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In previous posts, we saw how to optimize data between GraphQL Server and a backend server using the concepts of lookaheads and field filtering. In this post, we are going to leverage look aheads for other forms of optimization - especially prefetching resources.

In the previous posts, we saw how to optimize the data transfer between the GraphQL server and a data provider - backend server. In this post, I'm going to talk about how we can handle the complexities we discussed in previous posts with a Dataloader

In this post, we are going to take a look at one of the primary optimizations we can achieve by using GraphQL Lookaheds - Field filtering. We will dive deep into what the different complexities are for applying lookaheads to optimize data between GraphQL server and a backend server.

GraphQL offers a way to optimize the data between a client and a server. We can use the declarative nature of a GraphQL query to perform lookaheads. Lookaheads provide us a way to optimize the data between the GraphQL server and a backend data provider - like a database or another server that can return partial responses.

This post is about how do we detect some of the cases where we have possibly repeated code in the same project or across multiple projects. This code if necessary can be extracted into a common module so as to avoid fixing bugs multiple times.

This post is about how to effectively model timers for the web and go through the shortcomings of each of those models.