This is probably referring to "zero changes to your driver code" and not "zero changes to the SQL you send over this driver".

Translating between SQL dialects is notoriously hard and attempts to translate [1] are working in 95% of cases. But the last 5% would require 5x amount of work. That's because "SQL dialect" also includes weird edge cases of type inference of things like COALESCE(5, FALSE) and emulation of system catalogs (pg_catalog, information_schema).

[1] https://github.com/tobymao/sqlglot

Recommend checking out https://github.com/tobymao/sqlglot if you are interested in this capability for other SQL dialects

Tools like this are helpful for:

- Rendering SQL in a consistent way, eg for snapshot testing

sqlglot – Python SQL Parser and Transpiler

In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.

Cool! Is this built with sqlglot[1] on the back end?

[1] https://github.com/tobymao/sqlglot

Wanted to give sqlglot a shoutout as it saved me a ton of time.

I'm not sure if the fix is reassuring or not: https://github.com/duckdb/duckdb/pull/9411/files

It's an interesting question!

The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.

It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.

[1] https://github.com/bacalhau-project/bacalhau

[2] https://github.com/duckdb/duckdb

[4] Switch to Push-Based Execution Model by Mytherin · Pull Request #2393 · duckdb/duckdb (github.com)

it depends on your query obviously.

In general, I did very deep benchmarking of pg, clickhouse and duckdb, and I sure didn't make stupid mistakes like this: https://news.ycombinator.com/item?id=36990831

My dataset has 50B rows and 2tb of data, and I think columnar dbs are very overhiped and I chose pg because:

- pg performance is acceptable, maybe 2-3x times slower than clickhouse and duckdb on some queries if pg is configured correctly and run on compressed storage

- clickhouse and duckdb start falling apart very fast because they specialized on very narrow type of queries: https://github.com/ClickHouse/ClickHouse/issues/47520 https://github.com/ClickHouse/ClickHouse/issues/47521 https://github.com/duckdb/duckdb/discussions/6696

This action installs duckdb with the version provided in input.

Duckdb: https://github.com/duckdb/duckdb - seems pretty popular, been keeping an eye on this for close to a year now.

The Parquet-Go library is very complex, not yet success to use it. So I ask whether DuckDB can provide API https://github.com/duckdb/duckdb/issues/7776