Observability is a software approach that allows organizations to track system performance by studying external output, helping to detect and address errors or issues proactively. If there is a problem anywhere in the distributed system, observability identifies the issue and pinpoints its exact location, signaling the proper team to quickly fix it and create a plan to prevent future occurrences.
This article will dive into observability, how it sets itself apart from traditional monitoring tools, why it’s essential in today's integrated business systems, and how you can implement it effectively in your organization.
Table of contents
- Why monitoring alone isn’t enough
- What is observability?
- The three pillars of observability
- How is observability different from traditional monitoring?
- Monitoring and observability work together for a complete solution
- How observability works in different business functions
- The benefits of observability
- Getting started with observability
- Conclusion
- FAQs
Why monitoring alone isn’t enough
In simpler systems, application performance monitoring was often enough to catch known issues by tracking a few key metrics. But today’s complex, high-velocity, and high-volume systems rely on microservices, making it nearly impossible to manage everything manually and address issues as they arise. Observability picks up where monitoring leaves off, providing the depth of understanding required to detect and resolve issues across an organization’s entire system.
That’s why observability is crucial. It lets you see everything that’s happening in real time and helps you get to the root cause of problems faster, even for issues you're not aware of. Observability lets you track all the moving parts of your system and troubleshoot more effectively, ensuring smooth interactions and continuous improvement.
What is observability?
Observability offers automated, real-time visibility into system operations via unified dashboards that monitor performance over time. The dashboard interface displays collected telemetry data from distributed systems, such as logs, metrics, and traces. Various teams can use this telemetry data to quickly detect issues, point to the cause and location of the problem, and resolve it without manually troubleshooting each system.
Moreover, observability can integrate telemetry data with artificial intelligence (AI) so it can propose questions and generate insights against the data to improve system health further. This approach is especially valuable in complex, multi-layered environments such as software solutions, infrastructure components, connected networks, and business applications.
The three pillars of observability
Metrics, logs, and traces are three standard data outputs that give teams— IT operations, DevOps, site reliability engineers (SRE), and software developers—a clear view of distributed systems' performance. These tools are essential for monitoring system health, especially in cloud and microservices environments, helping teams catch and resolve issues before they lead to downtime, erode customer trust, or disrupt operations.
Metrics: Indicate a problem
Metrics are numerical data collected over time that explain the system's performance. Common examples include response times, CPU usage, and error rates. Metrics are typically set up to trigger alerts when thresholds are crossed or when a system’s performance deviates from expected patterns, enabling proactive responses.
Example: If a web server's response time suddenly spikes, metrics will alert engineers to investigate the cause before it impacts the user experience.
Logs: Offer details on the problem
Logs are records of events within a system that provide real-time visibility into issues as they arise. They capture the timing of a problem and link it to other system events, helping to uncover root causes.
Example: If a user requests a web page that fails to load, logs will capture the exact time of the request, the details of the failure, and any associated error messages, helping engineers diagnose the issue.
Traces: Pinpoint the problem's location
Traces track the path of a request or transaction as it flows through different parts of a system. By following this journey, tracing reveals how each component—whether it’s a large service, a specific function, or an individual method—contributes to the overall process. This deeper visibility into each step allows teams to pinpoint where delays, errors, or failures occur, helping to diagnose and resolve problems faster.
Services: Larger components in the system
like authentication or payment processing
that manage primary system functions
Functions: Smaller logic units within a service,
handling focused tasks such as validating
user input
Methods: Individual operations or code blocks
within functions that execute specific tasks,
like calculating totals or querying a database
Tracing helps teams and software engineers see how these elements interact, allowing them to identify precisely where delays, errors, or failures occur.
Example: In a microservices architecture, tracing can map the journey of a customer request through different services like authentication, billing, and inventory, identifying any slow or failing component—whether it’s a service, function, or method—causing delays.
How is observability different from traditional monitoring?
Think of traditional monitoring as a home security system. It's great for alerting you when someone opens a door or window but won't notice if a tree falls on your roof or your basement leaks. Why? Because it's not set up to detect these things, it only watches the specific things you've told it to. If something happens outside that narrow field of view, you’ll miss it.
