Advice for properly timing Django queryset evaluation

[rsomani95]

Hello, I was hoping to get some advice on how to best go about scoping Django querysets that hold vector data to ensure that the CosineDistance calculation is properly scoped to the relevant rows only. Here's how I'm currently querying the Django ORM:

Python:

from pgvector.django import CosineDistance

user_id = '...'
collection_id = '...'
distance = 0.8

scoped_queryset = (
    VSFItem.objects
    .filter(seg__assets__user__id=user_id)
    .filter(seg__assets__collection__id=collection_id)
)
embedding = VSFItem.objects.first().vector
similar_faces_qset = (
    scoped_queryset
    .alias(distance=CosineDistance("vector", embedding))
    .filter(distance__lt=distance)
)

Which, on calling print(similar_faces_qset.query), shows that the following SQL code has been generated:

SELECT "vsf_item"."seg_id",
       "vsf_item"."vector",
FROM   "vsf_item"
       INNER JOIN "segs"
               ON ( "vsf_item"."seg_id" =
                    "segs"."id" )
       INNER JOIN "assets"
               ON ( "segs"."asset_id" =
                    "assets"."id" )
WHERE  ( "assets"."user_id" =
         b679c79c - 0684 - 4f92 - 9a35 - 29824d62e569
         AND "assets"."collection_id" =
             e3faf9d4 - a7dc - 4710 - ad27 - 85619d34619c
         AND ( "vsf_item"."vector" <=> [-0.734, 0.1616 ... ]) < 0.8 ) 

---

The WHERE clause here, specifically the AND ( "vsf_item"."vector" <=> [-0.734, 0.1616 ... ]) < 0.8 ) suggests to me that the vector calculation might be happening across all rows of the "vsf_item" table. Is that accurate? If so, do you have any recommendations on how to best scope computation given the above queries? (I'm doing scoping similar in spirit to the above in different parts of my project, so was hoping for general guidelines re. best practices).

Thanks!

Adjacent issue that's worth mentioning: pgvector/pgvector#203 (comment)

[ankane]

Hi @rsomani95, Postgres will figure out the best way to execute the query (in most situations), which involves minimizing work. You can use EXPLAIN ANALYZE to see the details.

[rsomani95]

@ankane EXPLAIN ANALYZE was helpful, thanks.
It suggests to me that the concern presented above is true i.e. the vector filter is being applied on all rows of the table.

First, here's a simplified version of the code above:

        # `embeddable_cls` is a Django Model
        # `candidate_ids` is a list of ids to limit the scope of the vector filtering

        scoped_qset = embeddable_cls.objects.filter(id__in=candidate_ids)

        queryset = (
            scoped_qset
            .annotate(distance=CosineDistance(embedding_name, embedding))
            .filter(distance__lt=query_arguments.distance)
        )

        filtered_ids = list(queryset.values_list("id", flat=True))

In this case, candidate_ids is a precomputed list of 33 ids. The entire table has 178 rows in total.
I'd expect the cosine distance to only be computed on those 33 ids, and not 178 rows. However, this is the generated SQL:

