I have a not-so-complex (imho) filtering logic based on several conditions in my Django models. There is one particular query which takes an inusual amount of time to finish.

The query is built based on those two querysets:

queryset = self.serializer_class.Meta.model.valid_pricelist_objects.filter(
      ) |
    # pylint: disable=line-too-long
return queryset


return super().get_queryset().filter(
    Q(drug_prices__pricelist__active=True), # Lista de precios activa
    # Q(drug_pictures__is_main=True), # Que tenga una imagen
    # TODO: Hacer filtros por pais PriceListCountries
        Q(drug_prices__pricelist__expires=False) | # Que tenga precios que no caducan o
            Q(drug_prices__pricelist__expires=True), # Que tenga precios que si caducan Y
            Q(drug_prices__pricelist__datestart__date__lte=timezone.now()),  # Fecha de inicio menor que hoy Y
            Q(drug_prices__pricelist__dateend__date__gte=timezone.now())  # Fecha final mayor que hoy

The second querysets wraps the first one (via the super() call). The resulting query looks like this:

    INNER JOIN "monetary_drugprice" ON ( "phdrug_phdrug"."id" = "monetary_drugprice"."drug_id" )
    INNER JOIN "monetary_pricelist" ON ( "monetary_drugprice"."pricelist_id" = "monetary_pricelist"."id" )
    INNER JOIN "monetary_drugprice" T4 ON ( "phdrug_phdrug"."id" = T4."drug_id" )
    INNER JOIN "monetary_pricelist" T5 ON ( T4."pricelist_id" = T5."id" )
    INNER JOIN "monetary_pricelistdestinations" ON ( T5."id" = "monetary_pricelistdestinations"."pricelist_id" )
    LEFT OUTER JOIN "organization_organizationdata" ON ( "monetary_pricelistdestinations"."to_organization_data_id" = "organization_organizationdata"."id" )
    LEFT OUTER JOIN "organization_organization" ON ( "organization_organizationdata"."organization_id" = "organization_organization"."id" ) 
        "phdrug_phdrug"."active" = TRUE 
        AND "monetary_pricelist"."active" = TRUE 
        AND (
            "monetary_pricelist"."expires" = FALSE 
            OR (
                "monetary_pricelist"."expires" = TRUE 
                AND ( "monetary_pricelist"."datestart" AT TIME ZONE'UTC' ) :: DATE <= '2019-01-22' 
                AND ( "monetary_pricelist"."dateend" AT TIME ZONE'UTC' ) :: DATE >= '2019-01-22' 
        AND (
            "monetary_pricelistdestinations"."to_all_insurances" = TRUE 
            OR "organization_organization"."uuid" = 'b51773d4-05f8-43a2-86ef-0098b31725d8' 
    "phdrug_phdrug"."default_description" ASC

Running the query with EXPLAIN ANALYZE I get this:

