What is observability?
Observability is the ability to understand and react to the state of an IT system or application. Like monitoring, observability relies on outputs, logs, and performance metrics. But in contrast to monitoring alone, observability can help you proactively apply those metrics to troubleshoot and optimize your systems and applications. For example, by observing system events, automation tools can respond to issues as they arise to help keep systems efficient and stable.
What are the benefits of observability?
As software systems have grown more complex, so has the challenge of handling the growing streams of outputs and metrics. Observability absorbs and extends classic monitoring systems and helps teams identify the root cause of issues. It allows stakeholders to answer questions about their applications and businesses, and to make predictions.
Observability has grown in popularity alongside other computing trends, including the widespread adoption of microservices, the growing reliance on distributed architectures, and the rise of platform engineering as a discipline.
In modern software systems and cloud computing, observability supports reliability, performance, and security. Some of the benefits of observability include:
Improved reliability
Detect and resolve issues before they escalate, minimizing downtime and ensuring that systems remain available to users.
Efficient troubleshooting
Quickly identify the root cause of issues and efficiently resolve them with insights into a system’s behavior.
Optimized performance
Identify areas for optimization, such as bottlenecks in the system or underused resources, allowing for more efficient resource allocation and improved performance.
Data-driven decision-making
Receive up-to-date system performance and behavior information, supporting data-driven decision making and continuous improvement.
There is no 1 standard way to implement observability. With so many different tools and technologies in use, an observability strategy requires bringing together your choice of tools in specific ways that work for you.
What is platform engineering?
What are the challenges in putting observability to work?
Even with an observability strategy designed for your needs, organizational challenges can make it difficult to realize the benefits. Some common headwinds you might encounter can include:
Complexity
As IT environments continue to grow to include more components, the number of interactions among systems increases exponentially. This makes it difficult to predict how changes to 1 part of the system will affect others, complicating the task of maintaining reliability. Details matter, and sourcing properly labeled data and metadata can be challenging in complex environments.
Disconnected teams
Team organization is another significant challenge. Any given system might not have a single, direct owner. Cross-team collaboration is essential for turning observability insights into effective actions. Data needs to be accessible, and it needs to flow to the systems where the right teams can use it for analysis.
Rapid change
There’s always something new when it comes to observability and effective systems management. Teams require ongoing training to stay current on practices, tools, and technologies. This can be time-consuming, expensive, or both.
Technology and tool sprawl
As platforms, tools, and vendors change, old applications and infrastructures are inevitable. This can create gaps in efficiency, skills, and security. Collaboration becomes difficult without standardized observability tools and practices.
What is cloud-native observability?
As more organizations adopt cloud-native infrastructure, teams are seeing a growing need for observability built for these environments. Cloud-native observability is the practice of monitoring, analyzing, and troubleshooting modern, cloud-native applications built using microservices architecture and deployed in containers or serverless environments.
Cloud-native observability tools are designed to collect and analyze data from all these cloud-native technologies and provide insights into system performance in these environments.
The cloud-native observability pillars typically include:
Metrics: Focused on collecting quantitative data about your Kubernetes environment and applications. Metrics can include data such as central processing unit (CPU) and memory usage, network traffic, and request latencies. Kubernetes provides a number of built-in metrics, but you may also need to use additional tools or libraries to collect more detailed metrics.
Logs: Focused on collecting and analyzing log data from your Kubernetes environment and applications. Logs can provide valuable insights into the behavior of your applications and can be used to troubleshoot issues, identify performance bottlenecks, and detect security threats.
Traces: Focused on collecting data about the execution of requests or transactions across your Kubernetes environment and applications. Traces can help you understand how requests or transactions are processed by your applications, identify performance issues, and optimize your application's performance.
Events: Focused on collecting data about important events that occur within your Kubernetes environment, such as application deployments, scaling events, and errors. Events can help you monitor the health of your Kubernetes environment and quickly respond to issues as they arise.
Observability and event-driven automation
Event-driven automation is the capability to respond to changing conditions in an IT environment through appropriate actions without manual intervention.
Events are detectable changes in operating conditions that are significant to managing IT infrastructure or delivering an IT service. Observability tools can help identify events that indicate the changes of state in applications, hardware, software, cloud instances, or other technologies.
Once an observability system detects an event, automation tools can execute the appropriate actions to address or remediate it. Automation can help you get more out of existing tools by taking actions based on their observability data. For example, you can use observability tools to combine capacity and performance metrics with event-driven automation to automatically provision containers, cloud infrastructure, virtual machines, and other technologies when they’re needed.
Events from application workloads can trigger actions for better productivity. For example, development teams can automatically run hardening and compliance checks when code is checked in. Teams can flexibly craft these automation scenarios by picking the alert that triggers a response and designing the actions to take.
