x-boilerplate VS dataloader

Heres a full ts with auth, cruds, etc : https://github.com/kaviarjs/x-boilerplate

I know it's not Sequelize, but this boilerplate with MongoDB can serve as an example on how we do things GraphQL, how to structure files, etc.

In terms of structure, how we do it, you can see aworking example here (very opinionated), but it can serve you as a way to wrap your mind around all these concepts.

You mean you want to see the source code? We do have such servers built with our framework. There you have guidelines on how to do things and here you've got a boilerplate. I cannot share the source code of a real production app with you unfortunately, but if you have specific questions (how to protect against nested deep attacks, etc) I can gladly answer you.

Start playing now with the boilerplate

Start hacking away with ready-to-use boilerplate: https://github.com/kaviarjs/x-boilerplate

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