#graphql resolver mutation

[codeonedigest]

GraphQL Resolver Explained with Examples for API Developers

Full Video Link - https://youtube.com/shorts/PlntZ5ekq0U Hi, a new #video on #graphql #resolver published on #codeonedigest #youtube channel. @java @awscloud @AWSCloudIndia @YouTube #youtube @codeonedigest #graphql #graphqlresolver #codeo

 Resolver is a collection of functions that generate response for a GraphQL query. Actually, resolver acts as a GraphQL query handler. Every resolver function in a GraphQL schema accepts four positional arguments. Root – The object that contains the result returned from the resolver on the parent field. args – An object with the arguments passed into the field in the query. context – This is…

[codezup]

GraphQL Resolvers: A Step-by-Step Guide

A Step-by-Step Guide to Using GraphQL Resolvers for Optimized Queries 1. Introduction GraphQL resolvers are the backbone of a GraphQL server, acting as the bridge between the schema and your data sources. They define how each field in your schema is fetched, computed, or mutated. This guide will walk you through the process of writing resolvers for optimized queries, helping you handle complex…

[learning-code-ficusoft]

Introduction to GraphQL for Full Stack Applications

What is GraphQL?

GraphQL is a query language for APIs and a runtime for executing those queries by leveraging a type system defined for the data. Developed by Facebook in 2012 and open-sourced in 2015, GraphQL provides a flexible and efficient alternative to REST APIs by allowing clients to request exactly the data they need — nothing more, nothing less.

Why Use GraphQL for Full Stack Applications?

Traditional REST APIs often come with challenges such as over-fetching, under-fetching, and versioning complexities. GraphQL solves these issues by offering:

Flexible Queries: Clients can specify exactly what data they need.

Single Endpoint: Unlike REST, which may require multiple endpoints, GraphQL exposes a single endpoint for all queries.

Strongly Typed Schema: Ensures clear data structure and validation.

Efficient Data Fetching: Reduces network overhead by retrieving only necessary fields.

Easier API Evolution: No need for versioning — new fields can be added without breaking existing queries.

GraphQL vs. REST: Key Differences

Core Concepts of GraphQL

1. Schema & Types

GraphQL APIs are built on schemas that define the data structure.

Example schema:graphqltype User { id: ID! name: String! email: String! }type Query { getUser(id: ID!): User }

2. Queries

Clients use queries to request specific data.graphqlquery { getUser(id: "123") { name email } }

3. Mutations

Used to modify data (Create, Update, Delete).graphqlmutation { createUser(name: "John Doe", email: "john@example.com") { id name } }

4. Subscriptions

Enable real-time updates using Web Sockets.graphqlsubscription { newUser { id name } }

Setting Up GraphQL in a Full Stack Application

Backend: Implementing GraphQL with Node.js and Express

GraphQL servers can be built using Apollo Server, Express-GraphQL, or other libraries.

Example setup with Apollo Server:javascriptimport { ApolloServer, gql } from "apollo-server"; const typeDefs = gql` type Query { hello: String } `;const resolvers = { Query: { hello: () => "Hello, GraphQL!", }, };const server = new ApolloServer({ typeDefs, resolvers });server.listen().then(({ url }) => { console.log(`Server running at ${url}`); });

Frontend: Querying GraphQL with React and Apollo Client

Example React component using Apollo Client:javascriptimport { useQuery, gql } from "@apollo/client";const GET_USER = gql` query { getUser(id: "123") { name email } } `;function User() { const { loading, error, data } = useQuery(GET_USER); if (loading) return <p>Loading...</p>; if (error) return <p>Error: {error.message}</p>; return <div>{data.getUser.name} - {data.getUser.email}</div>; }

GraphQL Best Practices for Full Stack Development

Use Batching and Caching: Tools like Apollo Client optimize performance.

Secure the API: Implement authentication and authorization.

Optimize Resolvers: Use DataLoader to prevent N+1 query problems.

Enable Rate Limiting: Prevent abuse and excessive API calls.

