Am 16.07.22 um 18:53 schrieb Mladen Gogala:
> On 7/15/22 04:36, Martin Kalcher wrote:
>> Dear list,
>>
>> i am dealing with an application that processes fairly large arrays of
>> integers. It makes heavy use of the intarray extension, which works
>> great in most cases. However, there are two requirements that cannot
>> be addressed by the extension and are rather slow with plain SQL. Both
>> can be met with shuffling:
>>
>> - Taking n random members from an integer array
>> - Splitting an array into n chunks, where each member is assigned to a
>> random chunk
>>
>> Shuffling is currently implemented by unnesting the array, ordering
>> the members by random() and aggregating them again.
>
>
> Martin, have you considered PL/Python and NumPy module?
Hey Mladen,
thank you for your advice. Unfortunately the performance of shuffling
with NumPy is about the same as with SQL.
create function numpy_shuffle(arr int[])
returns int[]
as $$
import numpy
numpy.random.shuffle(arr)
return arr
$$ language 'plpython3u';
select arr[1:3]::text || ' ... ' || arr[3999998:4000000]::text
from (
select numpy_shuffle(arr) arr from numbers
) shuffled;
-------------------------------------------------------
{674026,3306457,1727170} ... {343875,3825484,1235246}
Time: 2315.431 ms (00:02.315)
Am i doing something wrong?
Martin
