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PostgreSQL is the bedrock on which many of today’s organizations are built. The versatility, reliability, performance, and extensibility of PostgreSQL make it the perfect tool for a large variety of operational workloads.

The one area in which PostgreSQL has historically been lacking is analytics, which involves queries that summarize, filter, or transform large amounts of data. Modern analytical databases are designed to query data in data lakes in formats like Parquet

The OpenStreetMap (OSM) database builds almost 750GB of location data from a single file download. OSM notoriously takes a full day to run. A fresh open street map load involves both a massive write process and large index builds. It is a great performance stress-test bulk load for any Postgres system. I use it to stress the latest PostgreSQL versions and state-of-the-art hardware. The stress test validates new tuning tricks and identifies performance regressions.

Two years ago, I presented (video

Postgres is being used more and more for analytical workloads. There’s a few hidden gems I recently ran across that are really handy for doing SQL for data analysis, ROLLUP and CUBE. Rollup and cube don’t get a lot of attention, but follow along with me in this post to see how they can save you a few steps and enhance your date binning

Since its inception Crunchy Data has released new builds and packages of Postgres on the day community packages are released. Yesterday's minor version release was the first time we made the decision to press pause on a release. Why did we not release it immediately? There appeared to be a very real risk of breaking existing installations. Let's back up and walk through a near miss of Postgres release day.

Yesterday when Postgres 17.1 was released there appeared to be breaking changes in the Application Build Interface (ABI). The ABI is the contract that exists between PostgreSQL and its extensions. Initial reports showed that a number of extensions could be affected, triggering warning sirens

Large language models (LLM) provide some truly unique capacities that no other software does, but they are notoriously finicky to run, requiring large amounts of RAM and compute.

That means that mere mortals are reduced to two possible paths for experimenting with LLMs:

• Use a cloud-hosted service like OpenAI

It is never immediately obvious how to go from a simple SQL query to a complex one -- especially if it involves intricate calculations. One of the “dangers” of SQL is that you can create an executable query but return the wrong data. For example, it is easy to inflate the value of a calculated field by joining to multiple rows.

Use Crunchy Playground to follow allow with this blog post using a Postgres terminal:

Postgres Playground w/ Sample Data

You followed all the best practices, your sales dates are stored in perfect timestamp format …. but now you need to get reports by day, week, quarters, and months. You need to bin, bucket, and roll up sales data in easy to view reports. Do you need a BI tool? Not yet actually. Your Postgres database has hundreds of functions that let you query data analytics by date. By using some good old fashioned SQL - you have powerful analysis and business intelligence with date details on any data set.

In this post, I’ll walk through some of the key functions querying data by date.

We are excited to announce the release of Crunchy Postgres for Kubernetes 5.7! This latest version brings a wealth of new features and enhancements designed to make your Postgres deployments on Kubernetes more flexible, efficient, secure, and robust than ever before.

We have highlighted a few of the features that we are excited about below. You can also check out the release notes for more details

Today, we’re excited to release pg_parquet - an open source Postgres extension for working with Parquet files. The extension reads and writes parquet files to local disk or to S3 natively from Postgres. With pg_parquet you're able to:

• Export tables or queries from Postgres to Parquet files
• Ingest data from Parquet files to Postgres
• Inspect the schema and metadata of existing Parquet files

Code is available at: https://github.com/CrunchyData/pg_parquet/.

Read on for more background on why we built pg_parquet or jump below to get a walkthrough of working with it.

JSON_TABLE, new in Postgres 17

If you missed some of the headlines and release notes, Postgres 17 added another huge JSON feature to its growing repository of strong JSON support with the JSON_TABLE feature. JSON_TABLE lets you query JSON and display and query data like it is native relational SQL. So you can easily take JSON data feeds and work with it like you would any other Postgres data in your database.

There is something new you may not have seen in the release notes for Postgres 17. No, not a new feature - I mean inside the actual release notes themselves! The Postgres project uses the git program to track commits to the project, and now each item in the release notes has a link to the actual commit (or multiple commits) that enabled it.