The word observability has its root in control theory. R.E. Kálmán in 1960 defined it as a measure of how well you can infer the internal states of a system from knowledge of its external outputs. Observability is such a powerful concept because it allows you to understand the internal state of a system without the complexity of the inner workings. In other words, you can figure out what’s going on just by looking at the output.
I’ve spent most of my career working with tech in various forms, and for the last ten years or so, I’ve focused a lot on building, maintaining, and operating robust, reliable systems. This has led me to put a lot of time into researching, evaluating, and implementing different solutions for automatic failure detection, monitoring, and more recently, observability. Before we get started: What is observability?
Two years ago, I wrote a long retrospective of observability for its third anniversary. It includes a history of instrumentation and telemetry, a detailed explanation of the technical spec, and why the whole “three pillars” thing is nonsense. At the time, it’s what was needed to steer conversations away from silly rabbit holes about data types and back to what matters: how we understand our systems.
We’re honored to be included in Enterprise Management Associates’ EMA Top 3 Award for Observability Platforms. This award recognizes software products that help enterprises reach their digital transformation goals by optimizing product quality, time to market, cost, and ability to innovate—all the things we’re passionate about at LogDNA.
Yoga is to ideal human health what observability is to an application’s ideal functioning. It is well established that observability is a critical factor for the successful implementation and maintenance of cloud-native, serverless, cloud-agnostic, and microservices-based applications. Well-established observability helps DevOps and development teams cross the boundaries of complex systems and get complete visibility into their functioning.
Modern application architectures are complex, typically consisting of hundreds of distributed microservices implemented in different languages and by different teams. As a developer, SRE, or DevOps engineer, you are responsible for the reliability and performance of these complex systems. But while you might have metrics that will help you debug when there’s an issue, metrics alone can’t help you narrow down and ultimately identify the root cause.
Sampling is the practice of extracting a subset of data from a dataset to make conclusions about that larger dataset. It’s far from a perfect solution, but when it’s implemented with Refinery, Honeycomb’s trace-aware sampling proxy, sampling can help you manage very high volumes of complex event data.
We are excited to announce that Elastic Observability has earned the Enterprise Management Associates Top 3 Award for Observability in 2021, a recognition of our commitment to empowering customers with products and features that advance digital transformation and solve real-life problems. This award is driven by EMA’s exhaustive, quantitative research into the top challenges and use cases facing developers, DevOps, SREs, IT professionals, and business professionals.
To celebrate our 13th birthday today, I sat down with Catchpoint's co-founders and my friends, Mehdi Daoudi, Chief Executive Officer, Drit Suljoti, Chief Product and Technology Officer, and J. Scotte Barkan, Chief Technology Officer (dialing in from Long Island after a long week of patch fixes), for an informal chat. We looked back to the days when they all met at DoubleClick prior to the three of them (along with Veronica Ellis, now a Principal Engineer at Eventbrite) founding Catchpoint.
