pathom3 VS dataloader

yeah, I picked cheshire just to create a minimal reproducible example. In fact what I was trying to do was developing a Pathom3 server with nbb and deploy it to GCP Cloud Functions. I thought I needed muuntaja to convert from edn to JSON and back. And since muuntaja wasn't working I tried the first library it came to mind.

Definitely check out pathom3 as well. It's a much improved version of pathom2.

Wilker Lucio (https://github.com/wilkerlucio) will be presenting Phantom 3 (https://github.com/wilkerlucio/pathom3). Pathom way provides a new way to think about and manipulate information. It both works for computing information locally or integrating data in a distributed architecture. You can find more info at: https://blog.wsscode.com/pathom-3-is-coming/

When I began working with GraphQL, I had concerns about the N+1 query problem. In my research, I came across the DataLoader pattern and its implementation on GitHub. While I explored various examples of its usage, I still struggled to grasp how it operates internally. Join me in delving a bit deeper into GraphQL DataLoader! :)

DataLoader: it is the standard solution to N+1 problem.

Additionally, you can use DataLoader or similar tools to optimize data fetching and avoid over-fetching or under-fetching data. Ultimately, performance and load tests ensure that your GraphQL API delivers optimal performance, meets response time expectations, and provides a smooth experience for users, even under heavy loads.

The most common practice is to turn N+1 into 1+1 using dataloaders (https://github.com/graphql/dataloader for JS, there are equivalents for most implementations). The N resolvers invoke a single batched loader which receives a list of keys and returns a list of values.

Let's take Payload, for example. Surprise, surprise. We have a relationship field, and it can store IDs to other related documents which are seamlessly merged in when you retrieve documents from the DB. We leverage the dataloader pattern to batch together all "populations" required for a given query, returning them all super fast and with as few separate queries to the DB as possible. We actually even outperform SQL-based frameworks quite a bit. In a purely relational test, we were 3x faster than Directus and 7x faster than Strapi while both were running Postgres, and we were on MongoDB.

Also, have a look at Facebook's Dataloader[0].

[0] https://github.com/graphql/dataloader

In the next article, I'm going to implement a GraphQL server with dataloader using the tools we introduced.

The one example of using Scoped dependency that comes to my mind, it's HTTP request level caching for libs like dataloader.

you would fetch these 1000 rows via dataloader that batches all requests for this relation to a single query... solving the n+1 issue