Cloud native success is a delicate balancing act. You must continuously take advantage of new and exciting technologies while you simultaneously keep operations rock-stable and reliable.
This isn’t easy. Microservices architecture adoption on container-based infrastructure means you can iterate changes quickly and pivot swiftly to meet the rapidly evolving needs of your customers. So you do.
But every time you introduce a new tool, make a process adjustment or change an app or infrastructure component, you risk creating a problem within your environment. What did you break? Where? There are frequently too many complexities and variables in cloud native to quickly triage.
Then there are the other, familiar-but-different risks your DevOps and site reliability engineering (SRE) teams face in their new cloud native setups:
- Human error abounds: 42% of enterprises experienced downtime due to human error in the last three years.
- External malicious actors keep trying: 40% of global businesses have suffered a cloud-based data breach in the last 12 months.
- Longer mean time to detect and mean time to remediate affect operations: Because of little visibility into complex cloud and hybrid environments, it’s harder to get back online fast.
- Poor app performance loses customers: After waiting just three seconds, 50% of potential customers will abandon your website.
All this directly affects your business. A recent ITIC survey found that the hourly cost of downtime now exceeds $300,000 for 91% of businesses, and nearly half (44%) said that a single hour of downtime can cost more than $1 million.
In the on-premises world, application monitoring tools have helped track down and mitigate these problems. In cloud native environments, not so much.
Learn the difference between observability and monitoring
Monitoring is simply the process of observing and recording the activity of a system. Monitoring tools collect data about how an application is functioning. The software then sends that data to a dashboard to analyze, and perhaps trigger alerts if previously established thresholds are exceeded.
Monitoring keeps on top of the health of your applications, helping you to stay vigilant to known points of failure.
As a superset of monitoring, observability includes all of these capabilities, plus more. That’s because you need more, and more varied, tools when troubleshooting complex, cloud native distributed systems. The kinds of failures you will encounter are not predictable or even known ahead of time. Observability helps your teams catch and remediate the so-called “unknown unknowns” in the new cloud native world.
Observability is not a completely new idea or category of technology; its roots are in monitoring. Both monitoring and observability are an evolution of control theory, a mathematical concept that uses feedback from complex systems to change their behaviors so operators can reach desired goals. The underlying principle is that a system’s visible “outputs” can help users infer what’s happening internally.
A major difference in objectives
But the most important difference between monitoring and observability is the immense gap between their respective objectives.
Monitoring is used to watch over and improve application performance.
Observability is more about using internal measurements of a cloud native system to influence a business-centric outcome or goal. What is the impact on users? On customers? How can you iterate more flexibly? And how can you deliver more benefits more quickly to the business as a whole? Observability is about having a bigger-picture approach to keep systems up and running.
The “three pillars” of observability, redux
Here’s a rundown of the three types of telemetry:
- Metrics: Measurements taken of a system, typically within or over a set period of time. Metrics help businesses detect where there might be a problem.
- Logs: Timestamped records of an event or events. Logs describe the nature of a problem, and why it is happening.
- Distributed traces: A record of events that occur along the path of a request. Distributed traces indicate where a problem is to help troubleshoot an issue.
Though these three types of telemetry are essential in achieving observability, a growing number of voices say observability is more than just data collection and analysis.
One way to think about observability is to focus on the outcomes. This approach defines three phases of observability: know, triage, and understand. The key difference from the traditional definition is that during each phase, the focus is to alleviate the impact on users and customers as quickly as possible.
Here’s how the three phases work:
- Know: First you must know there is a problem. If there’s a fire in the house, the first sign is usually the smell of smoke. In a cloud native environment, it’s essential to get an alert or on-call page to start the remediation process.
- Triage: Then gather your resources to fix the problem. Put the fire out. Make sure your users and customers are back to business as usual. Only then can you concern yourself with why and how an issue happened.
- Understand: Finally, try to figure out the “why” after you resolve the problem. Then you apply your learnings to the system to ensure it doesn’t happen again.
The four elements of observability
Leading observability tools tend to share certain characteristics. Here are four of the key ones to look for when evaluating observability platforms.
- Embrace interoperability
The data that feeds into observability tools (metrics, logs, and traces) come from a broad range of sources or instrumentation. This data provides visibility into both apps and infrastructure and can come from instrumentation within apps, services, cloud, mobile apps or containers. The data also comes in a variety of formats: open source, industry standard, or proprietary.
