Possibly the biggest observability story at this year’s KubeCon was the announcement by Morgan McClean, an OpenTelemetry co-founder, that “OpenTelemetry Logging hit 1.0”. The OTel logging architecture consists of four main components and all of these have now been designated as stable. Achieving a consensus around logging was always going to be a monumental task given its relatively loosely structured nature and the fact that there are so many varied implementations in the wild.
One of the major gains of the release is that Logging runs in the context of the Collector, meaning that logs can more easily be correlated with other telemetry such as traces and metrics. OpenTelemetry have a keen awareness that “logs are relatively computationally expensive to capture” and have attempted to tackle this issue by specifying a new logging data model, which aims to reduce both CPU consumption and storage requirements.
Read the InfoQ report from KubeCon here.
If you are an SRE, when an outage happens you will know about it pretty quick. With security breaches the picture is rather less clear as, by their nature, they are designed to go undetected. Intrusion detection therefore is often based on a mixture of tools designed to spot unusual spikes, suspicious patterns or failed logon attempts.
This article by Fatih Koç argues that one of the major difficulties involved in identifying attacks is that of correlating signals across multiple sources such as Falco, Prometheus, Kubernetes Audit Logs etc. In this article, he outlines a strategy for extracting relevant data from each of these sources and pulling it together into a single observability dashboard.
The first line of cyber defence is normally at the perimeter - preventing attackers from entering your network in the first place. The next line of defence is intrusion detection. This can often take the form of anomaly detection using a variety of heuristics.
There are also some more creative possibilities, such as the canary solution adopted by Grafana. Just as the canary in the coalmine sings to alert underground workers to the presence of toxic gases, Grafana’s canary was designed to alert them to the possible presence of intruders in their domain.
Load testing can be simple in theory but in modern distributed architectures, it involves a lot more than throwing requests at an individual service. This article on the Airbnb engineering blog looks at how the company’s engineers use the Impulse load-testing framework to handle a number of more complex requirements such as dependency mockingand managing messaging and asyncronous calls.
Unfortunately, at the moment Impulse is just an internal Airbnb framework, so you won’t be able to get your hands on it at present. At the same time, the article provides a valuable blueprint for tackling advanced, real world load testing scenarios.
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.