As we know, observability systems ingest vast amounts of metrics data. As we also know, this can potentially involve considerable cost. At present, metrics are capable of providing us with rich analytics into the current and past performance of our systems. What is missing from the picture at the moment seems to be the ability to use the vast amounts of metrics we harvest for predictive analytics. The spectacular feats accomplished by LLM's have led to heightened expectations that AI/ML can make forecasts that can be reliably used to predict and prevent outages and potential error conditions.
Unfortunately, the techniques employed by LLM's to construct apparently meaningful textual content do not translate to predicting future patterns in time series data. Whilst detecting anomalies in time series data in general is complex, generating forecasts from observability metrics tends to be even more difficult. This is partly because the best results arise from training models on a single metric, whereas observability systems may ingest millions of different metrics. The patterns of the data are also likely to be more unpredictable and exhibit characteristics such as sparsity and extreme right skew.
Victoria Metrics are one of the leading vendors taking on this challenge, and last year they rolled out their Anomaly Detection feature for their enterprise customers. A recent post on their blog summarises a number of key updates to their toolkit. The first of these is Presets - which can target well-known metrics types such as those generated by the Kubernetes Node Exporter. They have also released the MAD (Median Absolute Deviation) model but warn that it is not suited to seasonal or trendful data.
There is really excellent documentation both on the tooling and on anomaly detection in general on the VM web site. At the same time, these are quite sophisticated instruments and obtaining meaningful results probably requires both an understanding of the characteristics of your data sets as well as some fluency in data science principles.
Datadog have not yet released any time series forecasting tooling to production. They have, however, recently published an update on Toto , an internal project evaluating anomaly detection methods. Toto stands for Time Series Optimized Transformer for Observability and Datadog describe it as "a state-of-the-art time series forecasting foundation model optimized for observability data".
Whilst numerous time series foundation models have been released in the past year, Datadog say that theirs is the first to include a significant quantity of observability data. In benchmarking their model, Datadog used two measures - symmetric mean absolute percentage error (sMAPE) and symmetric median absolute percentage error (sMdAPE). These are measure selected as they are designed to minimise the influence of extreme outliers which can often be found in observability data sets.
Datadog claim that on both metrics, Toto outperformed all other models used in the benchmark. Unfortunately, whilst this sounds promising, the model is still under development and it will be some time before it is deployed to production systems.
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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