It was one of those announcements that was, in almost equal parts, both seismic and inevitable. Having spent many years providing the platform on which other vendors have built performant and scalable observability solutions, it seemed to be just a matter of time before ClickHouse would enter the market themselves. It's a move that comes us a surprise to absolutely nobody and this is the culmination of a process that has arguably been in the works for some time. The company engineers had already gained experience of building their own observability backend when they replaced Datadog with their own in-house solution.
Even before entering the market as a vendor, they have had a strong presence in the space as thought leaders, spelling out their vision for SQL-based observability. And then, of course, came the acquisition of HyperDX. It was never likely that this was going to be a case of HyperDX being shuttered away as an in-house observability solution.
As Altinity’s Josh Lee has pointed out in a number of illuminating talks, ClickHouse was not just a database - it was an observability infrastructure in need of a UI. The HyperDX acquisition meant that that need was fulfilled and it was only a matter of time before a ClickHouse observability solution would enter the market. The blog announcing the launch also shed new light on the logic behind the HyperDX acquisition. Although they are not a vendor with a large share of the market in financial terms, they were, nevertheless, a strong cultural and technological fit.
Even though they do not have a strong profile in the corporate market, their commitment to open source means that they have been built up a strong following amongst the developer community – an approach which has also been a highly successful strategy for vendors such as SigNoz. In addition, the ClickHouse engineers were highly impressed with the tight integration between the HyperDX front end and the database engine. The current focus of HyperDX is on developers, but given the existing ClickHouse database customer base, with its roster of tech behemoths, you would also expect they are well placed to offer an end-to-end solution for hyper-scale observability.
As commentators such as Dotan Horovits have observed, there always dangers in depending on vendor-managed OSS projects. In this case though, we don't see ClickHouse pulling the plug. Firstly, open source is truly in the DNA of the company's founders and engineers. Secondly the experience of Terraform and Redis has shown that the community can quickly fork the project and align product development with the needs of the community. ClickHouse is used by many of the largest companies on the planet as well as a whole swathe of observability vendors. There would be a large pool of highly skilled engineers who could step into the breach. The fact that HyperDX also built up its reputation and developer following as an OS project is also a factor. Obviously, anything can happen but shifting to a more restrictive license would not be without risks.
As we know, there are already a number of full stack solutions on the market that are built on the ClickHouse backend. So, what is the advantage of ClickStack over these existing offerings? According to the release blog, one of the biggest advantages is superior integration between the application layer and the database. Having built the database engine, ClickHouse engineers have the knowledge to maximise performance. In reality, we wonder whether this will be a major issue for all but the most performance hungry of customers. Dash0, for example, is built on ClickHouse and it delivers lightning fast performance.
ClickStack, however, is not just about speed. It also addresses other critical concerns within the observability lifecycle. The ClickStack solution offers full OpenTelemetry support and ships with an OpenTelemetry Collector. For companies that have in-house observability expertise this may not be too much of a win as there is already a ClickHouse oTel exporter, for others the convenience of having a ready-made pipeline may be attractive. There is a set of 11 SDKs for popular languages such as Python, Java and Golang – interestingly there is no .NET SDK.
The release article describes ClickStack as being 'opinionated' and one of the forms this takes is a preference for the 'wide events' model pioneered by Honeycomb. This is a model that rejects the separation of signals implied by the 'three pillars' approach and instead captures the full context of 'transactions' such as web requests. One of the major trends in observability systems in recent years has been queryless debugging and intuitive, GUI tooling for system exploration. ClickStack ships with Event Deltas – a tool for quickly identifying anomalies and performance regressions.
As you would expect from any system entering the market today, it also supports correlation across signals and session replay. For some customers, the icing on the cake maybe the ability to define custom data schemas – which adds a whole new dimension of flexibility. There are a number of options for deployment. You can get started by running a Docker image which combines all of the stack components into a simple bundle. Alternatively, you can choose to deploy HyperDX front end to a ClickHouse Cloud backend.
Apart from the fact that ClickStack inherits an existing user base and that it is a powerful combination of proven technologies, we think that there is one other major reason why the product will succeed and gain market share, and that is execution. As well as building a world class database engine, over the past few years the company has also displayed a relentless capacity for innovation and product development. They have consistently delivered on building out their core product as well as forging partnerships and delivering a swathe of integrations and plugins.
They are by no means the only company in the space that have demonstrated this level of execution - Embrace, Grafana and Dash0 are other notable examples. Dash0, for example, went from a standing start to 100 paying customers within 6 months and their appearance at this year's KubeCon in London evinced boldness and ambition in spades.
Given that ClickStack is already starting from a strong position, we would expect them to follow a similar trajectory. Is this bad news for other vendors. Not necessarily. As we have repeatedly said, it sometimes feels as if the observability space is like a young universe going through its rapid expansion phase. The market is both growing and diversifying. We are increasingly seeing that there is no one-size-fits-all solution and observability is not a zero sum game.
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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