What is Observability?
Observability provides proactive insights, automated analytics, and actionable intelligence from collected data.
What is Observability?
Observability Definition
Observability is the ability to provide insights, automated analytics, and actionable intelligence through the application of cross-domain data correlation, machine learning (ML), and AIOps across massive real-time and historical metrics, logs, and trace data.
However, the concept of observability isn’t something new—it comes from control theory in engineering. It deals with automating the control of a dynamic system (such as a car, airplane, or oil pipeline), so its dynamicity can be maintained at the desired level—based on its feedback signal. A system is said to be “observable” if its current state can be estimated from its external outputs only—not its composition or architecture—the more granular the external outputs, the better the system’s observability.
Observability tools are designed to collect and aggregate as many metrics as possible from each system component, including infrastructure, applications, serverless services, middleware, and databases, to provide comprehensive views into the internal states of a system at the most critical point: when data is sent to another system for processing and usage.
The three pillars of observability
Understanding how observability works can be explained in more detail by examining the telemetry data upon which it’s built, referred to as the three pillars of observability. When combined, these insights offer business and IT leaders a blueprint for developing a modern approach to systems management. These pillars act as the glue allowing each one to provide near-infinite layers of real-time insight into the broader spectrum of system performance across the entirety of the enterprise.
1. Performance metrics
Metrics are usually (but not always) time series numerical data designed to be calculated, aggregated, or averaged. Nothing in the business world can define success as much as powerful metrics. Businesses now apply metrics to almost everything they do, spotting trends at the onset to help determine the best course of action.
While most monitoring tools can collect metrics from popular platforms and systems to report on trends or anomalies over time, they often provide limited insights when something is broken.
With an observability solution, metrics can now provide critical data for building responses by measuring precise system performance values. Observability offers the hard facts regarding items such as service-level indicators, latency, and downtime values. The metrics built from these system data points present organizations with actionable visualizations of overall or specific system performance to help stay a step ahead of emerging problem spots or performance bottlenecks.
2. Gathering and analysis of logs
Logs are detailed records of events from every piece of software, user action, and network activity. Logs are a tried and proven way of obtaining valuable information regarding system health.
For example, an event can be a microservice performing a single database transaction. Underneath this event, there would be multiple components emitting and recording their own log messages. The API making the call to the microservice will log its call. The microservice code could send custom status messages to the programmatic log handler when it performs the operation. The container service, such as Kubernetes or Docker, would also have its own log, and so would the VM’s OS running the container, in the form of Syslog. The network, too, will have its own flow logs. And finally, the database engine will record the transaction along with the access information.
What all these logs give you are time-stamped, immutable, step-by-step records of every event a component sees. Along with this detailed information, logs contain valuable metadata. To have an observable system, each of these logs will have to be collected and correlated to the event. However, logs alone can’t give a complete picture of system performance.
Instead of the time-consuming tasks of digging into logs on a per-system basis, an observability solution is designed to centralize event and log data alongside other performance insights giving teams the ability to gain visibility across the entire enterprise. It can also catalog logs for future analysis or have them invoke specific alert tasks for predetermined events. This greatly increases response times, allowing teams to develop proactive solutions to solve and prevent the same issues from recurring.
3. Traces
Traces record the end-to-end journey of every call made within a distributed system architecture during a unit of work or transaction. A trace will clearly show every touchpoint the transaction interacted with during its course of action. In other words, the trace records every call made to fulfill the request, the chain of calls from one touchpoint to another, the times of the calls, and the latency between each hop.
Tracing issues to determine the root cause can be a frustrating, manual task in distributed networks. And as networks have extended to the cloud, the Edge, and the Internet of Things, it’s gotten worse. There are so many more routes into, out of, and through your infrastructure than a few years ago.
However, observability introduces the ability to centralize these tasks for rapid tracing execution. Called distributed tracing (or distributed request tracing), it can reach across the enterprise to give domain- and system-agnostic visibility into system functions.
