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Managing time series data at scale can be a challenge. PostgreSQL offers many powerful data processing features such as indexes, COPY and SQL—but the high data volumes and ever-growing nature of time series data can cause your database to slow down over time.

Fortunately, Postgres has a built-in solution to this problem: Partitioning tables by time range.

Partitioning with the Postgres declarative partitioning feature can help you speed up query and ingest times for your time series workloads. Range partitioning lets you create a table and break it up into smaller partitions, based on ranges (typically time ranges). Query performance improves since each query only has to deal with much smaller chunks. Though, you’ll still be limited by the memory, CPU, and storage resources of your Postgres server.

The good news is you can scale out your partitioned Postgres tables to handle enormous amounts of data by distributing the partitions across a cluster. How? By using the Citus extension to Postgres. In other words, with Citus you can create distributed time-partitioned tables. To save disk space on your nodes, you can also compress your partitions—without giving up indexes on them. Even better: the latest Citus 10.2 open-source release makes it a lot easier to manage your partitions in PostgreSQL.

Today, we are excited to announce PostgreSQL 14’s General Availability (GA) on Azure’s Hyperscale (Citus) option. To our knowledge, this is the first time a major cloud provider has announced GA for a new Postgres major version on their platform one day after the official release.

Starting today, you can deploy Postgres 14 in many Hyperscale (Citus) regions. In upcoming months, we will roll out Postgres 14 across more Azure regions and also release it with our new Flexible Server option in Azure Database for PostgreSQL.

This announcement helps us bring the latest in Postgres to Azure customers as new features become available. Further, it shows our commitment to open source PostgreSQL and its ecosystem. We choose to extend Postgres and share our contributions, instead of creating and managing a proprietary fork on the cloud.

In this blog post, you’ll first get a glimpse into some of our favorite features in Postgres 14. These include connection scaling, faster VACUUM, and improvements to crash recovery times.

We’ll then describe the work involved in making Postgres extensions compatible with new major Postgres versions, including our distributed database Citus as well as other extensions such as HyperLogLog (HLL), pg_cron, and TopN. Finally, you’ll learn how packaging, testing, and deployments work on Hyperscale (Citus). This last part ties everything together and enables us to release new versions on Azure, with speed.

Citus 10.2 is out! If you are not yet familiar with Citus, it is an open source extension to Postgres that transforms Postgres into a distributed database—so you can achieve high performance at any scale. The Citus open source packages are available for download. And Citus is also available in the cloud as a managed service, too.

You can see a bulleted list of all the changes in the CHANGELOG on GitHub. This post is your guide to what’s new in Citus 10.2, including some of these headline features.

With the 10.1 release to the Citus extension to Postgres, you can now monitor the progress of an ongoing shard rebalance—plus you get performance optimizations, as well as some user experience improvements to the rebalancer, too.

Whether you use Citus open source to scale out Postgres, or you use Citus in the cloud, this post is your guide to what’s new with the shard rebalancer in Citus 10.1.

And if you’re wondering when you might need to use the shard rebalancer: the rebalancer is used when you add a new Postgres node to your existing Citus database cluster and you want to move some of the old data to this new node, to “balance” the cluster. There are also times you might want to balance shards across nodes in a Citus cluster in order to optimize performance. A common example of this is when you have a SaaS application and one of your customers/tenants has significant more activity than the rest.

There is some good news for those of you wanting to shard your Postgres database in the cloud, so that as your data grows you have an easy way to scale out your Postgres database. I’m delighted to announce that Citus 10—the latest open source release of the Citus extension to Postgres—is now generally available in Hyperscale (Citus).

Hyperscale (Citus) is a built-in option in the Azure Database for PostgreSQL managed service, which has been around for a couple of years to help those of you who would rather focus on your application—and not on spending cycles managing your database.

Citus 10.1 is out! In this latest release to the Citus extension to Postgres, our team focused on improving your user experience. Some of the 10.1 fixes are operational improvements—such as with the shard rebalancer, or with citus_update_node. Some are performance improvements—such as for multi-row INSERTs or with citus_shards. And some are fixes you’ll appreciate if you use Citus with lots of Postgres partitions.

Given that the previous Citus 10 release included a bevy of new features—including things like columnar storage, Citus on a single node, open sourcing the shard rebalancer, new UDFs so you can alter distributed table properties, and the ability to combine Postgres and Citus tables via support for JOINs between local and distributed tables, and foreign keys between local and reference tables—well, we felt that Citus 10.1 needed to prioritize some of our backlog items, the kinds of things that can make your life easier.

This post is your guide to the what’s new in Citus 10.1. And if you want to catch up on all the new things in past releases to Citus, check out the release notes posts about Citus 10, Citus 9.5, Citus 9.4, Citus 9.3, and Citus 9.2.

If you have a large PostgreSQL database that runs on a single node, eventually the single node’s resources—such as memory, CPU, and disk—may deliver query responses that are too slow. That is when you may want to use the Citus extension to Postgres to distribute your tables across a cluster of Postgres nodes.

In your large database, Citus will shine for large tables, since the distributed Citus tables will benefit from the memory across all of the nodes in the cluster. But what if your Postgres database also contains some small tables which easily fit into a single node’s memory? You might be wondering: do you need to distribute these smaller tables, even though there wouldn’t be much performance gain from distributing them?

Fortunately, as of the Citus 10 release, you do not have to choose: you can distribute your large tables across a Citus cluster and continue using your smaller tables as local Postgres tables on the Citus coordinator.

One of the new features in Citus 10 that enables you to use a hybrid “local+distributed” Postgres database is that you can now JOIN local tables and distributed tables. (The other new Citus 10 feature has to do with foreign keys between local and reference tables.)

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.