GraphQL is quickly becoming mainstream as one of the best ways to get data into your React application. When we see people modernize their app architecture and move to React, they often want to migrate their API to GraphQL as part of the same effort. But while React is super easy to adopt in a small part of your app at a time, GraphQL can seem like a much larger investment. In this talk, we’ll go over the fastest and most effective ways for React developers to incrementally migrate their existing APIs and backends to GraphQL, then talk about opportunities for improvement in the space. If you’re using React and are interested in GraphQL, but are looking for an extra push to get it up and running at your company, this is the talk for you!
GraphQL is an application layer query language developed by Facebook that allows clients to define queries for retrieving multiple resources from an API in a single request. It uses a type system and schema to define the data and operations available. GraphQL aims to solve issues with REST APIs like over-fetching and under-fetching data by allowing clients to specify exactly what data they need.
It is a basic presentation which can help you understand the basic concepts about Graphql and how it can be used to resolve the frontend integration of projects and help in reducing the data fetching time
This presentation also explains the core features of Graphql and why It is a great alternative for REST APIs along with the procedure with which we can integrate it into our projects
GraphQL is a wonderful abstraction for describing and querying data. Apollo is an ambitious project to help you build apps with GraphQL. In this talk, we'll go over how all the parts—Client, Server, Dev Tools, Codegen, and more—create an end-to-end experience for building apps on top of any data.
## Detailed description
In today's development ecosystem, there are tons of options for almost every part of your application development process: UI rendering, styling, server side rendering, build systems, type checking, databases, frontend data management, and more. However, there's one part of the stack that hasn't gotten as much love in the last decade, because it usually falls in the cracks between frontend and backend developers: Data fetching.
The most common way to load data in apps today is to use a REST API on the server and manage the data manually on the client. Whether you're using Redux, MobX, or something else, you're usually doing everything yourself—deciding when to load data, how to keep it fresh, updating the store after sending updates to the server, and more. But if you're trying to develop the best user experience for your app, all of that gets in the way; you shouldn't have to become a systems engineer to create a great frontend. The Apollo project is based on the belief that data loading doesn't have to be complicated; instead, you should be able to easily get the data you want, when you want it, and it should be managed for you just like React manages updating your UI.
Because data loading touches both the frontend and backend of your app, GraphQL and Apollo have to include many parts to fulfill that promise of being able to seamlessly connect your data together. First, we need client libraries not only for React and JavaScript, but also for native iOS and Android. Then, we must bring server-side support for GraphQL queries, mutations, and most recently subscriptions to every server technology and make those servers easier to write. And finally, we want not only all of the tools that people are used to with REST APIs, but many more thanks to all of the capabilities enabled by GraphQL.
In this talk, we'll go over all of the parts of a GraphQL-oriented app architecture, and how different GraphQL and Apollo technologies come together to solve all of the parts of data loading and management for React developers.
This document is a presentation on GraphQL fundamentals. It was created by Virbhadra S. Ankalkote from BORN Group. The presentation covers what GraphQL is, who created it, why it was created, and how it works. It defines GraphQL as a query language for APIs that fulfills queries with existing data. It notes that Facebook developed GraphQL in 2012 for their native mobile app. In comparison to REST APIs, GraphQL allows clients to get precisely the data they need in one request. The presentation explains the GraphQL specification and includes sections on reading and writing data, using variables and fragments, and limitations such as indefinite querying depth.
GraphQL is quickly becoming mainstream as one of the best ways to get data into your React application. When we see people modernize their app architecture and move to React, they often want to migrate their API to GraphQL as part of the same effort. But while React is super easy to adopt in a small part of your app at a time, GraphQL can seem like a much larger investment. In this talk, we’ll go over the fastest and most effective ways for React developers to incrementally migrate their existing APIs and backends to GraphQL, then talk about opportunities for improvement in the space. If you’re using React and are interested in GraphQL, but are looking for an extra push to get it up and running at your company, this is the talk for you!
GraphQL is an application layer query language developed by Facebook that allows clients to define queries for retrieving multiple resources from an API in a single request. It uses a type system and schema to define the data and operations available. GraphQL aims to solve issues with REST APIs like over-fetching and under-fetching data by allowing clients to specify exactly what data they need.
It is a basic presentation which can help you understand the basic concepts about Graphql and how it can be used to resolve the frontend integration of projects and help in reducing the data fetching time
This presentation also explains the core features of Graphql and why It is a great alternative for REST APIs along with the procedure with which we can integrate it into our projects
GraphQL is a wonderful abstraction for describing and querying data. Apollo is an ambitious project to help you build apps with GraphQL. In this talk, we'll go over how all the parts—Client, Server, Dev Tools, Codegen, and more—create an end-to-end experience for building apps on top of any data.
## Detailed description
In today's development ecosystem, there are tons of options for almost every part of your application development process: UI rendering, styling, server side rendering, build systems, type checking, databases, frontend data management, and more. However, there's one part of the stack that hasn't gotten as much love in the last decade, because it usually falls in the cracks between frontend and backend developers: Data fetching.
The most common way to load data in apps today is to use a REST API on the server and manage the data manually on the client. Whether you're using Redux, MobX, or something else, you're usually doing everything yourself—deciding when to load data, how to keep it fresh, updating the store after sending updates to the server, and more. But if you're trying to develop the best user experience for your app, all of that gets in the way; you shouldn't have to become a systems engineer to create a great frontend. The Apollo project is based on the belief that data loading doesn't have to be complicated; instead, you should be able to easily get the data you want, when you want it, and it should be managed for you just like React manages updating your UI.
Because data loading touches both the frontend and backend of your app, GraphQL and Apollo have to include many parts to fulfill that promise of being able to seamlessly connect your data together. First, we need client libraries not only for React and JavaScript, but also for native iOS and Android. Then, we must bring server-side support for GraphQL queries, mutations, and most recently subscriptions to every server technology and make those servers easier to write. And finally, we want not only all of the tools that people are used to with REST APIs, but many more thanks to all of the capabilities enabled by GraphQL.
In this talk, we'll go over all of the parts of a GraphQL-oriented app architecture, and how different GraphQL and Apollo technologies come together to solve all of the parts of data loading and management for React developers.
This document is a presentation on GraphQL fundamentals. It was created by Virbhadra S. Ankalkote from BORN Group. The presentation covers what GraphQL is, who created it, why it was created, and how it works. It defines GraphQL as a query language for APIs that fulfills queries with existing data. It notes that Facebook developed GraphQL in 2012 for their native mobile app. In comparison to REST APIs, GraphQL allows clients to get precisely the data they need in one request. The presentation explains the GraphQL specification and includes sections on reading and writing data, using variables and fragments, and limitations such as indefinite querying depth.
GraphQL can be one of the best ways to make your product development more fun and productive. In this presentation I talk about how GraphQL makes your life simpler, and how to write and deploy a GraphQL API with Apollo Server 2.0 and serverless deployment via Netlify Functions.
GraphQL is a query language for APIs that uses a type system to define queries and mutations. It originated at Facebook in 2011 to address issues with REST APIs like multiple round trips and misaligned data shapes. GraphQL allows clients to specify exactly what data they need in a single request, making it more efficient than REST. It has gained popularity with over 300 billion requests per day on Facebook and adoption by companies like GitHub for their APIs.
GraphQL is a query language for APIs that allows flexible querying of data from a server. It was originally created by Facebook in 2012 and open sourced in 2015. Some key benefits of GraphQL include allowing apps to control the specific data received from servers instead of receiving all possible data like with REST APIs, and GraphQL queries mirroring the response structure. GraphQL schemas define query and mutation parameters as well as return data types.
The document discusses GraphQL, Relay, and some of their benefits and challenges. Some key points covered include:
- GraphQL allows for declarative and UI-driven data fetching which can optimize network requests.
- Relay uses GraphQL and allows defining data requirements and composing queries to fetch nested data in one roundtrip.
- Benefits include simpler API versioning since fields can be changed without breaking clients.
- Challenges include verbose code, lack of documentation, and not supporting subscriptions or local state management out of the box.
- Overall GraphQL aims to solve many data fetching problems but has a complex setup process and learning curve.
- Deepak Shevani works at Microsoft for the Azure group and recently became interested in GraphQL.
- GraphQL was created by Facebook in 2015 to improve client-server communication when rebuilding their native mobile apps. It defines a query language and provides a server-side runtime to fetch the desired data.
- GraphQL allows clients to get precisely the data they need through queries, reducing over- and under-fetching of data compared to REST APIs.
This document discusses various GraphQL tooling options. It describes GraphQL Config as a common configuration format, the GraphQL CLI for tying together multiple tools, and GraphQL Playground for exploring schemas. It also outlines GraphQL Binding for reusing APIs, GrAMPS for building microservices, GraphQL Import for splitting schemas, Apollo Tracing for performance insights, and GraphQL Yoga as an all-in-one server implementation.
