Egghead - Prevent Scaling Issues With GraphQL Data Loaders

[LeeAndro]

Released 12/2021MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English | Duration: 10 Lessons (12m) | Size: 73.1 MB

Most developers would raise an eyebrow if they saw database queries being done in a for-loop, but GraphQL provides just enough abstraction that it isn't always intuitive exactly how many s each resolver fires at scale, nor is it obvious how to batch operations efficiently and still return the correct results to the correct consumer

You'll learn how to use the GraphQL Data Loader pattern to improve the performance of your application, and solve scaling issues before they become a problem.​

To do this, we'll first implement our own naive version of the pattern to understand why the API is shaped how it is. Then we will switch over to the official DataLoader package and explore the benefits further.

Implement a cache layer to optimize your requests
Batch requests so your Database isn't overloaded
Build a performant GraphQL API

HomePage:

https://anonymz.com/https://egghead.io/courses/use-graphql-data-loaders-to-prevent-scaling-issues-by-batching-caching-database-requests-3b5d4442

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