Citus Blog

PGConf Silicon Valley, November 14-16, is barely under a month away and it's shaping up to be another great conference this year. Last year we had over 300 attendees to make it one of the larger Postgres conferences around, and this year we're expecting...

Everybody counts, but not always quickly. This article is a close look into how PostgreSQL optimizes counting. If you know the tricks there are ways to count rows orders of magnitude faster than you do already.

The problem is actually underdescribed...

SQL is a very powerful language for analyzing and reporting against data. At the core of SQL is the idea of joins and how you combine various tables together. One such type of join: outer joins are useful when we need to retain rows, even if it has no match on the other side.

And while the most common type of join, inner join, against tables A and B would bring only the tuples that have a match for both A and B, outer joins give us the ability to bring together from say all of table A even if they don’t have a corresponding match in table B. For example, let's say you keep customers in one table and purchases in another table. When you want to see all purchases of customers, you may want to see all customers in the result even if they did not do any purchases yet. Then, you need an outer join. Within this post we’ll analyze a bit on what outer joins are, and then how we support them in a distributed fashion on Citus.

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: