Byteconf GraphQL is a 100% free single-day conference with the best GraphQL speakers and teachers in the world. Conferences are great, but flights, hotels, and tickets are expensive, so not everyone can go. Byteconf is streamed on YouTube, for free, so anyone and everyone can attend. RSVP to get your free ticket, and we'll see you on January 31st!
Power of GraphQL Query Language
GraphQL is a query language that provides an efficient, powerful and flexible approach to developing web APIs. GraphQL has gained immense popularity in the last few years with many Fortune 500 companies using them for their product development. In this talk, we will learn the core concepts of the GraphQL Query Language. You will learn about Types, Queries and Mutations, and how they are used to work with your API data. I will be showcasing these concepts using the GitHub's Public GraphQL API. You can follow along, and have fun learning the core concepts of the GraphQL Query Language. The purpose of this talk, is to spark your interest in GraphQL and understand the fundamental concepts of the GraphQL Query Language and Schema.
The Future of Real-time | Offline | Data
Complexity, and the consistent attempts to reduce complexity, is at the core of the evolution of technology. As technology evolves, we then find harder problems to solve and are presented with new challenges. In the client space, we've seen innovation that has addressed how we deal with modern application concerns like real-time and offline data while GraphQL has continued to gain in market share. What happens when we take the advancements that GraphQL has introduced as a paradigm and combine them with a mental model that all data should be local and offline first, with eventual consistency to your database as a second thought? In this talk, I'll talk about a data store paradigm that allows developers to work with a single, local database and source of truth, and the idea that you should not have to make more than one write action to have (eventual) consistency across the client and server.
GraphQL as a scalable & performant data API for serverless
Accessing and working with data is not easy with serverless, because traditional methods of database access don't work well. In this talk, I will discuss the key problems around working with databases when building serverless application logic and approaches to solving them. I will motivate problems like connection pooling, cold-starts, handling spiky concurrent loads, database transactions and transient failures. I will then present GraphQL as a possible solution for building a high-performance data API that can scale to serverless workloads. I will talk about the pros & cons of this approach. Finally, I will do some live code demos and make the problems and solutions discussed previously more concrete!
Fullstack Graph With GRANDstack: GraphQL, React, Apollo, and Neo4j Database
Learn why representing data as a graph is a win when building your API - both for API developers and consumers and especially if you are working with graph data in the data layer, such as with a graph database like Neo4j. In this talk we dive into backend considerations for GraphQL and show how to leverage the power of representing your API data with graph using GraphQL and graph databases on the backend. After this talk you will:
GraphQL Without a Database
Your frontend developers are pushing to get started with GraphQL, but you don't have the backend capacity to migrate your existing REST APIs to GraphQL? Or you want to have a GraphQL API next to your existing endpoints that are based on REST, without having to rewrite all your controllers? In this talk I'll show how to wrap existing REST APIs into one single GraphQL endpoint on both the client and server side. This allows you to access the power of GraphQL without having to change any of your existing code or connect to a database.
Query Analysis - Rebooted
There are several tracing tools available publicly and privately for looking into execution time of GraphQL queries. These are super useful and key for integrators writing queries. But as API owners we also need to know how our API is being used. This is important for making deprecations, for choosing where we might want to optimise, understanding general usage patterns and even answering support queries. It's also key to not slow down execution gathering that data. To that end GitHub has several tools and relies heavily on data pipelines. We track parse and execution errors, which objects API queries touch, and more. All of this is beneficial when ensuring the smooth running of the API. This talk will focus on our experiences doing this, what data we've found useful, and how you might want to approach this in your APIs.
Continuous Mining of Github with GraphQL
Two Six Labs
Leveraging the Power of a Typed Schema: Dynamic User Interfaces with GraphQL
Reduce development time and create consistent and dynamic front-end interfaces which are always up-to-date with your back-end GraphQL API by utilizing the introspection query to reveal an API's schema, automatically generate queries and mutations, and, ultimately, hydrate typed React components.
Mutating Highly Dynamic Data at Shopify
GraphQL currently provides polymorphic types to model complex objects but cannot allow the mutations for interface object types. Over the course of the talk, I will be discussing the problem in detail, illustrating my personal experiences with this problem when working in Shopify’s core monolith, and then providing some lessons learned from our implementations.
I will be talking about two main implementations of how we solved this problem at Shopify, exploring their benefits and drawbacks. Further, I’ll be illustrating how Shopify has been sharing our implementations with the greater GraphQL community by working with the GraphQL working group on creating a native solution in an upcoming GraphQL specification.
Full-stack Mocking with Apollo and GraphQL
A common tagline for GraphQL is that it's a query language for your API or a way of asking questions of our data. This is true, but GraphQL also provides us with a type system for our APIs. In this talk, we'll take a closer look at how this type system in combination with Apollo Server allows us to mock data sources on the client and server. We'll learn to create custom mocks on the server, and we'll use the client directive to enable mocking for client-side fields. These techniques will help you move faster as you prototype and build your own solutions with GraphQL.
What is GraphQL - the misconceptions
When you first heard of GraphQL, did you think it was a SQL type language? Did you think it was a graphing software? Did you think it was used for graph type data structures only? You are not alone! There are a lot of misconceptions about what is GraphQL and what the Graph and QL in GraphQL means. In this talk, I will be debunking common misconceptions about GraphQL.
Instant distributed, scalable and secure GraphQL endpoints with FaunaDB
Brecht De Rooms
There are several things to consider when setting up a GraphQL endpoint; choosing a suitable database, handling the n+1 problem, using a cache, security rules. GraphQL has moved the many requests problem from the HTTP layer to the database layer. Instead of many REST calls, we only need to execute one GraphQL request. However, one GraphQL request often results in many database calls, which basically moves the performance problem to the database layer.
FaunaDB is a distributed database that comes out-of-the-box with GraphQL and a flexible security layer. Import your schema, let FaunaDB generate the collections and indexes to support your schema, and you can benefit from a scalable, distributed GraphQL endpoint in 30 seconds. Due to the composable nature and graph-like querying features of the Fauna Query Language (FQL), FaunaDB can offer a GraphQL endpoint that compiles GraphQL queries to FQL 1:1. No more n+1 problem, no need of a cache, and since each GraphQL query translates to one query, you can also take advantage of FaunaDB's strong consistency and flexible security model.
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