GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.

graphql

Learn Graphql

When your application communicates with multiple GraphQL services, it becomes problematic to distinguish them by operation type and name alone. Learn how to take the service's endpoint into account when intercepting GraphQL queries and mutations with Mock Service Worker.

- [`graphql.link()`](https://mswjs.io/docs/api/graphql#graphqllinkurl)

Endpoint-Based GraphQL Mocking in MSW

Let's improve our schema-first GraphQL mocks by intercepting _all_ GraphQL operations—both queries and mutations—using a single Mock Service Worker `graphql.operation()` request handler. 

- [`graphql.operation()`](https://mswjs.io/docs/api/graphql#graphloperationresolver)

Mock All GraphQL Operations in MSW

Operation-based GraphQL mocking allows you to move fast without investing time into schemas or resolvers. 

But, when integrating Mock Service Worker into an existing application, going schema-first can be advantageous. Here's how you can route every GraphQL request to be resolved against a GraphQL schema, mocked or real, and use mock resolvers to control individual types and fields. 

Resolve GraphQL Requests Against a Mock Schema in MSW

Error responses in GraphQL are a bit unique. They must still produce a "200 OK" response and list the errors in the response body instead. Let's follow the GraphQL specification to produce a valid GraphQL error response in our Mock Service Worker handlers. 

Mock GraphQL Errors in MSW

In this lesson, you will use the Mock Service Worker `graphql.mutation()` request handler to allow the users to leave reviews for the movie they've watched. 

- [`graphql` namespace](http://mswjs.io/docs/api/graphql)

Intercept and Mock GraphQL Mutations in MSW

Let's spice things up by adding GraphQL to our application. 

Since you are prototyping mock-first, you don't have to write any GraphQL schemas or define any resolvers. Instead, you will utilize Mock Service Worker to intercept valid GraphQL queries by their _type_ and _name_, and respond with the same semantic JSON responses matching the queries.

- [Describing GraphQL APIs](http://mswjs.io/docs/network-behavior/graphql)

Intercept and Mock GraphQL Queries in MSW

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Hi! My name is [Artem](https://kettanaito.com). In this course, I will teach you how to mock REST and GraphQL APIs with [Mock Service Worker](https://mswjs.io). Together, we will build a movie streaming application and you will learn how to intercept both server-side and client-side requests and mock responses using standard JavaScript API. This is a state-of-the-art mock-first prototyping with MSW.

- Join my [Discord server](https://kettanaito.com/discord) to go through the course with others, ask questions, and share feedback. See you there!

Introduction to Mock REST and GraphQL APIs with Mock Service Worker

Controlling the network is the key to fast prototyping, bullet-proof tests, and efficient debugging. 

Mocking the APIs you use gives you this kind of control but most methods in the client and server come up short. 

That's where Mock Service Worker does it right.

With it you can intercept and take control of requests no matter where they're being made.

In this course, you will learn how to:

* setup and your first request handler 
* mock complex flows like authentication
* describe dynamic requests
* respond with HTTP streams,
* and use schema-first GraphQL mocking

After this course you'll be more than ready to bring MSW into your app and start mocking any kind of API you're using!

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments. 

Controlling the network is the key to fast prototyping, bullet-proof tests, and efficient debugging. Mocking the APIs you use gives you this kind of control.

Mock REST and GraphQL APIs with Mock Service Worker

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

When a vendor manages their shops, they might want to click into the shop and mange the products associated with that shop. Let's create a page for that.

I'll create a details route that will host this information. On this page we will write a `GetShop` query that will take in the shop id that is pulled from the route. Similar to the shops page, we will iterate over the products on the shop and add buttons to manage individual products.

Create Shop Details View that uses GraphQL to Query Fauna by Shop Id

It would be great to show a list of shops that a vendor has created. To do this we will need to create a new query `getShopsByOwnerId` that accepts a string that will be our user id. Like before, we'll merge this schema change into Fauna and update our roles to make this request.

On the frontend we'll create a shop list component and pass in the user token that Auth0 gives us for the logged in user.

Query Data by Property with GraphQL and Fauna in Next.js

Fauna is a flexible database so we can update our schema without breaking any existing functionality. You'll notice that our products all have hard coded images. In this lesson, we'll update our `Product` to include an image and import those changes to Fauna.

Update the Product GraphQL Data Model and Upload Changes to Fauna

Next.js is a minimalistic framework for server-rendered React applications.

Query Data with GraphQL in Next.js from a Fauna Database

With our GraphQL schema in hand, we can create a full-fledged Fauna database. We'll import our GraphQL schema into a newly created database and explore the mutations and queries that were generated for us by creating and searching for a shop.