Overview of GraphQL
How it is different from REST
When you should consider using it and when you should not
Incremental demos until calling GraphQL from an React application: https://github.com/bary822/graphQL-techtalk
This document discusses transitioning a web application from a REST API to a GraphQL API. It covers why the company adopted GraphQL to address over-fetching and under-fetching issues, choosing the Apollo GraphQL client library, and a staged approach to transitioning including creating a GraphQL server, isolated GraphQL components, and updating legacy stores with component queries. It also provides tips on handling caching keys, response headers, mobile REST clients, server rendering, and taking a staged approach to the transition.
GraphQL Munich Meetup #1 - How We Use GraphQL At CommercetoolsNicola Molinari
The document describes commercetools' experience with adopting GraphQL for their merchant center application. They were initially fetching category data with multiple REST requests, but were able to fetch it in one GraphQL query along with additional fields like number of subcategories and products. This solved a performance issue and allowed them to easily extend their API. They also migrated other parts of their client to use GraphQL, reducing network requests and complexity. Overall, GraphQL allowed them to optimize queries and extend their API with minimal effort.
GraphQL is a declarative data fetching method that provides a single endpoint for queries. It was developed by Facebook in 2012 and addresses challenges with REST like over- and under-fetching of data. GraphQL uses a schema to define types and their relations, and allows fetching nested data in a single query to the server. It supports queries to read data, and mutations to write data by creating, updating, or deleting data records.
In this talk, we shared some of our highlights of the GraphQL Europe conference.
You can see the full coverage of the conference here: https://www.graph.cool/talks/
GraphQL is a query language for APIs that allows clients to fetch exactly what they need from a data source. Apollo Server is a library that makes it easy to build GraphQL servers with Node.js. It provides features like combining multiple GraphQL schemas into one. GraphQL uses SDL to define the schema and relationship between types of data. Resolvers are responsible for returning data in response to GraphQL operations like queries, mutations, and subscriptions. Scalar types define basic data types in GraphQL like Int, String and ID.
Wrapping and securing REST APIs with GraphQLRoy Derks
This document discusses using GraphQL to wrap and secure REST APIs. It describes problems with REST APIs like multiple endpoints, over-fetching and under-fetching data, and versioning issues. GraphQL solves these problems with a single endpoint that returns flexible data structures. The document then provides steps to implement GraphQL, including using schemas to define queries and mutations, retrieving data from existing services and APIs, adding authentication middleware, and building a GraphQL server with Node.js and Apollo.
AWS Finland September Meetup - Using Amazon Neptune to build Fashion Knowledg...Uri Savelchev
Amazon Neptune is a graph database that can be used to build a fashion knowledge graph. It is based on Blazegraph and provides additional features like replication, redundancy, backups and technical support. The document discusses using Neptune to model a knowledge graph with RDF triples and querying it with SPARQL. It notes some issues with Neptune like bugs, slow restoration from backups, and performance problems with complex queries. Overall, Neptune is suitable for knowledge graphs but requires working on queries, denormalizing data, and testing backup strategies.
This document discusses building a full stack graph application with Neo4j AuraDB, GitHub Actions, GraphQL, Next.js, and Vercel. It covers how to get data into Neo4j, build a GraphQL API with Neo4j and the GraphQL library, work with graphs on the frontend using GraphQL and React, and deploy the full application to Vercel. Code examples and resources are provided for each part of the process.
How easy (or hard) it is to monitor your graph ql service performanceRed Hat
- GraphQL performance monitoring can be challenging as queries can vary significantly even when requesting the same data. Traditional endpoint monitoring provides little insight.
- Distributed tracing using OpenTracing allows tracing queries to monitor performance at the resolver level. Tools like Jaeger and plugins for Apollo Server and other GraphQL servers can integrate tracing.
- A demo showed using the Apollo OpenTracing plugin to trace a query through an Apollo server and resolver to an external API. The trace data was sent to Jaeger for analysis to help debug performance issues.
apidays LIVE Paris - The Rise of GraphQL for database APIs by Karthic Raoapidays
apidays LIVE Paris - Responding to the New Normal with APIs for Business, People and Society
December 8, 9 & 10, 2020
The Rise of GraphQL for database APIs
Karthic Rao, Founder of Pixelbytestudio
GraphQL can be one of the best ways to make your product development more fun and productive. In this presentation I talk about how GraphQL makes your life simpler, and how to write and deploy a GraphQL API with Apollo Server 2.0 and serverless deployment via Netlify Functions.
