Implementing PostgreSQL User-Defined Table Functions in YugabyteDB
Learn how to implement user-defined table functions, in all variants, using generalized use cases.
December 16, 2019
How It Works
December 16, 2019
An Introduction to PostgreSQL Table Functions in YugabyteDB
Read through an introduction to PostgreSQL’s table functions and their use in distributed SQL databases. The blog series will cover the basics of table functions, the use of built-in SQL table functions, and the implementation of user-defined table functions.
December 13, 2019
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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December 2, 2019
Using Stored Procedures in Distributed SQL Databases
These days, most monolithic SQL databases support stored procedures. This support first emerged in commercially available offerings in the late nineteen eighties. However, stored procedure support is not yet standard in distributed SQL databases. In fact, YugabyteDB is just one of two in this category—supporting stored procedures written in PostgreSQL’s PL/pgSQL. (Aurora also supports stored procedures.) This post recaps the case for stored procedures that motivated their introduction all those years ago. And it describes a performance experiment that measures one of the benefits brought by using stored procedures: the reduction in client-server round trips for multi-statement business transactions.
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November 22, 2019
YugabyteDB Community Update, Tricks and Tips – Nov 22, 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, the Forum, GitHub or Stackoverflow. We’ll also review upcoming events, new blogs and documentation that has been published since the last update. Ok, let’s dive right in:
How do data compactions work in YugabyteDB?
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October 29, 2019
The Benefit of Partial Indexes in Distributed SQL Databases
If a partial index is used, instead of a regular one, on a nullable column—where only a small fraction of the rows have not null values for this column—then the response time for inserts, updates, and deletes can be shortened significantly. As a bonus, the response times for single row selects shorten a little bit too. This post explains what a partial index is, shows how to create one, describes the canonical use case that calls for a partial index,
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October 22, 2019
How YugabyteDB Scales to More than One Million Inserts Per Second
YugabyteDB is engineered to scale beyond 1 million inserts per second. It achieves this high level of performance through sharding and horizontal scaling, allowing it to support applications and services that need rapid data insertion and retrieval.
October 21, 2019
Working with PostgreSQL Data Types in YugabyteDB
In this blog post we’ll be focusing on PostgreSQL data types, and test their compatibility with YugabyteDB. You can find a quick list of supported data types in our docs.
October 18, 2019
YugabyteDB Community Engineering Update, Tricks and Tips – Oct 18, 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, the Forum, GitHub or Stackoverflow. We’ll also review upcoming events, new blogs and documentation that has been published since the last update. Ok, let’s dive right in:
Running yb-ctl status does not give node information
Question
AndrewLiuRM over on the forums asked why the yb-ctl command doesn’t give node information when issued after a manual deployment onto a Docker container.
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October 1, 2019
Recapping My Internship at Yugabyte – Jayden Navarro
It was a warm day in early October, and two large white tents occupied the lawn that sits between the Gates, Hewlett, and Packard buildings. Companies pasted the word “AI” in big bold letters across their banners, and students formed long lines, resumes in-hand, eager to learn about the Next Big Thing and how often the company cafeteria serves Poké.
Six months prior I had made the decision to leave my comfortable job as a Software Engineer in the networking industry and pursue a Masters degree in Computer Science at Stanford,
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