On 7/16/22 16:21, Martin Kalcher wrote:
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
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
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
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