Executed SQL
SELECT "ozu_api_segment"."id"
  FROM "ozu_api_segment"
 WHERE ("ozu_api_segment"."id" IN ('36d3abe1-2cf5-4796-8445-a7529ff0c8fb'::uuid, 'f1d02d3d-7861-40c6-95ae-627e791983cf'::uuid, '32a6be12-617b-4ef8-b580-698b99628b01'::uuid, '2f7cb18d-7e09-4b6b-976a-b16db475ce2f'::uuid, '5655dd82-fe55-426d-b54e-38663e1f13a2'::uuid, '1cca49ea-e27f-40af-82f3-d37417073ffe'::uuid, '887ad098-be19-4575-ac30-679cb65a150a'::uuid, 'a48b75f2-ca12-4e52-b843-664067bf03e4'::uuid, '4c72595c-fade-4af2-84e1-723b0dc23422'::uuid, '9973b052-27d0-4378-b7ba-0c56027e3c21'::uuid, '93f1b0aa-ea42-4b85-b216-7bc2b2e15a16'::uuid, 'bb6eded5-8855-4766-b892-dcb420710ec3'::uuid, 'ea2d008f-3108-4fe1-b82b-c0c0b4784005'::uuid, '6e2609a4-4e7a-4fb1-865e-9b9d707b08a1'::uuid, 'a176518f-f7cf-4171-9802-9f191caa7d87'::uuid, '02b0ced8-490b-4ef2-9300-bdf702102843'::uuid, '62e8f839-3023-4da8-b531-59a6dd61fd25'::uuid, 'f74db32a-705f-436b-bcf0-da480e535755'::uuid, 'b8cc4c88-5f4c-4408-aea3-6bac56cd4caf'::uuid, '2825b039-2a51-45cc-83e0-51ad0c4a4af9'::uuid, '33c0bc96-57f6-4415-9b4c-38890c53332e'::uuid, 'd7b35172-d824-43ae-8a89-e1974db7d9f2'::uuid, '7b3ae0c2-6526-437a-b10e-491826d197bf'::uuid, '24a5182d-ce1c-43a8-9dc0-02b0f6fc81a0'::uuid, 'fc94f78c-6120-4efc-8512-5b89f8b85298'::uuid, '2d98b2e0-3c5f-4067-9aea-3f00472342c2'::uuid, 'eda81005-1182-40fd-b486-7d1ea9a690b7'::uuid, '62dedc3d-b2f2-414b-b2bc-6c6bf57b7cda'::uuid, '432b3a21-acc8-416f-921e-541a03eb0460'::uuid, '0ea707b5-a54a-4a59-b362-95b0383c77a5'::uuid, 'cbf00545-2ab4-48ac-bcde-3b535e36e45d'::uuid, 'cbf8e005-5197-441a-ba45-5bb450b69707'::uuid, '6ee8ee78-c360-48c2-b6f5-1bf73cddaa9d'::uuid) AND ("ozu_api_segment"."visual_cinema_clip_embedding" <=> '[0.0003764629364013672,-0.020965576171875,0.04302978515625,0.0278472900390625,-0.0251007080078125,-0.003215789794921875,-0.0175018310546875,0.06768798828125,-0.00933074951171875,-0.007747650146484375,-0.034759521484375,-0.022918701171875,0.0253753662109375,0.0235748291015625,0.043487548828125,0.01320648193359375,0.00804901123046875,-0.004238128662109375,-0.0309906005859375,-0.041534423828125,-0.0245208740234375,-0.0198516845703125,-0.031005859375,-0.061187744140625,0.0034580230712890625,0.01380157470703125,0.00882720947265625,0.0016460418701171875,-0.04913330078125,0.08111572265625,0.08233642578125,0.0239105224609375,-0.0213470458984375,0.066162109375,-0.0027675628662109375,-0.0251617431640625,0.0285491943359375,-0.0015277862548828125,0.0206146240234375,0.0693359375,0.026153564453125,-0.0021762847900390625,0.034698486328125,-0.00156402587890625,-0.020599365234375,0.060394287109375,-0.053131103515625,-0.0164794921875,0.03680419921875,-0.0090789794921875,0.033233642578125,0.0079803466796875,-0.01136016845703125,-0.00011813640594482422,0.09735107421875,-0.004077911376953125,-0.036041259765625,0.01226043701171875,0.0308380126953125,0.00339508056640625,-0.03497314453125,0.02703857421875,-0.028045654296875,0.02752685546875,-0.005718231201171875,-0.0013713836669921875,0.004856109619140625,-0.01421356201171875,-0.0207061767578125,0.052337646484375,-0.004245758056640625,0.04864501953125,0.0146484375,0.05718994140625,-0.01105499267578125,0.020660400390625,-0.0141754150390625,0.048248291015625,-0.044036865234375,0.0196685791015625,-0.0120391845703125,0.0082550048828125,0.01031494140625,0.020599365234375,0.0198211669921875,0.021331787109375,-0.0211029052734375,0.027679443359375,-0.0019426345825195312,0.0173187255859375,0.004001617431640625,-0.068603515625,-0.00823211669921875,-0.00957489013671875,0.059722900390625,0.007549285888671875,-0.0120391845703125,0.086181640625,-0.03289794921875,0.010986328125,-0.021697998046875,-0.084228515625,-0.05377197265625,0.081787109375,0.01377105712890625,0.01006317138671875,-0.044403076171875,-0.045379638671875