How Much CPU & Memory? Make It Work podcast

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Make it Work «
How much CPU & Memory?

2y ago 35:36

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This episode looks into the observability tool Parca & Polar Signals Cloud with Frederic Branczyk and Thor Hansen. We discuss experiences and discoveries using Parca for detailed system-wide performance analysis, which transcends programming languages.

We highlight a significant discovery related to kube-prometheus and the unnecessary CPU usage caused by Prometheus exporter's attempts to access BTRFS stats, leading to a beneficial configuration change for Kubernetes users globally.

We also explore Parca Agent's installation on Kubernetes 1.28 running on Talos 1.5, the process of capturing memory profiles with Parca, and the efficiency of the Parca Agent in terms of memory and CPU usage.

We touch upon the continuous operation of the Parca Agent, the importance of profiling for debugging and optimization, and the potential of profile-guided optimizations in Go 1.22 for enhancing software efficiency.

🎬 Screensharing videos that go with this episode:

🎁 Access the audio & all videos as a single conversation at makeitwork.gerhard.io

LINKS

EPISODE CHAPTERS

(00:00) - Intro
(02:21) - kube-prometheus discovery & fix
(06:29) - Parca Agent on K8s 1.28 running as Talos 1.5
(06:49) - How to capture memory profiles with Parca?
(08:42) - pprof.me
(10:42) - Data retention in Parca
(11:42) - A real-world memory issue debugging example
(16:05) - How much memory is Parca Server expected to use?
(17:39) - How much memory is the Parca Agent expected to use?
(19:42) - What about Parca Agent CPU usage?
(21:57) - Is Parca Agent meant to run continously?
(23:03) - Other Parca stories worth sharing
(25:19) - What are the things that you are looking forward to in 2024?
(27:23) - Golang Profile Guided Optimisations with Parca
(30:22) - Frederic's surprise screen share
(34:02) - Wrap-up

15 episodes

#Tech #Gerhard Lazu #Kubernetes #Cloud Native #Infrastructure #DevOps #Platform Engineering #Software Engineering #Bare Metal #Cloud IT