An observability solution equips IT organizations with the framework needed to pinpoint failures quickly across applications, networks, and systems. It provides a one-stop console for continuously monitoring impacted systems until resolution is achieved, which is pivotal in giving IT operations (Ops) the capability to ensure service delivery while the end-user experience remains unaffected.
Observability vs. monitoring: what's the difference?
The core comparison between observability and monitoring starts with this crucial fact: monitoring is reactionary, and observability is based on proactive response.
Monitoring: Monitoring is the systematic process of collecting and analyzing information, such as logs and performance metrics. Monitoring tools help track errors, identify issues, and send alerts and notifications. Monitoring helps teams understand the current state of infrastructure and applications.
Observability: Observability goes beyond monitoring and helps expedite problem resolution by providing actionable insights. An observability strategy will dig deeper into the “what” of occurrences to reveal the “why” (root cause) happening behind the scenes. These actionable insights are built to be highly accurate based on holistic data performance.
Most enterprises have some form of continuous monitoring for their environment, which usually involves watching and alerting on a set of metrics across hardware and software components. And when a metric value goes above a set threshold, an alert fires. The Ops team looks at the alarm and investigates the underlying root cause.
Now, this is a kind of observability in itself. The system exposes the metrics as its external output, and the monitoring tool observes it. But this is as far as the analogy goes. It’s not full observability. Why? Remember, the Ops team must investigate the root cause of the metric value crossing the threshold. They know something went wrong but must do all the legwork by digging deeper. This may involve looking at other metrics and correlating them or running diagnostic commands in system consoles. In other words, a monitoring solution tells you something isn’t right yet can’t tell you why it isn’t right. A fully configured observability solution can help prevent this extra work.
Observability vs. APM vs. monitoring
How does observability differ from application performance management (APM) and monitoring? In this video, learn how observability can assist with deepening your understanding of the holistic health of your environment beyond the application stack and network and what this deeper visibility can help improve.
Why is observability important?
As organizations of all sizes progress their digital transformation initiatives and modernize applications, they still need to manage their complex, diverse, and distributed network, cloud, system, application, and database infrastructures.
It’s essential for teams to have visibility across the full IT stack for improved and effective analysis and troubleshooting. In support of these initiatives, the importance of observability is seen in its primary strength: to enable organizations to move from reactive to proactive postures by providing unified insights from across their IT ecosystems.
You can learn more about how observability can help transform your organization by downloading this free eBook.
Common challenges observability can help solve
The diversification of IT systems has illuminated the gaps within monitoring as traditional monitoring solutions are built to capture infrastructure and application telemetry data while providing metrics around uptime and downtime issues. These monitoring tools typically can’t aggregate data input from multiple dashboards or existing instrumentation, making them ineffective as a comprehensive monitoring system. This can lead to various teams implementing their own monitoring and infrastructure management tools to handle specific IT issues and requirements. When these individual divisions, departments, and IT teams across an organization have single-solution tools, not only can it exaggerate work silos, but it can also further strain budgets.
Limited visibility creating work silos
The partitioning of tools across an organization can create a reliance on disparate (and often duplicate) tooling in which the data is unable to be easily viewed or analyzed in relation to each other. This ultimately creates working silos between departments, process overload, and an overall lack of escalation visibility or coordinated prioritization.
Multiple tools increasing IT operational costs
Toolset creep can lead to insufficient visibility over enterprise assets and introduce potentially costly business risks through performance and hygiene gaps. Without a central source of truth, common activities like enterprise resource planning (ERP) can require laborious manual tasks to gain any meaningful insights. This makes it difficult to quickly map asset-to-service dependencies with accuracy or speed, which can affect overall business value.
Inefficient workflows leading to poor service delivery
The astounding flood of telemetry data and notifications generated by having numerous systems-monitoring tools is often overwhelming and can affect the ability to distill actionable insights. The network, cloud, system, application, and database dynamics can create challenges in understanding asset-to-service dependencies, assessing baselines, and meeting service-level objectives (SLOs). Less connected insights can make it difficult to identify and resolve problems effectively.