Upload a GraphQL Schema to Create a FaunaDB Database and Create a Shop

The next step is to model the data that we are going to use for our application, we'll have a Shop and Product. Shops will 'have many' Products. 

You'll see how to type this in a GraphQL schema with the different properties we want on Shop and Product. Additionally we will be introduced to the faunadb `@relation` directive which is similar to a foreign key constraint. 

[Read more about the @relation directive in the docs.](https://docs.fauna.com/fauna/current/api/graphql/directives/d_relation)

```typescript
# schema.graphql
type Shop {
  name: String!
  description: String!
  coverImg: String!
  products: [Product]!
  ownerID: String!
}

type Product {
  name: String!
  description: String!
  price: Float!
  category: String!
  shop: Shop! @relation
}
```

Model our Application Data with a GraphQL Schema


Next.js is one of the most popular server-side-rendered React frameworks you can use to build production-ready serverless applications in record time. 

On the other hand, Fauna is a flexible, serverless, distributed document-relational database. Fauna has a powerful GraphQL API and custom resolver support. 

Using Next.js and Fauna, you can build robust, feature-rich, production-ready full-stack applications without the hassle of managing any servers or databases.

In this course, Shadid guides you through building your very own full-stack serverless marketplace application using Next.js, GraphQL, and Fauna. 

You will learn,

- How to work with Apollo GraphQL Client in Next.js
- How to setup Fauna GraphQL
- Data modeling in Fauna
- How to write custom Fauna GraphQL Resolvers (UDFs)
- How to authenticate with Auth0?
- Stripe payment 



Full-Stack Serverless Applications with Next.js & Fauna

In this course, Colby is going to do a deep dive into creating a full store for your products. You'll learn how to set up and use GraphCMS as your content management system. You'll learn about the Cloudinary UI extension and how to use it to host your images. You'll learn about Snipcart and using the useSnipcart hook to handle the cart state and the checkout. You'll learn how to implement Stripe as your payment gateway. And lastly, you'll learn how to deploy a Next js app to vercel from github with environment variables.

snipcart

Ecommerce Product Management & Storefront with GraphCMS, Snipcart, & Next.js

Apollo provides a universal GraphQL API on top of your existing services, so you can build new application features fast without waiting on backend changes.

apollo

We are going to start visualizing what our data looks like by using graphcms' Playground. Here we can query our data and explore our content API right from the dashboard. 

We are going to use Next.js getStaticProps to query our data at compile time so we don't have to make the browser wait for that request when it's getting loaded. 

To actually make our query, we are going to be using [Apollo Client](https://www.apollographql.com/docs/react/).

Query the GraphCMS GraphQL API with Apollo GraphQL

graphcms

To manage my product data, I'm going to use [graphCMS](https://graphcms.com/) which is a content management system that's going to give us a content API backed by GraphQL. You'll need to login, or register an account, with just the free version for this course. I will also run you through the initial setup for graphCMS. 

Set up GraphCMS to Manage Data with a GraphQL API

If users will be often querying the number of comments without also querying the list of comments

Then fetching the entire comment collection is wasteful. It's also not compatible with any pagination features that don't return the entire collection.

In this lesson, we will create a custom Data Loader specifically for counting items

Count the Items in a Collection with a Custom Data Loader

The simplest way to count the comments on a post is to use a Data Loader that returns all of the comments and return the length.

As long as the primary access pattern is to always fetch the number of comments along with the comments themselves, the Data Loader will batch them together and you'll get the count for free with no extra database requests.

Count the Items in a Collection By Reusing a Data Loader

One-to-many relationships are a prime candidate for scaling issues. In this lesson we'll add a new loader to fetch all the comments for a given post ID.

Fetching Many Items by Key with GraphQL DataLoaders

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.



Mocking WebSocket APIs with Mock Service Worker


When attempting to load the same module twice in JavaScript you'll hit a cache and code won't re-run. In scenarios where you actually do want to have state in your modules, you'll have to use a cache-busting technique by passing a query parameter to a dynamic import. This lesson walks through how to add a query parameter, how to add the appropriate types for loading dynamic modules in TypeScript, and when these techniques are valuable.

Import the Same JavaScript Module Multiple Times with Cache Busting



The include keyword in GitHub Actions empowers you to add custom variables to your matrix strategy, providing flexibility beyond standard configurations. In this video, we'll explore how include allows you to define specific combinations of variables, enhancing control over your workflow runs. You'll see how this feature enables you to tailor jobs for unique environments or scenarios not covered by the default matrix options. By leveraging include, you can efficiently manage complex build processes with greater precision. This technique helps you streamline your CI/CD pipeline, making it more adaptable to your project's evolving needs.

