Citus Blog

A lot of people look to Citus for a solution that scales out their Postgres database, whether on-prem or as open source or in the cloud, as a fully-managed database as a service. And yet, a common question even before looking at Citus is: "what kind of performance can I get with Postgres?" The answer is: it depends. The performance you can expect from single node Postgres comes down to your workload, both on inserts and on the query side and how large that single node is. Unfortunately, "it depends" often leaves people a bit dissatisfied.

Fortunately, there are some fermi estimates, or in laymans terms ballpark, of what performance single node Postgres can deliver. These ballparks apply both to single-node Postgres, but from there you can start to get estimates of how much further you can go when scaling out with Citus. Let's walk through a simplified guide for what you should expect in terms of the read performance and ingest performance for queries in Postgres.

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Wouldn’t it be great if you could run SQL queries on your data in DynamoDB? While this isn’t possible directly, there is an alternative: With Podyn, you can automatically replicate the schema, data, and changes in your DynamoDB tables to Postgres. Once your data is flowing into Postgres, you can start using a wide array of features including views, indexes, rollup tables, and advanced SQL queries. And if you chose Dynamo because you needed scale beyond what you thought a single Postgres instance could provide, you can replicate it into Citus.

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There are a lot of things that are everyday occurrences for engineering teams. Deploying new code, deploying a new service, it's even fairly common to deploy a net new data store or language. But migrating from one database to another is far more rare. While migrating your database can seem like a daunting task, there are lessons you can learn from others—and steps you can take to minimize risk in migrating from one database to another.

At Citus Data, we've helped many a customer migrate from single node Postgres, like RDS or Heroku Postgres, to a distributed Citus database cluster, so they can scale out and take advantage of the compute, memory, and disk resources of a distributed, scale-out solution. So we've been privy to some valuable lessons learned, and we’ve developed some best practices. Here you can find your guide for steps to follow as you start to create your migration plan to Citus.

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In the beginning there was Postgres

We love Postgres at Citus. And rather than create a newfangled database from scratch, we implemented Citus as an extension to Postgres. We've talked a lot on our blog here about you can leverage Citus, about key use cases, and different data models and sharding approaches. But we haven’t spent a lot of time explaining how Citus works. So if you want to dive deeper into how Citus works, here we're going to walk through how Citus shards the data all the way through to how the executors run queries.

Distributing data within Citus

Citus gets its benefits from sharding your data which allows us to split the data across multiple physical nodes. When your tables are significantly smaller due to sharding your indexes are smaller, vacuum runs faster, everything works like it did when your database was smaller and easier to manage.

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"Thirty years ago, my older brother was trying to get a report on birds written that he'd had three months to write. It was due the next day.

We were out at our family cabin in Bolinas, and he was at the kitchen table close to tears, surrounded by binder paper and pencils and unopened books on birds, immobilized by the hugeness of the task ahead. Then my father sat down beside him, put his arm around my brother's shoulder, and said, 'Bird by bird, buddy. Just take it bird by bird.'"

— Bird by Bird: Some Instructions on Writing and Life, by Anne LaMott

When we started working on Citus, our vision was to combine the power of relational databases with the elastic scale of NoSQL. To do this, we took a different approach. Instead of building a new database from scratch, we leveraged PostgreSQL’s new extension APIs. This way, Citus would make Postgres a distributed database and integrate with the rich ecosystem of tools you already use.

When PostgreSQL is involved, executing on this vision isn’t a simple task. The PostgreSQL manual offers 3,558 pages of features built over two decades. The tools built around Postgres use and combine these features in unimaginable ways.

After our Citus open source announcement, we talked to many of you about scaling out your relational database. In every conversation, we’d hear about different Postgres features that needed to scale out of the box. We’d take notes from our meeting and add these features into an internal document. The list would keep getting longer, and longer, and longer.

Like the child writing a report on birds, the task ahead felt insurmountable. So how do you take a solid relational database and make sure that all those complex features scale? You take it bird by bird. We broke down the problem of scaling into five hundred smaller ones and started implementing these features one by one.

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