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Basic CRUD Operations Using Hasura GraphQL with Distributed SQL on GKE

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. GraphQL provides a complete and understandable description of the data in your API,

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Cloud Native Tips and Tricks for YugabyteDB – March 27, 2020

Cloud Native Tips and Tricks for YugabyteDB – March 27, 2020

In this blog post, we answer some common questions from YugabyteDB users to help you in your own application development and deployment. We’ll also review upcoming events, new documentation, and blogs that have been published since the last post. Got questions? Make sure to ask them on our YugabyteDB Slack channel, Forum, GitHub, or Stackoverflow.

Before we dive in, we wanted to let you know that the Yugabyte team has been working from home in order to do our part with social distancing and to help with containment efforts.

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Getting Started with pgbench and Distributed SQL on GKE

Getting Started with pgbench and Distributed SQL on GKE

pgbench is a simple program for running benchmark tests on PostgreSQL. It runs the same sequence of SQL commands over and over, possibly in multiple concurrent database sessions, and then calculates the average transaction rate (transactions per second). By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. However, it is easy to test other cases by writing your own transaction script files.

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Distributed SQL Tips and Tricks – March 13, 2020

Distributed SQL Tips and Tricks – March 13, 2020

Welcome to this week’s tips and tricks blog where we recap some distributed SQL questions from around the Internet. We’ll also review upcoming events, new documentation, and blogs that have been published since the last post. Got questions? Make sure to ask them on our YugabyteDB Slack channel, Forum, GitHub, or Stackoverflow. Ok, let’s dive right in.

How can I UPSERT multiple rows with an update?

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Monitoring YugabyteDB with Prometheus and Grafana in Kubernetes

Monitoring YugabyteDB with Prometheus and Grafana in Kubernetes

Prometheus has matured into a robust time-series metrics monitoring solution since it was first open-sourced in 2012. CNCF incubated it as its second project after Kubernetes in 2016 followed by graduation in 2018. Today it is arguably the most popular option for monitoring Kubernetes cluster metrics as well as container-based applications. Combined with Grafana for visualization, it becomes a potent combination for dashboarding performance of applications. Nodes in a YugabyteDB cluster have exposed a Prometheus endpoint for easy metrics collection right from the inception of the open source project.

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Getting Started with Falco Runtime Security and Cloud Native Distributed SQL on Google Kubernetes Engine

Getting Started with Falco Runtime Security and Cloud Native Distributed SQL on Google Kubernetes Engine

Falco is an incubating CNCF project that provides cloud native, open source runtime security for applications running in Kubernetes environments. Falco monitors process behaviors to detect anomalous activity and help administrators gain deeper insights into process execution.  Behind the scenes, Falco leverages the Linux-native extended Berkeley Packet Filter (eBPF) technology to analyze network traffic and audits a system at the most fundamental level, the Linux kernel. Falco then enriches this data with other input streams,

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Cloud Native Meets Distributed SQL: Bringing Microservices, Kubernetes, Istio & YugabyteDB Together with Hipster Shop Demo

Cloud Native Meets Distributed SQL: Bringing Microservices, Kubernetes, Istio & YugabyteDB Together with Hipster Shop Demo

Polyglot persistence is the widely accepted database implementation strategy when it comes to decomposing monoliths into microservices. In practice, this requires every microservice to model its data needs independently using a database that is purpose-built for that particular model, and thereafter store the data in an independent database instance. While independent database instances as a deployment paradigm makes sense from an decoupled microservices architecture standpoint, choosing multiple different databases each with a specialized data model is usually justified in the context of performance,

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Announcing YugabyteDB on Crossplane, the Open Source Multicloud Control Plane

Announcing YugabyteDB on Crossplane, the Open Source Multicloud Control Plane

We are excited to announce that YugabyteDB is now available as a self-managed database service on Crossplane, the open-source multicloud control plane. Built on top of our recent Rook Kubernetes Operator for YugabyteDB, this offering makes YugabyteDB one of the first distributed SQL databases available on Crossplane.

Benefits of the Joint Solution

With Crossplane as the single control plane, users can provision and manage multiple YugabyteDB clusters across Google Kubernetes Engine,

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Announcing the Rook Operator for YugabyteDB

Announcing the Rook Operator for YugabyteDB

We are excited to congratulate the Rook community on the release on 1.1! We are also pleased to announce that the Rook operator for YugabyteDB is now available from rook.io and also on Github. This release extends the Rook storage operator as a custom resource, as well as provides an additional way to easily create, natively view and manage YugabyteDB within a Kubernetes cluster. In this blog we’ll summarize how to get started with the operator.

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Best Practices and Recommendations for Distributed SQL on Kubernetes

Best Practices and Recommendations for Distributed SQL on Kubernetes

YugabyteDB and Kubernetes have very complementary design principles because they both rely on an extensible and flexible API layer, as well as a scale-out architecture for performance and availability. In this blog post we’ll look at best practices and recommendations when choosing Kubernetes as the cluster foundation for a distributed SQL system. This will begin with a review of relevant architectural decisions of the YugabyteDB. Then we’ll walk you through how to handle the provisioning,

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