Traditional monitoring in software systems works like the camera focusing in only one direction. It keeps track of specific metrics you've chosen, like how much traffic your application handles or how much memory your servers use. While useful, its scope is limited. If something unexpected comes up, basic monitoring might miss it entirely.
Observability, on the other hand, goes one step further than traditional monitoring, providing a 360-degree perspective to catch issues wherever they occur. However, the reality is that observability and monitoring are both vital to gain full insight into your environment, covering both applications and infrastructure.
Monitoring and observability work together for a complete solution
Monitoring alerts you when something goes wrong, while observability helps you understand why it’s happening and where it’s coming from. For instance, you might configure your observability solution to track an application’s throughput (how much data it processes) or its compute capacity (how much power your system has available). You can also set up error thresholds, which activate an alert when too many failed logins or transactions occur. With traditional monitoring, you’d only know that something is happening, but not the location or cause of that event. It also wouldn't tell you if the issue is impacting your users.
In today's integrated systems, often built with distributed architectures like microservices, identifying the root cause of an issue can be challenging. Each microservice performs a specific task; when one fails, it’s not always clear where the failure originated.
Example: Streaming services
Take streaming services like Netflix, which uses microservices to handle different tasks. One microservice might manage user authentication, another might recommend videos, and another may control video playback. If something breaks, traditional monitoring will alert you to the issue but won’t tell you exactly where it’s happening. Observability, however, collects detailed data from each microservice, helping you quickly pinpoint the problem and understand its impact on the user experience.
How observability works in different business functions
Observability helps businesses get a real-time view of their systems' functioning and provides insights and fixes to keep things running smoothly. Each business function or system has unique observability needs. We’ve outlined a few of them below.
Networks
In networks, observability focuses on continuously monitoring how well the system works, how much traffic passes through, and whether it’s secure and available. Standard metrics include bandwidth used, how quickly data moves (latency), and if data is lost (packet loss). These metrics help the network run efficiently. Logs keep a record of security events like access attempts and firewall activities. Tracing data flow through the network helps identify where traffic might be slowed down or blocked.
Software development
Observability is essential during the software development lifecycle. DevOps teams integrate real-time observability tools to monitor system behavior, providing early feedback on how components interact and perform under various scenarios. This immediate insight enables developers to detect bottlenecks, errors, or inefficiencies as they write code, allowing them to optimize features and address issues before they become embedded.
Data management
Data observability focuses on assessing and measuring the health, quality, and reliability of the data flowing through systems. It tracks data anomalies, freshness, completeness, and how data flows to ensure accuracy and trustworthiness. This capability is critical in data pipelines, analytics, and AI models, where poor data quality can lead to wrong results.
Business operations
Business observability monitors metrics like sales, customer retention, and system uptime. Metrics give you a snapshot of overall business health, while traces follow internal processes or customer journeys. Logs capture events like transactions, helping businesses fine-tune their operation and solve issues more quickly.
Security
Security observability means constantly monitoring things like unauthorized access attempts, unusual user behavior, and malware. Logs capture audit trails and flag suspicious activity, while metrics help track how often incidents occur. Traces map potential attack paths, assisting businesses to respond quickly to security risks and maintain system integrity.
Infrastructure
In infrastructure management, observability tracks the hardware and cloud resources that support software systems. It monitors metrics like CPU usage, memory, disk activity, and network performance across servers, virtual machines, and containers. Metrics track how well servers, storage, and databases perform, while logs capture system updates and failures. Traces show how different components interact, which helps identify and fix issues in distributed systems.
The benefits of observability
Observability improves many processes, including the overarching task of application performance monitoring. In cloud-native architectures or microservices, it can be hard to see inside each system component to find the problem. Observability overcomes this limitation and can find performance bottlenecks in specific system components. It can detect, analyze, and resolve problems or slowdowns before they impact system performance or usability.
Whether optimizing system performance, improving reliability, or enhancing customer experience, observability offers essential benefits that help businesses thrive. Here are some of the top advantages observability brings to the table.
Faster issue detection and resolution
Observability gives teams a real-time view of how systems are doing so they can reduce false alerts, catch issues as they happen, and even spot potential problems before they impact users. With metrics, logs, and traces, anomalies in system behavior are found, allowing teams to act proactively and prevent major incidents.
Improved collaboration across teams
Observability helps break down the barriers between development, operations, and security teams. It increases productivity because everyone has access to the same data and insights, making it easier to collaborate and work together to solve problems and keep things running smoothly.
Supports application performance monitoring
Most companies use a mix of on-premise systems, cloud environments, and container applications like Kubernetes. Observability offers a unified view of these distributed systems so operations teams get a direct view of everything from cloud resource utilization to system performance. Observability helps teams identify patterns and resolve problems quickly, like uncovering cloud latency or finding a problem in a Kubernetes cluster.
Better decision-making
Observability provides data-driven, actionable insights that empower teams to make informed decisions. Whether optimizing infrastructure, improving application performance, or enhancing customer experiences, an observability solution gives teams the information they need to make intelligent choices based on real-time data.
Improved customer experience
Businesses can link telemetry data such as metrics, logs, and traces with the overall business goals. For example, if an application is running slowly and causing delays for users, that problem could decrease sales numbers or increase the number of frustrated customers. With observability, teams can quickly identify the slowdown, fix it, and ensure the app doesn’t impact revenue or customer satisfaction.
Getting started with observability
Implementing observability might seem complicated, but it doesn't have to be. By taking a structured approach, you can benefit from the real-time insights it offers. Observability isn't just about using the right tools—it's about creating a strategy that aligns with your business goals and ensures your team knows how to act on the collected data.
The proper foundation is essential to make a unified observability platform work for your organization. This means assembling a dedicated team, choosing the right metrics, documenting everything correctly, and ensuring all teams can access and understand the data. A successful observability and monitoring framework enables teams to collaborate more effectively, identify and resolve issues faster, and continuously improve system performance.
To help you get started, we've outlined some essential steps below to guide your team.
Build your team
Start by forming a small group to own the observability and monitoring process. This team should create a strategy that fits your organization's goals and define the key metrics you'll need to monitor.
Pick the right metrics
Identify the most critical metrics for your systems—whether that's logs, traces, operational data, telemetry data, system interactions, or user behavior. Ensure you collect data from across your tech stack to comprehensively view your system's health.
Document everything
Documenting how your data is collected and shared is vital so everyone is on the same page. This step helps different teams collaborate easily, especially in larger organizations where teams may work independently.
Create your observability pipeline
Set up a process to funnel all data into a centralized platform. From there, you can route it to analytics tools or store it for later analysis, ensuring that the right people can act on the insights.
Train your team
Regular training keeps everyone up to speed. It’s essential to ensure everyone is comfortable with the data and knows how to use it to improve system performance.
By following these straightforward steps, you’ll be well on your way to creating a solid observability framework that helps you stay ahead of issues, keeps systems running smoothly, and delivers a better user experience.
Conclusion
Today’s systems operate quickly and in volume, making manual management impossible. This creates a challenge for identifying and resolving issues in real time, which impacts the ability to ensure consistent quality and reliability.
Implementing observability does more than speed up issue resolution; it establishes a resilient framework that grows with your systems. When a system is reliable, teams can focus on innovation rather than troubleshooting recurring issues. Observability can detect problems as they arise—or even before they become apparent—addressing the challenge of "unknown unknowns," where issues are hidden until they impact performance.
During software development, observability helps developers build applications that are easier to monitor and manage, with embedded metrics that provide real-time insights and even forecast potential issues.
Starting with a clear observability strategy and the right tools, your team is well-prepared for future challenges, ensuring systems run smoothly as your business grows. Going forward, observability will be essential for staying agile and equipped to handle the rapid changes in the tech landscape.
If interested, learn more about MongoDB’s database observatory tools to see if it’ll be a good fit for you, or your company!