The complications of putting together the necessary logging and forensics can also make incident response management a nightmare. False positives can’t be investigated correctly, and the inability to quickly solve issues leads to issue and alert fatigue. This makes it nearly impossible to predict problems or determine the proper system capacity scaling, causing unpredictable performance bottlenecks, outages, and poor customer experiences.
Benefits of using an observability solution
The importance of an observability solution is seen in its primary purpose: to enable organizations to move from reactive to proactive postures by providing unified insights from across their IT ecosystems.
Deeper insights for improved collaboration
Having a single pane of glass for multiple teams across the enterprise can help organizations develop solutions and maintain system readiness more holistically. Your developers and software engineers can see the insights needed from the same platform your Ops team will use. Your SecOps can check the logs from the same observation solution also used by DevOps and site reliability engineers (SREs).
And an observability solution can assist with breaking down operational silos while helping end shadow IT operations by allowing organizations to explore their infrastructure from one seamless platform. This presents new opportunities for cross-team collaboration to resolve issues and improve service delivery, ultimately lowering risk factors for the business.
Optimize costs
An observability solution can provide a path out of the diminishing returns caused by using multiple monitoring tools brought on to solve specific performance issues by providing a comprehensive, integrative approach to optimizing infrastructure management.
A unified observability platform can help reduce total cost of ownership by consolidating the number of tools needed to manage all the systems within a distributed network. And implementing an observability solution built to grow with and offer flexibility throughout your digital transformation and cloud migration journeys can result in significant cost savings and faster ROI by turning data deluge into business value.
Streamline processes
With observability, workflows focused on optimizing system performance become smoother and easier to manage. The influx of automation options, including analytics, systems management, and troubleshooting, can also dramatically evolve day-to-day operations.
An observability solution can provide enterprises with a centralized dashboard view across complex distributed systems. This is one of the core advantages of observability: the ability to eliminate blind spots in IT infrastructure while bolstering incident responsiveness. With full-stack observability, you can easily pinpoint errors—letting teams concentrate on fixing, then proactively implementing automated steps to remediate the issue instead of merely finding them.
What is Observability?
Observability Definition
Observability is the ability to provide insights, automated analytics, and actionable intelligence through the application of cross-domain data correlation, machine learning (ML), and AIOps across massive real-time and historical metrics, logs, and trace data.
However, the concept of observability isn’t something new—it comes from control theory in engineering. It deals with automating the control of a dynamic system (such as a car, airplane, or oil pipeline), so its dynamicity can be maintained at the desired level—based on its feedback signal. A system is said to be “observable” if its current state can be estimated from its external outputs only—not its composition or architecture—the more granular the external outputs, the better the system’s observability.
Observability tools are designed to collect and aggregate as many metrics as possible from each system component, including infrastructure, applications, serverless services, middleware, and databases, to provide comprehensive views into the internal states of a system at the most critical point: when data is sent to another system for processing and usage.
The three pillars of observability
Understanding how observability works can be explained in more detail by examining the telemetry data upon which it’s built, referred to as the three pillars of observability. When combined, these insights offer business and IT leaders a blueprint for developing a modern approach to systems management. These pillars act as the glue allowing each one to provide near-infinite layers of real-time insight into the broader spectrum of system performance across the entirety of the enterprise.
1. Performance metrics
Metrics are usually (but not always) time series numerical data designed to be calculated, aggregated, or averaged. Nothing in the business world can define success as much as powerful metrics. Businesses now apply metrics to almost everything they do, spotting trends at the onset to help determine the best course of action.
While most monitoring tools can collect metrics from popular platforms and systems to report on trends or anomalies over time, they often provide limited insights when something is broken.
With an observability solution, metrics can now provide critical data for building responses by measuring precise system performance values. Observability offers the hard facts regarding items such as service-level indicators, latency, and downtime values. The metrics built from these system data points present organizations with actionable visualizations of overall or specific system performance to help stay a step ahead of emerging problem spots or performance bottlenecks.
2. Gathering and analysis of logs
Logs are detailed records of events from every piece of software, user action, and network activity. Logs are a tried and proven way of obtaining valuable information regarding system health.
