What is observability?
Observability refers to the ability to monitor, measure, and understand the state of a system or application by examining its outputs, logs, and performance metrics. In modern software systems and cloud computing, observability plays an increasingly crucial role in ensuring the reliability, performance, and security of applications and infrastructure.
The importance of observability has grown due to the increasing complexity of software systems, the rise of platform engineering as a discipline, the widespread adoption of microservices, and the growing reliance on distributed architectures.
observability absorbs and extends classic monitoring systems and helps teams identify the root cause of issues. It allows stakeholders to answer questions about their application and business, including forecasting and predictions about what could go wrong. A diverse collection of tools and technologies are in use, which leads to a large matrix of possible deployments. This has architectural consequences, so teams must understand how to set up their observability systems in a way that works for them.
What are the aspects and trends of observability?
Artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are increasingly used in observability platforms to provide automated anomaly detection, root cause analysis, and predictive insights. These technologies help reduce the time and effort needed to identify and address issues in complex systems.
Platform engineering
Observability empowers platform engineers by enabling them to move beyond flagging individual metrics and instead query and explore data comprehensively across all services. This expanded visibility uncovers crucial relationships and dependencies that traditional monitoring might miss, allowing teams to troubleshoot complex issues far more effectively and ensure all system components work together smoothly and stably. Through observability, platform engineering teams can cultivate not only a responsive but also a resilient platform, gaining the depth needed to identify, address, and prevent issues. This proactive approach significantly increases overall system reliability and supports the smooth, consistent operation of critical applications.
Hybrid and multicloud environments
As organizations increasingly adopt hybrid cloud and multicloud strategies, observability tools are required to provide a view of the entire infrastructure, regardless of where applications and services are deployed.
Edge devices
The future growth of edge devices, Internet of Things (IoT) devices, or other local computing devices will lead to new challenges in monitoring and managing these environments. They need to provide real-time insights and fast response times. This may involve creating lightweight agents for data collection, using edge-friendly data formats and protocols, and incorporating decentralized data processing and analysis techniques, still with robust security and privacy features in place.
Observability in DevOps
As observability becomes increasingly important for ensuring the reliability and performance of cloud-native applications, there is a greater focus on observability in the DevOps process. This includes the integration of observability tools into the DevOps toolchain, as well as the use of observability data to drive continuous improvement in application performance and reliability.
Increasing use of open source observability tools
Open source observability tools like Grafana, Jaeger, Kafka, OpenTelemetry, and Prometheus have become increasingly popular in recent years, and this trend is likely to continue. This is partly driven by the desire to reduce costs associated with proprietaryobservability tools and the flexibility and customization options offered by open-source tools.
Increasing adoption of cloud-native infrastructure
As more organizations adopt cloud-native infrastructure, the need for observability tools specifically designed for these environments will likely grow. With the increasing amount of data generated by cloud-native applications and infrastructure, ML and AI will become increasingly important in the cloud-native observability space. These technologies can help identify anomalies and performance issues before they impact end-users, enabling organizations to proactively address issues before they cause significant problems.
What is platform engineering?
What are the benefits of observability?
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 resolve them efficiently with deep insights into the behavior of a system.
Optimized performance
Identify areas for optimization, such as bottlenecks in the system or underutilized resources, allowing for more efficient resource allocation and improved performance.
Data-driven decision-making
Receive up-to-date system performance and behavior information, enabling data-driven decision making and continuous improvement.
How is observability different from monitoring?
Observability and monitoring are related concepts but have some key differences. observability refers to the ability to ask questions about your system by examining its behavior from the outside.
As more organizations adopt cloud-native infrastructure, the need for observability tools specifically designed for these environments is likely to grow. Cloud-native observability tools are designed to collect and analyze data from microservices, containers, and other cloud-native technologies and provide insights into system performance in these environments.
In a nutshell, cloud-native observability is a practice of monitoring, analyzing, and troubleshooting modern, cloud-native applications built using microservices architecture and deployed in containers or serverless environments. The cloud-native observability pillars typically include the following:
Metrics: Focused on collecting quantitative data about your Kubernetes environment and applications. Metrics can include data such as 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.
How can I resolve system issues with observability data?
Observability is critical for platform engineering, site reliability engineering (SRE), and DevOps because it ensures the reliable and efficient operation of systems. The importance of Observability lies in its ability to provide deep insights into the performance and behavior of a system, enabling proactive monitoring, troubleshooting, and optimization.
For a platform engineer, developer, operations team, or site reliability engineer, certain steps need to be taken to identify, analyze, and resolve issues in any software system using observability data. This is called a “debug journey.”
Where it arises from monitoring, alerts, or user-reported incidents, start the observability journey by detecting the issue.
Once detected, the team must determine the severity and prioritize it. This triage process involves assessing the impact on users, systems, and overall performance.
With prioritized items, investigate the collected observability data to identify patterns and correlations.
After identifying potential correlations and patterns, the team dives deeper into the data to find the root cause of the issue.
With the cause identified, the fix can be implemented with a code change, hotfix, or infrastructure adjustment, and the team continues to monitor the system to see if the resolution is good.
Observability for platform engineering, DevOps, and SRE requires a combination of tools, processes, and expertise to effectively monitor, troubleshoot, and optimize systems, and it plays a critical role in enabling businesses to deliver high-quality digital services to their customers. Red Hat OpenShift Observability can provide the information needed to develop a system baseline, and then monitor and alert on deviations from that baseline, giving the capability for reduced Mean Time To Detection (MTTD) and Mean Time To Resolution (MTTR).
Why choose Red Hat for Observability?
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.
An enterprise application platform with a unified set of tested services for bringing apps to market on your choice of infrastructure.
Red Hat Advanced Cluster Management for Kubernetes includes capabilities that unify multicluster management, provide policy-based governance, extend application life-cycle management, and proactive cluster health and performance monitoring.
Red Hat Insights continuously analyzes platforms and applications to predict risk, recommend actions, and track costs so enterprises can better manage hybrid cloud environments.
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