GraphQL Workshop Medley to build cloud native apps with Open Source Distributed SQL database

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YugateDB is a popular open-source Distributed SQL database designed and built for cloud native applications from the ground up. YugateDB allows developers to easily scale the RDBMS workloads for internet-scale apps while natively handling the cloud infrastructure outages supporting cross-region and cross Datacenter deployments. In this workshop, participants will get firsthand experience implementing GraphQL applications using three of the most popular GraphQL engines in Hasura, Prisma, and Spring GraphQL connected to the YugateDB database.

This workshop will provide quick start guides for GraphQL developers for getting started with YugateDB. The workshop will include steps for creating a YugateDB database instance, configuring the database for the respective GraphQL engine, and the best practices for writing the GraphQL queries for YugateDB. From this workshop session, developers will know all the main concepts of getting started with YugateDB and GraphQL to solve the business use case at hand using YugateDB.

Nikhil Chandrappa
Nikhil Chandrappa
Eric Pratt
Eric Pratt
Marko Rajcevic
Marko Rajcevic
Taylor Mull
Taylor Mull
155 min
06 Dec, 2021

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Video Summary and Transcription

The GraphQL Workshop Medley covers Distributed SQL, Yugabyte, and Popular GraphQL Servers. YugabyteDB bridges the gap between SQL and NoSQL systems, offering strong SQL features and horizontal scalability. It integrates seamlessly with GraphQL and supports regional and multi-cloud deployments. The workshop explores creating Hasura and YugabyteDB instances, configuring the cluster and database, applying migrations, and working with Hasura, Prisma, and Apollo. It also covers scaling GraphQL workloads with YugabyteDB, query tuning, and optimizing performance.

1. Introduction to Workshop and Agenda

Short description:

Welcome to the GraphQL Workshop Medley! We'll cover Distributed SQL, Yugabyte, and Popular GraphQL Servers. I'm Eric Pratt, a senior solutions engineer at Yugibyte. With me are Nikhil, an engineer on the ecosystem team, and Marco Rejcic, a solutions engineer at Ubite. We'll provide an overview of distributed SQL databases and GraphQL, and then dive into hands-on sessions. Join us for real-time poll app, social post app, and space explorer app. Time permitting, we'll discuss tuning performance for GraphQL queries with distributed SQL.

Welcome everybody. My name is Eric Pratt. Today we're going to be going over our GraphQL Workshop Medley. We're getting started with Distributed SQL, Yugabyte, and some Popular GraphQL Servers. My name is, as I said, Eric Pratt. I'm a senior solutions engineer here at Yugibyte on the cloud team. Previously, I was a premium support engineer over at Datastats. I have two others with me, and I will let them introduce themselves.

Hey everyone, my name is Nikhil. I'm one of the engineers on the ecosystem team. We build integrations with popular developer tools like GraphQL, Spring Framework, and hosts of other cloud-native projects. If you have any questions on the integrations with distributed SQL and GraphQL. You can reach us out on our Slack. That's what we do daily in our engineering efforts. Prior to this, I was at Ubuntu. I was a senior data architect there, building cloud-native solutions. Thanks, Brad.

Hi everyone. My name is Marco Rejcic. I'm also a solutions engineer here at Ubite. Previously in solutions engineering at Oracle focused on both cloud and on-premise technologies. So excited to speak to you today. As Nikhil mentioned, we're very active on our Community Slack channel. So if you're able to download Ubite locally or using the cloud or whatever, have any questions, feel free to find us there.

This is our workshop agenda. We're just going to give you a brief overview, getting started with distributed SQL databases and GraphQL. We're going to have a brief overview of our open source database, our open source offering for Ubite. And then we're going to do some hands-on sessions for implementing these. So you're going to do a real-time poll app that'll be with Marco and Asura. For me, we have a social post app with Prisma and Ubite. And then finally, Nico will be doing a space explorer app, which I think is pretty cool, with Apollo Platform and Ubite. At the end of those, if we have some time, we will be going over tuning performance for GraphQL queries with distributed SQL and some do's and don'ts that we've come across as we've worked with customers over the last year or so.

2. Introduction to GraphQL and Lugabyte DB

Short description:

Getting started with GraphQL, you can query and mutate data via GraphQL constructs, evolve the API without versioning, and use out-of-the-box pagination and filtering. GraphQL is a robust query language for your API, supporting both GraphQL and generic REST APIs. Examples are provided to demonstrate data retrieval using GraphQL. We will explore different GraphQL frameworks such as Casura, Prisma, and Apollo in relation to Lugabyte. Lugabyte DB is an open-source distributed SQL database that bridges the gap between traditional SQL and NoSQL systems, aiming to be the go-to database for cloud-native applications. The founders have experience with Oracle, Cassandra, and HPASE at Facebook, where they encountered the need for scalable NoSQL systems due to rapid user growth.

So getting started with GraphQL, we have your app, we have your GraphQL server, our database. We have abstract layers that kind of go over our database so you can query mutate data via the GraphQL constructs, build your schema and evolve the domain models. We can evolve the API without versioning, which I think is kind of nice. And then you have out-of-the-box pagination and filtering. We'll call it disparate data sources and then finally venting support.

So the GraphQL, right, it's just query language for your API, you build your queries, you can get exactly what you need. You can combine a few different resources in a single request. It's pretty robust. And you can, like we said previously, you can evolve your API without versioning and supports event-based system. So we kinda have an example here of a query. If you guys are familiar with GraphQL, this should look pretty familiar. And then, you know, it has your generic rest APIs. So your gets, posts, it supports all of those. Here's some examples that we have on how those work with a particular response. And then for GraphQL, we have our ways for retrieving data, right? Which is our posts. You can see the request there. Here's the query that we would run on this side. So let's say we wanna get the author name and articles from a particular thing table, and then we have that response here that you'll get. And as we work through a lot of these examples, or a lot of these different workshops, we will be running through quite a few of these, so you'll get kinda familiar with those for the different GraphQL, Casura, Prisma and then Apollo. So you get to kinda see what each of those has and how they kind of interact with Lugabyte. It's pretty cool. I think you guys are gonna like it.

So I'm gonna turn it over to Marko here as we will be going over the open source distributed SQL database and how kinda fundamentals of Lugabyte. So before we get into the fun part of the live demo, we wanted to just catch everybody up what we're doing here at Lugabyte DB, why we feel this is important for cloud native applications and really what Lugabyte DB is for those that are unfamiliar. So Lugabyte DB is an 100% open source distributed SQL database. Our goal is to make it the go-to cloud native database for cloud native applications. We're really trying to bridge the two worlds between traditional SQL and the strengths that those types of systems provide with the strengths of typical NoSQL systems. Our founders have vast experience with both Oracle as well as building out Cassandra and HPASE at Facebook in the mid 2000s. And if you guys are, know anything about Facebook in the mid 2000s, that's kind of where they had this crazy user growth and really had to start moving towards NoSQL systems because the traditional database systems that they were using just could not scale to what they wanted.

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