Скачать или смотреть Five Common Problems in GraphQL Apps And How to Fix Them

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okay, let's dive into five common problems encountered while building graphql applications, along with detailed explanations, code examples, and strategies to fix them.

*1. the n+1 problem (and its graphql variant)*

*the problem:*

the n+1 problem is a performance bottleneck that occurs when your application makes an initial database query to fetch a list of items, and then makes n additional queries to fetch data related to each item in that list. in a standard rest api, you might fetch a list of posts, then make separate calls for each post's author. in graphql, this manifests when you have a query that fetches a list of objects and then traverses a relationship to fetch more data, resulting in a similar explosion of queries.

*why it's a problem in graphql:*

while graphql allows clients to request precisely the data they need, it can unintentionally exacerbate the n+1 problem. clients might request nested fields, which can trigger inefficient data fetching if the graphql server isn't properly optimized.

*example:*

let's say we have a schema with `user` and `post` types, where each post has an `author`:



now, a client sends this query:



*naive implementation (n+1 problem):*

here's a simplified (and problematic) resolver implementation in node.js using `graphql`:



in this example:

1. the `users` resolver fetches all users (1 query).
2. for each user, the `user.posts` resolver fetches that user's posts (n queries).
3. for each post, the `post.author` resolver fetches the author (n * m queries).

that's a lot of database round trips!

*solutions:*

*dataloader:* dataloader is a batching and caching utility designed to solve the n+1 problem. it's a core component of many graphql server implementations.

1. *batching:* dataloader collects individual requests for the same data over a short period (e.g., within a single graphql request) and batches them into a single database query.
2. *caching:* dataloader caches the ...

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