Citus Blog

Today, we’re excited to announce our latest release of our distributed database—Citus 7.2. With this release, we’re making Citus more of a drop-in replacement for your single-node Postgres database, so you don’t need to adapt your SQL for a distributed system.

For multi-tenant applications where the single-tenant queries were scoped to a single machine, Citus already provided full SQL support. . The improvements in Citus 7.2 take our support for distributed SQL one big step further. With Citus database version 7.2, we now extend our distributed SQL support to queries that run on data spread across a cluster of machines. This becomes particularly important for real-time analytics workloads, where even the most complex SELECT queries need to be parallelized across machines.

If you’re into bulleted lists, here’s the quick overview of what’s new in Citus database version 7.2 for distributed queries that span across machines. For an overview of other recent Citus features check out these blogs about distributed transactions and Citus 7.1.

Years ago Citus used to have multiple methods for distributing data across many nodes (we actually still support both today), there was both hash-based partitioning and time-based partitioning. Over time we found big benefits in further enhancing the features around hash-based partitioning which enabled us to add richer SQL support, transactions, foreign keys, and more. Thus in recent years, we put less energy into time-based partitioning. But… no one stopped asking us about time partitioning, especially for fast data expiration. All that time we were listening. We just thought it best to align our product with the path of core Postgres as opposed to branching away from it.

Postgres has had some form of time-based partitioning for years. Though for many years it was a bit kludgy and wasn't part of core Postgres. With Postgres 10 came native time partitioning, and because Citus is an open source extension to Postgres that means anyone using Citus gets to take advantage of time-based partitioning as well. You can now create tables that are distributed across nodes by ID and partitioned by time on disk.

We have found a few Postgres extensions that make partitioning much easier to use. The best in class for improving time partitioning is pg_partman and today we'll dig into getting time partitioning set up with your Citus database cluster using pg_partman.

Today we're excited to announce that you can now use our fully-managed database as a service, Citus Cloud, to manage protected health information (PHI) and to build HIPAA-compliant applications on top of Postgres. For those of you building apps in healthcare environments regulated by the Health Insurance Portability and Accountability Act (HIPAA, you can feel safer knowing you now have a scalable Postgres database that meets your healthcare compliance requirements. .

If you're building an application on top of Postgres and you need a combination of horizontal scale as well as HIPAA compliance, reach out to us if you want more information about getting a Business Associate Agreement (BAA) with Citus Data in place.

When it comes to building large-scale, multi-tenant applications, Microsoft's ASP.NET platform is a strong choice. Like other popular web frameworks such as Express and Django, ASP.NET is used to build web applications and APIs. It's been around for a while, but don't let that fool you: ASP.NET packs some serious muscle. After all, it powers one of the biggest Q&A networks on the web: Stack Exchange!

In the past, ASP.NET apps could only run on Windows servers. That's changed with the latest version, ASP.NET Core, which is fully open source and cross-platform. ASP.NET Core runs anywhere you need it to (Windows, Mac, Linux, Docker) and features a modern middleware pipeline, a rich package ecosystem, and blazing-fast performance.

My experience working on multi-tenant enterprise apps has taught me that it's never too early to design for scale. How you architect your code matters, as does how you architect your data. In the past, the apps I worked on were designed around a database-per-tenant model—unfortunately, the database-per-tenant model didn’t scale and caused problems once our app reached thousands of customers (aka tenants). In this post, I’ll show you a different approach to scale the underlying database with ASP.NET: sharding. With sharding you can leave behind the drawbacks of the database-per-tenant model and can scale infinitely.

In this blog post, I'll show you how to build your multi-tenant app with scale in mind. You'll learn how to use ASP.NET Core's middleware pipeline plus the sharding features of Postgres and Citus to build a scalable multi-tenant application on ASP.NET Core. Along the way we’ll start to build the MVP of our very own StackExchange. Let's get started!

Dimitri Fontaine. Photo by Oleg Bartunov

My colleagues at Citus Data and I are super excited to announce that Dimitri Fontaine is joining the Citus Data team. Dimitri is a Major Contributor to the PostgreSQL development project and has authored (and still maintains) key components in the PostgreSQL ecosystem, including the extension framework, pgloader and event triggers, to name a few.

Sharding is one of those database topics that most developers have a distant understanding of, but the details aren't always perfectly clear unless you've implemented sharding yourself. In building the Citus database (our extension to Postgres that shards the underlying database), we've followed a lot of the same principles you'd follow if you were manually sharding Postgres yourself. The main difference of course is that with Citus, we’ve done the heavy lifting to shard Postgres and make it easy to adopt, whereas if you were to shard at the application layer then there’s a good bit of of work needed to re-architect your application.

I've found myself explaining how sharding works to many people over the past year and realized it would be useful (and maybe even interesting) to break it down in plain English.

What Postgres and distributed database topics got the most attention on our Citus Data blog in 2017? Out of the 47 new posts we published last year, it’s pretty clear that many of you were interested in sharding relational databases, whether it be Ozgun’s principles of sharding or Craig’s post on figuring out which sharding data model is right for you. Heck, the five sharding data models post was so popular that it even got re-published recently on HackerNoon.