Use GitHub Actions Matrix Include for Complex Job Configurations

While running `zod` schema validation agains domain-rich objects, the error message saying that _a string is not a number_ might not be really useful.

Especially, when you need to provide a meaningful feeback to the user, e.g. after they have submitted a form with lots of data.

`zod` allows to customize messages on a very granular level. We can also extract repeatable customization to `zod` error maps.

Finally, in order to process an error message further (e.g. display it to the user), we need to handle the `zod` validation errors effectively. Especially, when a big object validation has failed and we could receive a handful of errors.

That's what we learn in this lesson. 🔥

Customize zod Error Messages and Handle zod Errors Effectively

customize zod validation messages, customize zod schema property messages, reuse error maps across different schemas, navigate through zod error objects

You might want to run `zod` schema checks in different styles.

Sometimes you prefer to conditionally throw the error - that's what `parse` does. But another time you'd prefer to receive a `boolean` property that determines whether the validation was successful or not - and perform further actions accordingly.

Additionally, the error-throwing API of `parse` works extremely well with [TypeScript assertion functions](https://www.typescriptlang.org/docs/handbook/release-notes/typescript-3-7.html#assertion-functions). Their combination is just... perfect! 👌

That's what we learn in this lesson.

Use parse and safeParse Runtime Type-checks with zod

use zod parse function to conditionally throw ZodError, use zod safeParse to get a boolean value and run typescript type guard over success property

In this lesson we learn that TypeScript cannot guarantee correct shapes of responses. That's due to TS performing checks in compile-time, while the response will be availbale in runtime - when TS doesn't exist anymore.

In some situations, such as HTTP requests, runtime type checks such as `zod` are essential for providing quick feedback around data-related errors.

Understand where are the possible mistakes to make while using TypeScript with HTTP calls. Use `fetch` and `axios` along with `zod` schema checks.

Also, note that similar mistakes or mistakes could happen while using web storage (e.g. `localStorage`)

Validate HTTP API Response with zod, fetch and axios

use zod as an HTTP response validation tool along with fetch and axios in order to make sure that the response has expected data structure

In this lesson we learn that the `zod` schemas become the single source of truth.

We shall not update TypeScript types manually, since they are generated automatically from the schema.

Same with validation functions.

Whenever the schema changes, all the rest will update accordingly. It's an important mind shift.

Treat zod Schemas as Single Source of Truth

Zod Schemas are the single source of truth for both typescript types, and interfaces - and runtime validation functions. Schemas should be updated first.

`zod` is powerful, but writing the schemas manually for existing JSON objects, especially big ones, can be time-consuming.

But that's what `quicktype` is for! You can get 80% work done instantly! 🥳

Depending on your need - whether you want to automate the process, or do it just one time, you can:
- use an online app
- use an IDE extension
- run a CLI script
- generate a script to be executed within CI/CD environment/tooling

And this is what we learn in this lesson! 🔥🔥🔥

Get started with zod quickly!

Generate zod Schemas Automatically using Quicktype App, Extensions and Scripts

Use quicktype app, extension or scripts to automatically generate zod schemas from existing json files. Save time and get started with zod quickly!

`zod` is a powerful library allowing to run runtime type-checks.

It's complementary to TypeScript, which does type checks in compile time.

In this lesson we create few schemas that represent primitive type values, along with `zod` utilities which make these more precise. We also create object schemas and nest one inside another.

All schemas are being validated against example values. We analyze how to interpret error messages in case of failures.

Perform Runtime Type-checks on Primitives and Objects with zod

Create zod schemas to validate primitive type and object type values in runtime. Analyze the errors to see how a value doesn't correspond to schema expectations


Using Cursor Chat, you can easily reference an entire directory of files and ask it to generate anything based on those files. This opens up the possibility of using your current code as a reference guide to create new code that follows existing patterns. With this approach, the result can be a polished prompt you can use with any AI to generate consistent and reliable output.

Focus less on crafting the perfect prompt upfront - what matters most is testing and iterating based on the AI's responses. The more results you see from the prompt, the more you can reinforce the good results and prune out the bad results until your prompt is polished enough that you can rely on the prompt to give you consistent quality results.


GraphQL

Secure GraphQL

Securing GraphQL Backends with JWTs

Up and Running

Build a full-stack JAMstack application with Gatsby, Hasura, and Auth0

Getting Started With Hasura and GraphQL

Learn Apollo Client Hooks

For Your Porfolio

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

React Real-Time Messaging with GraphQL using urql and OneGraph

Build a CRUD-ready GraphQL API + Admin UI with KeystoneJS

Going Serverless with GraphQL

Scalable Offline-Ready GraphQL Applications with AWS AppSync & React

GraphQL Data in React with Apollo Client

Building Serverless Web Applications with React & AWS Amplify

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