Presented by:


Ozgun Erdogan

Citus Data

Ozgun is a co-founder and the CTO at Citus Data. Prior to Citus, Ozgun worked as a software developer for four years in the Distributed Systems Engineering team at Amazon. There, he proposed, designed, and implemented novel algorithms on distributed caching and consistency; and also worked on building systems for scalable data analytics. Ozgun earned his M.S. in Computer Science from Stanford University, and his B.S. from Galatasaray University. He also holds patents on distributed cache consistency and load balancing.

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, 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. In this talk, we're first going to talk about motivations behind scaling your SaaS (multi-tenant) database and several heuristics we found helpful on deciding when to scale. We'll then describe three design patterns that are common in scaling SaaS databases: (1) Create one database per tenant, (2) Create one schema per tenant, and (3) Have all tenants share the same table(s). Next, we'll highlight the tradeoffs involved with each design pattern and focus on one pattern that scales to hundreds of thousands of tenants. We'll also share an example architecture from the industry that describes this pattern in more detail. Last, we'll talk about key PostgreSQL properties, such as semi-structured data types, that make building multi-tenant applications easy. We'll also mention Citus as a method to scale out your multi-tenant database. We'll conclude by answering frequently asked questions on multi-tenant databases and Q&A.

2017 March 29 06:40
50 min
Liberty II
PGConf US 2017