Databases

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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An Introduction to Distributed SQL: Glossary of Terms

An Introduction to Distributed SQL: Glossary of Terms

In 2017 we introduced YugabyteDB, an open source, high performance, cloud native database for mission-critical applications. As a team, we have worked first hand on a number of databases such as Apache HBase, Apache Cassandra (from even before it was open sourced), Oracle, and RocksDB. We were the team that built and ran Facebook’s NoSQL platform that powered a number of user-facing, real-time applications. Over the years, we have had the opportunity and good fortune to become intimately familiar with what it takes to power mission-critical applications in a cloud-native architecture.

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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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Document Data Modeling in YugabyteDB with the JSON Data Types

Document Data Modeling in YugabyteDB with the JSON Data Types

YugabyteDB has two JSON data types, json and jsonb, to let you store documents in a column in a YSQL table and to do content-based queries with index support. YSQL is PostgreSQL compatible and it therefore supports every one of the rich set of about thirty five JSON-specific operators and functions that users of PostgreSQL’s JSON functionality might already know, and rely upon. These features let you handle semi-structured data,

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Automating YugabyteDB Deployments with AWS CloudFormation

Automating YugabyteDB Deployments with AWS CloudFormation

YugabyteDB is easy to get started with on the infrastructure of your choice including public cloud platforms, private cloud environments, and any Kubernetes distribution. For example, you can quickly customize and deploy in AWS thanks to CloudFormation templates. AWS CloudFormation is one of the many ways to automate a public cloud deployment in a consistent manner.

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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YugabyteDB Engineering Update – March 17, 2020

YugabyteDB Engineering Update – March 17, 2020

We are excited to announce that YugabyteDB 2.1.2 is GA! You can read the official release notes here. This release shipped with over 30 new enhancements and fixes.

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. We have also transitioned to online meetings with our customers,

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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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