After some more digging I did arrive at something which seems more efficient than what I had:
struct_schema = pa.struct([('field0', pa.int32()), ('field1', pa.int8())])
nparray = x = np.array([(0, 10), (1, 20)], dtype=[('field0', ' wrote:
> After some more digging I did arrive at something which seems more efficient than what I had:
>
> struct_schema = pa.struct([('field0', pa.int32()), ('field1', pa.int8())])
> nparray = x = np.array([(0, 10), (1, 20)], dtype=[('field0', ' struct_array = pa.array(nparray, type=struct_schema)
>
> This looks easy, although I'm not sure how much copying is done down below.
>
> I now have an issue with the Rust implementation since I'm not sure how do I access or iterate over the rows of the resulting StructArray.
>
> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
> On Sunday, March 21, 2021 10:52 AM, Hagai Har-Gil wrote:
>
>> Hi,
>>
>> I'm trying to efficiently convert incoming numpy.recarray's to pyarrow.StructArray and I'm unsure how to do so with the least amount of copying.
>>
>> My use case involves real time data processing of numpy.recarrays in Rust. I'm happily using the IPC protocol to transfer data to Rust's arrow implementation which will do the heavy lifting. I'll need to iterate on the recarray-turned-StructArray line-by-line, each time yielding all fields of a specific row, so the StructArray format is quite fitting. However, doing the actual conversion in an efficient manner seems harder than expected. The fields (=individual arrays) of a numpy.recarray aren't stored in a contiguous manner, so any numpy.recarray -> pyarrow.Array conversion first has to copy the data to standard pyarrow.Array buffers, and then re-construct the StructArray structure by interleaving the arrays. I was unable to find in the docs or in previous discussions here a better approach for this type of pre-processing step.
>>
>> Since I'm using IPC I'll eventually need to have the pyarrow.StructArray wrapped in a pyarrow.RecordBatch if that makes any difference.
>>
>> Thanks in advance.