How can I optimize this JOIN query? - sql

Question

From pg_stat_statements I have this query that's taking 900 ms on average. What is the recommended way going forward in optimizing this query? I do have indexes but not sure where the bottleneck could be. Here's the EXPLAIN ANALYZE.
EXPLAIN ANALYZE
SELECT "listing_variants".*
FROM "listing_variants"
INNER JOIN "links" ON "links"."listing_variant_id" = "listing_variants"."id"
INNER JOIN "product_variants" ON "product_variants"."id" = "links"."product_variant_id"
INNER JOIN "listings" ON "listing_variants"."listing_id" = "listings"."id"
WHERE "listings"."sales_channel_id" = 31
AND "listing_variants"."is_linked" = 'f'
AND (listing_variants.available_quantity != product_variants.available_quantity);
gives
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=5283.71..6960.01 rows=524 width=232) (actual time=54.138..54.138 rows=0 loops=1)
Join Filter: (listing_variants.available_quantity <> product_variants.available_quantity)
-> Hash Join (cost=5283.42..6648.69 rows=720 width=236) (actual time=54.137..54.137 rows=0 loops=1)
Hash Cond: (links.listing_variant_id = listing_variants.id)
-> Index Only Scan using index_on_product_listing_variant_id on links (cost=0.29..1205.14 rows=30643 width=8) (actual time=0.026..6.112 rows=30863 loops=1)
Heap Fetches: 6799
-> Hash (cost=5261.53..5261.53 rows=1728 width=232) (actual time=45.407..45.407 rows=368 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 65kB
-> Hash Join (cost=1671.82..5261.53 rows=1728 width=232) (actual time=11.147..45.075 rows=368 loops=1)
Hash Cond: (listing_variants.listing_id = listings.id)
-> Seq Scan on listing_variants (cost=0.00..3412.77 rows=42577 width=232) (actual time=0.018..29.882 rows=42713 loops=1)
Filter: (NOT is_linked)
Rows Removed by Filter: 30863
-> Hash (cost=1661.68..1661.68 rows=811 width=4) (actual time=10.585..10.585 rows=811 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 29kB
-> Bitmap Heap Scan on listings (cost=30.57..1661.68 rows=811 width=4) (actual time=0.362..10.224 rows=811 loops=1)
Recheck Cond: (sales_channel_id = 31)
Heap Blocks: exact=668
-> Bitmap Index Scan on index_listings_on_sales_channel_ext_svc_updated (cost=0.00..30.37 rows=811 width=0) (actual time=0.242..0.242 rows=821 loops=1)
Index Cond: (sales_channel_id = 31)
-> Index Scan using product_variants_pkey on product_variants (cost=0.29..0.42 rows=1 width=12) (never executed)
Index Cond: (id = links.product_variant_id)
Planning time: 1.437 ms
Execution time: 54.366 ms
Thanks!

Answer 1

Use JOIN Over Exists only when you need to select data from multiple tables, which you are not doing here. That's a first step of optimization. In your case, with join it is polluting your resultset by returning multitude of same data rows depending on multiple of data available in joined secondary tables.
SELECT "listing_variants".*
FROM "listing_variants"
WHERE "listing_variants"."is_linked" = 'f'
AND EXISTS(SELECT 1 FROM "links" ON "links"."listing_variant_id" = "listing_variants"."id"
JOIN "product_variants" ON "product_variants"."id" = "links"."product_variant_id"
AND "listing_variants"."available_quantity" != "product_variants"."available_quantity"
JOIN "listings" ON "listing_variants"."listing_id" = "listings"."id"
AND "listings"."sales_channel_id" = 31);
Other than that your query is pretty straightforward, and well indexing & data partitioning can only contribute to further optimization.