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Four Compelling Use Cases for PostgreSQL Table Functions

Welcome to part three of a three-part series of posts on PostgreSQL’s table functions. These functions can be easily leveraged in a distributed SQL database like YugabyteDB, which is PostgreSQL compatible.

In this final post, I’ll discuss four realistic use cases that call for user-defined table functions and I’ll explain how each case is met using complete, self-contained code examples.

If you missed the two previous posts, make sure to check out:

Use Case #1: Parameterized View

In my blog post “Using Stored Procedures in Distributed SQL Databases”,

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Implementing PostgreSQL User-Defined Table Functions in YugabyteDB

Welcome to part two of a three-part series of posts on PostgreSQL’s table functions. These functions can be easily leveraged in a distributed SQL database like YugabyteDB, which is PostgreSQL compatible.

In part one I gave a brief introduction to PostgreSQL’s table functions. Part three will cover some realistic use cases. I’ll introduce this second post by quoting that paragraph:

A regular language plpgsql user-defined function is implemented using the plain return statement.

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An Introduction to PostgreSQL Table Functions in YugabyteDB

Welcome to the first of a three-part series of posts on PostgreSQL’s table functions. These functions can be easily leveraged in a distributed SQL database like YugabyteDB, which is PostgreSQL compatible.

This series follows on from my “Using Stored Procedures in Distributed SQL Databases” post. In this series of posts we’ll cover:

  • What table functions are and why they’re useful
  • Demonstrate the use of some built-in SQL table functions
  • Introduce how you can implement a user-defined table function,

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YugabyteDB Community Update, Tricks and Tips – Dec 13, 2019

Welcome to this week’s community update where we recap a few interesting questions that have popped up in the last week or so on the YugabyteDB Slack channel, Forum, GitHub or Stackoverflow. We’ll also review upcoming events, new blogs and documentation. Ok, let’s dive right in:

How best to configure clusters across deployment types

Ava over on StackOverflow asked how to best setup configurations for different deployment models like single AZ,

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YugabyteDB Engineering Update – Dec 9, 2019

We are pleased to announce that YugabyteDB 2.0.7 is live!  You can read the official release notes of this and previous versions here. This release is shipping with almost 30 new enhancements and bug fixes.

What’s YugabyteDB? It is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. Yugabyte’s SQL API (YSQL) is PostgreSQL wire compatible

YugabyteDB 2.0.7 Release Notes


  • Transparent read restarts for general queries.

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9 Techniques to Build Cloud-Native, Geo-Distributed SQL Apps with Low Latency

This post is an in-depth look at the various techniques that applications needing low latency and high availability can leverage while using a geo-distributed SQL database like YugabyteDB so that the negative impacts of an high-latency, unreliable Wide Area Network (WAN) are minimized.

Geo-Distributed SQL is the Future of RDBMS

Enterprises are increasingly moving to cloud-native applications powered by microservices architecture. These applications run on elastic cloud infrastructure such as serverless frameworks and containers.

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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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Distributed SQL on Google Kubernetes Engine (GKE) with YugabyteDB’s Helm Chart

The glory days of the heavy-weight hypervisor are slowly fading away, and in the last few years, containerization of applications and services is the new reality. With containerization, enterprises can prototype, deploy, and meet scale demands more quickly. To systematically and efficiently manage these large-scale deployments, enterprises have bet on technologies like Kubernetes (aka k8s), a powerful container orchestrator, to get the job done. Kubernetes was originally developed by Google, but it has been open sourced since 2014 and is today developed by a large community of contributors.

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Rise of Globally Distributed SQL Databases – Redefining Transactional Stores for Cloud Native Era

At last month’s KubeCon + CloudNativeCon in Seattle, the single biggest change from previous container-related conferences was the excitement among the end user companies around their adoption of Kubernetes and the associated cloud native infrastructure ecosystem. The CNCF End User Community page today lists 50+ enterprises and 21+ case studies including those from industry bellwethers such as Capital One, Netflix, Nordstrom and Pinterest. There is a common adoption pattern among all these case studies —

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Understanding How YugabyteDB Runs on Kubernetes

As we reviewed in “Docker, Kubernetes and the Rise of Cloud Native Databases”, Kubernetes has benefited from rapid adoption to become the de-facto choice for container orchestration. This has happened in a short span of only 4 years since Google open sourced the project in 2014. YugabyteDB’s automated sharding and strongly consistent replication architecture lends itself extremely well to containerized deployments powered by Kubernetes orchestration. In this post we’ll look at the various components involved in getting YugabyteDB up and running as Kubernetes StatefulSets.

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