ezyang's blog

Tensor libraries like PyTorch and JAX have developed compact and accelerated APIs for manipulating n-dimensional arrays. N-dimensional arrays are kind of similar to tables in database, and this results in the logical question which is could you setup a Tensor-like API to do queries on databases that would be normally done with SQL? We have two challenges:

• Tensor computation is typically uniform and data-independent. But SQL relational queries are almost entirely about filtering and joining data in a data-dependent way.
• JOINs in SQL can be thought of as performing outer joins, which is not a very common operation in tensor computation.

However, we have a secret weapon: first class dimensions were primarily designed to as a new frontend syntax that made it easy to express einsum, batching and tensor indexing expressions. They might be good for SQL too.

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One of the things that I learned in grad school is that even if you’ve picked an important and unsolved problem, you need some reason to believe it is solvable–especially if people have tried to solve it before! In other words, “What’s different this time?” This is perhaps a dreary way of shooting down otherwise promising research directions, but you can flip it around: when the world changes, you can ask, “What can I do now that I couldn’t do before?”

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