Solution to Over-Fetching
1. Query Optimization
- Fine-tuning Data Interchange: Developing API endpoints or database queries to handle data fields based on specific needs.
- Selective Data Transfer: Expressing the particular data fields to be sent to avoid transferring redundant data.
2. Pagination and Filtering
- Implement Pagination: Breaks data retrieval into smaller, manageable chunks to prevent over-fetching.
- Benefits: Makes data easier to handle and reduces the risk of fetching unnecessary data.
- Include Filtering Options: Allows users to search for data that meets specific conditions.
3. Client-Side Caching
- Use caching mechanisms on the client side to store previously fetched data.
- Purpose: Reduce redundant requests and prevent over-fetching of data.
- Mechanism: Store data locally on the client side for future use.
- Benefits: Improves performance by minimizing the need to fetch data repeatedly.
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.
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