Conclusion

GraphQL provides a powerful and efficient way to manage data fetching in full-stack applications. By using GraphQL, developers can optimize API performance, reduce unnecessary data transfer, and create a more flexible architecture. 

Whether you’re working with React, Angular, Vue, or any backend framework, GraphQL offers a modern alternative to traditional REST APIs.

WEBSITE: https://www.ficusoft.in/full-stack-developer-course-in-chennai/

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Advanced Topics Covered in a Full Stack Developer Course

A Full Stack Developer course is designed to equip students with a broad range of skills needed to build and maintain complex web applications. While foundational topics such as HTML, CSS, and JavaScript are essential, advanced topics are crucial for developing expertise and tackling more sophisticated challenges in full stack development. As the field evolves, Full Stack Developer courses increasingly integrate advanced topics to prepare students for the demands of modern web development. Here’s an exploration of some of the advanced topics commonly covered in a Full Stack Developer course.

1. Microservices Architecture

Microservices architecture is an advanced approach to designing and deploying applications. Unlike monolithic applications, which are built as a single, unified unit, microservices break down an application into smaller, loosely coupled services that communicate through APIs. In a Full Stack Developer course, you might cover:

Service Design: Learning how to design microservices that are independently deployable and scalable.

API Integration: Using RESTful APIs or GraphQL to enable communication between services.

Service Orchestration: Managing interactions between microservices and handling service discovery and load balancing.

Resilience and Fault Tolerance: Implementing strategies to ensure that failures in one service do not impact the entire system.

Understanding microservices architecture is crucial for building scalable and maintainable applications, particularly in complex environments.

2. DevOps Practices and Tools

DevOps practices aim to streamline and automate the development and operations lifecycle, fostering collaboration between development and IT teams. Full Stack Developer courses often cover:

Continuous Integration/Continuous Deployment (CI/CD): Setting up automated pipelines for building, testing, and deploying code. Tools like Jenkins, GitLab CI, and Travis CI are commonly used.

Infrastructure as Code (IaC): Managing and provisioning infrastructure using code with tools like Terraform, Ansible, and AWS CloudFormation.

Containerization and Orchestration: Using Docker for containerization and Kubernetes for orchestrating containerized applications.

Monitoring and Logging: Implementing tools for monitoring application performance and collecting logs to ensure system reliability and troubleshoot issues.

Proficiency in DevOps practices is essential for modern development environments that emphasize efficiency and automation.

3. Advanced JavaScript Concepts

JavaScript is a core technology for web development, and mastering advanced concepts is vital for building dynamic applications. Topics covered might include:

Asynchronous Programming: Techniques such as Promises, async/await, and handling asynchronous operations effectively.

JavaScript Design Patterns: Implementing design patterns like Singleton, Factory, and Observer to write more efficient and maintainable code.

Functional Programming: Applying principles of functional programming such as higher-order functions, closures, and immutability to JavaScript.

Web Performance Optimization: Techniques for optimizing JavaScript performance, including code splitting, lazy loading, and minimizing reflows and repaints.

A deep understanding of advanced JavaScript concepts enhances your ability to create high-performance and responsive web applications.

4. GraphQL

GraphQL is a query language for APIs that offers a more flexible and efficient alternative to REST. Full Stack Developer courses might cover:

Schema Design: Defining a GraphQL schema to specify the types of data and relationships available in the API.

Resolvers: Implementing functions that handle requests and return data based on the schema.

Queries and Mutations: Understanding how to write queries to fetch data and mutations to modify data.

Integration with Front-End Frameworks: Using GraphQL with front-end frameworks like React to optimize data fetching and state management.

GraphQL’s ability to provide precise and efficient data fetching makes it a valuable skill for modern web development.

5. Advanced Database Management

Effective database management is crucial for handling complex data interactions and maintaining application performance. Topics include:

Database Design: Advanced techniques for designing normalized and denormalized database schemas, optimizing queries, and ensuring data integrity.

NoSQL Databases: Exploring NoSQL databases such as MongoDB, CouchDB, or Cassandra, and understanding when to use them compared to traditional relational databases.