GraphQL is a query language for APIs that uses a type system to define queries and mutations. It originated at Facebook in 2011 to address issues with REST APIs like multiple round trips and misaligned data shapes. GraphQL allows clients to specify exactly what data they need in a single request, making it more efficient than REST. It has gained popularity with over 300 billion requests per day on Facebook and adoption by companies like GitHub for their APIs.
GraphQL is a query language for APIs that allows flexible querying of data from a server. It was originally created by Facebook in 2012 and open sourced in 2015. Some key benefits of GraphQL include allowing apps to control the specific data received from servers instead of receiving all possible data like with REST APIs, and GraphQL queries mirroring the response structure. GraphQL schemas define query and mutation parameters as well as return data types.
The document discusses GraphQL, Relay, and some of their benefits and challenges. Some key points covered include:
- GraphQL allows for declarative and UI-driven data fetching which can optimize network requests.
- Relay uses GraphQL and allows defining data requirements and composing queries to fetch nested data in one roundtrip.
- Benefits include simpler API versioning since fields can be changed without breaking clients.
- Challenges include verbose code, lack of documentation, and not supporting subscriptions or local state management out of the box.
- Overall GraphQL aims to solve many data fetching problems but has a complex setup process and learning curve.
- Deepak Shevani works at Microsoft for the Azure group and recently became interested in GraphQL.
- GraphQL was created by Facebook in 2015 to improve client-server communication when rebuilding their native mobile apps. It defines a query language and provides a server-side runtime to fetch the desired data.
- GraphQL allows clients to get precisely the data they need through queries, reducing over- and under-fetching of data compared to REST APIs.
This document discusses various GraphQL tooling options. It describes GraphQL Config as a common configuration format, the GraphQL CLI for tying together multiple tools, and GraphQL Playground for exploring schemas. It also outlines GraphQL Binding for reusing APIs, GrAMPS for building microservices, GraphQL Import for splitting schemas, Apollo Tracing for performance insights, and GraphQL Yoga as an all-in-one server implementation.
Overview of GraphQL
How it is different from REST
When you should consider using it and when you should not
Incremental demos until calling GraphQL from an React application: https://github.com/bary822/graphQL-techtalk
This document discusses transitioning a web application from a REST API to a GraphQL API. It covers why the company adopted GraphQL to address over-fetching and under-fetching issues, choosing the Apollo GraphQL client library, and a staged approach to transitioning including creating a GraphQL server, isolated GraphQL components, and updating legacy stores with component queries. It also provides tips on handling caching keys, response headers, mobile REST clients, server rendering, and taking a staged approach to the transition.
GraphQL Munich Meetup #1 - How We Use GraphQL At CommercetoolsNicola Molinari
The document describes commercetools' experience with adopting GraphQL for their merchant center application. They were initially fetching category data with multiple REST requests, but were able to fetch it in one GraphQL query along with additional fields like number of subcategories and products. This solved a performance issue and allowed them to easily extend their API. They also migrated other parts of their client to use GraphQL, reducing network requests and complexity. Overall, GraphQL allowed them to optimize queries and extend their API with minimal effort.
GraphQL is a declarative data fetching method that provides a single endpoint for queries. It was developed by Facebook in 2012 and addresses challenges with REST like over- and under-fetching of data. GraphQL uses a schema to define types and their relations, and allows fetching nested data in a single query to the server. It supports queries to read data, and mutations to write data by creating, updating, or deleting data records.
In this talk, we shared some of our highlights of the GraphQL Europe conference.
You can see the full coverage of the conference here: https://www.graph.cool/talks/
GraphQL is a query language for APIs that allows clients to fetch exactly what they need from a data source. Apollo Server is a library that makes it easy to build GraphQL servers with Node.js. It provides features like combining multiple GraphQL schemas into one. GraphQL uses SDL to define the schema and relationship between types of data. Resolvers are responsible for returning data in response to GraphQL operations like queries, mutations, and subscriptions. Scalar types define basic data types in GraphQL like Int, String and ID.
Wrapping and securing REST APIs with GraphQLRoy Derks
This document discusses using GraphQL to wrap and secure REST APIs. It describes problems with REST APIs like multiple endpoints, over-fetching and under-fetching data, and versioning issues. GraphQL solves these problems with a single endpoint that returns flexible data structures. The document then provides steps to implement GraphQL, including using schemas to define queries and mutations, retrieving data from existing services and APIs, adding authentication middleware, and building a GraphQL server with Node.js and Apollo.
