With the acceleration of complexity, scale, and dynamic systems architectures, under-resourced IT teams are under increasing pressure to understand when there is abnormal behavior, identify the precise reason why this occurred, quickly remediate the issue, and prevent this behavior in the future. IT operations, application, infrastructure, and development teams all look to the topic of observability as the silver bullet to solve their problems.
To summarize the textbook definition, which originates from engineering and control theory, observability is essentially the ability to understand what is happening inside of a system from the knowledge of its external outputs. But rather than focusing on only the textbook definition, it’s more important to understand the end goals of observability: why do you need observability and what are you trying to achieve from it?
Why do you need observability?
In the world of software, observability helps cross-functional teams understand and answer specific questions about what’s happening in highly distributed systems. Observability is the ‘how’ to empower you to understand what is slow or broken, as well as quickly understanding exactly what needs to be done to improve performance.
However, since modern cloud environments are dynamic and constantly increasing in scale and complexity, most problems are neither known nor monitored. Observability addresses this common issue of these “unknown unknowns”, enabling you to continuously and automatically understand new types of problems that arise. Only once you think about observability, in terms of its ultimate purpose and value to the business, will CIOs and business executives start to care about what can often become a very myopic and technical topic.
How do you make a system ‘observable’?
If you have read about observability, you have been told that collecting the measurements of metrics, distributed traces, and logs are the three key pillars to achieving success. However, observing telemetry from back-end applications does not provide the full picture of how your systems are behaving. Neglecting the front-end perspective potentially skews or even misrepresents the understanding of how your applications and infrastructure are performing in the real world, to real users. Extending the three pillars approach, IT teams must augment telemetry collection with user experience data to eliminate blind spots:
- Metrics – Values represented as counts or measures that are often calculated or aggregated over a time period. Metrics can originate from a variety of sources, including infrastructure, hosts, services, as well as cloud platforms and external sources.
- Distributed traces – Displays activity for a transaction or request as it flows through applications and show how services connect, including code-level details.
- Logs – Structured or unstructured text that record discreet events that occurred at a specific time.
- User experience – Extends traditional observability telemetry by adding the outside-in user perspective of a specific digital experience on an application, even in pre-production environments. Most commonly in the form of Real User Monitoring (RUM) but can be observed by synthetic monitoring or even a recording of the actual session—better known as session replay. Extends telemetry by adding in data for APIs, third-party services, errors occurring in the browser, user demographics, and application performance from the user perspective.
Why the three pillars of observability aren’t enough
Obviously, data collection is only the start. Once you’re able to use that telemetry to achieve the end goals of improving end-user experience and business outcomes, only then can you really say you have achieved the purpose of observability.
While IT organizations have the best of intentions and strategy, they often overestimate the ability of already overburdened teams with limited resources to constantly observe, understand, and act upon an impossibly overwhelming amount of data and insights.
- Data silos – Multiple agents, disparate data sources, and siloed monitoring tools make it hard to understand interdependencies across applications, multiple clouds, and digital channels such as web, mobile, and IoT.
- Volume, velocity, and complexity – It’s near impossible to get answers from the sheer amount of raw data collected from every component in everchanging modern cloud environments, such as Kubernetes and containers, that can spin up and down in seconds.
- Manual instrumentation and configuration – When IT resources are forced to manually instrument and change code for every new type of component or agent, they spend most of their time trying to set up observability, rather than innovating based on insights from observability data.
- Lack of pre-production – Even with load testing in pre-production, developers still don’t have a way to observe or understand how real users will impact applications and infrastructure, before pushing code into production.
- Wasting time troubleshooting – Application, operations, infrastructure, development, and digital experience teams are pulled into war rooms, wasting valuable time guessing and trying to make sense of telemetry and come up with answers. These war rooms drain an organization’s constrained resources and take the focus away from deciding the best course of action and executing.
Making observability actionable and scalable for IT teams
Observability must be done in a manner so that resource-constrained teams are able to action upon the myriad of telemetry data collected in real-time, to prevent business-impacting issues from propagating further or even occurring in the first place:
- Context & topology – Instrumenting in a way that creates an understanding of relationships between every interdependency in highly dynamic, multi-cloud environments of potentially billions of interconnected components. Rich context metadata enables real-time topology maps and understanding causal dependencies vertically throughout the stack, as well as horizontally across services, processes, and hosts.
- Continuous automation – Automatic discovery, instrumentation, and baselining of every system component on a continuous basis, shifts IT effort away from manual configuration work to value-add innovation projects that can prioritize understanding of the things that matter. Observability becomes ‘always-on’ and scalable so constrained teams can do more with less.
- AI-assistance – Exhaustive fault-tree analysis combined with code-level visibility unlock the ability to pinpoint the root cause of anomalies that don’t rely on time-consuming human trial and error, guessing, and correlations. Additionally, causation-based AI can automatically detect any unusual change points to discover unknown unknowns that are not understood or monitored.
- Open ecosystem – Extending observability to include external data sources, such as OpenTelemetry, which is an open-source project led by vendors such as Dynatrace, Google, and Microsoft. OpenTelemetry expands telemetry collection and ingestion for platforms that provide topology mapping, automated discovery and instrumentation, and actionable answers required for observability at scale.
So, what do I do?
Don’t just take it from us, hear how Nancy Gohring from 451 Research succinctly defines Observability from an analyst’s perspective.
Since you can’t waste months or years trying to build your own tools, and testing out multiple vendors that enable you to achieve one piece of the observability pie, you need a solution that can help make all of your systems and applications observable, give you actionable answers, and provide technical and business value as fast as possible.
Talk to someone from Dynatrace who can quickly demonstrate how to achieve intelligent observability, so you can start improving digital experience and business outcomes and show your IT executives and business stakeholders how investing in observability delivers immediate business value. Or read our observability eBook to learn more about approaching observability with the end goal in mind.