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From Wes McKinney <wesmck...@gmail.com>
Subject Re: [C++] Apply Gandiva Filter to a RecordBatch
Date Sun, 05 Apr 2020 03:07:48 GMT
Try the arrow::compute::Take function

https://github.com/apache/arrow/blob/master/cpp/src/arrow/compute/kernels/take.h#L121

On Sat, Apr 4, 2020 at 8:19 PM Yue Ni <niyue.com@gmail.com> wrote:
>
> Hi Wes,
>
> Thanks for the reply, but I don't think this is what I am looking for.
>
> It seems to me this `result.to_array()` will only return the array for the selection
vector (https://github.com/apache/arrow/blob/b07c2626cb3cdd3498b41da9feedf7c8319baa27/python/pyarrow/gandiva.pyx#L130),
but it is not clear to me how I can use the selection vector to filter the original record
batch.
>
> If I understand correctly, in this test case, `result.to_array().equals(pa.array(range(1000),
type=pa.uint32()))` is asserting that the selection vector has integer index values from [0,
1000), but I am looking for to obtain an array in the filtered record batch which should be
an array of floats here. I know I can iterate indices in the selection vector and use it to
retrieve each row in original record batch columns, but I am not certain if this is the right
way to do it. For example, if I have multiple columns in the original record batch, do I need
to iterate the selection vector multiple times to filter each of the column? Since this is
a common task, I expect there is an easy/efficient API to do this.
>
> Basically, I am looking for something like:
> selection_vector = filter.evaluate(record_batch, pa.default_memory_pool())
> filtered_column_arrays_in_record_batch = record_batch.filter(selection_vector) # what
is the API for doing this?
>
> In the C++ test cases, the closest thing I find is to construct a gandiva projector to
use the selection vector, but every test case there requires client to construct a gandiva
expression to build the projector (for example, in this test case, a {sum_expr} is used for
constructing the projector, https://github.com/apache/arrow/blob/967728fe4654e5d53bc0789e64e5a9ba7f27f263/cpp/src/gandiva/tests/filter_project_test.cc#L121
).
>
> I wonder if the filtering can be done without involving creating a projection expression.
At the same time, if projector is expected to be used for doing this, what projector expressions
should be used if I want to keep all the columns as they are but just with some rows filtered
based on the criteria given?
>
> On Sun, Apr 5, 2020 at 7:27 AM Wes McKinney <wesmckinn@gmail.com> wrote:
>>
>> You can see an example of filtering via the Python bindings
>>
>> https://github.com/apache/arrow/blob/master/python/pyarrow/tests/test_gandiva.py#L89
>>
>> This creates a gandiva::Filter using gandiva::Filter::Make, which can
>> be used to filter a RecordBatch
>>
>> Is this what you need?
>>
>> On Fri, Apr 3, 2020 at 7:12 PM Yue Ni <niyue.com@gmail.com> wrote:
>> >
>> > Hi there,
>> >
>> > I am using the gandiva C++ library for processing RecordBatch. I would like
to know how I can apply gandiva::Filter for a RecordBatch so that I can do some filtering
without using the projector.
>> >
>> > Since I don't find any documentation for it, I read some source code about its
usage, and here are the test cases I found about its usage:
>> > 1) https://github.com/apache/arrow/blob/967728fe4654e5d53bc0789e64e5a9ba7f27f263/cpp/src/gandiva/tests/filter_test.cc
>> > 2) https://github.com/apache/arrow/blob/967728fe4654e5d53bc0789e64e5a9ba7f27f263/cpp/src/gandiva/tests/filter_project_test.cc
>> >
>> > From my reading, I find it is possible to get a SelectionVector by using the
gandiva::Filter, at the same time, you can use the SelectionVector with the gandiva::Projector
to filter RecordBatch when doing projection. My questions are:
>> > 1) if I don't want to do any projection but simply filtering, what is the recommended
way to do it?
>> > 2) I am trying to handle the case like "SELECT * FROM table WHERE blah", is
it recommended to apply filtering without projection in this case or is there any alternative
approach doing it?
>> >
>> > Thanks.
>> >
>> > Regards,
>> > Yue
>> >

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