I made something like this for Morgan Stanley some years ago, a structurally typed eager variant of Haskell with static elimination of type class constraints (so no runtime penalty for overloading) and uses LLVM for the back-end:
http://github.com/morgan-stanley/hobbes/
We used it for processing and analyzing billions of events per day. Using structural algebraic types let us encode market data (obviously) but also complex data structures inside trading systems that we could correlate with market data.
As you say, Haskell-ish expressions are much more compact and capable than SQL, which was one of the reasons I wanted to build it.
It also had a somewhat novel compression method (“types as probabilities” if you like the old Curry-Howard view), which was very important to manage billions of daily events.
We used it for processing and analyzing billions of events per day. Using structural algebraic types let us encode market data (obviously) but also complex data structures inside trading systems that we could correlate with market data.
As you say, Haskell-ish expressions are much more compact and capable than SQL, which was one of the reasons I wanted to build it.
It also had a somewhat novel compression method (“types as probabilities” if you like the old Curry-Howard view), which was very important to manage billions of daily events.
Good times.