There are several advantages to cloud platforms over traditional server-based infrastructures and processing capabilities. However, cloud computing frequently falls short in one crucial area. In serverless cloud settings, traditional observability technologies don’t perform well, if at all. This indicates that cloud native observability is a must for any open-source cloud computing solution.
In this blog we are going to cover:
- What is observability?
- Observability Vs Monitoring
- Why do we need observability?
- How does observability work?
- Benefits of observability
- Conclusion
What is observability?
The capacity to discern the condition of a complicated system based on its outputs is known as observability. The capacity to log, monitor, and trace different servers, application processes, data processes, and hardware activities are required for computer observability.
IT infrastructure is made up of hardware and software components that create records of every system activity automatically. Application logs, system logs, security logs, and a variety of other sorts of data are used to track anything from system sign-ins to security risks and occurrences. The capacity of monitoring and analyzing, that drives observability and generates actionable insights, rather than the event logs themselves, is the key to establishing genuine observability of IT infrastructure and cloud computing systems. IT firms can use observability platform software products to automate event log gathering and analysis.
Observability Vs Monitoring
Observability is often used synonymously with monitoring, but monitoring is only one of the subcategories of cloud-native observability and does not do justice to its scope. Observability and monitoring are complementary, with each having a distinct function.
Monitoring alerts you when something isn’t working well, whereas observability helps you figure out why. Monitoring is a subset of observability and a crucial task. Only visible systems can be monitored.
Monitoring keeps track of an application’s overall health. It compiles information on the system’s performance in terms of access speeds, connectivity, downtime, and bottlenecks. Observability, on the other hand, delves deeper into the “what” and “why” of application operations by offering granular and contextual information into failure modes.
Learn More About Monitoring
Why do we need observability?
It’s crucial to be observable since it provides you with more control over-complicated systems. Simple systems are easier to handle since they have fewer moving components. Monitoring CPU, memory, databases, and networking conditions are frequently sufficient to comprehend these systems and apply the proper solution to a problem.
Because distributed systems have a greater number of interconnected pieces, the number and types of failures that might occur are also greater. Furthermore, distributed systems are updated on a regular basis, and each modification might result in a new sort of failure. Understanding a present problem in a distributed context is a huge difficulty, partly because it creates more “unknown unknowns” than simpler systems. Monitoring typically fails to appropriately address problems in these complex systems because it requires “known unknowns.”
Because it allows you to ask questions about your system’s behavior when difficulties emerge, observability is better suited to the unpredictability of distributed systems. Observability can answer queries like “Why is X broken?” and “What is causing delay right now?”
How does observability work?
By integrating with existing instrumentation embedded into application and infrastructure components, as well as offering tools to add instrumentation to these components, observability platforms continually identify and gather performance telemetry. It can be divided into three primary pillars:
Source: School Of SRE
Logs: Logs are timestamped, comprehensive, and immutable recordings of application events that are granular, timestamped, and complete. Logs may be used to build a high-fidelity, millisecond-by-millisecond record of each occurrence, replete with context, that developers can ‘play back’ for troubleshooting and debugging reasons, among other things.
Figure: ELK Architecture (Source: http://elastic-stack.readthedocs.io)
Metrics: Metrics (also known as time series metrics) are basic indicators of application and system health across time, such as how much memory or CPU capacity an application consumes in a five-minute period or how much delay an application encounters during a surge in demand.
Traces: Traces record the end-to-end ‘journey’ of every user request, from the UI or mobile app through the entire distributed architecture and back to the user.
Following the collection of telemetry, the platform correlates it in real-time to provide DevOps teams, site reliability engineering (SRE) teams, and IT staff with complete, contextual information – the what, where, and why of any event that could indicate, cause, or be used to address an application performance issue.
Benefits of observability
The underlying benefit of it is that assuming all other factors are equal, a more observable system is simpler to understand, monitor, update with new code, and fix than a less visible one. More precisely, observability helps a business achieve its Agile/DevOps/SRE goals of providing higher-quality software quicker by allowing it to:
Discover and address unknown issues: One of the most significant limitations of monitoring systems is that they only look for ‘known unknowns,’ or unusual circumstances that you are already aware of. Observability identifies circumstances you would not be aware of or think to search for, then monitors their link to specific performance concerns, providing context for root cause identification and resolution.
Catch and resolve issues early in development: Monitoring is baked into the early stages of the software development process using observability. DevOps teams can spot and repair bugs in new code before they affect the user experience or service level agreements (SLAs).
Scale observability automatically: You may, for example, set instrumentation and data aggregation as part of a Kubernetes cluster configuration and begin collecting telemetry from the time it starts spinning up until it stops spinning up.
Enable automated remediation and self-healing application infrastructure: Combining observability with AIOps machine learning and automation skills allows you to foresee and fix issues based on system outputs without the need for management participation.
Conclusion
Response time is critical to any business, as latency or reliability issues in end-user experience can significantly impact brand reputation and adoption, thereby leading to a loss in revenue. While traditional monitoring may suffice in a relatively simple and limited-service environment, tracing is required in highly distributed and sophisticated systems. Wherever observability is carefully incorporated into the software, it provides a comprehensive capacity for exploration and root-cause analysis in a live system, enhancing troubleshooting capabilities and expediting issue discovery and resolution, more frequently proactively. As a result of the built-in observability, the program becomes more durable over time.
Related/References
- KCNA Certification Exam (Kubernetes and Cloud Native Associate)
- Kubernetes and Cloud Native Associate (KCNA): Step-by-Step Activity Guide (Hands-on Lab)
- GitOps: Everything You Need To Know
- Containers for Beginners: What, Why and Types
- Kubernetes for Beginners – A Complete Beginners Guide
- Kubernetes Architecture | An Introduction to Kubernetes Components
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