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Category: Containers

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

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

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. GraphQL is often used for microservices, mobile apps, and as an alternative to REST.

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Run the REST Version of Spring PetClinic with Angular and Distributed SQL on GKE

Run the REST Version of Spring PetClinic with Angular and Distributed SQL on GKE

Java developers know that Spring Data makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. When YugabyteDB is combined with Spring, Java developers are able to leverage their familiarity with PostgreSQL while gaining the added benefits of distributed SQL. These “out-of-the-box” benefits include geo-data distribution, high performance, and horizontal scalability, which are impossible or difficult to achieve with monolithic SQL databases.

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Staying Connected During COVID-19: Join Us at KubeCon + CloudNativeCon Europe 2020

Staying Connected During COVID-19: Join Us at KubeCon + CloudNativeCon Europe 2020

We are excited to sponsor KubeCon + CloudNativeCon Europe 2020 and participate alongside other open source and cloud native communities coming together to drive cloud native computing forward. Although many people across the world remain physically distanced (us included), we believe that coming together as a community and maintaining a sense of connection are still very essential and important. That’s why we are happy to take part in the virtual KubeCon + CloudNativeCon event taking place August 17-20,

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Distributed SQL Change Management with Liquibase and YugabyteDB on GKE

Distributed SQL Change Management with Liquibase and YugabyteDB on GKE

Liquibase is an open source and extensible change management project that supports a variety of databases including Snowflake, MySQL, and PostgreSQL via JDBC. Liquibase allows users to easily define changes in SQL, XML, JSON, and YAML. These changes are then managed in a version control system so the changes can be documented, ordered, and standardized. For more information on the features and benefits of Liquibase, check out their documentation site.

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Getting Started with SQLPad and Distributed SQL on Google Kubernetes Engine

Getting Started with SQLPad and Distributed SQL on Google Kubernetes Engine

SQLPad is an MIT licensed web app written in React and Node.js for writing and running SQL queries and visualizing the results. SQLPad supports PostgreSQL, MySQL, SQL Server, Crate, Vertica, Presto, SAP HANA, Cassandra, Snowflake, Google BigQuery, SQLite, and many more via ODBC. Because YugabyteDB is PostgreSQL compatible, most third-party tools and apps will work “out of the box.” SQLPad is no exception here.

In this blog post we’ll show you how to:

  • Install a 3 node YugabyteDB cluster on Google Kubernetes Engine
  • Build the sample Northwind database
  • Build and configure SQLPad
  • Start the required SQLPad processes
  • Launch the SQLPad UI and issue a test query to validate the deployment

New to distributed SQL or YugabyteDB?

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Getting Started with Longhorn Distributed Block Storage and Cloud Native Distributed SQL

Getting Started with Longhorn Distributed Block Storage and Cloud Native Distributed SQL

Editor’s note: This post was originally published on the Rancher blog, and has been cross-posted here and updated as of July 21, 2020 to account for new versions of software available.

Longhorn is cloud native distributed block storage for Kubernetes that is easy to deploy and upgrade, 100 percent open source, and persistent. Longhorn’s built-in incremental snapshot and backup features keep volume data safe, while its intuitive UI makes scheduling backups of persistent volumes easy to manage.

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Highly Available Prometheus Metrics for Distributed SQL with Thanos on GKE

Highly Available Prometheus Metrics for Distributed SQL with Thanos on GKE

In the last few years, Prometheus has gained huge popularity as a tool for monitoring distributed systems. It has a simple yet powerful data model and query language, however, it can often pose a bit of a challenge when it comes to high availability as well as for historical metric data storage. Adding more Prometheus replicas can be used to improve availability, but otherwise, Prometheus does not offer continuous availability. For example, if one of the Prometheus replicas crashes,

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Part 1: Deploying a Distributed SQL Backend for Apache Airflow on Google Cloud

Part 1: Deploying a Distributed SQL Backend for Apache Airflow on Google Cloud

Apache Airflow is a popular platform for programmatically authoring, scheduling, and monitoring workflows. Airflow has been deployed by companies like Adobe, Airbnb, Etsy, Instacart, and Square. The advantage of defining workflows as code is that they become more maintainable, versionable, testable, and collaborative. Airflow is used to author these workflows as directed acyclic graphs (DAGs) of tasks. Airflow’s scheduler executes your tasks on an array of workers while following the specified dependencies.

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Getting Started with Distributed SQL on Azure Kubernetes Service

Getting Started with Distributed SQL on Azure Kubernetes Service

Microsoft’s Azure Kubernetes Service (AKS) offers a highly available, secure, and fully managed Kubernetes service for developers looking to host their applications on containers in the cloud. AKS features elastic provisioning, an integrated developer experience for rapid application development, enterprise security features, and the most available regions of any cloud provider.

YugabyteDB is a natural fit for AKS because it was designed to support cloud native environments since its initial design.

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