Citus Blog

Distributed databases often require you to give up SQL and ACID transactions as a trade-off for scale. Citus is a different kind of distributed database. As an extension to PostgreSQL, Citus can leverage PostgreSQL’s internal logic to distribute more sophisticated data models. If you’re building a multi-tenant application, Citus can transparently scale out the underlying database in a way that allows you to keep using advanced SQL queries and transaction blocks.

In multi-tenant applications, most data and queries are specific to a particular tenant. If all tables have a tenant ID column and are distributed by this column, and all queries filter by tenant ID, then Citus supports the full SQL functionality of PostgreSQL—including complex joins and transaction blocks—by transparently delegating each query to the node that stores the tenant’s data. This means that with Citus, you don’t lose any of the functionality or transactional guarantees that you are used to in PostgreSQL, even though your database has been transparently scaled out across many servers. In addition, you can manage your distributed database through parallel DDL, tenant isolation, high performance data loading, and cross-tenant queries.

For many SaaS products, a common database problem is having one customer that has so much data, it adversely impacts other customers on the shared machine. This leads many to ask, “What do I do with my largest customer?”

Tenant isolation is a great way to solve this issue. Effectively it allows you to control which tenant or customer in particular you want to isolate on a completely new node. By separating a tenant, you get dedicated resources with more memory and cpu processing power.

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.

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.

Relational databases are the first choice of data store for many applications due to their enormous flexibility and reliability. Historically the one knock against relational databases is that they can only run on a single machine, which creates inherent...

Citus is a distributed database that extends (not forks) PostgreSQL. Citus does this by transparently sharding database tables across the cluster and replicating those shards.

After open sourcing Citus, one question that we frequently heard from users related to how Citus replicated data and automated node failovers. In this blog post, we intend to cover the two replication models available in Citus: statement-based and streaming replication. We also plan to describe how these models evolved over time for different use cases.

Citus 6.0 allows you to scale out your transactional relational database with minimal changes to your application, thus reducing complexity over other alternatives while still allowing scale. If you're building a multi-tenant application and outgrow a single node Postgres, by sharding based on tenant with Citus 6.0 you can linearly add more memory and processing power to your database without a large re-architecting of your application. You can still maintain referential integrity, and to your application it's still just standard Postgres.

If you’re building a SaaS application, you probably already have the notion of tenancy built in your data model. Typically, most information relates to tenants / customers / accounts and your database tables capture this natural relation.

With smaller amounts of data (10s of GB), it’s easy to throw more hardware at the problem and scale up your database. As these tables grow however, you need to think about ways to scale your multi-tenant database across dozens or hundreds of machines.

After our blog post on sharding a multi-tenant app with Postgres, we received a number of questions on architectural patterns for multi-tenant databases and when to use which. At a high level, developers have three options:

Whether you’re building marketing analytics, a portal for e-commerce sites, or an application to cater to schools, if you’re building an application and your customer is another business then a multi-tenant approach is the norm. The same code runs for all customers, but each customer sees their own private data set, except in some cases of holistic internal reporting.

Early in your application’s life customer data has a simple structure which evolves organically. Typically all information relates to a central customer/user/tenant table. With a smaller amount of data (10’s of GB) it’s easy to scale the application by throwing more hardware at it, but what happens when you’ve had enough success and data that you have no longer fits in memory on a single box, or you need more concurrency? You scale out, often painfully.