Citus Blog

Citus is a distributed database that extends (not forks) PostgreSQL for large workloads. One challenge associated with building a distributed relational database (RDBMS) is that they require notable effort to deploy and operate. To remove these operational barriers, we’ve been thinking about offering Citus as a managed database for a while now.

Naturally, we were also worried that providing a native database offering on AWS could split our startup’s focus and take up significant engineering resources. (Honestly, if the founding engineers of the Heroku Postgres team didn’t join Citus, we might have decided to wait on this.) After having Citus Cloud publicly available for eight months though, we are now more bullish on the cloud then ever.

It turns out that targeting an important use case for your customers and delivering it to them in a way that removes their pain points, matters more than anything else. In this blog post, we’ll only focus on removing operational pain points and not on use cases: Why is cloud changing the way databases are delivered to customers? What AWS technologies Citus Cloud is using to enable that in a unique way?

Your database is a key part of your stack, and when things act up in your application getting insights into it are key. With Citus Cloud you have a number of dashboards with metrics you can look into as well as centralized logging. In addition to the centralized logging, you also have the ability to drain your logs to the provider of your choice. This means you can have all your Citus Cloud logs (both the coordinator and distributed nodes) integrated with the rest of your application logs.

We're excited to announce U2F Fido (Yubikey) support for Citus Cloud to make the experience of keeping your account and data secure even easier. Within the Account Security section of the Citus Cloud Console you'll now see a section to add your new device. If you already have a U2F click Register New Device then you'll be prompted to activate it, and you're done.

If you already have a Yubikey then you know all the benefits it brings, however when testing many of our customers were unaware of them or weren't using them already. We felt it would be worth it to spend some time explaining why they're great as well as creating a few guides for how to set them up on the most common services you may be using.

Getting an example schema and data is often one of the more time consuming parts of testing a database. To make that easier for you, we're going to walk through Citus with an open data set which almost any developer can relate to–github event data. If you already have your own schema, data, and queries you want to test with, by all means use it. If you need any help with getting setup, join us in our Slack channel and we'll be happy to talk through different data modeling options for your own data.

An overview of the schema and queries

The data model we're going to work with here is simple, we have users and events. An event can be a fork or a commit related to an organization and of course many more.

At Citus we want to build a database you can trust. Part of that is that we continue to advance Citus from both a usability and performance standpoint. Another part of that is that we ensure your data is safely stored and secure–we do this through...

From nearly the beginning of the Citus Cloud service, we've had an internal formation provisioned and managed by the service itself. Dogfooding in this manner brings all the usual benefits such as gaining operational knowledge, customer empathy, and etc.

However, more interesting than yet another blog post going over the importance of dogfooding is the two different ways we're using our Citus formation. Setting up a distributed table requires a bit more forethought than a normal Postgres table, because the choice of shard column has a big impact on the types of queries and joins you can do with the data.

We're going to look at two cases here: a time-series metrics table and an events table.

We want the process of scaling your data to be easier–whether it's Citus 5.2 making it easier for multitenant applications to scale, ORM support, or real-time analytics and search of your data you shouldn't have to re-engineer everything as your data...

It has been several months since the launch of Citus Cloud, and we’d like to share one part of its design with you. In particular, the fundamental unit of organization for our hosted service on top of AWS is concurrent state machines. In what follows...

At Citus we want to enable you to build real-time applications across large amounts of data with ease. One part of that is Citus makes it simple for you to shard your data and use scale-out capabilities to leverage all your processing power. Another part is Citus Cloud: our managed, hosted offering of Citus running on AWS.

Today taking advantage of Citus becomes even easier with our Citus Cloud database going into general availability. You can read on to discover what’s included with Citus Cloud or sign-up to get started right away.

At Citus we believe in making databases easier. Key to that is empowering users to scale Postgres beyond the typical limits of a single node. Our latest Citus release makes it easier than ever to scale memory and processors while retaining access to familiar SQL queries and rich Postgres features. But database management can be tricky even in the single-node case, so we at Citus have been hard at work building the next step in our journey to make databases easier: Citus Cloud, an on-demand cloud service on top of Amazon Web Services available today in private beta. *Citus Cloud is now fully GA, you can learn more here