Database Optimization: Techniques for indexing, query optimization, and caching strategies to improve database performance.

Data Migration and Backup: Managing data migration processes, backups, and recovery strategies to ensure data reliability.

Mastering advanced database management techniques ensures efficient and scalable data handling in your applications.

6. Security Best Practices

Security is a critical concern in web development, and understanding advanced security practices is essential. Topics covered might include:

Authentication and Authorization: Implementing robust authentication mechanisms (e.g., OAuth, JWT) and managing user permissions and roles.

Encryption: Applying encryption for data at rest and in transit to protect sensitive information.

Security Vulnerabilities: Identifying and mitigating common vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).

Secure Development Lifecycle: Integrating security practices into the development lifecycle to proactively address potential threats.

Applying security best practices helps safeguard applications and user data against malicious attacks.

7. Progressive Web Apps (PWAs)

Progressive Web Apps combine the best features of web and mobile applications to deliver enhanced user experiences. Advanced topics include:

Service Workers: Implementing service workers to enable offline functionality, background sync, and push notifications.

Web App Manifest: Creating a manifest file to configure the appearance and behavior of the PWA when installed on a user’s device.

Performance Optimization: Techniques for optimizing PWA performance, including caching strategies and resource optimization.

PWAs offer a seamless experience across different devices and network conditions, making them an important skill for modern web development.

8. Cloud Computing and Serverless Architectures

Cloud computing and serverless architectures are transforming how applications are deployed and managed. Topics might include:

Cloud Platforms: Understanding major cloud platforms such as AWS, Azure, and Google Cloud Platform, and their services for computing, storage, and databases.

Serverless Computing: Using serverless services like AWS Lambda, Azure Functions, or Google Cloud Functions to run code without managing servers.

Scalability and Load Balancing: Implementing strategies for auto-scaling applications and distributing traffic across multiple servers.

Proficiency in cloud computing and serverless architectures is essential for building scalable and cost-effective applications.

Conclusion

Advanced topics covered in a Full Stack Developer course play a crucial role in preparing students for the complex and evolving demands of the web development industry. By mastering concepts such as microservices architecture, DevOps practices, advanced JavaScript, GraphQL, and cloud computing, you can position yourself as a well-rounded and highly skilled developer. These advanced skills not only enhance your technical expertise but also ensure that you are equipped to tackle the challenges and opportunities that lie ahead in the dynamic field of full stack development.

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Understanding and Implementing GraphQL in Mobile Apps

In the dynamic world of mobile app development, integrating robust and efficient APIs is crucial. GraphQL, an open-source data query language developed by Facebook, has emerged as a powerful tool for developers. Unlike traditional REST APIs, GraphQL allows clients to request exactly the data they need, minimizing over-fetching and under-fetching of data. If you're looking to enhance your mobile app development, especially if you're a mobile app development company in Chennai, understanding and implementing GraphQL can provide significant advantages.

Why Choose GraphQL for Mobile Apps

GraphQL’s query language enables clients to fetch only the necessary data, reducing the amount of data transferred over the network. This efficiency is especially beneficial for mobile apps, which often operate on limited bandwidth.

Enhanced Performance

By avoiding multiple round trips to the server, GraphQL improves the performance of your mobile apps. The single request and response structure streamlines data retrieval, enhancing user experience with faster load times.

Strongly Typed Schema

GraphQL uses a strongly typed schema, which helps in defining the structure of the data queries and responses. This schema ensures that both the client and server understand the data format, reducing errors and increasing reliability.

Implementing GraphQL in Mobile Apps

Set Up the Server: Install a GraphQL server. You can use popular libraries like Apollo Server or Express-GraphQL.

Define the Schema: Create your schema by defining the types and the queries your app will support.

Resolvers: Implement resolver functions for your queries to fetch the necessary data from your database or other APIs.

Client Integration: Use a GraphQL client, such as Apollo Client or Relay, to connect your mobile app to the GraphQL server.

