If you are visiting FOSDEM for the first time, then the experience can be both bewildering and overwhelming. Set on the sprawling campus of the Universite Libre Brussels, it attracts in excess of 8,000 visitors and includes more than 800 lectures.
If, like us, you were a first time visitor it is easy to feel a sense of dislocation as you criss-cross the terrain from one warren of bunker-like corridors to another. However, the small fleet of rock festival size food trucks selling pizza, Belgian waffles and french fries are a very useful reference point for meeting friends and getting your bearings.
Whilst the ethos of the event is open source (entry is free and you don't even have to register), it obviously needs to cover its costs. Luckily, though, the commercial sponsors who support FOSDEM keep a low profile and the sessions are technical in nature rather than being marketing-driven.
It is a vast conference with a huge number of streams - each of which has its own location (referred to as a devroom). One of the most eagerly awaited sessions in the Observability DevRoom was Nikola Grcevski and Mario Macias of Grafana Labs showcasing their eBPF-powered Beyla product. They didn't disappoint and the audience were rewarded with an amazing demonstration of zero instrumentation tracing. The queues for the DIY Observability talk were long enough for people to be posting photos of them on Twitter. In the space of 20 minutes, Robert Hodges walked through the process of setting up a fully open source telemetry pipeline.
We mentioned Quickwit in our last newsletter. Company Co-founder François Massot also received an enthusiastic reception for his demonstration of the product's ability to ingest telemetry at scale and search at high speed. Those are just some of the highlights of a hugely successful day in the Observability DevRoom. If you were unable to attend we have compiled below a listing of videos and slide decks from each presentation.
AutoInstrumentation for Node.js on Kubernetes by Richard Hartmann
Video recording
When Prometheus Met OpenTelemetry by Pavol Loffay
Video and slides
Strategic sampling: Architectural Approaches to Efficient telemetry by Benedikt Bongartz, Julius Hinze
Slides
Unifying Observability: The Power of a Common Schema by Christos Markou, Alex Wert
Video and slides
Linux load average and other silly metrics by Franck Pachot
Video and slides
Fast cheap DIY Monitoring by Robert Hughes
Video
Implementing distributed traces with eBPF by Nikola Grcevski, Mario Macias
Video
What’s possible in observability when we have frame pointers by Matthias Loibl, Jon Seager
Video and Slides
Modern application observability with Grafana and Quickwit by Francois Massot
Slides
What is CI/CD observability, and how to bring observability to CI/CD pipelines? by Dimitris Sotirakis, Giordano Ricci
Slides
Introducing Observability to an airline by James Belchamber
Video
Netdata: Open Source, Distributed Observability Pipeline - Journey and Challenges by Costa Tsaousis
Slides
A final thought - we are going to need a bigger DevRoom for 2025!
Stories about Uber architecture always seem to be interesting, not least because they always involve technology at huge scale - such as this trillion record migration from DynamoDB. This article, however, is actually interesting on a number of levels. As well of being of technical interest it also provides some fascinating insight into internal team topologies and management processes - which are also fundamentally important aspects of managing observability at scale. Whilst most organisations will only operate at a fraction of Uber’s scale, every organisation is seeking to minimise costs and improve service to users, and the article provides a number of insights which would be of interest to most observability practitioners.
A survey carried out by McKinsey in 2021 found that 57% of respondents were already using Machine Learning to support at least one business function. ML is no longer a niche concern but is becoming a core component of development and CI/CD practices. As this post from the Datadog blog notes, the efficacy of ML models will inevitably degrade over time, so monitoring their performance and reliability is critical. The article really drives home the point that ML is a domain with its own specific behaviours, and effective monitoring requires building out new processes, metrics and even infrastructure to cover issues such as Data Drift, Prediction Drift and Concept Drift. Whilst the article does use some specialist terms, it is a highly readable and practical guide to the subject of ML monitoring.
It sounds like it could be a sub-plot in the film Inception, but this is a really interesting article from the Observe blog on how they use an instance of their Observe system to monitor their Observe cloud platform. Observe not only have to support fast reads for complex user queries, they also have to support ingesting one petabyte of telemetry per day. As you can see from the above diagram, Kafka and Snowflake form two of the pillars of the backend architecture. This three-part series offers a fascinating insight into Observe’s own internal observability strategy as well as being a great exemplar of the eat your own dog food principle. This is an article which is of great value to anybody with an interest in large-scale observability architectures.
Most of us have experienced the anguish of bill shock at some point. Being hit with a huge bill for mobile roaming charges on return from your holiday or getting a penalty notice for an inadvertent motoring infringement that happened weeks back. Those are just small pinpricks though, compared to the 50,000 volts of financial burn felt by companies mentioned in this transcript of a scintillating talk by Erik Peterson, CEO of CloudZero. He argues, persuasively, that engineering decisions are buying decisions. In the case mentioned in the headline, a decision to turn on one section of debug code led to vast volumes of logs being emitted and racking up over $1m in costs.
This is a really engaging blog post by Infrastructure Engineer Jack Lindamood, where he reviews nearly every infrastructure decision he made over four years working at a start-up. Each choice is graded with a Regret, Endorse or an occasional Unsure. Whilst not explicitly observability-related, it will however, have resonance for any engineer forced to make technological choices (which is probably all of us). The article contains much distilled wisdom and some strong opinions, as well as general observations on the challenges and trade-offs faced by infrastructure engineers.
The RAG pattern has really gained traction over the past year as it allows enterprises to leverage the power of LLM's to gain insights into their own data. This is a fascinating and (occasionally technical) article which details how Incident IO used vector embeddings to mine through their data and discover related incidents. The article explains the techniques involved with great clarity and provides really helpful advice on creating embeddings to find hidden patterns in your own data.
When you think of large corporations pushing the technology envelope, Chik-Fil-A might not be the first name to come to mind. However, the highly distributed nature of their infrastructure presents massive observability challenges, which they have met with some very impressive engineering. The scale of their task is daunting - 2,800 Edge Kubernetes clusters, tens of thousands of IoT devices and billions of MQTT messages each month. This is a really fascinating article on managing IoT observability at scale.
In this blog article, Bijit Ghosh of Deutsche Bank discusses best practices for observability across the full AI system lifecycle. He composes a custom system which knits together a range of technologies including structlog, Flask, Prometheus and Kibana as well as AI-specific tools such as MLFlow and CausalML. It’s a comprehensive article which exhibits a clear understanding of both observability and AI technologies.
A great example of managing the complexities of Observability engineering. Jay Taylor from InfluxDB builds out a solution using the Telegraf, InfluxDB, Grafana stack.
An in-depth look at monitoring K8S with the increasingly popular VictoriaMetrics platform. This follows an end-to-end process from crafting your own Helm chart to configuring alert rules.
This has really raised a few eyebrows. A forensic analysis by Nikolay Sivko of coroot on how just a few OpenTel meta tags can potentially explode your ingestion fees.
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