Introduction

Heroku is excited to introduce nine new dyno types to our fleets and product offerings. In 2014, we introduced Performance-tier dynos, giving our customers fully dedicated resources to run their most compute-intensive workloads. Now in 2024, today's standards are rapidly increasing as complex applications and growing data volumes consume more memory and carry heavier CPU loads.

With these additional dyno types, we’re excited to enable new use cases on Heroku with enhanced compute and memory specifications. Some use case examples include real-time processing against big data/real-time analytics, large in-memory cache applications such as Apache Spark or Hadoop...

Your app is slow. It does not spark joy. This post will use memory allocation profiling tools to discover performance hotspots, even when they're coming from inside a library. We will use this technique with a real-world application to identify a piece of optimizable code in Active Record that ultimately leads to a patch with a substantial impact on page speed.

In addition to the talk, I've gone back and written a full technical recap of each section to revisit it any time you want without going through the video.

I make heavy use of theatrics here, including a Japanese voiceover artist, animoji, and some edited clips of Marie Kondo's Netflix TV show. This recording was done...