Citus Blog

Articles tagged: benchmarks

My main advice when running performance benchmarks for Postgres is: “Automate it!”

If you’re measuring database performance, you are likely going to have to run the same benchmark over and over again. Either because you want a slightly different configuration, or because you realized you used some wrong settings, or maybe some other reason. By automating the way you’re running performance benchmarks, you won’t be too annoyed when this happens, because re-running the benchmarks will cost very little effort (it will only cost some time).

However, building this automation for the database benchmarks can be very time-consuming, too. So, in this post I’ll share the tools I built to make it easy to run benchmarks against Postgres—specifically against the Citus extension to Postgres running in a managed database service on Azure called Hyperscale (Citus) in Azure Database for PostgreSQL.

Here’s your map for reading this post: each anchor link takes you to a different section. The first sections explore the different types of application workloads and their characteristics, plus the off-the-shelf benchmarks that are commonly used for each. After that you can dive into the “how to” aspects of using HammerDB with Citus and Postgres on Azure. And yes, you’ll see some sample benchmarking results, too.

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Marco Slot

Making Postgres stored procedures 9X faster in Citus

Written byBy Marco Slot | November 21, 2020Nov 21, 2020

Stored procedures are widely used in commercial relational databases. You write most of your application logic in PL/SQL and achieve notable performance gains by pushing this logic into the database. As a result, customers who are looking to migrate from other databases to PostgreSQL usually make heavy use of stored procedures.

When migrating from a large database, using the Citus extension to distribute your database can be an attractive option, because you will always have enough hardware capacity to power your workload. The Hyperscale (Citus) option in Azure Database for PostgreSQL makes it easy to get a managed Citus cluster in minutes.

In the past, customers who migrated stored procedures to Citus often reported poor performance because each statement in the procedure involved an extra network round trip between the Citus coordinator node and the worker nodes. We also observed this ourselves when we evaluated Citus performance using the TPC-C-based workload in HammerDB (TPROC-C), which is implemented using stored procedures.

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Dimitri Fontaine

PostgreSQL 11 and Just In Time Compilation of Queries

Written byBy Dimitri Fontaine | September 11, 2018Sep 11, 2018

PostgreSQL 11 is brewing and will be released soon. In the meantime, testing it with your own application is a great way to make sure the community catches all the remaining bugs before the dot-zero release.

One of the big changes in the next PostgreSQL release is the result of Andres Freund’s work on the query executor engine. Andres has been working on this part of the system for a while now, and in the next release we are going to see a new component in the execution engine: a JIT expression compiler!

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