A simple way of describing observability is how well you can understand the system from the output. In control theory, observability is defined as how engineers can infer the internal states of a system from knowledge of that system's external outputs.
Observability gives engineers a proactive approach to analyze and optimize their systems based on the data it generates. Observability platforms provide a centralized way to collect, store, analyze, and visualize logs, metrics, and traces to provide a connected real-time view of all the operational data in your software system, as well as the flexibility to ask questions about your applications and infrastructure to understand system behavior and get to the answers you need to improve system performance.
Why observability matters to modern digital business
Modern systems are complex, open-source, cloud-native microservices running on Kubernetes clusters and cloud infrastructure. They’re being developed and deployed faster than ever—by distributed teams and components.
Organizations today rely on DevOps teams, continuous delivery, and agile development, making the whole software delivery process faster than ever before, which in turn can make it more difficult to detect issues when they arise.
When things went wrong back in the days of mainframes and static operations, it was pretty easy to understand why, and pre-configured static alerts based on known parameters to alert an operator of an issue. This was sufficient since these systems failed in similar ways over and over again.
As systems became more complex, monitoring tools attempted to shed light on what was happening with software performance. You could trace application performance with monitoring data and time-series analytics. It was a manageable process.
Today, the complexity is overwhelming. The possible causes of failure are abundant—and can feel infinite when you are staring at a screen, frustrated. Is a server down? Is your cloud provider having an outage? Did someone push new code that's impacting end-user behavior?
When working on these complex, distributed systems, identifying a broken link in the chain can be nearly impossible without an observability solution. Now that microservices architectures are commonplace, responsibilities are distributed across teams, there's not a discrete app owner, and many teams need to be involved. Teams need to understand, analyze, and troubleshoot application areas they don’t necessarily own. Now you need modern tools like distributed tracing, which allows you to trace requests—and bottlenecks—through all parts of a distributed system.
The business case for implementing observability within your organization is clear. In the 2023 Observability Forecast, we found that two out of five (40%) said observability improved system uptime and reliability. Even more telling, just over half of respondents said they received $500K+ total value per year from their observability practice. We crunched the numbers: there was a 100% median ROI across all respondents on their observability spend.
Confused about observability vs monitoring?
To understand the difference between the two starts with really understanding the holes in “traditional monitoring” systems.
Issues with conventional monitoring
Conventional monitoring can only track known unknowns. That means it won’t help you succeed in the complex world of microservices and distributed systems. It only tracks the things you know to ask about in advance (for example: “What’s my application’s throughput?”, “What does compute capacity look like?”, “Alert me when I exceed a certain error budget.”)
Observability is the key
Observability gives you the flexibility to understand patterns you hadn’t even thought about before, the unknown unknowns.
The power to not just know that something is wrong…but to also understand why.
Observability AND monitoring
To be clear, observability doesn't eliminate the need for monitoring. Monitoring just becomes one of the techniques used to achieve observability.
Think of it this way: Observability (a noun) is the approach to how well you can understand your complex system. Monitoring (a verb) is an action you take to help in that approach.
What are the components of better observability?
Observability in modern systems has four fundamental pieces: metrics, events, logs, and traces, often referred to as MELT. But this alone will not get you the insights you need to build and operate better software systems. The following are areas of focus that can help you get the best out of observability:
Open instrumentation is using code (agents) to track and measure data flowing through your software application. Open instrumentation means gathering telemetry data without being tied to vendor-specific entities that produce that data. Examples of open-source or telemetry data sources include vendor-agnostic observability frameworks like OpenTelemetry and Prometheus.
Correlation and context
Understanding the bigger picture is vital, especially for large enterprise applications with enormous amounts of raw telemetry data. The telemetry data collected must be analyzed for correlations and context, so humans can make sense of any patterns and anomalies that arise.
Organizations need the flexibility to create their own context and curation with custom applications based on their unique business objectives.
To ensure that your modern infrastructure is always available, you need to accelerate incident response. AIOps solutions use machine learning models to automate IT operations processes such as correlating, aggregating, and prioritizing incident data. These tools help you eliminate false alarms, proactively detect issues, and accelerate mean time to resolution (MTTR).
So, what’s the value of an observability tool?
Improve customer experience
Observability tools empower engineers and developers to create better customer experiences despite the increasing complexity of the digital enterprise. With observability, you can collect, explore, alert, and correlate all telemetry data types; understand user behavior; deliver a better digital experience that delights your users; and increases conversion, retention, and brand loyalty.
Decrease downtime and improve MTTR
Observability also makes it easier to drive operating efficiencies and fuel innovation and growth. For example, a team can use an observability platform to understand critical incidents that occurred and proactively prevent them from recurring.
