Best Practices For Avoiding Fetching Issues
1. Query Optimization
- In optimized data queries related to only the meaningful fields and records, avoid required data fetching and unnecessary the development of network.
2. Pagination and Partial Responses
- Apply pagination recommendation to reduce the number of data which are fetched per each request, and use partial response mechanism for the purpose of finding only the necessary fields.
3. Data Caching
- Retrieve the data that is often in demand from different levels (e.g., client-side, server-side and intermediate caches), in order to avoid unnecessary redundancy and increase speed.
4. GraphQL and Data Graphs
- Develop GraphQL – a query language for APIs – that allows for specifying exact data requirements, minimizing over-fetching and under-fetching problems with performance optimization through efficient data retrieval.
5. Data Modeling and Denormalization
- Enhance data models and think about mixture denormalization methods to hold data which are related together.
What Are Over-Fetching and Under-Fetching?
Fetching data in GraphQL is a fundamental concept that involves retrieving information from a server or database. Unlike traditional REST APIs, GraphQL allows clients to request only the specific data they need, minimizing over–fetching and under–fetching.
In this article, We will explore the concepts of fetching, over–fetching, and under–fetching in GraphQL, along with their challenges and solutions in detail and so on.