#optimization
6 posts
Memoizing an n-ary function in TypeScript
In this post, I talk about an approach to memoizing a n-ary function in JavaScript / TypeScript preserving the flow of types.
How to avoid logging sensitive data in GraphQL
Metrics, Logging, and Tracing are some primary forms of monitoring we use in our services. In this post, I talk about how we can leverage the power of GraphQL to prevent sensitive information ending up in these monitoring tools.
GraphQL Optimization - Lookaheads - Prefetching
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.
GraphQL Optimization - Dataloaders
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
GraphQL Optimization - Field Filtering
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 Optimization - Lookaheads
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.