PostgreSQL 13 is a lucky release! There are some very impactful new features and the release is coming out at its regularly scheduled time in the fall.

One of my favorite parts around the PostgreSQL release process

My introduction to databases and PostgreSQL was for web application development and statistical analysis. I learned just enough SQL to get the queries to return the right answers. Because of my work with PostGIS

tl;dr -- If you are not actively monitoring your PostgreSQL clusters, and alerting on anomalous conditions, you are "flying blind". Don't do that!

At Crunchy Data, we distribute and support PostgreSQL as a product, in multiple forms, to many enterprise customers. We also make our products available via GitHub

If you're an application developer, analyst, data scientist, or anyone who's had to figure out how to work with relational databases, chances are you're familiar with indexes. At least to the extent that you know they somehow help speed up your queries. (That's where I'd left my understanding of indexes for a good amount of time). You may have also seen that PostgreSQL automatically creates an index for a column with a primary key or unique constraint

Prometheus is a popular open source monitoring tool and we have many customers that leverage it when using the Crunchy PostgreSQL Operator or Crunchy PostgreSQL High Availability

I'll admit it: I used to have triskaidekaphobia, otherwise known as a "fear or avoidance of the number 13." After all, I grew up in an area with many tall buildings where the elevators have no 13th floor

More and more frequently, customers are being given access to company databases for purposes of account management, receiving customer support, or placing and tracking an order. Although this provides great convenience for the end user, it also opens the database up to certain vulnerabilities

Python is one of many procedural languages (PLs) included in standard PostgreSQL distributions. In this post, we'll take a quick look at how to get started with using PL/Python to write Postgres functions

I'm a big fan of data in general. Data can tell you a lot about what users are doing and can help you gain all sorts of insights. One such aspect is in making recommendations based on past history or others that have made similar choices. In fact, years ago I wrote a small app to see if I could recommend wines based on how other ones were rated. It was a small app that I shared among just a handful of friends, some with similar taste, some with different taste. At first it was largely an academic exercise of writing a recommendation engine, but if I could find some new wines I liked along the way, then great. Turns out it was a lot more effective at recommending things than I expected, even with only a small handful of wines rated.

The other thing I'm a fan of is Postgres

There you are writing some SQL, having a great time. Uh oh, you need to iterate over each item in a result set and apply a function. You think, "Now I am going to have to write a stored procedure." Well today's post will give you an alternative by using lateral joins in Postgres