Practical Steps

Start by setting up a GraphQL server using tools like Apollo Server or Express-GraphQL. These libraries provide the essential infrastructure for handling GraphQL queries and mutations.

Step 2: Define Your Schema

Define the schema for your API. The schema outlines the types of data and the queries and mutations that your clients can perform. This step is crucial for setting the foundation of your GraphQL implementation.

Step 3: Create Resolvers

Resolvers are functions that handle the data fetching for your queries and mutations. Implement these resolvers to connect your schema to your data sources, whether it's a database, REST API, or other services.

Step 4: Integrate the Client

Finally, integrate a GraphQL client into your mobile app. Apollo Client and Relay are popular choices that provide powerful tools for managing GraphQL queries and state in your app.

Advantages of Using GraphQL

GraphQL simplifies data management by allowing clients to request exactly what they need. This capability reduces the complexity of handling multiple endpoints, making it easier to manage and scale your API.

Real-Time Data with Subscriptions

GraphQL supports real-time data through subscriptions. Subscriptions enable clients to receive updates whenever specific data changes, which is particularly useful for features like notifications or live updates in your mobile app.

Increased Developer Productivity

GraphQL enhances developer productivity by providing a more flexible and efficient way to interact with APIs. The strongly typed schema and powerful query language enable faster development cycles and easier debugging.

Key Considerations

Implement proper security measures when using GraphQL. Ensure that your queries and mutations are properly authenticated and authorized to prevent unauthorized access to sensitive data.

Performance Optimization

Optimize your GraphQL server for performance. Implement caching strategies and use batching techniques to minimize the load on your server and improve response times.

Understanding and implementing GraphQL in mobile apps can significantly enhance the efficiency and performance of your applications. Whether you're a mobile app development company in Chennai or a solo developer, leveraging GraphQL can streamline your data fetching processes and provide a better user experience. For professional assistance in implementing GraphQL and developing high-performance mobile apps, contact Creatah today. Let us help you take your mobile app to the next level!

Contact Creatah now to get started on your next mobile app project!

[mani4869]

GraphQL in MuleSoft

Integrating GraphQL with MuleSoft enables you to offer a modern, powerful API interface for your applications, allowing clients to request the data they need and nothing more. GraphQL, a query language for APIs developed by Facebook, provides a more efficient and flexible alternative to the traditional REST API approach. When combined with MuleSoft’s Anypoint Platform, you can leverage GraphQL to design, build, and manage APIs that offer tailored data retrieval options to your API consumers.

Implementing GraphQL in MuleSoft

As of my last update, MuleSoft’s Anypoint Platform does not natively support GraphQL in the same direct manner it supports REST or SOAP services. However, you can implement GraphQL over the APIs managed by MuleSoft through custom development. Here’s how you can approach it:

Define Your GraphQL Schema:

Start by defining a GraphQL schema that specifies the types of data you offer, including objects, fields, queries, and mutations. This schema acts as a contract between the client and the server.

Implement Data Fetchers:

You need to implement a resolver or data fetcher for each field in your schema. In the context of MuleSoft, you can implement these fetchers as Java classes or scripts that execute logic to retrieve or manipulate data from your backend systems, databases, or other APIs managed by MuleSoft.

Expose a GraphQL Endpoint:

Use an HTTP Listener in your Mule application to expose a single GraphQL endpoint. Clients will send POST requests to this endpoint with their query payloads.

You can handle these requests in your Mule flows, parsing the GraphQL queries and passing them to the appropriate data fetchers.

Integrate GraphQL Java Libraries:

Leverage existing GraphQL Java libraries, such as graphql-java, to parse the GraphQL queries, execute them against your schema, and format the response according to the GraphQL specification.

You may need to include these libraries in your Mule project and call them from your custom components or scripts within your flows.

Manage Performance and Security:

Implement caching, batching, and rate limiting to optimize performance and manage the load on your backend systems.

Secure your GraphQL endpoint using MuleSoft’s security policies, OAuth2 providers, or JWT validation to protect against unauthorized access.