AWS Finland September Meetup - Using Amazon Neptune to build Fashion Knowledg...Uri Savelchev
Amazon Neptune is a graph database that can be used to build a fashion knowledge graph. It is based on Blazegraph and provides additional features like replication, redundancy, backups and technical support. The document discusses using Neptune to model a knowledge graph with RDF triples and querying it with SPARQL. It notes some issues with Neptune like bugs, slow restoration from backups, and performance problems with complex queries. Overall, Neptune is suitable for knowledge graphs but requires working on queries, denormalizing data, and testing backup strategies.
This document discusses building a full stack graph application with Neo4j AuraDB, GitHub Actions, GraphQL, Next.js, and Vercel. It covers how to get data into Neo4j, build a GraphQL API with Neo4j and the GraphQL library, work with graphs on the frontend using GraphQL and React, and deploy the full application to Vercel. Code examples and resources are provided for each part of the process.
How easy (or hard) it is to monitor your graph ql service performanceRed Hat
- GraphQL performance monitoring can be challenging as queries can vary significantly even when requesting the same data. Traditional endpoint monitoring provides little insight.
- Distributed tracing using OpenTracing allows tracing queries to monitor performance at the resolver level. Tools like Jaeger and plugins for Apollo Server and other GraphQL servers can integrate tracing.
- A demo showed using the Apollo OpenTracing plugin to trace a query through an Apollo server and resolver to an external API. The trace data was sent to Jaeger for analysis to help debug performance issues.
apidays LIVE Paris - The Rise of GraphQL for database APIs by Karthic Raoapidays
apidays LIVE Paris - Responding to the New Normal with APIs for Business, People and Society
December 8, 9 & 10, 2020
The Rise of GraphQL for database APIs
Karthic Rao, Founder of Pixelbytestudio
Scaling Your Team With GraphQL: Why Relationships MatterJoel Bowen
Have you used GraphQL yet? If you haven't, you're likely to encounter the technology soon. If you have, you might have questions about how to scale your team and what best practices large and small companies are following. What principles should I be following when developing a new GraphQL API? What tooling is available to help me be more productive and spend less time writing boilerplate code? How can many teams with many apps across one company share one graph?
GraphQL Conf 2019 just wrapped up in Berlin where I was fortunate to hear meet thoughtful leaders on these and other subjects related to GraphQL. So, whether you're brand new to GraphQL or have an active project using GraphQL, I'd like to share some of what I have learned so you can be productive with GraphQL and your teams - while keeping relationships at the center of our work.
This document provides an introduction and overview of GraphQL, including:
- A brief history of GraphQL and how it was created by Facebook and adopted by other companies.
- How GraphQL provides a more efficient alternative to REST APIs by allowing clients to specify exactly the data they need in a request.
- Some key benefits of GraphQL like its type system, declarative data fetching, schema stitching, introspection, and versioning capabilities.
- Some disadvantages like potential complexity in queries and challenges with rate limiting.
GraphQL - A query language to empower your API consumers (NDC Sydney 2017)Rob Crowley
The shift to microservices, cloud native and rich web apps have made it challenging to deliver compelling API experiences. REST, as specified in Roy Fielding’s seminal dissertation, has become the architectural pattern of choice for APIs and when applied correctly allows for clients and servers to evolve in a loosely coupled manner. There are areas however where REST can deliver less than ideal client experiences. Often many HTTP requests are required to render a single view.
While this may be a minor concern for a web app running on a WAN with low latency and high bandwidth, it can yield poor client experiences for mobile clients in particular. GraphQL is Facebook’s response to this challenge and it is quickly proving itself as an exciting alternative to RESTful APIs for a wide range of contexts. GraphQL is a query language that provides a clean and simple syntax for consumers to interrogate your APIs. These queries are strongly types, hierarchical and enable clients to retrieve only the data they need.
In this session, we will take a hands-on look at GraphQL and see how it can be used to build APIs that are a joy to use.
Tutorial: Building a GraphQL API in PHPAndrew Rota
This document discusses building a GraphQL API in PHP. It provides an overview of GraphQL concepts like queries, fields, types and schemas. It then outlines the steps to build a GraphQL API in PHP using the graphql-php library:
1. Define object types and the Query root type in the schema
2. Initialize the GraphQL schema instance
3. Execute GraphQL queries against the schema and return the result
By following these steps, one can build an API for querying a database of PHP conferences and speakers to understand how to build GraphQL APIs in PHP.
