Thanks a lot for the insight Mike,
My question might then be quite naive: let's say I have a list of ORM
entities on one side, and an accordingly sorted numpy array of computed
features, how would I merge back attributes on entities?
Let's say I have a list like :
user_list = [User(id=1, dist=None), User(id=2, dist=None)]
and a pandas DataFrame (or numpy array) like:
dist_df =
id dist
1 123
2 90
How would I correlate those 2 into:
[User(id=1, dist=123), User(id=2, dist=90)]
Would the way to go be a simple for loop? Like:
for user in user_list:
user.dist = dist_df.loc[user.id, 'dist']
Or is there something included in SQLAlchemy for this kind of task?
Regards,
Pierre
Le mer. 11 oct. 2023 à 15:07, Mike Bayer <
[email protected]> a écrit :
>
>
> On Wed, Oct 11, 2023, at 4:22 AM, Pierre Massé wrote:
>
> Dear all,
>
> I have a requirement that makes me think that I need to "mass patch" some
> ORM objects. However, I am open to any suggestions regarding the way to
> answer my requirements.
>
> I have defined an ORM object which represents a user, holding longitude
> and latitude (among other attributes). At some point, I want to query many
> of those users, and send them back holding the geographical distance from a
> certain point (defined by longitude and latitude) along with their other
> data.
>
> Computing the distance is computationally heavy, and I noticed that I
> could greatly improve performance by mass computing those distances, using
> numpy for example.
>
> My question is: would it be possible to split my flow in 2 :
> - a flow that queries the data that is simply available in the database,
> as ORM entities
> - a flow that queries lon/lat as a numpy array, perform the distance
> computation
> and afterward merge those 2 in the queried ORM entities?
>
>
> This is a straightforward programming task. Query for the set of objects
> you want, assemble the appropriate values into a numpy array, do wahtever
> numpy thing you need, then merge back. you'd likely want to ensure you
> can correlate numpy rows back to original objects most likely by keeping a
> sort order between your result set and your numpy array.
>
> not stated here is if these numpy-calculated values as assembled onto ORM
> objects are also database-column mapped, it sounds like they are not
> (otherwise those values would be in the database), so the matrix values can
> be applied to plain attributes on the objects directly.
>
> now where this may be more challenging, not sure if this is what you're
> asking, is if you want this to happen implicitly for all queries or
> something like that. there's ways to do this depending on the programming
> patterns you are looking to achieve however I'd certainly start simple with
> a function like "apply_lat_long_to_list_of_objects(obj)".
>
>
>
> It is important to me that I finally get back a list of ORM entities fully
> populated, because my whole downstream process is built around this
> assumption.
>
> Thanks a lot for your insights on the matter!
>
> Regards,
>
> Pierre
>
> PS: giving me a "SQLAlchemy fast distance computation" won't do the trick,
> because I have other kinds of computations that may not be optimizable this
> way.
>
>
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>
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