Testing and Documentation

Testing: Use Postman, Insomnia, or GraphQL Playground to test your GraphQL API. These tools allow you to craft queries, inspect the schema, and see the results.

Documentation: Although GraphQL APIs are self-documenting through introspection, consider providing additional documentation on everyday use cases, query examples, and best practices for clients.

Challenges and Considerations

Query Complexity: GraphQL allows clients to request deeply nested data, which can lead to performance issues. Consider implementing query complexity analysis and depth limiting to mitigate this.

Error Handling: Design your error handling strategy to provide meaningful error messages to clients while hiding sensitive system details.

N+1 Problem: Be mindful of the N+1 problem, where executing a GraphQL query could result in many more data fetching operations than expected. Use techniques like data loader patterns to batch requests and reduce the number of calls to backend services.

Demo Day 1 Video:

You can find more information about Mulesoft in this Mulesoft Docs Link

Conclusion:

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Laravel and GraphQL: Revolutionizing Web Development Beyond REST APIs

In the ever-evolving world of web development, the integration of Laravel with GraphQL is emerging as a powerful alternative to traditional REST APIs. This combination offers a highly efficient and flexible way to develop web applications, providing developers with the tools to create more dynamic, scalable, and user-friendly web solutions. As we delve into this transformative approach, it's essential to understand the distinct advantages and possibilities it brings to the table.

The Limitations of REST APIs

REST (Representational State Transfer) has been the cornerstone of web application development for years. It uses HTTP requests to perform CRUD (Create, Read, Update, Delete) operations on data. However, the RESTful approach often leads to over-fetching or under-fetching of data, where a client either gets too much unnecessary data or needs to make additional requests to fetch everything required. This inefficiency can significantly affect the performance and speed of web applications, particularly those requiring real-time data interactions.

Enter GraphQL

Developed by Facebook in 2015 and open-sourced, GraphQL presents a query language for APIs and a runtime for executing those queries by using a type system defined for your data. Unlike REST, GraphQL allows clients to precisely define the data they need, which means no more over-fetching or under-fetching. This level of efficiency is a game-changer for web applications, ensuring faster load times and a more seamless user experience.

Hire Laravel Developers to elevate your web development project with Laravel and GraphQL? Hire Laravel developers today and unlock the full potential of modern web application development.

Why Laravel and GraphQL?

Laravel, a PHP framework known for its elegance and simplicity, pairs remarkably well with GraphQL. This pairing leverages Laravel's robust backend capabilities with GraphQL's efficient data retrieval system, offering a compelling alternative to REST APIs. Here are some key benefits of integrating Laravel with GraphQL:

Precise Data Fetching: Developers can query exactly what they need, no more, no less, optimizing data transfer and improving application performance.

Single Endpoint: GraphQL uses a single endpoint for all queries, simplifying the API structure and making it easier to maintain.

Real-time Data with Subscriptions: GraphQL subscriptions enable real-time updates to the client, a feature that's incredibly useful for chat applications, live feeds, and any application requiring real-time data.

Strong Typing System: The type system in GraphQL ensures that queries against your API are valid at runtime, reducing the chances of errors.

Avail Laravel Development Services to leverage the power of Laravel and GraphQL for your projects. Transform your ideas into reality with our expert team.

Integrating GraphQL in Laravel

The integration process involves setting up a GraphQL server in Laravel, defining your schemas (types, queries, mutations, and possibly subscriptions), and implementing the logic to resolve these queries. Laravel packages like Lighthouse provide an elegant and straightforward approach to setting up a GraphQL server, allowing you to define your schema in GraphQL SDL (Schema Definition Language) and offering directives to add functionalities like authentication and caching with minimal boilerplate.

Challenges and Considerations

While the benefits are clear, integrating GraphQL into Laravel projects does come with its challenges. Developers need to be mindful of:

Complexity in Query Handling: As queries become more complex, the logic to resolve them efficiently can also become more intricate.