GraphQL across the stack: How everything fits togetherSashko Stubailo
My talk from GraphQL Summit 2017!
In this talk, I talk about a future for GraphQL which builds on the idea that GraphQL enables lots of tools to work together seamlessly across the stack. I present this through the lens of 3 examples: Caching, performance tracing, and schema stitching.
Stay tuned for the video recording from GraphQL Summit!
Graphql for Frontend Developers Simplifying Data Fetching.docxssuser5583681
In today’s digital landscape, the demand for efficient and flexible APIs (Application Programming Interfaces) has grown exponentially. Developers are constantly seeking ways to improve data retrieval and manipulation processes while ensuring seamless integration between client applications and server resources. One technology that has gained significant popularity in recent years is GraphQL Server.
This document summarizes and compares several GraphQL libraries for Java: graphql-java, graphql-java-kickstart, and dgs-framework. It discusses their features for defining schemas and types, handling data fetching and caching, performing mutations, handling errors, testing functionality, and code generation capabilities. Overall, dgs-framework requires the least amount of boilerplate code, supports testing and code generation more fully, and is designed specifically for use within Spring Boot applications.
GraphQL is an open-source data query and manipulation language for APIs that allows clients to get precisely the data they need from a server in a single request. It provides a complete description of the available data in an API and gives clients control over the data received. In contrast, REST APIs typically require multiple requests to get all needed data. While REST is an architectural style for designing APIs, GraphQL is a specification and query language. GraphQL is gaining popularity as it can address limitations of REST like overfetching data and making multiple requests.
GraphQL is an open-source data query and manipulation language for APIs that allows clients to get precisely the data they need from a server in a single request. It provides a complete description of the available data in an API and gives clients control over the data received. While REST uses a server-driven architecture with multiple endpoints, GraphQL uses a single endpoint and client-driven model. Some key advantages of GraphQL include fetching exact data needs to avoid over- or under-fetching, and stitching together schemas from multiple sources into a unified API.
1. The document discusses how to use GraphQL with ApolloJS and provides an overview of both technologies. It introduces the presenter and covers GraphQL history, companies using GraphQL, advantages over REST, how to use GraphQL, and what ApolloJS is and its products.
2. It then summarizes how to set up an ApolloJS server including installing ApolloServer, defining types and schemas, resolving queries and mutations, using DataLoader for performance, subscriptions, and directives.
3. The presentation concludes with references for further reading on GraphQL and ApolloJS.
GraphQL has grown out of its baby shoes and is becoming the new standard for client-server communication. When it was introduced 2 years ago, there merely was any tooling that would help developers using it except for Facebook's reference implementation in JavaScript as well as corresponding middleware for Express so you could embed it in your web server. By now, the situation has changed drastically and a plethora of tools, libraries and services have entered the GraphQL ecosystem, providing great improvements to workflows and overall developer experience. In this talk, Nikolas will give an overview of the most relevant tools that exist in the GraphQL ecosystem today, ensuring you can make the best choices when starting your own GraphQL journey.
GraphQL and its schema as a universal layer for database accessConnected Data World
GraphQL is a query language mostly used to streamline access to REST APIs. It is seeing tremendous growth and adoption, in organizations like Airbnb, Coursera, Docker, GitHub, Twitter, Uber, and Facebook, where it was invented.
As REST APIs are proliferating, the promise of accessing them all through a single query language and hub, which is what GraphQL and GraphQL server implementations bring, is alluring.
A significant recent addition to GraphQL was SDL, its schema definition language. SDL enables developers to define a schema governing interaction with the back-end that GraphQL servers can then implement and enforce.
Prisma is a productized version of the data layer leveraging GraphQL to access any database. Prisma works with MySQL, Postgres, and MongoDB, and is adding to this list.
Prisma sees the GraphQL community really coming together around the idea of schema-first development, and wants to use GraphQL SDL as the foundation for all interfaces between systems.
