Are Objection.js WHERE IN queries slowing my Node.js application down?

Question

Ok, so this is basically a yes/no answer. I have a node application running on Heroku with a postgres plan. I use Objection.js as ORM. I'm facing 300+ms response times on most of the endpoints, and I have a theory about why this is. I would like to have this confirmed.

Most of my api endpoints do around 5-10 eager loads. Objection.js handles eager loading by doing additional WHERE IN queries and not by doing one big query with a lot of JOINS. The reason for this is that it was easier to build that way and that it shouldn't harm performance too much.

But that made me thinking: Heroku Postgres doesn't run on the same heroku dyno as the node application I assume, so that means there is latency for every query. Could it be that all these latencies add up, causing a total of 300ms delay?

Summarizing: would it be speedier to build your own queries with Knex instead of generating them through Objection.js, in case you have a separately hosted database?

Answer 1

Hereby I can confirm that every query takes approx. 30ms, regardless the complexity of the query. Objection.js indeed does around 10 separate queries because of the eager loading, explaining the cumulative 300ms.

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Just FYI; I'm going down this road now ⬇

I've started to delve into writing my own more advanced SQL queries. It seems like you can do pretty advanced stuff, achieving similar results to the eager loading of Objection.js

select 
  "product".*,
  json_agg(distinct brand) as brand,
  case when count(shop) = 0 then '[]' else json_agg(distinct shop) end as shops,
  case when count(category) = 0 then '[]' else json_agg(distinct category) end as categories,
  case when count(barcode) = 0 then '[]' else json_agg(distinct barcode.code) end as barcodes
from "product"
inner join "brand" on "product"."brand_id" = "brand"."id"
left join "product_shop" on "product"."id" = "product_shop"."product_id"
left join "shop" on "product_shop"."shop_code" = "shop"."code"
left join "product_category" on "product"."id" = "product_category"."product_id"
left join "category" on "product_category"."category_id" = "category"."id"
left join "barcode" on "product"."id" = "barcode"."product_id"
group by "product"."id"

This takes 19ms on 1000 products, but usually the limit is 25 products, so very performant.