Last year I attended FOSDEM for the first time and, I have to say, I was pretty overwhelmed. This year I returned and, I have to say, I was still pretty overwhelmed. So what is it that sets FOSDEM apart? For me it is three things:
1. It's free. Free as there is no cost for entry. But it is also free in the sense of free spirit and free thought.
2. It's about the tech. Some conferences consist of a relentless series of sales pitches. You might gain insights but often it will be insights about a certain commercial product rather than a deeper or transferable insight about technology or practice.
3. It covers everything. As an observability specialist, I naturally made a beeline for the observability room on Sunday, but the range of topics at FOSDEM is extraordinary. There were tracks on everything from Python to Bootloaders and Railways to Quantum Computing.
There is also something quite egalitarian and democratic about FOSDEM. It is a great leveller. There are no corporate logos, no fast-track check-ins and no executive lounges. Whether you are a newbie coder or a tech oligarch (tbh I don't think I spotted any) you all have to stand in line for your Belgian waffles or your bottle of Jupiler just the same.
The interest in observability this year seemed to be even greater than last year, with the lecture theatre being close to full for many of the events. There was also a great representation of open source observabilitity companies - VictoriaMetrics, CoRoot, SigLens, Altinity and Grafana were all in attendance. There were also a number of leading speakers - Bryan Boreham, Ricardo Castro, Roman Khavronenko and Josh Lee to name but a few. For good measure, there were also representatives from Kotlin and even a presence from CERN.
The format for the Observability day is pretty intensive. There is a packed schedule of 30 minute talks, each of which is followed by a ten minute break. Pretty much any queue for anything at FOSDEM can easily eat up ten minutes, so balancing your need for a caffeine shot with the length of the break is a challenge. In fact it is a challenge you are not going to win. It's one or the other.
The quality of the sessions was really excellent throughout the day. Unfortunately reviewing all of them would turn this article into a dissertation-length read. Here then, are a few of my personal highlights.
If you are ever thinking about giving a presentation at an event, then this is a fantastic object lesson in how to engage your audience. Are there really parallels between the way a Formula 1 team handles a blowout and the the way we can manage IT incidents? Well, it turns out that there really are. In F1 every millisecond counts and can be the difference not only between coming first and second but can also have huge implications for prize money. As Ricardo points out, minimising "downtime" in F1 is not just a matter of technical expertise - there also really important organisational principles which can be transferred over to other domains.
ClickHouse has established itself as one of the fastest, most scalabale and user-friendly database systems on the planet. It is also the backend for a number of full stack observability systems such as SigNoz and Groundcover. What if you wanted to follow the example of these vendors and build your own open-source observability solution on top a ClickHouse backend? This expert talk by Josh Lee maps out what that journey might look like, whilst also covering some of the potential limitations and drawbacks.
If you are like me, then you will feel that the most exhilirating sessions at an event are ones where people at the top of their game take you on a deep dive into how they tackled a really difficult challenge. Hearing about solutions is great, but hearing about trial and error and failed hypotheses can often be a more gripping narrative. This talk was a fascinating insight into the Grafana team's struggle, often bruising and painful, to solve one of observability's hard problems - i.e. full fidelity eBPF tracing. This was kind of like playing Snakes and Ladders with the Linux kernel, where every breakthrough seemed to be followed by a gotcha.
In his FOSDEM profile, Roman Khavronenko modestly describes himself as a Software Engineer, but he also happens to be a Co-Founder at observability powerhouse Victoria Metrics. Meta observability - or how you can ensure that you monitor your monitoring is a really important and interesting topic and in this thoroughly entertaining talk Roman pulled back the curtain on how the team at VM set about the task. As with much IT best practice, many of the principles boil down to common sense and pragmatism. The advice included ensuring that you have relevant and up to date documentation, instrumenting your system and creating dashboards and alerts. In other words, monitor your monitoring using the same principles that you might apply to monitoring your core systems.
Enjoy the city! For me, Brussels is one of those cities that punches above its weight. Each time I go there I discover a bit more of its charm and magnificence. At FOSDEM you are only a few minutes walk away from a really charming quarter with any number of fascinating side streets, cool bars and outstanding cafes. Getting around is also super-easy. You don't need to buy any tickets in advance to use public transport - just swipe in to any mode of transport with your bank card. Get there a day early or stay a day extra and soak up the atmosphere!
Even if you are not familiar with the name of Brendan Gregg, you are almost certainly familiar with the fruits of his labours. Brendan is the creator of the Flame Graph - one of the most important and iconic visualisations in the observability toolkit.
We featured the Flame Graph in our recent tribute to the work of UX designers in the observability arena - but you should also visit Brendans’ web site.
Brendan’s latest innovation is the AI Flame Chart. This is an evolution of the original flame graph and its ambitious aim is to help reduce the vast financial and environmental costs entailed in the use of LLM’s. This means that whereas the original flame graph was focused on CPU cycles, the latest generation sets its sights on reducing GPU load. The article discusses the considerable complexities involved in mapping GPU programs back to their corresponding CPU stacks.
The names of some of the instruction sets look intimidating to the uninitiated but the basic concept of the graph is quite simple - the wider the bar, the more resource it consumes.
If you have ever had to grapple with a 3,000 line Helm chart to deploy your observability infrastructure, you may be forgiven for thinking that there must be a better way to do this. Whilst YAML has a certain formal elegance, its syntax struggles to express the architectures and relationships embedded in highly complex systems.
Whilst Pulumi have tackled this problem by enabling the use of high level programming languages for IaC, System Initiative are taking a fundamentally more radical approach. Their goal is nothing other than completely reinventing IaC from the ground up. The blog article for the launch of the product is an incredibly ambitious statement of intent. The terms ‘game changer’ and ‘paradigm shift’ tend to be thrown around somewhat casually, this might be a case where their usage is appropriate.
So, what are they proposing? Well, System Initiative is IaC without the code. It is a kind of digital canvas where you manipulate digital twins of your systems. Is the future here or is this the Platform Engineering equivalent of science fiction? Read the article and decide for yourself!
Zomato is a restaurant aggregator and food delivery service that generates vast volumes of metrics. As their company grew, they adopted a Prometheus/Thanos-based architecture - running some 144 Prometheus servers. As metrics volumes continued to skyrocket, even this architecture started to creak and the Zomato SRE team began the search for an alternative solution.
In this article on the Zomato blog, the team discuss why they opted to migrate to Victoria Metrics as well as discussing a number of features of the system which enable them to achieve better performance, lower costs and greater scalability.
The technical challenges were pretty daunting - the project involved migrating over 800 dashboards, 300 microservices and 2.2 billion active time series. We would commend this article not just for its technical insights but also for taking a warts-and-all approach in documenting some of the technical limitations of the VM solution.
Grafana dashboards have been put to all sorts of uses over the years - for everything from space missions to monitoring milk production. In this fun but highly informative article Ivana Huckova and Sven Grossman walk us through building an observability system for bird song. Whilst this might sound slightly quirky, the techniques could be applied to all manner of applications which need to record and analyse audio inputs.
The article is a great showcase for a number of Grafana capabilities - including installing Alloy on a Raspberry Pi and adding context to Dashboard data by dynamically query sources such as Wikipaedia and the Open-Meteo weather information service.
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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