If you want to learn more about Citus on Microsoft Azure, read this post about Hyperscale (Citus) on Azure Database for PostgreSQL.

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Articles tagged: performance

One common challenge with Postgres for those of you who manage busy Postgres databases, and those of you who foresee being in that situation, is that Postgres does not handle large numbers of connections particularly well.

While it is possible to have a few thousand established connections without running into problems, there are some real and hard-to-avoid problems.

Since joining Microsoft last year in the Azure Database for PostgreSQL team—where I work on open source Postgres—I have spent a lot of time analyzing and addressing some of the issues with connection scalability in Postgres.

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When working on the internals of Citus, an open source extension to Postgres that transforms Postgres into a distributed database, we often get to talk with customers that have interesting challenges you won’t find everywhere. Just a few months back, I encountered an analytics workload that was a really good fit for Citus.

But we had one problem: the percentile calculations on their data (over 300 TB of data) could not meet their SLA of 30 seconds.

To make things worse, the query performance was not even close to the target: the percentile calculations were taking about 6 minutes instead of the required 30 second SLA.

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Marco Slot

What’s new in the Citus 9.4 extension to Postgres

Written byBy Marco Slot | September 5, 2020Sep 5, 2020

Our latest release to the Citus extension to Postgres is Citus 9.4. If you’re not yet familiar, Citus transforms Postgres into a distributed database, distributing your data and your SQL queries across multiple nodes. This post is basically the Citus 9.4 release notes.

If you’re ready to get started with Citus, it’s easy to download Citus open source packages for 9.4.

I always recommend people check out docs.citusdata.com to learn more. The Citus documentation has rigorous tutorials, details on every Citus feature, explanations of key concepts—things like choosing the distribution column—tutorials on how you can set up Citus locally on a single server, how to install Citus on multiple servers, how to build a real-time analytics dashboard, how to build a multi-tenant database, and more…

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This year, I was so excited about doing a workshop about optimizing Python & Django apps with Postgres superpowers for the PyCon 2020 conference.

Working with other developers on performance is something I always find amazing. So props to the Python people at Microsoft who encouraged my team to create a workshop on Postgres for PyCon 2020. Thank you to Nina Zakharenko, Dan Taylor, & Crystal Kelch.

Alas, we had to change our plans and find other ways to share the PostgreSQL workshop content that we had prepared. So I created a video on the topic of database performance for Django developers, to help teach you the PostgreSQL tips and tricks that have served me well in optimizing my Django apps. These tips are what I call “Postgres superpowers.”

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Some of you have been asking, “what’s happening with the Citus open source extension to Postgres?” The short answer is: a lot. More and more users have adopted the Citus extension in order to scale out Postgres, to increase performance and enable growth. And you’re probably not surprised to learn that since Microsoft acquired Citus Data last year, our engineering team has grown quite a bit—and we’ve been continuing to evolve and innovate on the Citus open source extension.

Our newest release is Citus 9.2. We’ve updated the installation instructions on our Download page and in our Citus documentation, and now it’s time to take a walk through what’s new.

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How do you know if the next update to your software is ready for hundreds of millions of customers? It starts with data. And when it comes to Windows, we’re talking lots of data. The Windows team measures the quality of new software builds by scrutinizing 20,000 diagnostic metrics based on data flowing in from 800 million Windows devices. At the same time, the team evaluates feedback from Microsoft engineers who are using pre-release versions of Windows updates.

At Microsoft, the Windows diagnostic metrics are displayed on a real-time analytics dashboard called “Release Quality View” (RQV), which helps the internal “ship-room” team assess the quality of the customer experience before each new Windows update is released. Given the importance of Windows for Microsoft’s customers, the RQV analytics dashboard is a critical tool for Windows engineers, program managers, and execs.

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Dimitri Fontaine

PostgreSQL 11 and Just In Time Compilation of Queries

Written byBy Dimitri Fontaine | September 11, 2018Sep 11, 2018

PostgreSQL 11 is brewing and will be released soon. In the meantime, testing it with your own application is a great way to make sure the community catches all the remaining bugs before the dot-zero release.

One of the big changes in the next PostgreSQL release is the result of Andres Freund’s work on the query executor engine. Andres has been working on this part of the system for a while now, and in the next release we are going to see a new component in the execution engine: a JIT expression compiler!

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Marco Slot

High performance distributed DML in Citus

Written byBy Marco Slot | July 25, 2018Jul 25, 2018

One of the many unique abilities of SQL databases is to transform data using advanced SQL queries and joins in a transactional manner. Commands like UPDATE and DELETE are commonly used for manipulating individual rows, but they become truly powerful when you can use subqueries to determine which rows to modify and how to modify them. It allows you to implement batch processing operations in a thread-safe, transactional, scalable manner.

Citus recently added support for UPDATE/DELETE commands with subqueries that span across all the data. Together with the CTE infrastructure that we’ve introduced over the past few releases, this gives you a new set of powerful distributed data transformation commands. As always, we’ve made sure that queries are executed as quickly and efficiently as possible by spreading out the work to where the data is stored.

Let’s look at an example of how you can use UPDATE/DELETE with subqueries.

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Craig Kerstiens

ZFS Private Beta on Citus Cloud

Written byBy Craig Kerstiens | July 19, 2018Jul 19, 2018

ZFS is a open source file system with the option to store data on disk in a compressed form. Itself ZFS supports a number of compression algorithms, giving you flexibility to optimize both performance and how much you store on disk. Compressing your data on disk offers two pretty straightforward advantages:

  1. Reduce the amount of storage you need—thus reducing costs
  2. When reading from disk, requires less data to be scanned, improving performance

To date, we have run Citus Cloud—our fully-managed database as a service that scales out Postgres horizontally—in production on EXT4. Today, we’re excited to announce a limited beta program of ZFS support for our Citus Cloud database. ZFS makes Citus Cloud even more powerful for certain use cases. If you are interested in access to the beta contact us to get more info, or continue reading to learn more about the use cases where ZFS and Citus and Postgres can help.

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