Tutorials on Apollo Client

Consider the following code snippet of a React component, <App /> , that... You may have noticed that the data sent back by the mutation provides the user's information in a logIn field, and any data returned from a successful mutation automatically gets added to the local Apollo Client cache. Therefore, why do we have a user state variable when we could just access the user's information via the data field in the mutation result object? For example, like this:

Liking a tweet on Twitter. Marking an e-mail as read in your G-Mail inbox. These type of simple, low-stake actions seem to happen so quickly that you can perform one action after another without having to wait for the previous to finish resolving. As the defining trait of optimistic UIs , these actions give the feeling of a highly responsive and instant UI. Psychologically speaking, they trick the user into thinking that an action has completed even though the network request it sends to the server has not been fully processed. Take, for example, the like button of a tweet. You can scroll through an entire feed and like every single tweet with zero delays between successive tweets. To observe this, open up a Twitter feed and your browser's developer console. Within the developer console, switch to the network tab and select the "Slow 3G" option under the throttling dropdown to simulate slow 3G network speeds. Slowing down network speeds lets us see the UI updates happen before the server returns a response for the action. Then, filter for network requests sent to a GraphQL API endpoint containing the text "FavoriteTweet" (in the request URL), which tells the server to mark the tweet as liked by the current user. When you click on a tweet's like button, the heart icon disappears, the like count increments by one and the text color changes to pink despite the network request still pending. While the server handles this request, the updates to the UI give the illusion that the server already finished processing the request and returned a successful response.

RESTful APIs adhere to a reliable architectural standard for transferring data statelessly over the HTTP protocol. Every endpoint of an API semantically describes how a resource should be created ( POST ), read ( GET ), updated ( PUT / PATCH ), deleted ( DELETE ), etc. Large, data-driven applications consume data from multiple third-party/in-house sources, and each one exposes a unique set of endpoints to manage different resources. Adapting these applications to support a wide range of platforms and device sizes (commonly mobile, desktop and web) may present several problems: Using Facebook's GraphQL query language, the client specifies its exact data requirements to the server via a single endpoint. Establishing a schema (written with the syntax of the GraphQL Schema Definition Language) creates a contract between the client and server that defines what data can be read from and written to the data graph by the client. This data graph centralizes all of the APIs consumed by your application by mapping each field to a resolver that populates it with a value retrieved from an endpoint of one of these APIs, a database, etc. A client can fetch data from a GraphQL server via plain HTTP and then manually update the UI accordingly. However, GraphQL clients such as Apollo Client abstract away the low-level implementation details of these features underneath a declarative API. Built by the Apollo GraphQL team, Apollo Client is an open-source GraphQL client that provides a lot of out-of-the-box functionality for communicating with a GraphQL server: