I’ve been noticing that the query used in v1.x of my pg_bloat_check.py script (obtained from the check_postgres.pl module) was not always accurate and was often not reporting on bloat that I knew for a fact was there (Ex: I just deleted over 300 million rows, vacuumed & analyzed the table and still no bloat? Sure it could happen, but highly unlikely). So I continued looking around and discovered the pgstattuple

This is the third and final post of the series intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. The information below provides sample use of R Functions against the NDVI dataset.

As introduced in the previous posts, the combination of PostgreSQL and R provides users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. It is our hope that these posts will cause those building analytic applications to give PostgreSQL a second look.

The first post in this series

This is the second in a series of posts intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language.  This post builds on the example introduced in the initial post by demonstrating the steps associated with preprocessing the Normalized Difference Vegetation Index

This is the first in a series of posts intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. This initial post introduces PL/R and R, provides set up instructions for following the Spatial Analytics example to be used in this series of posts, and provides introductory instruction on Geocoding with PostGIS, R and PL/R.

While PostgreSQL is known for and widely popular as a transactional database due to its SQL compliance, reliability, data integrity and ease of use, it is less commonly associated with analytic applications.

The combination of PostgreSQL and R provide users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database.

This series of blog posts will provide users with information about:

• Introduction to R and PL/R
• Geocoding with PostGIS, R and PL/R
• Example of Spatial Analytics – NDVI Processing
• Example of Spatial Analytics – Sample Analytics Functions

Crunchy Data is pleased to announce an initial implementation of a PostgreSQL Operator for Kubernetes to build on our work with PostgreSQL Containers. This initial implementation provides a user with the ability to perform certain PostgreSQL functions including creating PostgreSQL clusters, performing database backup and restores and viewing persistent volume claims.

Operators

Crunchy Data recently announced the publication of the PostgreSQL Security Technical Implementation Guide (STIG) by the United States Defense Information Systems Agency (DISA), making PostgreSQL the first open source database to provide a published STIG.

While the STIG was authored for the benefit of the U.S. Government, the DISA PostgreSQL STIG offers security-conscious enterprises a comprehensive guide for the configuration and operation of open source PostgreSQL. Enterprises can refer to the STIG as for guidance on PostgreSQL security best practices they consider open source PostgreSQL as an alternative to proprietary, closed source, database software.

Importantly, compliance with the STIG guidance requires only open source software and documentation. The PostgreSQL STIG is based on open source, unmodified PostgreSQL 9.x used in conjunction with certain open source PostgreSQL extensions – most notably, pgaudit

This blog provides guidance on how to build a PostgreSQL cluster using the new Kubernetes feature - StatefulSet. Using this StatefulSet capability provides a very simple, Kubernetes native, mechanism to make clustering decisions when deploying a PostgreSQL cluster.

The Crunchy PostgreSQL Container Suite

UPDATE

PLEASE READ THE UPDATED VERSION: AN EASY RECIPE FOR CREATING A POSTGRESQL CLUSTER WITH DOCKER SWARM

The below content has been deprecated in favor of An Easy Recipe for Creating a PostgreSQL Cluster with Docker Swarm

Today, I am wrapping up my preview of next week's PGConf.EU conference. I'm really excited about all of the excellent topics and speakers that we get to choose from! Once again, here's the full Schedule

Today, I will continue with my preview of the exciting talks at the upcoming PGConf.EU conference. In Part 1, I discussed the talks that will happen on Wednesday. Today, I want to dive into the Thursday sessions.

Starting in early on Thursday morning,if you haven’t tracked all the fantastic progress we’ve made with PostgreSQL 9.6 then definitely go to Magnus Hagander