From a machine learning perspective, vectors are a well-known concept that are effectively immutable fixed-length n-dimensional values that are then later used either as part of a model or in conjunction with a model after the fact.
While we could have this be non-frozen and not call it a vector, I'd be inclined to still make the argument for a layer of syntactic sugar on top that met ML users where they were with concepts they understood rather than forcing them through the cognitive lift of figuring out the Cassandra specific contortions to replicate something that's ubiquitous in their space. We did the same "Cassandra-first" approach with our JSON support and that didn't do us any favors in terms of adoption and usage as far as I know.
So is the goal here to provide something specific and idiomatic for the ML community or is the goal to make a primitive that's C*-centric that then another layer can write to? I personally argue for the former; I don't see this specific data type going away any time soon.
but as you point out it has the problem of allowing nulls.
If nulls are not allowed for the elements, then either we need a) a new type, or b) add some way to say elements may not be null…. As much as I do like b, I am leaning towards new type for this use case.
So, to flesh out the type requirements I have seen so far
1) represents a fixed size array of element type
* on write path we will need to validate this
2) element may not be null
* on write path we will need to validate this
3) “frozen” (is this really a requirement for the type or is this just simpler for the ANN work? I feel that this shouldn’t be a requirement)
4) works for all types (my requirement; original proposal is float only, but could logically expand to primitive types)
Anything else?
The key thing about a vector is that unlike lists or tuples you really don't care about individual elements, you care about doing vector and matrix multiplications with the thing as a unit.
That maybe true for this use case, but “should” this be true for the type itself? I feel like no… if a user wants the Nth element of a vector why would we block them? I am not saying the first patch, or even 5.0 adds support for index access, I am just trying to push back saying that the type should not block this.
(Maybe this is making the case for VECTOR FLOAT[N] rather than FLOAT VECTOR[N].)
Now that nulls are not allowed, I have mixed feelings about FLOAT[N], I prefer this syntax but that limitation may not be desired for all use cases… we could always add LIST<TYPE, N> and ARRAY<TYPE, N> later to address that case.
In terms of syntax I have seen, here is my ordered preference:
1) TYPE[size] - have mixed feelings due to non-null, but still prefer it
2) QUALIFIER TYPE[size] - QUALIFIER is just a Term we use to denote this semantic…. Could even be NON NULL TYPE[size]
That’s a bounded ring buffer, not a fixed length array.
This definitely isn’t a tuple because the types are all the same, which is pretty crucial for matrix operations. Matrix libraries generally work on arrays of known dimensionality, or sparse representations.
Whether we draw any semantic link between the frozen list and whatever we do here, it is fundamentally a frozen list with a restriction on its size. What we’re defining here are “statically” sized arrays, whereas a frozen list is essentially a dynamically sized array.
I do not think vector is a good name because vector is used in some other popular languages to mean a (dynamic) list, which is confusing when we also have a list concept.
I’m fine with just using the FLOAT[N] syntax, and drawing no direct link with list. Though it is a bit strange that this particular type declaration looks so different to other collection types.
It's been a while, so I may be missing something, but do we already have fixed-size lists? If not, I don't see why we'd try to make this fit into a List-shaped problem.
