|
- Demystifying Cache Normalization - Apollo GraphQL Blog
Data normalization Normalization is a technique used to organize data in a way that reduces data redundancy Typically, when we’re structuring data to be stored somewhere (whether that be a database, a client-side cache, or a JSON object), we want to reduce the amount of duplicate data saved Ideally, we aim to have no duplicate data
- Data Normalization, Caching, and Cache Eviction: Data Lifecy
Data normalization is an integral part of relational database design It is the process by which data is transformed into atomic components to reduce redundancy and improve efficiency Apollo explains their process of data normalization in the documentation, but to summarize the InMemoryCache data normalization flow: Distinct objects are
- Normalized Caching - urql Documentation
Normalized Caching In GraphQL, like its name suggests, we create schemas that express the relational nature of our data When we create and query against a Query type we walk a graph that starts at the root Query type and walks through relational types
- Data Normalization in Frontend Apps Using Normalizr and JSON API Normalizer
When normalizing data we need to follow few rules: data structure should be flat each entity should be stored as different object property relationships with other entities should be created based on ids This post will present two packages that help with data normalization in frontend apps JSON API Normalizer JSON API is a standard that helps API developers to create consistent responses and
- Configuring the Cache - Apollo Angular
Data Normalization The InMemoryCache normalizes query response objects before it saves them to its internal data store Normalization involves the following steps: The cache generates a unique ID for every identifiable object included in the response The cache stores the objects by ID in a flat lookup table
|
|
|