For example, an event can be a microservice performing a single database transaction. Underneath this event, there would be multiple components emitting and recording their own log messages. The API making the call to the microservice will log its call. The microservice code could send custom status messages to the programmatic log handler when it performs the operation. The container service, such as Kubernetes or Docker, would also have its own log, and so would the VM’s OS running the container, in the form of Syslog. The network, too, will have its own flow logs. And finally, the database engine will record the transaction along with the access information.
What all these logs give you are time-stamped, immutable, step-by-step records of every event a component sees. Along with this detailed information, logs contain valuable metadata. To have an observable system, each of these logs will have to be collected and correlated to the event. However, logs alone can’t give a complete picture of system performance.
Instead of the time-consuming tasks of digging into logs on a per-system basis, an observability solution is designed to centralize event and log data alongside other performance insights giving teams the ability to gain visibility across the entire enterprise. It can also catalog logs for future analysis or have them invoke specific alert tasks for predetermined events. This greatly increases response times, allowing teams to develop proactive solutions to solve and prevent the same issues from recurring.
3. Traces
Traces record the end-to-end journey of every call made within a distributed system architecture during a unit of work or transaction. A trace will clearly show every touchpoint the transaction interacted with during its course of action. In other words, the trace records every call made to fulfill the request, the chain of calls from one touchpoint to another, the times of the calls, and the latency between each hop.
Tracing issues to determine the root cause can be a frustrating, manual task in distributed networks. And as networks have extended to the cloud, the Edge, and the Internet of Things, it’s gotten worse. There are so many more routes into, out of, and through your infrastructure than a few years ago.
However, observability introduces the ability to centralize these tasks for rapid tracing execution. Called distributed tracing (or distributed request tracing), it can reach across the enterprise to give domain- and system-agnostic visibility into system functions.
An observability solution equips IT organizations with the framework needed to pinpoint failures quickly across applications, networks, and systems. It provides a one-stop console for continuously monitoring impacted systems until resolution is achieved, which is pivotal in giving IT operations (Ops) the capability to ensure service delivery while the end-user experience remains unaffected.
Observability vs. monitoring: what's the difference?
The core comparison between observability and monitoring starts with this crucial fact: monitoring is reactionary, and observability is based on proactive response.
Monitoring: Monitoring is the systematic process of collecting and analyzing information, such as logs and performance metrics. Monitoring tools help track errors, identify issues, and send alerts and notifications. Monitoring helps teams understand the current state of infrastructure and applications.
Observability: Observability goes beyond monitoring and helps expedite problem resolution by providing actionable insights. An observability strategy will dig deeper into the “what” of occurrences to reveal the “why” (root cause) happening behind the scenes. These actionable insights are built to be highly accurate based on holistic data performance.
Most enterprises have some form of continuous monitoring for their environment, which usually involves watching and alerting on a set of metrics across hardware and software components. And when a metric value goes above a set threshold, an alert fires. The Ops team looks at the alarm and investigates the underlying root cause.
Now, this is a kind of observability in itself. The system exposes the metrics as its external output, and the monitoring tool observes it. But this is as far as the analogy goes. It’s not full observability. Why? Remember, the Ops team must investigate the root cause of the metric value crossing the threshold. They know something went wrong but must do all the legwork by digging deeper. This may involve looking at other metrics and correlating them or running diagnostic commands in system consoles. In other words, a monitoring solution tells you something isn’t right yet can’t tell you why it isn’t right. A fully configured observability solution can help prevent this extra work.
Observability vs. APM vs. monitoring
How does observability differ from application performance management (APM) and monitoring? In this video, learn how observability can assist with deepening your understanding of the holistic health of your environment beyond the application stack and network and what this deeper visibility can help improve.
Why is observability important?
As organizations of all sizes progress their digital transformation initiatives and modernize applications, they still need to manage their complex, diverse, and distributed network, cloud, system, application, and database infrastructures.