,0.029083251953125,-0.055206298828125,-0.0205078125,-0.046600341796875,0.065673828125,0.08233642578125,0.0302276611328125,-0.01033782958984375,0.007793426513671875,0.06170654296875,-0.01270294189453125,-0.01256561279296875,0.07794189453125,-0.010528564453125,-0.01049041748046875,0.025848388671875,-0.087158203125,-0.00937652587890625,-0.03289794921875,0.0038127899169921875,0.07275390625,0.054290771484375,-0.026885986328125,-0.00829315185546875,-0.042633056640625,0.0025577545166015625,-0.0017404556274414062,0.0302886962890625,-0.0264434814453125,0.01045989990234375,-0.039154052734375,0.0299530029296875,-0.0036773681640625,-0.071533203125,0.0008149147033691406,0.063720703125,-0.042327880859375,0.01448822021484375,-0.0246734619140625,-0.06878662109375,-0.002063751220703125,0.0078582763671875,0.0012798309326171875,0.01201629638671875,-0.01220703125,0.0279541015625,0.06134033203125,0.07977294921875,-0.0794677734375,-0.004276275634765625,-0.01059722900390625,-0.0411376953125,0.03948974609375,-0.0259246826171875,0.09283447265625,-0.067626953125,-0.052490234375,0.01441192626953125,-0.03497314453125,-0.048858642578125,-0.11151123046875,-0.0335693359375,0.0606689453125,0.0148468017578125,-0.00479888916015625,0.0007290840148925781,0.034027099609375,0.023834228515625,-0.01861572265625,-0.00646209716796875,0.0027523040771484375,0.032073974609375,-0.09417724609375,-0.0217742919921875,-0.0283660888671875,-0.01605224609375,0.0113983154296875,0.1824951171875,0.0107879638671875,-0.019866943359375,0.027679443359375,-0.0411376953125,0.005558013916015625,-0.048614501953125,-0.0231781005859375,-0.00989532470703125,-0.0123138427734375,0.0149383544921875,0.0266571044921875,0.024078369140625,0.0005826950073242188,-0.06280517578125,0.0181121826171875,-0.002910614013671875,-0.0167083740234375,-0.024993896484375,-0.0137939453125,-0.0289154052734375,0.047119140625,-0.025054931640625,-0.006130218505859375,0.0233306884765625,-0.0297698974609375,-0.03363037109375,0.0004024505615234375,-0.0301971435546875,-0.025360107421875,-0.033111572265625,-0.05364990234375,-0.03509521484375,0.0079345703125,0.0298004150390625,-0.004131317138671875,0.031341552734375,0.0289764404296875,0.026702880859375,0.05780029296875,-0.024078369140625,0.005283355712890625,-0.066162109375,0.04840087890625,0.078369140625,0.01142120361328125,-0.004451751708984375,0.044189453125,0.0200347900390625,0.006839752197265625,0.0247955322265625,0.014617919921875,0.003009796142578125,-0.0012531280517578125,0.0194244384765625,-0.034332275390625,-0.06524658203125,0.05279541015625,0.033721923828125,0.039886474609375,0.006412506103515625,-0.0239410400390625,-0.012237548828125,0.050018310546875,-0.057861328125,0.047882080078125,-0.0260772705078125,-0.05377197265625,-0.046966552734375,-0.0280303955078125,-0.041778564453125,0.0249786376953125,-0.005756378173828125,-0.01372528076171875,-0.09185791015625,0.02880859375,0.0198516845703125,-0.0295562744140625,0.019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< 0.6)

and this is the output of EXPLAIN ANALYZE:

QUERY PLAN
Seq Scan on ozu_api_segment  (cost=0.08..9.12 rows=11 width=16) (actual time=0.497..0.638 rows=10 loops=1)
  Filter: ((id = ANY 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AND ((visual_cinema_clip_embedding <=> 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< '0.6'::double precision))
  Rows Removed by Filter: 168
Planning Time: 0.539 ms
Execution Time: 0.685 ms

If I'm reading this correctly, 10 rows are being returned and 168 are removed, and the distance query is being run on 10 + 168 = 178 rows i.e. all rows in the table. Is that correct?

[rsomani95]

This might be the solution: https://www.postgresql.org/docs/current/queries-with.html

Will try this out and share updates.