Citus Blog

A number of SaaS applications have data models where they want to have their customers interact with only their data. At the enterprise end you have companies like Salesforce and Workday that fall into this bucket, but we see a ton of small ones as well. If you're just getting started figuring out how you should approach your data so it can scale in the future, it doesn't have to be hard.

Here we're going to walk through an example data model that you can use as a basis for learning how you could apply the same to your own multi-tenant application.

Microservices and NoSQL get a lot of hype, but in many cases what you really want is a relational database that simply works, and can easily scale as your application data grows. Microservices can help you split up areas of concern, but also introduce complexity and often heavy engineering work to migrate to them. Yet, there are a lot of monolithic apps out that do need to scale. If you don't want the added complexity of microservices, but do need to continue scaling your relational database then you can with Citus. With Citus 6.1 we're continuing to make scaling out your database even easier with all the benefits of Postgres (SQL, JSONB, PostGIS, indexes, etc.) still packed in there.

With this new release customers like Heap and ConvertFlow are able to scale from single node Postgres to horizontal linear scale. Citus 6.1 brings several improvements, making scaling your multi-tenant app even easier. These include:

• Integrated reference table support
• Tenant Isolation
• View support on distributed tables
• Distributed Vaccum / Analyze

All of this with the same language bindings, clients, drivers, libraries (like ActiveRecord) that Postgres already works with.

Give Citus 6.1 a try today on Citus Cloud, our fully managed database-as-a-service on top of AWS, or read on to learn more about all that’s included in this release.

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.

Indexes are an essential tool for optimizing database performance and are becoming ever more important with big data. However, as the volume of data increases, index maintenance often becomes a write bottleneck, especially for advanced index types which use a lot of CPU time for every row that gets written. Index creation may also become prohibitively expensive as it may take hours or even days to build a new index on terabytes of data in postgres. As of Citus 6.0, we’ve made creating and maintaining indexes that much faster through parallelization.

Today we’re happy to announce our new activerecord-multi-tenant Ruby library, which enables easy scale-out of applications that are built on top of Ruby on Rails and follow a multi-tenant data model.

This Ruby library has evolved from our experience working with customers, scaling out their multi-tenant apps, and patching some restrictions that ActiveRecord and Rails currently have when it comes to automatic query building. It is based on the excellent acts_as_tenant library, and extends it for the particular use-case of a distributed multi-tenant database like Citus.