N+1 Query Problem: Similar to REST, inefficient queries can lead to performance issues, though tools and techniques like the DataLoader pattern can help mitigate this.

Learning Curve: For teams accustomed to REST, the shift to GraphQL requires a learning investment, though the long-term benefits often outweigh the initial effort.

Case Studies and Success Stories

Numerous case studies highlight the successful integration of Laravel with GraphQL, showcasing significant improvements in application performance, developer productivity, and user satisfaction. From e-commerce platforms streamlining their data retrieval processes to social media apps enhancing their real-time data interactions, the Laravel and GraphQL combination is proving to be a formidable tool in modern web development.

Connect with top Laravel companies to ensure your project is in the hands of professionals who can deliver cutting-edge web solutions tailored to your needs.

The Future of Web Development with Laravel and GraphQL

The demand for efficient, flexible, and scalable solutions is at an all-time high as the web development landscape continues to shift towards more dynamic, complex, and user-centric applications. Integrating Laravel with GraphQL is not just an alternative to REST APIs; it's a forward-looking approach that aligns with the future of web development. By embracing this powerful combination, developers can unlock new possibilities, create richer user experiences, and build web applications that stand the test of time.

Embrace the future of web development with Laravel and GraphQL, and start building more efficient, flexible, and user-friendly web applications today.

[govindhtech]

AWS CDK database queries in PostgreSQL and MySQL

With support for the AWS Cloud Development Kit (AWS CDK), AWS are now able to connect to and query your current MySQL and PostgreSQL databases. This is a new feature that allows you to construct a secure, real-time GraphQL API for your relational database, either inside or outside of Amazon Web Services (AWS). With merely your database endpoint and login credentials, you can now construct the full API for all relational database operations. You can use a command to apply the most recent modifications to the table schema whenever your database schema changes.

With the release of AWS Amplify GraphQL Transformer version 2, which was announced in 2021, developers can now create GraphQL-based app backends that are more feature-rich, adaptable, and extensible with little to no prior cloud experience. In order to create extensible pipeline resolvers that can route GraphQL API requests, apply business logic like authorization, and interact with the underlying data source like Amazon DynamoDB, this new GraphQL Transformer was completely redesigned.

But in addition to Amazon DynamoDB, users also desired to leverage relational database sources for their GraphQL APIs, including their Amazon RDS or Amazon Aurora databases. Amplify GraphQL APIs now support @model types for relational and DynamoDB data sources. Data from relational databases is produced into a different file called schema.sql.graphql. You may still build and maintain DynamoDB-backed types with standard schema.graphql files.

Upon receiving any MySQL or PostgreSQL database information, whether it is accessible publicly online or through a virtual private cloud (VPC), AWS Amplify will automatically produce a modifiable GraphQL API that can be used to securely connect to your database tables and expose CRUD (create, read, update, or delete) queries and mutations. To make your data models more frontend-friendly, you may also rename them. For instance, a database table with the name “todos” (plural, lowercase) may be accessible to the client as “ToDo” (single, PascalCase).

Any of the current Amplify GraphQL authorization rules can be added to your API with only one line of code, enabling the smooth development of use cases like owner-based authorization and public read-only patterns. Secure real-time subscriptions are accessible right out of the box because the produced API is based on AWS AppSync’s GraphQL capabilities. With a few lines of code, you can subscribe to any CRUD event from any data model.

Starting up the MySQL database in the AWS CDK

The AWS CDK gives you the significant expressive capability of a programming language to create dependable, scalable, and affordable cloud applications. Install the AWS CDK on your local computer to begin.

To print the AWS CDK version number and confirm that the installation is correct, use the following command.

Next, make your app’s new directory:

Use the cdk init command to set up a CDK application.

Add the GraphQL API construct from Amplify to the newly created CDK project.

Launch your CDK project’s primary stack file, which is often found in lib/<your-project-name>-stack.ts. Add the following imports to the top of the file:

Run the following SQL query on your MySQL database to create a GraphQL schema for a new relational database API.

$ cdk –version

Make sure the results are written to a.csv file with column headers included, and change <database-name> to the name of your schema, database, or both.

