Approaches to Keeping Apollo Cache Consistent with API Data after Mutations

Apollo Client is a popular GraphQL client used in React applications that simplifies communication with a GraphQL backend and improves user experience through caching. In this article, we will discuss methods for keeping the Apollo Cache up-to-date after successful data mutations.

One of the key features of Apollo is its normalized cache. When properly configured, it can detect situations where two different query results return parts of the same entity and merge those results into a single object in memory. This means that when there is an overlap between two queries, the result of the later query can update the values read by the earlier query if those fields have changed in the meantime.

For example, consider a UserComponent that updates and reads the latest age fetched by a query initiated by a different component (UserAgeComponent). Thanks to Apollo's knowledge that both queries refer to the same object in memory, updates propagate to all readers, provided the query fetch policy permits using the cache.

In addition to being normalized, the Apollo cache can also handle multiple different fields that refer to the same object in memory. This makes it easier to keep track of changes and maintain consistency in the cache.

However, when it comes to mutations, the cache can become inconsistent with the API data. This is because mutations modify the data on the server, but the cache may still hold the old data. To keep the cache up-to-date, there are a few approaches that developers can take.

The first approach is to manually update the cache after a mutation. This involves writing custom update functions that modify the cache based on the mutation's response. While this approach provides more control over the cache, it can be time-consuming and error-prone.

Another approach is to use Apollo's built-in update functions. These functions automatically update the cache based on the mutation's response, making it easier to maintain consistency. However, they may not work for more complex mutations or custom data structures.

Finally, developers can use a combination of both approaches. They can use Apollo's built-in update functions for simple mutations and write custom update functions for more complex ones. This approach provides the best of both worlds, allowing for greater control and flexibility.

In conclusion, keeping the Apollo Cache consistent with API data after mutations is crucial for maintaining data integrity and improving user experience. While there are multiple approaches to achieving this, developers should choose the one that best fits their needs and the complexity of their application. With the right approach, developers can ensure that their applications are always up-to-date and provide a seamless user experience.