As tech stacks and system architectures grow in size and complexity, DevOps, SRE, and IT teams have the increasingly demanding job of understanding and addressing issues in near real time across the myriad of tools and services that comprise their multicloud environments. Folks need to be able to monitor and act on different aspects of their system from performance availability to the user experience, and be able to remediate issues before they negatively affect the end user. Simple monitoring and troubleshooting has long lost the ability to keep up with the demand of these ever growing complex systems, and now observability has taken its place.
What is observability?
Observability is the practice of gaining insight into complex software systems by collecting, analyzing, and visualizing data outputs such as events, metrics, logs, and traces. It is essential for today’s distributed, multicloud systems. Every piece of hardware, service, container, tool, and microservice generates a record of activity. By observing these external outputs, developers and operations teams gain greater visibility into their dynamic systems to detect issues, troubleshoot problems, and make informed decisions when making changes or system improvements. In essence, observability is about knowing a house may catch on fire and taking steps to prevent it, while monitoring just points out that the house is on fire.
Traditional monitoring simply can’t manage anymore. Determining the root cause of an event amid distributed systems running thousands of processes across on-prem, private, and public clouds is practically impossible with all the disparities and interdependencies in a given system. On the other hand, observability works by collecting data from all parts of the system and allowing users to weave a complete narrative on system behavior for better performance, reliability, and user experiences.
How does troubleshooting and root cause analysis work in today’s systems? Developers and operations can collect and analyze data to quickly assess performance bottlenecks, troubleshoot, and make the appropriate changes. Potential security threats? System activity and user behavior can be tapped to identify potential threats and stop system interruptions. Only with observability can teams understand the cause and effect of events in their systems to ensure performance, reliability, and security.
What is the difference between monitoring and observability?
Although seemingly related, there are key differences between monitoring and observability — the former is passive and reactive while the latter is proactive in real time. Monitoring is the collection of data for tracking performance, availability, and other key metrics. It entails alerts and dashboards for specific metrics like CPU usage, network traffic, storage, etc. Monitoring is passive, forcing teams to wait for alert triggers before they take action.
Observability means collecting, analyzing, and visualizing data from various sources proactively to gain near real-time insight into system activity. More than monitoring key metrics, observability also includes collecting all data, from logs to events, to have the most complete picture in order to take the most effective action. In short, while monitoring focuses on specific metrics and alerts, observability focuses on gaining deeper understanding through a composite of entire systems, from cloud infrastructure to serverless functions.
Why is observability important?
Observability is imperative as complex systems have become commonplace. Complex distributed systems are composed of a higher number and variety of infrastructure, applications, and services. They are constantly being updated and changed, and every variable can become an issue. This dynamic nature of modern systems and applications means more instances of unknown or unmonitored problem areas. Observability ameliorates this by staying on top of new issues as they arise, irrespective of any predetermined monitoring or alerting processes.
Observability delivers value from DevOps to business operations. In DevOps, container services (e.g., Docker, Kubernetes, and others) and microservices break applications into smaller fragments for easy modification and redeployment. This can get cumbersome with interdependent microservices across nodes. Observability cuts through that complexity by exposing discrete aspects of an application to identify and resolve issues fast. For business, observability provides an indispensable view into the business impact of an organization’s digital infrastructure and services. It can measure user experience, help prioritize business decisions, and even boost conversions. Observability is the only way to discover problems before end users do and deliver the best experience with real-time system’s data.
What are potential challenges to observability?
Achieving observability is no easy feat, especially with the rapid adoption multicloud infrastructures and the cloud-native apps built on top of them. Cloud apps and infrastructure orchestrated on containers and microservices, generate large, varied sets of data at a higher rate. It can be overwhelming, oversaturating DevOps teams with information while impeding action.
There are also challenges that permeate all systems. Data silos are still common due to disparate data sources, monitoring tools, and interdependencies. And when processes like instrumentation and configuration are done manually, it is often highly dependent on institutional knowledge or tribal expertise, which exacerbates the problem.
How do you make your system observable?
Much like the systems of an organization have evolved, technical and organizational changes need to take place in a given business to keep up and minimize the possibility of incidents with the increase in complexity. Here’s what can be done to ensure you’re implementing observability effectively:
Clear objectives: If you’re building an e-commerce site, objectives might include maximizing conversions, minimizing cart abandonment, and minimizing page load times. Use these objectives to guide your observability efforts and ensure that you’re observing the right aspects of your infrastructure and services.
Make your system observable: Access to logs (structure and unstructured), metrics from infrastructure to services, tracing across workflows, and user data is key for observability to take place.
Tools and infrastructure: An organization looking to get the most out of observability needs the right tools, including tools for logging, monitoring, and event tracking.
Data collection and analysis: All data is good data, but focusing on specific areas can be helpful too. An organization may collect data on user behavior, node performance, containers, queries, network traffic, etc. This data should be analyzed in context to better troubleshoot and optimize system performance.
Data visualization: Dashboards, topological views, and more are useful for making sense of data. For instance, a dashboard that shows key metrics like active users, page load times, and orders processed can be useful for an e-commerce site. Views of node and container activity can translate into faster mean time to recovery (MTTR) for operations and DevOps teams.
How does observability help DevOps?
The right level of observability is invaluable in DevOps, as it enables teams to have granular yet comprehensive visibility into how systems are behaving.
- Infrastructure Monitoring: DevOps teams can track key metrics like CPU and memory usage, network latency, or disk utilization to proactively identify issues and optimize performance and uptime of their systems.
- Application Performance Monitoring (APM): Teams can track key performance metrics like response time, throughput, or error rates so they can identify bottlenecks and optimize application performance.
- Logging and Tracing: Teams can simply collect and analyze data related to user behavior, server performance, queries, or network traffic to identify patterns and trends for optimizing system performance or improve the user experience.
- Alerting and Notification: Teams can have smarter alerts when key metrics and events fall out of the normal range so they can take action before they become critical.
What are the benefits of observability?
When done right, observability provides a range of benefits for DevOps teams, including:
- Improved System Reliability: Teams can identify and address issues quickly, reducing downtime and improving reliability.
- Faster Meant Time to Recovery/Resolution (MTTR): Observability tools enable DevOps teams to better identify root cause and remediate it quickly, reducing downtime and minimizing the impact on users.
- Optimized System Performance: The right tools also enable DevOps teams to recognize and address system performance issues in order to optimize general performance and scalability.
- Better User Experience: With all the right data, DevOps teams can better understand how users interact with their application or service, identify pain points, and improve the user experience.
- Reduced Costs: Observability offers a lot of opportunities for cost savings through optimized system performance, less downtime, and better resource management.
What is the future of observability?
Distributed, Dynamic Environments Will Be the Norm
As organizations continue to move their applications to the cloud, adoption of cloud-native technologies like Kubernetes, serverless functions, and microservices will increase. DevOps teams will have to face new challenges inherent in the dynamic distributed systems.
Automation and Machine Learning
Machine learning and AI will play an increasingly important role in enabling teams to identify and address issues more quickly. Whether it is generative AI that helps with minimal prompting or algorithms that help identify patterns in trends, new smart technology will be necessary and welcomed.
