Citus Blog

Citus scales out Postgres for a number of different use cases, both as a system of record and as a system of engagement. One use case we're seeing implemented a lot these days: using the Citus database to power customer-facing real-time analytics dashboards, even when dealing with billions of events per day. Dashboards and pipelines are easy to handle when you’re at 10 GB of data, as you grow even basic operations like a count of unique users require non-trivial engineering work to get performing well.

Citus is a good fit for these types of event dashboards because of Citus’ ability to ingest large amounts of data, to perform rollups concurrently, to mix both raw unrolled-up data with pre-aggregated data, and finally to support a large number of concurrent users. Adding all these capabilities together, the Citus extension to Postgres works well for end users where a data warehouse may not work nearly as well. We've talked some here about various parts of building a real-time customer facing dashboard, but today we thought we'd go one step further and give you a guide for doing it end to end.

Citus 7.1 shipped just a few weeks back and included a number of great new features. In case you missed the details check out Ozgun’s blog or read up on what Citus is on our site. Today though we want to drill further into an important area in Postgres, counting.

Getting a distinct count of some value out of your database is a common question. We've talked about how to count more quickly on our blog before, and followed that up with how you can use probabilistic algorithms like HyperLogLog to do counts faster.

On Thursday December 14th we experienced a service outage across all of Citus Cloud, our fully managed database as a service. This was our first system wide outage since we launched the service in April of 2016.

We know that Citus Cloud customers trust us with their data, and its availability is critical. In this case we missed the mark and we’re sorry. We've worked over the last few days to thoroughly understand what went wrong and steps we can take to limit the likelihood of this problem occurring again. In addition, we found improvements we can make to our architecture and incident response processes to allow us to better respond to similar types of problems in the future.

We're big fans of Postgres and enjoy getting around to the various community conferences to give talks on relevant topics as well as learn from others. A few months ago we had a good number of Citus team members over at the largest Postgres conference in Europe. Additionally, three of our Citus team members gave talks at the conference. We thought for those of you that couldn't make the conference you might still enjoy getting a glimpse of some of the content. You can browse the full set of talks that were given and slides for them on the PGConf EU website or flip through the presentations from members of the Citus team below.

We rolled out a new database migration feature for the Citus fully-managed database as a service—the Warp migration feature—as part of our Citus Cloud 2 announcement. Today I wanted to walk through Citus Cloud’s warp migration feature in more detail. Before we drill in, we should probably take a step back and look at what typically (and sometimes painfully) goes on for a large database migration.

So about two weeks ago we had a stealth release of Citus 7.1. And while we have already blogged a bit about the recent (and exciting) update to our fully-managed database as a service–Citus Cloud—and about our newly-added support for distributed transactions, it’s time to share all the things about our latest Citus 7.1 release.

If you’re into bulleted lists, here’s the quick overview of what’s in Citus 7.1:

• Distributed transaction support
• Zero-downtime shard rebalancer
• Window function enhancements
• Distinct ON/count(distinct) enhancements
• Additional SQL enhancements
• Checking for new software updates