Many vendors I talk to are not even sure if their product is an observability product and then still others, whose products almost certainly are observability products, don't want to market them as observability products because they think that the space is becoming saturated. We have now kind of reached a consensus on what observability does not mean - i.e. that observability is not just about the three pillars. At the same time though, there is not much consensus about what it does mean. At the empirical end of the spectrum that are those who define observability as processing telemetry and then at the more abstract end there are positions such as "observability is about being able to ask questions of your system". You can have your observability burger your way! Some of us want to know if our K8S pods are overheating, and some of us want to understand our sales pipeline. But that's actually ok. As we say, it's a big tent.
What happened?? Why did that system go down? If we want to answer that question, then we obviously need to have historical data. The problem is that modern computer systems generate huge volumes of telemetry. Since we never in know in advance which system might fail, we play safe and cover all our bases. This gives us a warm feeling, but it also means that we have the engineering problem of ingesting that data and the economic problem of paying for it. To be honest, to an extent, the engineering problem of mass ingestion has been solved. Unfortunately, the problem of querying that mountain of data so that we can quickly make sense of it, is still hard.
But it's still not really possible. Ultimately, as an SRE or a DevOps engineer, I would rather not have to figure out what went wrong. I would really like a system that would be able to help me prevent the outage in the first place. Sure, it's great that my system can alert me that a pod went down with an OOM Kill, but if I'm paying a six or seven-figure sum for a state of the art system - can't it actually figure this stuff out pre-emptively? I mean, I have fed it several petabytes of historical data - why can't it be a bit more predictive? At the moment, it turns out that this is still a hard problem.
Root cause analysis is a seductive phrase but, in practice, it is something of a chimera. Unfortunately, the mechanics of cause and effect are not always visible. Even more unfortunately, causes themselves are not always mechanical. In loosely coupled, complex and highly distributed systems, they can sometimes only be inferred. And, as a growing body of theory tells us, failures in complex systems are often not mono-causal. Maybe it is even the case that attempting to pinpoint a single cause for an error condition is a particular prejudice of human inquiry rather than a self-evidently correct approach.
Complexity is pretty much taken for granted in modern IT landscapes. It is part of the wallpaper. And then we pile one layer of complexity on top of another. Distributed services, interconnected network topologies, enterprise messaging backbones. They are designed by clever people and they do complex, high tech stuff. The terabytes of logs, metrics, traces, events etc generated by these systems don't tell a story by themselves. Turning these huge heaps of data points into meaningful analyses requires deep domain knowledge as well as heavy-duty engineering and some highly skilled UI design. The complexity of observability systems is a function of the complexity they observe.
Very few organisations actually have dedicated observability specialists - at best they may have one or more DevOps or Platform engineers that have some familiarity with some aspects of one or two observability products. Most organisations don't have staff with experience and expertise in instrumenting their systems optimally or in the best ways to filter, forward, consolidate and configure that telemetry. Traversing a wide and unfamiliar landscape without a map or a compass is not a simple endeavour.
Procuring a unified observability system is actually a considerable undertaking. Effectively, you cannot really evaluate the system without re-instrumenting some of your existing services and infrastructure. It is not easy to do this without impacting an existing environment or spinning up a new one. You will also have to coordinate across multiple teams and set up a whole variety of testing scenarios. Most organisations simply do not have the time to go through this process over and again so that they can compare vendor tools. Often this means that customers don't end up with the best tool for the job.
If you are a developer and you want to know about OOP or the principles of RESTful API's, the chances are that the canonical texts are not written by a particular vendor. More likely they are the product of collaborations by networks of subject matter experts or reflect a coalescence of academic traditions.
In the observability realm, much of the narrative-making tends to have a more vendor-led feel. Those vendors though, are often focused on the concerns of big-ticket clients. The "problems", therefore, are often stated as dealing with petabyte scale ingestion or cardinality explosions or traces with tens of thousands of spans. This results in heroic feats of engineering that captivate audiences at conferences (me included!), but it may not reflect the actual day to day concerns of many practitioners.Organisations that have adopted OpenTelemetry will not have to go through the pain of re-instrumenting their code in order to evaluate or switch to a new vendor. Equally eBPF offers the possibility of zero code instrumentation for companies running compatible workloads. And, of course, AI tooling holds out the prospect of automation to help mitigate the observability skills gap. Will this lead us to a technological nirvana of systems functioning in perfect harmony? Hopefully not, the hard problems are the best ones.
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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