Improve team efficiency and innovation
When a new build is pushed out, they can see into the application performance and then drill down into the reasons why an error rate spikes or application latency rises. They can see which particular node has the problem.
There are so many other benefits, but here are a few we hear from our customers:
- A single source of truth for operational data.
- Verified uptime and performance.
- An understanding of the real-time fluctuations of your digital business performance.
- Better cross-team collaboration to troubleshoot and resolve issues faster.
- A culture of innovation.
- Greater operating efficiency to produce high-quality software at scale, accelerating time to market.
- Specific details to make better data-driven business decisions, and optimize investments.
Common catalysts for adopting observability
The 2023 Observability Forecast found that nearly half (49%) of the 1,700 respondents cited an increased focus on security, governance, risk, and compliance as the top strategy or trend driving the need for observability.
Other top drivers included the integration of business apps into workflows (38%), the adoption of artificial intelligence (AI) technologies (38%), the development of cloud-native application architectures (38%), migration to a multi-cloud environment (37%), and an increased focus on customer experience management (35%).
The report also found that only 1% of respondents indicated that their organizations had employed all 15 mature observability practice characteristics, such as the following best practices:
- Software deployment uses CI/CD practices (44%)
- Infrastructure that is provisioned and orchestrated using automation tooling (43%)
- Ability to query data on the fly (35%)
- Portions of incident response are automated (34%)
- Telemetry (metrics, events, logs, and traces) is unified in a single pane for consumption across teams (31%)
- Telemetry data includes business context to quantify the business impact of events and incidents (27%)
- Users broadly have access to telemetry data and visualizations (27%)
- Instrumentation is automated (25%)
- Telemetry is captured across the full tech stack (24%)
- Ingestion of high-cardinality data (21%)
Most common observability use cases
SREs and IT Operations teams are in charge of keeping complex systems—the apps that people rely on every day—up and running. But observability is everyone’s concern throughout the software development lifecycle.
Software engineering teams use observability to understand the health, performance, and status of software systems, including when and why errors occur. By looking at a system's outputs, such as events, metrics, logs, and traces, engineers can determine how well that system is performing.
Small teams and observability
Small teams can reap significant benefits from observability tools, particularly when faced with limited resources.
In the context of small cross-functional teams, where every member often wears multiple hats, the ability to monitor and analyze the performance of their systems is invaluable.
Observability tools provide a comprehensive view into the health and behavior of your applications and infrastructure, so your team can quickly identify and address issues. This is especially crucial because small teams may not have the luxury of dedicated personnel for each component of their stack.
By automating data collection and providing real-time insights, observability tools allow team members to focus their efforts more efficiently and reduce the time spent on reviewing and debugging individual servers.
If you’d like to see this in action, see how one of our customers improved efficiency significantly with New Relic.
Observability tools empower small teams to maximize their productivity, streamline troubleshooting, and ultimately deliver a more reliable and responsive user experience without straining their limited resources.
Observability and DevOps
Deployment frequency has increased dramatically with microservices. Too much is changing to realistically expect teams to predefine each and every possible failure mode in their environments. It's not just application code, but the infrastructure that supports it, and consumer behavior and demand.
Observability gives DevOps teams the flexibility they need to test their systems in production, ask questions, and investigate issues that they couldn’t originally predict.
Observability helps DevOps teams:
- Establish clear service-level objectives (SLOs) and put instrumentation in place to prepare and join forces toward measurable success.
- Rally around team dashboards, orchestrate responses, and measure the effects of every change to enhance DevOps practices.
- Review progress, analyze application dependencies and infrastructure resources, and find ways to continually improve the experience for the users of their software.
TL;DR on observability
Observability provides a proactive approach to troubleshooting and optimizing software systems effectively. It offers a real-time and interconnected perspective on all operational data within a software system, enabling on-the-fly inquiries about applications and infrastructure.
In the modern era of complex systems developed by distributed teams, observability is essential. Observability goes beyond traditional monitoring by allowing engineers to understand not only what is wrong but also why.
It encompasses open instrumentation, correlation, context analysis, programmability, and AIOps tools to make sense of telemetry data. Observability tools enhance customer experience, reduce downtime, improve team efficiency, and foster a culture of innovation across all teams.
Get started with observability. Try New Relic.
Modern observability empowers software engineers and developers with a data-driven approach across the entire software lifecycle. It brings all telemetry—events, metrics, logs, and traces—into a unified data platform with powerful full-stack analysis tools that enable them to plan, build, deploy, and run great software to deliver great digital experiences that fuel innovation and growth.
Dive into the 2023 Observability Forecast to see insights and best practices uncovered in the research.
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