It’s essential for teams to have visibility across the full IT stack for improved and effective analysis and troubleshooting. In support of these initiatives, the importance of observability is seen in its primary strength: to enable organizations to move from reactive to proactive postures by providing unified insights from across their IT ecosystems.
You can learn more about how observability can help transform your organization by downloading this free eBook.
Common challenges observability can help solve
The diversification of IT systems has illuminated the gaps within monitoring as traditional monitoring solutions are built to capture infrastructure and application telemetry data while providing metrics around uptime and downtime issues. These monitoring tools typically can’t aggregate data input from multiple dashboards or existing instrumentation, making them ineffective as a comprehensive monitoring system. This can lead to various teams implementing their own monitoring and infrastructure management tools to handle specific IT issues and requirements. When these individual divisions, departments, and IT teams across an organization have single-solution tools, not only can it exaggerate work silos, but it can also further strain budgets.
Limited visibility creating work silos
The partitioning of tools across an organization can create a reliance on disparate (and often duplicate) tooling in which the data is unable to be easily viewed or analyzed in relation to each other. This ultimately creates working silos between departments, process overload, and an overall lack of escalation visibility or coordinated prioritization.
Multiple tools increasing IT operational costs
Toolset creep can lead to insufficient visibility over enterprise assets and introduce potentially costly business risks through performance and hygiene gaps. Without a central source of truth, common activities like enterprise resource planning (ERP) can require laborious manual tasks to gain any meaningful insights. This makes it difficult to quickly map asset-to-service dependencies with accuracy or speed, which can affect overall business value.
Inefficient workflows leading to poor service delivery
The astounding flood of telemetry data and notifications generated by having numerous systems-monitoring tools is often overwhelming and can affect the ability to distill actionable insights. The network, cloud, system, application, and database dynamics can create challenges in understanding asset-to-service dependencies, assessing baselines, and meeting service-level objectives (SLOs). Less connected insights can make it difficult to identify and resolve problems effectively.
The complications of putting together the necessary logging and forensics can also make incident response management a nightmare. False positives can’t be investigated correctly, and the inability to quickly solve issues leads to issue and alert fatigue. This makes it nearly impossible to predict problems or determine the proper system capacity scaling, causing unpredictable performance bottlenecks, outages, and poor customer experiences.
Benefits of using an observability solution
The importance of an observability solution is seen in its primary purpose: to enable organizations to move from reactive to proactive postures by providing unified insights from across their IT ecosystems.
Deeper insights for improved collaboration
Having a single pane of glass for multiple teams across the enterprise can help organizations develop solutions and maintain system readiness more holistically. Your developers and software engineers can see the insights needed from the same platform your Ops team will use. Your SecOps can check the logs from the same observation solution also used by DevOps and site reliability engineers (SREs).
And an observability solution can assist with breaking down operational silos while helping end shadow IT operations by allowing organizations to explore their infrastructure from one seamless platform. This presents new opportunities for cross-team collaboration to resolve issues and improve service delivery, ultimately lowering risk factors for the business.
Optimize costs
An observability solution can provide a path out of the diminishing returns caused by using multiple monitoring tools brought on to solve specific performance issues by providing a comprehensive, integrative approach to optimizing infrastructure management.
A unified observability platform can help reduce total cost of ownership by consolidating the number of tools needed to manage all the systems within a distributed network. And implementing an observability solution built to grow with and offer flexibility throughout your digital transformation and cloud migration journeys can result in significant cost savings and faster ROI by turning data deluge into business value.
Streamline processes
With observability, workflows focused on optimizing system performance become smoother and easier to manage. The influx of automation options, including analytics, systems management, and troubleshooting, can also dramatically evolve day-to-day operations.
An observability solution can provide enterprises with a centralized dashboard view across complex distributed systems. This is one of the core advantages of observability: the ability to eliminate blind spots in IT infrastructure while bolstering incident responsiveness. With full-stack observability, you can easily pinpoint errors—letting teams concentrate on fixing, then proactively implementing automated steps to remediate the issue instead of merely finding them.
Visualize, observe, remediate, and automate your environment with a solution built to ensure availability and drive actionable insights.