SQL, JS, Excel are really hard to substitute because of how widely used they are by people. Even if something new comes up that it's objectively better, so far has always failed gaining traction because of this reality.
I wonder though, is such a dialect better for agents? Have you tried to measure if an agent performs better expressing queries in such a language instead of SQL?
Claude had no problem translating SQL into Prela, and because you have fine grained control over the query plan (a Prela query is a plan), it was able to optimize queries to be very fast
I'm more curious about going from text to Prela instead of going from text to SQL and measuring any difference in the performance there. On one hand models have been trained on a lot of SQL on the other hand they are really good in mathematical reasoning too so thinking in Perla might be a natural fit for them.
There are fewer foot guns in Prela in particular no NULLs which should help both humans and robots.
Having control over the execution plan is super interesting ! This is a very common frustration when writing SQL.
Do you think it would be possible to offer Prela as a direct interface to a relational database?
Yes, maybe not the language itself, but the ideas behind it. Tarski's Algebra of Relations is actually a better model for modern columns stores than the standard relational algebra, because a column is a binary relation from the primary key into its value.
It would be pretty easy to put a DuckDB data source into this code.
It might be pretty easy to use overloading to get special case implementations that form SQL queries progressively until the results need to be materialized as something like a dataframe for the function code to work on.