The growing number of sources, both proprietary and open source, means that observability tools must collect all data from all types of instrumentation to get a full picture of your environment.
DevOps and SREs thus need an observability platform that possesses comprehensive interoperability of all data through open instrumentation, no matter where —or what — it comes from.
- Abundant context
Context in IT systems is the same as in real life. It would be very difficult to interpret the “data” we humans take in every day without context. How things are said, where they are said, and even such things as weather and whether we are hungry can affect our interpretation of real-life information.
For observability, the same applies. Telemetry data is very important as it gets insights into the internal state of applications and infrastructure. But contextual intelligence is also important.
You may want to know how a system performed last week or yesterday. What is the configuration of the server the system is running on? Was there anything unusual about the workload when an issue occurred?
Leading observability platforms enable you to enrich your data with context to eliminate noise, identify the real problems, and easily figure out how to fix the issue.
- Programmable tools for customizable search and analysis
You also want the ability to customize your observability tools so they meet your specific business needs.
First, taking a step back. It’s important to understand that the key to any observability strategy is setting appropriate success metrics and establishing key performance indicators (KPIs) that tell you when your team meets those success metrics.
Still, traditional KPIs, although useful for monitoring and measuring app performance, don’t indicate how issues affect users, customers, and businesses that rely on cloud native environments. No one is connecting the dots.
The traditional answer has been to visualize KPIs in dashboards. DevOps and SRE professionals must get beyond dashboards to fully connect observability with business outcomes. They must create apps that provide an interactive experience with the KPIs that use automated workflows, and which integrate external data with internal metrics in real time.
This gives businesses simultaneous insights into the technology, the business and the users. Your teams can make data-driven decisions that target particular improvement KPIs. And the return on investment (ROI) and effectiveness of new software investments can be optimized. A programmable observability platform helps your teams understand data, systems and customers. This helps you get the right data to the right people to ensure any business that supports your infrastructure runs smoothly.
- An accurate source of truth
Because you have so much data coming from so many places, it is dizzying (and impossible) to switch between different observability tools. You want complete visibility into your entire system by seeing everything, from anywhere in real time.
How can observability help me?
Observability helps cloud native businesses in many ways. Here are four in particular that distinguish observability from basic monitoring:
Nurture a culture of innovation
Observability tells you very quickly what works and what doesn’t, so you constantly improve performance, reliability and efficiency in ways that benefit the business. As you grow your understanding of how technology supports your business, you can continuously optimize your infrastructure and services to align with customer expectations and avoid downtime or service interruption.
Make wise investments in new cloud and leading-edge tools
Engineering teams are no longer overseeing only physical computing hardware; now they’re constantly wrangling data and cloud infrastructure. By tracking business performance data, internal processes and customer-facing services instead of just system availability, IT can better prioritize any on-call pages or specific outages. It means IT can provide the necessary data for management to make critical investment decisions for future software, data collection, and cloud services.
Get real-time insight into digital business performance
When you aggregate many disparate levels and types of facts into dashboards you know precisely what is happening in your environment and how it affects your business.
Information can include standard telemetry data, resource optimization feedback, business-oriented KPIs and user experience metrics. Real-time collection allows you to respond to any incidents before your customers notice.
Accelerate time to market of cloud native applications
Agile workflows enable developers to quickly create, test, iterate, and repeat, to get cloud native applications into production faster and with fewer errors.
But frequent iterations in any system can introduce potential issues and increase the risk of deployment. DevOps teams can take the feedback from observability and diagnose and debug systems more swiftly and effectively with continuous delivery and continuous integration (CI/CD) to reduce time between feature testing and deployment.
Evolve or languish
DevOps engineers and SREs that manage cloud native environments face challenges daily. They must constantly make sense of the complexity of distributed systems, detect difficult-to-isolate issues and expedite troubleshooting so that the business isn’t affected by digital disconnects or even failures.
Monitoring tools have their place, but they’re not enough on their own. Today’s businesses must understand the direct connection between their technology they’re deploying and business success. They must support business needs with relevant data.
It’s also paramount to continuously stay on top of data collection to ensure developer productivity, meet fast time-to-market demands, and deliver an exemplary customer experience.
Observability is the natural step forward from monitoring software. It provides the competitive advantage to stay relevant in today’s cloud native market through data cost control, faster time to remediation and reduced downtime.
Curious as to how Chronosphere can help you upgrade to cloud native observability? Contact us for a demo today.