Many folks are surprised to hear that Postgres has parallel queries out of the box. This was released in small batches across a half dozen versions of Postgres, so the major fanfare for having parallelism got a little bit lost.

By default Postgres is configured for two parallel workers. The Postgres query planner will assemble a few plans for any given query and will estimate the additional overhead of performing parallel queries, and make a go or no-go decision. Depending on the settings and the calculations of the query planner, parallel queries are typically used by large and long running queries — like warehouse or analytical workloads.

Below is the output of a sample EXPLAIN

Crunchy Data is pleased to announce the publication of the Crunchy Data PostgreSQL 16 Security Technical Implementation Guide

As a team you often get handed a piece of software to deploy and manage, for example Red Hat's Ansible Automation Platform (AAP) or Quay. Red Hat's guide is to run and manage this in OpenShift and great, you're already comfortable with OpenShift and have a decent size deployment. Turns out pretty early on you've got a decision to make you didn't even realize was a decision, what are you going to do about the database? Most software needs a database – and the database of choice is overwhelmingly Postgres. But managing stateful systems is a different commitment than stateless apps.

In fact we have the conversation all the time with customers that need Postgres, want Postgres. While some want to manage and be responsible for their database others don't. Well today you've got another choice. You can still have your database integrated with Crunchy Postgres for Kubernetes

We are excited to introduce Crunchy Postgres for Kubernetes (CPK) 5.6, the latest version of our PostgreSQL Kubernetes operator. This release brings several new features that will elevate your PostgreSQL experience to new heights, ensuring better management, automation, and scalability.

Autogrow Postgres storage

In the evolving world of data management, ensuring consistency and accuracy across multiple database systems is paramount. Whether you're migrating data, synchronizing systems, or performing routine audits, the ability to compare data across different database platforms is crucial. Enter pgCompare

When your company has decided it's time to invest in more open source, Postgres is the obvious choice. Managing databases is not new and you already have established practices and requirements for rolling out a new database. One of the big requirements we frequently help new customers with on their Postgres adoption is data encryption. While the question is simple, there's a few layers to it that determine which is the right approach for you. Here we'll walk through the pros and cons of approaches and help you identify the right path for your needs.

Overview of At-Rest Encryption Methods

Today we're excited to announce a new scheduler for Crunchy Bridge. Scheduler makes it easy for you to create and manage automated database maintenance tasks such as:

• Refreshing materialized views
• Performing automated roll-ups

Last month we launched Crunchy Bridge for Analytics, a new managed PostgreSQL offering that lets you query your data lake directly from PostgreSQL. Since then, we have had quite a few exciting conversations with customers handling large amounts of data in PostgreSQL. A common question is of course: How does it work?

In this post, I wanted to shed some light on the internals. Crunchy Bridge for Analytics abstracts the query engine to offer fast analytics on data in Amazon S3 in PostgreSQL. In principle, it can support multiple query engines, and it likely will in the future, but the current query engine is DuckDB.

A bit of history: Distributed SQL pushdown in Citus

I love taking random spatial data and turning it into maps. Any location data can be put into PostGIS in a matter of minutes. Often when I’m working with data that humans collected, like historic locations or things that have not yet traditionally been done with computational data, I’ll find traditional Degrees, Minutes, Seconds (DMS) data. To get this into PostGIS and QGIS, you’ll need to convert this data to a different system for decimal degrees. There’s probably proprietary tools that will do this for you, but we can easily write our own code to do it. Let’s walk through a quick example today.

Let’s say I found myself with a list of coordinates, that look like this:

38°58′17″N 95°14′05″W

Whether you are managing a large table or setting up automatic archiving, time based partitioning in Postgres is incredibly powerful. pg_partman