Sashko Stubailo - The GraphQL and Apollo Stack: connecting everything togetherReact Conf Brasil
Apresentado na React Conf Brasil, em São Paulo, 7 de Outubro de 2017 #reactconfbr
I’ve been exploring the space of declarative developer tools and frameworks for over five years. Most recently, I was the founding member of the Apollo project at Meteor Development Group. My greatest passion is to make software development simpler, and enable more people to create software to bring good to the world.
https://medium.com/@stubailo
@stubailo
- Patrocínio: Pipefy, Globo.com, Meteor, Apollo, Taller, Fullcircle, Quanto, Udacity, Cubos, Segware, Entria
- Apoio: Concrete, Rung, LuizaLabs, Movile, Rivendel, GreenMile, STQ, Hi Platform
- Promoção: InfoQ, DevNaEstrada, CodamosClub, JS Ladies, NodeBR, Training Center, BrazilJS, Tableless, GeekHunter
- Afterparty: An English Thing
GraphQL is a query language for APIs and a server-side runtime. It allows fulfilling queries by using a type system you define for your data. Why use GraphQL? What are the pros and cons? We did research and summarised our conclusions.
GraphQL with .NET Core Microservices.pdfKnoldus Inc.
In this Webinar, will talk on GraphQL with .NET, that provides a modern and flexible approach to building APIs. It empowers developers to create efficient and tailored APIs that meet the specific needs of their applications and clients.
This document discusses GraphQL and Relay Modern. It begins with an overview of REST and its issues. It then covers what GraphQL is, how it was created at Facebook, and its motivations. Key aspects of GraphQL discussed include its single endpoint, hierarchical nature, strong typing, response mirroring the query, and introspection capabilities. The document also discusses Relay Modern and what it provides like declarative queries and mutations. It provides examples of simple GraphQL schemas and Relay components. In the end, it discusses subscriptions, routing, debugging, and resources for learning more.
The document discusses GraphQL and Relay Modern. It begins with an overview of REST and some of its issues. It then covers what GraphQL is, how it was created at Facebook, and some of its key features like being version-free and having a hierarchical data structure. The document also discusses Relay Modern and what it provides for managing GraphQL queries and mutations. It includes examples of using GraphQL schemas and Relay to fetch and display basic and list data.
Similar to Scaling your GraphQL applications with Dgraph (20)
The document discusses how a parent process can determine if a child process terminated normally or abnormally. It explains that the wait() system call returns the child's exit status to the parent. This status can be passed to the WIFEXITED() and WIFSIGNALED() macros to check if the child exited normally or was terminated by a signal. It then provides an example C program that uses fork() to create three child processes, each terminating differently (normal exit, abort(), division by zero) and calls wait() and the macros to print the termination type.
Program to find out whether the child process died normally or abnormally.If It died abnormally then the program finds out the signal which caused it .
First create a file in your Home directory by name seqno.txt,just write one character the
digit one( 1 ) into the file and save it.This program reads the number from the file , increments it
then writes back the incremented value back into the file.
This sequence o program is used to control print jobs in UNIX
Hackintoshrao:codes for GNU/LINUX
ILLUSTRATION OF ORPHAN PROCESS AND ASSIGNMENT OF INIT AS A PARENT TO SUCH A PROCESS
SYSTEM CALLS USED
1.fork()
2.getpid()
3.getppid()
4.sleep()
*/
#include<stdlib>
#include<unistd>
#include<time>
int main()
{
int pid;
pid=fork();
if(pid==0)
{
printf("\nChild process here \n");
printf("\nchild here..Parent id=%d,Process id=%d\n",getppid(),getpid());
sleep(3);
printf("\nChild process here,now its a orphan process,so init is my parent process now \n");
printf("\nchild here...Parent id=%d,Process id=%d\n",getppid(),getpid());
/*its clearly evident in the o/p that the parent process Id of the child process will now be =1,
the init process is made as a parent */
}
else{
sleep(1);
/*since parent process finishes earlier than the child ,the latter becomes a orphan process */
printf("\nparent process here \n");
printf("\nparent process..Process id=%d\n",getpid());
printf("\nparent process Exiting,making the child process a orphan .......\n\n") ;
}
}
This document discusses buffer overflows as a major software security problem. It begins by explaining how a buffer overflow occurs when a program writes past the end of an allocated buffer through errors like failing to check array bounds. This can allow attackers to execute arbitrary code by overwriting return addresses on the stack. The document covers various dynamic countermeasures implemented by compilers like stack canaries to help detect and prevent buffer overflow attacks. However, it notes these don't prevent all overflows like those on the heap.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
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ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
State of Artificial intelligence Report 2023kuntobimo2016
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The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
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Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
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We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
2. What to
expect?