Information technology service management (ITSM) tasks—including ticket enhancement and remediation such as service restarts and certificate rotation—are ideal starting points, but event-driven automation is flexible enough to handle many tasks across IT environments.
Red Hat® Ansible® Automation Platform includes Event-Driven Ansible, which can support artificial intelligence for IT operations (AIOps) and integrate with platforms such as Splunk, Dynatrace, IBM Instana, ITSM solutions, and many others.
How do I use observability to resolve system issues?
Observability is critical for platform engineering, site reliability engineering (SRE), and DevOps as a way to support reliable and efficient systems.
A “debug journey” starts when teams identify, analyze, and resolve issues in a system using observability data. The process begins with detecting the issue using monitoring, alerts, or user-reported incidents.
Once detected, teams determine the severity of the issue and prioritize it. This triage process involves assessing the impact on users, systems, and overall performance.
With these prioritized items, teams use observability data to investigate and identify patterns and correlations. After identifying potential correlations and patterns, teams dive deeper into the data to find the root cause of the issue.
With the root cause identified, teams can implement a fix. That could take the form of a code change, hotfix, or infrastructure adjustment. Finally, teams monitor the system to see if the resolution is effective.
Observability for platform engineering, DevOps, and SRE plays a critical role in businesses delivering high-quality digital services to their customers.
Red Hat OpenShift® Observability can provide the information needed to develop a system baseline and then alert on deviations from that baseline, helping reduce mean time to detection (MTTD) and mean time to resolution (MTTR).
How does observability support other IT trends?
AIOps
Observability plays a role in supporting AIOps, an approach that combines AI-driven insights with automated remediation. Observability platforms gather operational data, and machine learning algorithms identify patterns and anomalies. You can then connect these insights to an automation tool like Ansible Automation Platform to resolve issues. Organizations can automatically remediate issues as they’re detected, shortening MTTR, reducing manual intervention, and freeing IT teams to focus on higher-priority work.
Platform engineering
Platform engineering is a discipline within software development that focuses on improving productivity, application cycle time, and speed to market. Observability helps platform engineers query and explore data comprehensively across all services, rather than focusing on 1 individual metric at a time. Thanks to this expanded visibility, teams can troubleshoot complex issues more effectively and ensure all system components work together smoothly and stably.
Hybrid and multicloud environments
As organizations increasingly adopt hybrid cloud and multicloud strategies, they can deploy applications across many different kinds of infrastructure and take advantage of additional flexibility. Observability tools can provide a view of the entire infrastructure, regardless of where applications and services are deployed.
Edge devices
The growth of edge, Internet of Things (IoT) , and other local computing devices creates new challenges in monitoring and managing these environments. Observability for edge devices may involve creating lightweight agents for data collection, using edge-friendly data formats and protocols, and incorporating decentralized data processing and analysis techniques, while maintaining effective security and privacy controls.
DevOps
DevOps processes rely on observability to ensure the reliability and performance of cloud-native applications. This includes integrating observability tools into the DevOps toolchain, as well as using observability data to support continuous improvements in application performance and reliability.
Open source tools
Much of the observability ecosystem is built around open technologies. Open source observability tools including Grafana, Jaeger, Kafka, OpenTelemetry, and Prometheus have wide adoption. These tools provide cost advantages as well as flexibility, customization, and integrations with other tools.
Video: Red Hat OpenShift Observability across the Hybrid Cloud (1:47)
Why choose Red Hat for observability?
Red Hat’s portfolio of solutions includes support for your team’s observability strategy across any platform.
Red Hat OpenShift Observability solves modern architectural complexity by connecting observability tools and technologies to create a unified observability experience. The platform is designed to provide real-time visibility, monitoring, and analysis of various system metrics, logs, traces, and events to help users quickly diagnose and troubleshoot issues before they impact their applications or end users.
Other Red Hat products that can help you succeed in implementing a sound observability strategy include:
Red Hat OpenShift: An enterprise application platform with a unified set of tested services for bringing applications to market on your choice of infrastructure.
Red Hat Ansible Automation Platform: A trusted, versatile enterprise automation solution that can respond to observability data and orchestrate your entire IT estate with 1 platform.
Red Hat Advanced Cluster Management for Kubernetes: A collection of capabilities that unify multicluster management, provide policy-based governance, application lifecycle management, and proactive cluster health and performance monitoring.
Red Hat Lightspeed: An end-to-end system management tool that provides AI-powered guidance across Red Hat platforms, so you can better manage your hybrid cloud environments.
Observability and Event-Driven Ansible
Event-driven automation works with existing observability tools to respond to issues in an automated way. Learn more about Event-Driven Ansible today.
