The Distributed SQL Blog

Thoughts on distributed databases, open source and cloud native

Heroku Add-on for Yugabyte Cloud Now Available in Public Beta

Today, we are pleased to announce the public beta release of the Heroku Yugabyte Cloud add-on. With new accessibility to the Heroku marketplace, the process of deploying a fully-managed distributed SQL database in the cloud is now radically simple. For those of you who are new to distributed SQL, YugabyteDB is a Google Spanner-inspired, cloud native distributed SQL database that is 100% open source.

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Webinar Recap: CRUD Operations with Hasura GraphQL and Distributed SQL

For those of you who missed last week’s YugabyteDB Community Q&A session, which covered GraphQL fundamentals, here’s the playback:

If you are interested in further exploring the possibilities of Hasura GraphQL and Distributed SQL, check out these resources.

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Getting Started with Hasura GraphQL Remote JOINs on Multi-Cloud Distributed SQL

Remote Joins in Hasura GraphQL extend the concept of joining data across tables, to being able to join data across tables and remote data sources. In this blog post we are going to demonstrate this capability by configuring the following set up.

  • A 3 node YugabyteDB cluster running on GKE with a Hasura GraphQL Engine attached
  • A 3 node YugabyteDB cluster running on AKS with a Hasura GraphQL Engine attached
  • A Remote Schema and Remote Relationship configured
  • The ability to issue GraphQL queries that join data from two different databases,

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Deploying a Real-Time Location App with Hasura GraphQL Engine and Distributed SQL

Hasura is one of the leading vendors in the GraphQL ecosystem. They offer an open source engine that connects to your databases and microservices, and then auto-generates a production-ready GraphQL backend. GraphQL is a query language (more specifically a specification) for your API, and a server-side runtime for executing queries by using a type system you define for your data.

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GraphQL & Distributed SQL Tips and Tricks – July 10, 2020

Welcome to this week’s tips and tricks blog where we explore topics related to combining GraphQL and YugabyteDB to develop scalable APIs and services. We’ll also review upcoming events, new documentation, and blogs that have been published since the last tips and tricks post.

This next section is for those of you who might be new to either GraphQL or distributed SQL.

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INSERT INTO Yugabyte (We’re Hiring Developer Advocates)

It has been an exciting last few weeks here at Yugabyte! We closed our Series B funding round, Bill Cook (formerly of Pivotal and Greenplum) joined as our new CEO, we reaffirmed our commitment to open source, and achieved a bunch of cool community adoption milestones. We are hiring for several roles across engineering,

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Real-Time Scalable GraphQL and JAMstack with Gatsby, Hasura, and YugabyteDB

JAMstack is a new way of building websites and apps. It’s not a technology but rather an architectural pattern that is growing in popularity. In JAMstack, the JAM acronym stands for JavaScript, API, and Markup, and the main idea behind the technology is that web applications don’t have to rely on the application server to be fully functional and robust.

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Scaling a Hasura GraphQL Backend with Distributed SQL

GraphQL is taking the modern development world by storm having been adopted by companies like Facebook, GitHub and Intuit because it solves many of the common problems developers encounter when working with REST APIs. For example, it solves issues like overfetching (getting more data than your response needs) and underfetching (having to make multiple fetches to get all the data you need),

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Basic CRUD Operations Using Hasura GraphQL with Distributed SQL on GKE

Editor’s note: This post was updated July 20, 2020 with new Helm and YugabyteDB versions

GraphQL is an MIT-licensed project originally developed at Facebook in 2012 and open-sourced a few years later. Two popular GraphQL projects, Hasura and Apollo, have reported download numbers of 29 and 33 million, respectively. Why? Think of GraphQL as a query language for APIs and a runtime for fulfilling those queries with your existing data.

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