KubeCon EU took place in London last week. It was a huge event with representation from across the whole cloud native spectrum - from three person start-ups all the way up to Google. The buzz at the event was palpable - you could hear it, feel it and almost taste it - although that may have been down to the copious amounts of free coffee and Diet Coke. It also confirmed the growing strategic and commercial importance of observability. Full stack observability newcomers Dash0 were Platinum sponsors and stole the show with their Pit Stop booth, Grand Prix racewear and high-end swag.
In London, they say that you are never more than a few feet away from a rat and, at KubeCon, it seemed like you were never more than a few yards away from an Observability vendor or booth. SquaredUp backed on to Kerno, who sat opposite Incident.io. On the South side, Kloudfuse were just a few doors down from Honeycomb and if you then took a right turn from their crib you would find the bustling OpenTelemetry Observatory. The Observatory was home to a who’s who of the observability cosmos - Dotan Horovits, Daniel Gomez Blanco, Martin Thwaites, Adriana Villela and many other A-listers were in attendance. I was lucky enough to bump into Adnan Rahić, who gave me a really great walkthrough of BindPlane's truly amazing oTel Fleet management capabilities. Naturally, Datadog, Dynatrace, Grafana and New Relic were represented - but so were newcomers like Control Theory and SigLens.
Observability also featured heavily on the packed schedule of talks. There were over thirty talks on observability at the KubeCon main event as well as packed sessions at the co-located Observability Day. A highlight for me was Michele Mancioppi's eye-opening talk on the power of the Linux DL_PRELOAD function (no, I had never heard of it before either). Elsewhere Karthik Kalyanaraman of AI observability pacesetters Langtrace and Guangya Liu of IBM Instana spoke on Agentic AI Observability. It was also great to see the Perses project on the big stage with a talk on Dashboards as Code by Nicolas Takashi and Antoine Thébaud. Dynatrace spoke at so many sessions that they were almost running an event within an event:
A while ago, we published this piece on the unique aesthetics of observability tooling. Observability is not just about logs, metrics and traces – it is also about great visual design, and events like KubeCon literally give you the chance to see beautiful UI’s on the big screen. Seeing Datadog’s Kubernetes Active Remediation on a 48 inch long monitor was pretty jaw-dropping. Likewise, a demo of Dash0’s SIFT features really gave a great close-up view of the level of detail that goes into the application’s UX. As well as being slick it is also insanely fast.
At events like KubeCon there is so much happening that you have to make choices. If you are attending as an exhibitor, you want to speak to visitors at your booth but you also want to catch talks, meet friends and soak up the event. Something has to give. In the end it was the talks that had to make way - after all - you can catch up with them on YouTube.
I had the good fortune to catch up with the all-knowing Andrew Mallaband - a person whose observability memoirs will one day be a great read. I also finally picked up a copy of Buzzing Across Space signed by eBPF Jedi Bill Mulligan. An unexpected highlight was talking to Hanson Ho of Embrace - who is not only a mobile observability guru but is also the fount of all knowledge about Watford FC.
I had many talks with visitors at the SquaredUp stand, and there were a number of themes that emerged. One is the incredible diversity of observability needs. It is not a one-size-fits-all question. Some people wanted better visibility of their Cloud costs, others wanted anomaly detection across ten thousand hosts. There are also many different levels of maturity - some organisations have a fully built-out infrastructure, whereas others are just starting out on their observability strategy. Whilst there is a growing adoption of OpenTelemetry, for many it is still something of an unknown quantity. My feeling is that there is a great opportunity for vendors who can provide support and guidance to companies who are just beginning to dip their toes into the water.
Observability is a space that is expanding and evolving so fast that it is hard to really pin down the state of the market. What I am sensing is that it is a growing space rather than a zero-sum game. Observability is a very wide domain. New entrants into the market do not necessarily need to try to prize customers away from Splunk or Datadog to build a business. Instead, companies can carve out their own niche in an emerging ecosystem.
It’s an announcement that might have seemed unthinkable not long ago, but the porting of the revolutionary eBPF technology to Windows is now a reality. The ability to bring safe programmability to the kernel has resulted in enormous gains in fields such as security, networking and observability for Linux hosts, so applying the same principle to the Windows ecosystem is obviously an attractive proposition. It is not, though, without its own difficulties. There were a lot of hurdles to overcome and, inevitably, given the differences in OS architecture, this is not a full-fidelity replica of the Linux implementation.
This possibly foundational article by Pavel Yosifovich guides you through the steps involved in boldly going where few have gone before and creating your first eBPF program for Windows. One paragraph in the article begins with the sentence “this is where things get a bit hairy“ - for some that will likely be a challenge rather than a deterrent. This may not be cooking up nuclear fusion in your bedroom, but it does feel pretty radical.
As well as rolling out their Open AI observability solution, Elastic have also been very active within the OpenTelemetry project. C++ has a reputation for being something of a fearsome foe for observability practitioners. In this article on the Elastic blog, Haidar Braimaanie dons his protective gear and attempts to tame the beast with a soothing dose of OpenTelemetry instrumentation.
Unlike languages built in frameworks such as .NET, C++ does not have a standardized runtime environment that supports dynamic instrumentation across all platforms and compilers. C++ also uses a variety of build systems such as Makefiles and CMake, so that implementing instrumentation can be difficult and error-prone. In the article, Haidar looks at adding OpenTelemetry support to a C++ application running on Ubuntu 22.04. He also includes sample code for instrumenting the project with database spans and then observing the application in APM.
After reading this article you may want to give the C++ developer in your life a hug.
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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