Unique  (cost=10412.31..12666.32 rows=29084 width=143) (actual time=3373.496..3620.090 rows=6442 loops=1)
  ->  Sort  (cost=10412.31..10485.02 rows=29084 width=143) (actual time=3373.494..3460.790 rows=228667 loops=1)
        Sort Key: phdrug_phdrug.default_description, phdrug_phdrug.id, phdrug_phdrug.uuid, phdrug_phdrug.ean, phdrug_phdrug.parent_ean, phdrug_phdrug.reg_num, phdrug_phdrug.medika_code, phdrug_phdrug.atc_iv, phdrug_phdrug.product_type, phdrug_phdrug.fraction, phdrug_phdrug.active, phdrug_phdrug.loyal, phdrug_phdrug.patent, phdrug_phdrug.chronics, phdrug_phdrug.recipe, phdrug_phdrug.deal, phdrug_phdrug.specialized, phdrug_phdrug.armored, phdrug_phdrug.hight_speciality, phdrug_phdrug.temp_8_15, phdrug_phdrug.temp_15_25, phdrug_phdrug.temp_2_8, phdrug_phdrug.temp_less_15, phdrug_phdrug.new, phdrug_phdrug.mdk_internal_code, phdrug_phdrug.mdk_single_id, phdrug_phdrug.is_from_mdk_db, phdrug_phdrug.top, phdrug_phdrug.laboratory_id, phdrug_phdrug.specialty_id
        Sort Method: external merge  Disk: 31192kB
        ->  Hash Join  (cost=704.51..6166.54 rows=29084 width=143) (actual time=23.648..507.099 rows=228667 loops=1)
              Hash Cond: (monetary_drugprice.pricelist_id = monetary_pricelist.id)
              ->  Nested Loop  (cost=696.92..5604.95 rows=44105 width=147) (actual time=22.881..416.630 rows=457692 loops=1)
                    Join Filter: (phdrug_phdrug.id = monetary_drugprice.drug_id)
                    ->  Hash Join  (cost=696.51..1177.21 rows=4583 width=147) (actual time=22.864..38.841 rows=23577 loops=1)
                          Hash Cond: (phdrug_phdrug.id = t4.drug_id)
                          ->  Seq Scan on phdrug_phdrug  (cost=0.00..359.94 rows=11992 width=143) (actual time=0.438..3.593 rows=11992 loops=1)
                                Filter: active
                                Rows Removed by Filter: 2
                          ->  Hash  (cost=639.21..639.21 rows=4584 width=4) (actual time=22.339..22.339 rows=23577 loops=1)
                                Buckets: 32768 (originally 8192)  Batches: 1 (originally 1)  Memory Usage: 1085kB
                                ->  Nested Loop  (cost=3.99..639.21 rows=4584 width=4) (actual time=1.785..16.702 rows=23577 loops=1)
                                      ->  Nested Loop  (cost=3.58..9.11 rows=5 width=8) (actual time=1.756..1.874 rows=7 loops=1)
                                            ->  Hash Left Join  (cost=3.43..7.57 rows=5 width=4) (actual time=1.733..1.797 rows=7 loops=1)
                                                  Hash Cond: (monetary_pricelistdestinations.to_organization_data_id = organization_organizationdata.id)
                                                  Filter: (monetary_pricelistdestinations.to_all_insurances OR (organization_organization.uuid = 'b51773d4-05f8-43a2-86ef-0098b31725d8'::uuid))
                                                  Rows Removed by Filter: 130
                                                  ->  Seq Scan on monetary_pricelistdestinations  (cost=0.00..3.37 rows=137 width=9) (actual time=0.626..0.643 rows=137 loops=1)
                                                  ->  Hash  (cost=3.12..3.12 rows=25 width=20) (actual time=1.076..1.076 rows=25 loops=1)
                                                        Buckets: 1024  Batches: 1  Memory Usage: 10kB
                                                        ->  Hash Left Join  (cost=1.56..3.12 rows=25 width=20) (actual time=1.040..1.053 rows=25 loops=1)
                                                              Hash Cond: (organization_organizationdata.organization_id = organization_organization.id)
                                                              ->  Seq Scan on organization_organizationdata  (cost=0.00..1.25 rows=25 width=8) (actual time=0.501..0.504 rows=25 loops=1)
                                                              ->  Hash  (cost=1.25..1.25 rows=25 width=20) (actual time=0.513..0.513 rows=25 loops=1)
                                                                    Buckets: 1024  Batches: 1  Memory Usage: 10kB
                                                                    ->  Seq Scan on organization_organization  (cost=0.00..1.25 rows=25 width=20) (actual time=0.484..0.501 rows=25 loops=1)
                                            ->  Index Only Scan using monetary_pricelist_pkey on monetary_pricelist t5  (cost=0.14..0.31 rows=1 width=4) (actual time=0.007..0.007 rows=1 loops=7)
                                                  Index Cond: (id = monetary_pricelistdestinations.pricelist_id)
                                                  Heap Fetches: 7
                                      ->  Index Scan using monetary_drugprice_pricelist_id_1ce160ce on monetary_drugprice t4  (cost=0.42..110.21 rows=1581 width=8) (actual time=0.010..1.236 rows=3368 loops=7)
                                            Index Cond: (pricelist_id = t5.id)
                    ->  Index Scan using monetary_drugprice_drug_id_c2f278e5 on monetary_drugprice  (cost=0.42..0.78 rows=15 width=8) (actual time=0.002..0.009 rows=19 loops=23577)
                          Index Cond: (drug_id = t4.drug_id)
              ->  Hash  (cost=6.45..6.45 rows=91 width=4) (actual time=0.757..0.757 rows=93 loops=1)
                    Buckets: 1024  Batches: 1  Memory Usage: 12kB
                    ->  Seq Scan on monetary_pricelist  (cost=0.00..6.45 rows=91 width=4) (actual time=0.655..0.713 rows=93 loops=1)
                          Filter: (active AND ((NOT expires) OR (expires AND ((timezone('UTC'::text, datestart))::date <= '2019-01-22'::date) AND ((timezone('UTC'::text, dateend))::date >= '2019-01-22'::date))))
                          Rows Removed by Filter: 45
Planning time: 25.871 ms
Execution time: 3638.544 ms

If I paste this in explain.depesz.com, I get this result

query explain

Almost the entire time that the query takes to finish is spent sorting, as you can see here:

enter image description here

...and this is where I'm completely lost. What is it sorting? It's not the ORDER BY at the end of my query, I already tried removing that.

My guts are telling me that I'm missing an index (or multiple indexes?), but at this point I'm not sure which fields should I index. How can I improve the performance of this query?

1 Answer 1


The sort is being done to support the DISTINCT in the SQL query (as represented by the UNIQUE in the query plan). An alternative approach to sorting would be use hashing to get the DISTINCT values. Increasing your setting for "work_mem" could encourage that approach.

But why are you generating so many duplicates, just to throw them away? It is hard to determine this just be reading the text of the query, without knowing what the query means in terms of your data model.

Nothing stands out to me as being a missing index. But since we don't know which indexes you have, it is hard to say if an index is not used because it is missing, or if it is not used because the planner thinks a hash join will be faster than using the index.

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