Author: Jim Mlodgenski

This post was updated May, 2022 to include resources for forecasting models and insights for time series data. Many organizations need to store time series data. Some organizations have applications designed to store and query large amounts of time series data such as collecting metrics from a fleet of internet of things (IoT) devices. Others […]

When designing many applications today, developers rightfully think of the end-user first and focus on what the experience will be. How the data is ultimately stored is an implementation detail that comes later. Combined with rapid release cycles, “schema-less” database designs fit well, allowing for flexibility as the application changes. PostgreSQL natively supports this type […]

The PostgreSQL community continues its consistent cadence of yearly major releases with PostgreSQL 12. PostgreSQL 12 introduces features opening up new development opportunities while simplifying the management of some already robust capabilities, such as new ways to query JSON data, enhancements to indexes, and better performance with partitioned tables. In this post, I take a […]

Historically, the PostgreSQL community releases a new major version yearly, and with that, has a defined end of life (EOL) policy of older major versions. This allows version and upgrade decisions to be made on dates known well into the future. The community EOL policy is to support a major version for 5 years after […]

In this post, I take a close look at three exciting features in PostgreSQL 11: partitioning, parallelism, and just-in-time (JIT) compilation. I explore the evolution of these features across multiple PostgreSQL versions. I also cover the benefits that PostgreSQL 11 offers, and show practical examples to point out how to adapt these features to your applications.