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One of the main reasons people use Citus to transform Postgres into a distributed database is that with Citus, you can scale out horizontally while still enjoying PostgreSQL’s great RDBMS features. Whether you’re already a Postgres expert or are new to Postgres, you probably know one of the benefits of using a relational database is to have relations between your tables. And one of the ways you can relate your tables is of course to use foreign keys.

A foreign key ensures referential integrity, which can help you to avoid bugs in applications. For example, a foreign key can be used to ensure that a table of “orders” can only reference customer IDs that exist in the “customers” table.

If you have already heard about Citus 10, you know that Citus 10 gives you more support for hybrid data models, which means that you can easily combine regular Postgres tables with distributed Citus tables to get the best of the single node and distributed Postgres worlds.

This post will walk you through one of the new features in Citus 10: support for foreign keys between local Postgres tables and Citus reference tables.

It’s been an eventful time for Hyperscale (Citus) lately. If you’re interested in Postgres, distributed databases, and how to handle ever growing needs for your Postgres application or simply use Hyperscale (Citus), keep reading.

Citus is an open source extension to Postgres that enables horizontal scaling of your Postgres database. Citus distributes your Postgres tables, writes, and SQL queries across multiple nodes—parallelizing your workload and enabling you to use the memory, compute, and disk of a multi-node cluster. And Citus is available on Azure: Hyperscale (Citus) is a deployment option in Azure Database for PostgreSQL.

What’s really exciting to me is that we’ve made it easier and cheaper than ever to try and use Hyperscale (Citus). With Basic tier, you can now use Hyperscale (Citus) on a single node, parallelizing your operations and adopting a distributed database model from the very beginning. And you can now try Citus open source with a single docker run command—boom!

Citus is an extension to Postgres that lets you distribute your application’s workload across multiple nodes. Whether you are using Citus open source or using Citus as part of a managed Postgres service in the cloud, one of the first things you do when you start using Citus is to distribute your tables. While distributing your Postgres tables you need to decide on some properties such as distribution column, shard count, colocation. And even before you decide on your distribution column (sometimes called a distribution key, or a sharding key), when you create a Postgres table, your table is created with an access method.

Previously you had to decide on these table properties up front, and then you went with your decision. Or if you really wanted to change your decision, you needed to start over. The good news is that in Citus 10, we introduced 2 new user-defined functions (UDFs) to make it easier for you to make changes to your distributed Postgres tables.

Last month we released Citus 10 and we’ve received an overwhelming amount of positive feedback on the new columnar compression and single node Citus features, as well as the news that we’ve open sourced the shard rebalancer.

The new and exciting Citus 10 features are bringing in lots of new users of Citus open source and the managed Hyperscale (Citus) option in Azure Database for PostgreSQL. And many of you are asking:

One of the performance projects I’ve focused on in PostgreSQL 14 is speeding up PostgreSQL recovery and vacuum. In the PostgreSQL team at Microsoft, I spend most of my time working with other members of the community on the PostgreSQL open source project. And in Postgres 14 (due to release in Q3 of 2021), I committed a change to optimize the compactify_tuples function, to reduce CPU utilization in the PostgreSQL recovery process. This performance optimization in PostgreSQL 14 made our crash recovery test case about 2.4x faster.

The compactify_tuples function is used internally in PostgreSQL:

• when PostgreSQL starts up after a non-clean shutdown—called crash recovery
• by the recovery process that is used by physical standby servers to replay changes (as described in the write-ahead log) as they arrive from the primary server
• by VACUUM

So the good news is that the improvements to compactify_tuples will: improve crash recovery performance; reduce the load on the standby server, allowing it to replay the write-ahead log from the primary server more quickly; and improve VACUUM performance.

One of the big new things in Citus 10 is that you can now shard Postgres on a single Citus node. So in addition to using the Citus extension to Postgres to scale out Postgres across a distributed cluster, you can now also:

• Try out Citus on a single node with just a few simple commands
• Shard Postgres on a single Citus node to be “scale-out-ready”
• Simplify CI/CD pipelines by testing with single-node Citus

The Citus 10 release is chock full of new capabilities like columnar storage for Postgres, the open sourcing of the shard rebalancer, as well as the feature we are going to explore here: using Citus on a single node. No matter what type of application you run on top of Citus—multi-tenant SaaS apps, customer-facing analytics dashboards, time-series workloads, high-throughput transactional apps—there is something for everyone in Citus 10.

One of the main reasons people use the Citus extension for Postgres is to distribute the data in Postgres tables across multiple nodes. Citus does this by splitting the original Postgres table into multiple smaller tables and putting these smaller tables on different nodes. The process of splitting bigger tables into smaller ones is called sharding—and these smaller Postgres tables are called “shards”. Citus then allows you to query the shards as if they were still a single Postgres table.

One of the big changes in Citus 10—in addition to adding columnar storage, and the new ability to shard Postgres on a single Citus node—is that we open sourced the shard rebalancer.

Yes, that’s right, we have open sourced the shard rebalancer! The Citus 10 shard rebalancer gives you an easy way to rebalance shards across your cluster and helps you avoid data hotspots over time. Let’s dig into the what and the how.

Citus 10 is out! Check out the Citus 10 blog post for all the details. Citus is an open source extension to Postgres (not a fork) that enables scale-out, but offers other great features, too. See the Citus docs and the Citus github repo and README.

This post will highlight Citus Columnar, one of the big new features in Citus 10. You can also take a look at the columnar documentation. Citus Columnar can be used with or without the scale-out features of Citus.

Postgres typically stores data using the heap access method, which is row-based storage. Row-based tables are good for transactional workloads, but can cause excessive IO for some analytic queries.

Columnar storage is a new way to store data in a Postgres table. Columnar groups data together by column instead of by row; and compresses the data, too. Arranging data by column tends to compress well, and it also means that queries can skip over columns they don’t need. Columnar dramatically reduces the IO needed to answer a typical analytic query—often by 10X!

Development on Citus first started around a decade ago and once a year we release a major new Citus open source version. We wanted to make number 10 something special, but I could not have imagined how truly spectacular this release would become. Citus 10 extends Postgres (12 and 13) with many new superpowers:

• Columnar storage for Postgres: Compress your PostgreSQL and Citus tables to reduce storage cost and speed up your analytical queries.
• Sharding on a single Citus node: Make your single-node Postgres server ready to scale out by sharding tables locally using Citus.
• Shard rebalancer in Citus open source: We have open sourced the shard rebalancer so you can easily add Citus nodes and rebalance your cluster.
• Joins and foreign keys between local PostgreSQL tables and Citus tables: Mix and match PostgreSQL and Citus tables with foreign keys and joins.
• Functions to change the way your tables are distributed: Redistribute your tables in a single step using new alter table functions.
• Much more: Better naming, improved SQL & DDL support, simplified operations.

These new capabilities represent a fundamental shift in what Citus is and what Citus can do for you.

In my day to day, I get to work with many customers migrating their data to Postgres. I work with customers migrating from homogenous sources (PostgreSQL) and also from heterogenous database sources such as Oracle and Redshift. Why do people pick Postgres? Because of the richness of PostgreSQL—and features like stored procedures, JSONB, PostGIS for geospatial workloads, and the many useful Postgres extensions, including my personal favorite: Citus.

A large chunk of the migrations that I help people with are homogenous Postgres-to-Postgres data migrations to the cloud. As Azure Database for PostgreSQL runs open source Postgres, in many cases the application migration can be drop-in and doesn’t require a ton effort. The majority of the effort usually goes into deciding on and implementing the right strategy for performing the data migration.