Citus Blog

Archive for Nov 2022

As you may have heard, we recently made PostgreSQL 15 generally available in Azure Cosmos DB for PostgreSQL within just 1 week of the PostgreSQL 15 release. The Postgres 15 version is available for you whether you need to create a new cluster in Azure Cosmos DB for PostgreSQL, or upgrade your existing cluster. (Note: you can do in-place major version upgrades in Azure Cosmos DB for PostgreSQL.) And the PostgreSQL 15 support is available in all Azure regions that support Azure Cosmos DB for PostgreSQL.

You may be surprised since it's usually not the norm for a managed database service to start supporting the new major PostgreSQL version that early... This post will walk you through what's going on behind the scenes that enables us to do such a feat. Some background before diving in:

Azure Cosmos DB for PostgreSQL is powered by native Postgres and Citus open source—and enables you to run PostgreSQL at any scale, from a single node to a large, distributed cluster. Customers can also scale out as much as they want depending on their needs with many additional features. The Hyperscale (Citus) managed service recently moved into Azure Cosmos DB family (more info on the launch of Azure Cosmos DB for PostgreSQL in this blog post) and with that introduced try Azure Cosmos DB for PostgreSQL for free where you can try out PostgreSQL 15 with Citus 11.1.

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Thomas Munro

Reducing replication lag with IO concurrency in Postgres 15

Written byBy Thomas Munro | November 10, 2022Nov 10, 2022

Reducing replication lag with IO concurrency in Postgres 15

PostgreSQL 15 improves crash recovery and physical replication performance of some large and very busy databases by trying to minimise I/O stalls. A standby server might now have an easier time keeping up with the primary.

How? The change in PostgreSQL15 is that recovery now uses the maintenance_io_concurrency setting (default is 10, but you can increase it) to decide how many concurrent I/Os to try to initiate, rather than doing random read I/Os one at a time. With big and busy databases, when I/O concurrency increases, replication lag can be reduced.

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