Run the following command, substituting the path to the.csv file prepared in the previous step for <path-schema.csv>.

$ npx @aws-amplify/cli api generate-schema \

    –sql-schema <path-to-schema.csv> \

    –engine-type mysql –out lib/schema.sql.graphql

To view the imported data model from your MySQL database schema, open the schema.sql.graphql file.

If you haven’t already, establish a parameter for your database’s connection information, including hostname/url, database name, port, username, and password, in the AWS Systems Manager console’s Parameter Store. To properly connect to your database and run GraphQL queries or modifications against it, Amplify will need these in the following step.

To define a new GraphQL API, add the following code to the main stack class. Put the parameter paths that were made in the previous step in lieu of the dbConnectionConfg options.

This setting assumes that you can access your database online. Additionally, on all models, the sandbox mode is enabled to permit public access, and the default authorization mode is set to Api Key for AWS AppSync. You can use this to test your API before implementing more detailed authorization restrictions.

Lastly, launch your GraphQL API on the Amazon Cloud

Select the Queries menu along with your project. The newly developed GraphQL APIs, like getMeals to retrieve a single item or listRestaurants to list all products, are compatible with your MySQL database tables.

like instance, a new GraphQL query appears when you pick objects that have fields like address, city, name, phone number, and so on. You may view the query results from your MySQL database by selecting the Run button.

You get identical results when you run a query on your MySQL database.

Currently accessible

Any MySQL and PostgreSQL databases hosted anywhere within an Amazon VPC or even outside of the AWS Cloud are now compatible with the relational database support for AWS Amplify.

Read more on Govindhtech.com

[tecforfun]

How to write GraphQL Schemas, Queries, and Mutations, and their Resolvers

In this post, I am going to talk to you about how to define GraphQL schemas for Queries and Mutations, and then we will also learn to how implement resolvers for Query and Mutation Types. If you are new to this post, I encourage you to read this overview on GraphQL, and learn to set up the GraphQL server so that you can have a better understanding of this post. First of all, we need to be…

[a-journey-of-a-thousand]

Writing GraphQL Tests

Writing a test can seem deceptively simple in GraphQL. It comes in with a built-in ability to generate mocks for all your resolvers. You can set it in your Apollo server setup with:

mockEntireSchema: true

This will randomly generate mocked data for all your resolvers that match your schema. You can opt out of this if you want to write specific mocks in your tests.

This is a complete mutation test that checks that the query you send will hit the correct resolver and return your mocked info.

Isn’t that a little too simple?

The more I learn about GraphQL the more its simplicity eludes me. Sure the technology is quick to pick up but when it comes time to actually implement some APIs, it’s not so straightforward.

The innovation of GraphQL isn’t so much the spec but in the design of the API. GraphQL works best as a thin API gateway that catches all traffic between the front-end clients and anything that is back-end, and the thinner the better. A request is intercepted by the registered root, passed to queries, checked by a schema, then calls a resolver. Anything past a resolver is out of GraphQL world. 

So I guess it’s alright that it seems too “small”, the complexity is just further down the request path.

[codeonedigest]

GraphQL Query Tutorial with Examples for API Developers

Full Video Link - https://www.youtube.com/shorts/HDue72AK75o Hi, a new #video on #graphql #resolver published on #codeonedigest #youtube channel. @java @awscloud @AWSCloudIndia @YouTube #youtube @codeonedigest #graphql #graphqlresolver #graphqltutori

In GraphQL, you fetch data with the help of queries. A query is a GraphQL Operation that allows you to retrieve data from the server. GraphQL operation can either be a read or a write operation. A GraphQL query is used to fetch data from the backend system while a mutation is used to post data to the backend system. In either case, the operation is a simple string that a GraphQL server can parse…

[codezup]

GraphQL Resolver Authentication: Real-World Example

1. Introduction Authentication is a cornerstone of modern web applications, ensuring only authorized users can access specific functionalities and data. In GraphQL, unlike REST APIs, we often handle authentication within our resolvers – the functions responsible for fetching data and executing mutations. This approach allows for granular control and elegant integration with the data retrieval…

[opensourcefan]

Introducing GraphQL Concepts for Angular 8|7 Devs: Schemas, Resolvers, Queries & Mutations ☞ http://dev.geekwall.in/a3e106e4bd #angular #javascript

[inextures]

Building APIs with GraphQL and Flask

Assumptions

I’m assuming that y’all have some basic knowledge about Graphql & Flask.

