sgilmore10 opened a new pull request, #37525:
URL: https://github.com/apache/arrow/pull/37525
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### Rationale for this change
In order to add an `arrow.tabular.Table` class to the MATLAB Interface, we
first need to add a MATLAB class representing `arrow::ChunkedArray`s. This is
required because an `arrow::Table` is backed by a vector of
`arrow::ChunkedArray`s, and the output of its `column(int index)` method is an
`arrow::ChunkedArray`.
### What changes are included in this PR?
1. Introduced a new class called `arrow.array.ChunkedArray`.
2. `arrow.array.ChunkedArray` has the following properties:
1. `Type` - datatype of the `arrow.array.Array`s
2. `Length` - Sum of the `arrow.array.Array` lengths
3. `NumChunks` - Number of `arrow.array.Array`s
3. `arrow.array.ChunkedArray` has the following methods:
1. `chunk(index)` - Returns the `arrow.array.Array` stored at the
specified index
2. `fromArrays(array1, array1, ..., arrayN, Type=type)` - Creates a
`ChunkedArray` from the arrays provided. If `Type` is provided, all arrays are
expected to have the specified `Type`.
**Example Usage**
```matlab
>> a1 = arrow.array(1:100);
>> a2 = arrow.array(101:250);
>> a3 = arrow.array(251:300);
% Create a ChunkedArray from 3 Float64Arrays
>> c = arrow.array.ChunkedArray.fromArrays(a1, a2, a3)
c =
ChunkedArray with properties:
Type: [1×1 arrow.type.Float64Type]
NumChunks: 3
Length: 300
% Extract the first chunk and compare it to a1
>> c1 = c.chunk(1);
>> tf = isequal(c1, a1)
tf =
logical
1
% Create an empty ChunkedArray by providing the Type nv-pair
>> c = arrow.array.ChunkedArray.fromArrays(Type=arrow.timestamp())
c =
ChunkedArray with properties:
Type: [1×1 arrow.type.TimestampType]
NumChunks: 0
Length: 0
```
### Are these changes tested?
Yes. I added a new test class called `tChunkedArray.m` that contains unit
tests for the new class.
### Are there any user-facing changes?
Yes. Users can now create a `ChunkedArray` in the MATLAB Interface.
### Future Directions
1. In this PR, we deliberately didn't include a convenience constructor
function because we're not sure if we want users to create `ChunkedArray`s
themselves. We think users will mostly use `ChunkedArray` when extracting
columns from `Table`s.
2. We will implement more methods on `ChunkedArray`, such as `flatten()` and
`combineChunks()`, etc.
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