- ABOUT DGRAPH
- GRAPHQL BASICS
- GRAPHQL AND GRAPHS
- GRAPHS ALL THE WAY
DEMO
- SETTING UP
- THE ~GRAPHQL INTERFACE
- QUERY/MUTATION WITH
~GRAPHQL
4. Dgraph marches ahead
2015, DECEMBER
v0.1
2017, DECEMBER
v1.0, 3M$ Funding
2019, MAY
Series A, 11.5M$ and Rocking
5. We are bunch of fun loving people with teams in
San Francisco and Bengaluru
6. GraphQL basics
QUERY LANGAUGE
FOR API'S
SCHEMA FIRST
DESIGN
QUERY FROM FRONT
END
Schema defines and
controls the possible
queries
Call the functions defined in
Schema in your queries
from the frontned
Query language for API's? I
know about query language
for databases!
9. LET'S SEE...........
Why is there a
"Graph" in
GraphQL!?
THE IGNORED STORY
FOLLOWS
FOLLOWS
M
EN
TIO
N
S
PO
STS
USING
TAGS
CONTAINS
RETWEETS
User
Tweet
SourceLink
Hashtag
User
Tweet
10. GraphQL
operates
on the
"App data
graph"
THERE IS A GRAPH
STORY BEHIND
GRAPHQL
When you write a mobile or a web
app you operate on set of objects
which are connected through
relationships at some capacity,
that's an App Data Graph. And
GraphQL lets you not just represent
App Data graph, but also to
traverse through them.
FOLLOWS
FOLLOWS
M
EN
TIO
N
S
PO
STS
USING
TAGS
CONTAINS
RETWEETS
User
Tweet
SourceLink
Hashtag
User
Tweet
11. GraphQL shema defines the graph and the query helps
you traverse them
STEP 1
Let GraphQL schema
represent your app data
graph
STEP 2
Traverse through the Graph
using queries, modify graph
using mutation
STEP 3
Obtain a sub-tree of the
graph as a result of your
GraphQL query
12. From whiteboard model -> app model -> schema model
-> client query -> All in graphs -> But..!
STEP 1
Define you app graph on
white paper
STEP 2
Define schema and traverse
through the graphs to fetch
data using queries
STEP 3
Obtain a sub-tree of the
graph as a result of your
GraphQL query
13. GraphQL is
just a
spec, a
contract
IT DOESN'T DICTATE
THE IMPLEMENTATION
The implementation
onus is on the GraphQL
API server's resolvers.
The query could be
resolved by any
database or a
microservcie
15. No Join, No traversal. Anything works. Join / One Traversal. GraphDB slightly
excel.
Join of Join/ Traversal Depth 2.
GraphDB excels
Join of Join of Join/ Traversal Depth
3. GraphDB clearly wins.
16. When a graph
query can be
traversed like
a graph,
WHY COMPLICATE THINGS!? THE
ASYMPTOTIC COMPLEXITY OF
JOINS IS ALSO A FUNCTION OF
THE SIZE OF YOUR TABLES
Every database has its
use cases and strengths.
Graph databases are
specialized in storing the
connected data with
relationships as nodes
and edges and excel in
querying through
relationship traversals. If
your application can be
visualized as a graph,
welcome to the world
without joins!Just
traverse through
relationships without
complexities
17. The other way around is true too, if you are using
GraphQL, your application can be visualized as a
graph.
18. WELCOME TO DGRAPH
But what if your
graphs also
need scale,
speed,
resiliency?
THOUGHT OF THE DAY
19. The story
of
GraphQL+-
interface
GraphQL is the only graph QL to allow retrieving a
sub-graph from a larger dataset. This, along with
the simplicity of query expression, is what led us to
use GraphQL as the native query language for
Dgraph.
However, the journey has not been completely
smooth. GraphQL is not designed to be a database
query language. To make it work like one required
quite a few modifications to the spec. Concepts like
variables and query blocks are new ideas
introduced to allow more complex database
queries. Other concepts like filters and directives
are built upon existing spec ideas to provide richer
functionality. And lastly, unions and inheritance is
dropped entirely as concepts not relevant to a
database, but rather only to an application layer.
This is what resulted in GraphQL+-, the only
GraphQL based database query language.
22. Dgraph.io/about for rest of the awesome team
MANISH JAIN
Founder and CEO
@manishrjain
FRANCESC CAMPOY
VP of Product
@francesc
KARTHIC RAO
Developer Advocate
@hackintoshrao
26. Additional References
INSTAL, GET STARTED
docs.dgraph.io/get-started/
GITHUB
github.com/dgraph-io/
DGRAPH TOUR
https://tour.dgraph.io
TRY IT OUT
play.dgraph.io