If not then kindly go through below link:

Graphql: https://graphql.org/learn/

Flask: https://flask.palletsprojects.com/

Introduction

GraphQL is a query language for APIs and a server-side runtime that allows clients to request only the data that they need from APIs.

GraphQL is meant to be a more efficient and flexible alternative to REST.

Initial steps

Create virtual environment

Install requirements

Flask

Flask-sqlalchemy

Flask_login

Flask_migration

Psycopg2-binary

We are going to use the ariadne library to integrate graphql in flask for that install ariadne library using this command.

pip install ariadne

Configure flask app

Create models for your project and migrate

Create schema.graphql file which contain types of models field in your root directory

The Query and Mutation type

Types and fields

Every graphql service has a query and mutation type. These types are the same as regular types, but they are special because they define the entry point of every graphql query.

Put this code in schema.graphql

The most basic components of a GraphQL schema are object types, which just represent a kind of object you can fetch from your service, and what fields it has. In the GraphQL schema language, for example

Example

OrderAll is a graphql object type, meaning it’s a type with some fields.

success, errors and order  are fields on the OrderAll type. That means success, errors and order are the only fields that can appear in graphql query that operates on the OrderAll type.

String is one of the built-in scalar types;  these are types that resolve to a single scalar object, and can’t have sub-selection in query.

String! means that the field is non-nullable, meaning that the Graphql service always gives you value when you query this field. We’ll represent those with an exclamation mark.

[Order]! Represent an array of Order objects. you can get every item of the array when you query the order field. it can have sub-selections in the query.

Create function for graphql execution

Create resource.py  file your app for implement graphql resolver(functions)

Configure QueryType and MutationType from ariadne library  in project __init__ file

Import query and mutation in resource.py file it is used as wrapper in function which is map function to graphql query.

Create graphql query for get_all_user

First create model for user

Create get all user resolver

Create user model in model.py file

Add fields like username, email, mobile_number, password

Create to_dict function for return all data in dictionary it is helping for fetch particular data

Create function which return all users data in json

“*_” in users function is use for ignore unwanted list of parameter

In user_data we get list of all user data in json from database

This function returns success and users,  where success contains Boolean value and users contain a list of user data.

Read More Building APIs with GraphQL and Flask

[holyjak]

The first ±20 min is a great explanation of the problem with REST and multiple clients (e.g. UI components) with varying data needs, demonstrated on the APIs of the industry leaders Twitter (v1, v2), Spotify, Youtube. How do you model data when different clients need different subsets? It is impossible to define the container types to carry these subsets well: you either create "fat types" with all the possible attributes (and over-deliver) or you get a type explosion, defining a separate type for each use case. GraphQL helps but does not solve it - it still uses types so you end up with fat types for queries and type explosion for mutations.

(Data navigation = getting the subset of data you want (such as tweet's text, the author's name and homepage, ...). Navigation because it often requires multiple requests to fetch referred entities.)

The rest of the talk introduces a different approach, one based around individual attributes instead of artificial groupings thereof, and demonstrates how Pathom 3 implements this. We see how Pathom can easily wrap a REST API and even migrate from Twitter v1 to v2 without any impact on clients, we learn how to design an attribute-based backend from scratch, and finally we see how Pathom can even resolve a query by delegating parts to remote Pathom instances.

[iamadevelopers]

Introducing GraphQL Concepts for Angular 8|7 Devs: Schemas, Resolvers, Queries & Mutations ☞ http://dev.geekwall.in/a3e106e4bd #angular #javascript