- In control systems engineering, observability is defined as a measurement of how well a system’s internal states could be inferred from its external outputs.
- Meaning a system is considered observable if we can determine the behavior of the entire system in a finite time period from the system output. A system whose output doesn’t generate enough data to determine its behavior is considered to be unobservable
- This concept and terminology is now applied to distributed systems and cloud computing
- In the context of IT, it is the ability to collect external and internal state data about hardware and software to answer questions about systems behavior. Teams can leverage this data to investigate anomalies, engage in observability-driven development, and improve system performance and up-time
- IT system consists of hardware and software components which generates application logs, system logs, network logs and several other logs and data.
- The key to achieve observability is not these logs but the ability to monitor and analyze these logs and data along with KPIs using right tools and platform to achieve deep visibility into applications for faster, automated problem identification and resolution.
- In general, observability is the extent to which you can understand the internal state or condition of a complex system based on the analysis of its external outputs.
- The more observable a system, the more quickly and accurately you can navigate from an identified performance problem to its root cause, without additional testing or coding.
- In cloud computing, observability also refers to software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application and the hardware it runs on, in order to more effectively monitor, troubleshoot and debug the application to meet customer experience expectations, service level agreements (SLAs) and other business requirements.
- Modern Applications consist of microservices, different programming languages, containers, orchestration engines, serverless functions and they leverage agile development practices , CI/CD and DevOps. The objective behind such an architecture or practices is to reduce the time to market.
- This kind of architecture and system requires a higher quality telemetry that can be used to create a high-fidelity, context-rich, fully correlated record of every event, every application user request or transaction and this is what observability helps to achieve.
- The following are the 3 key elements to achieve observability
- Logs : granular, timestamped, immutable record of discrete events that happens over time
- Metrics: Numbers describing a particular process or activity measured over intervals of time such as how much memory or CPU capacity an application uses over a five-minute span, or how much latency an application experiences during a spike in usage.
- Traces: Records 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.
- Logs : granular, timestamped, immutable record of discrete events that happens over time
- After gathering these data, the platform correlates it in real-time to provide DevOps teams, site reliability engineering (SREs) teams and IT staff complete, contextual information – the what, where and why of any event that could indicate, cause, or be used to address an application issue.
- The goal of developing observability is to enable developers, DevOps Engineers, security analysts, IT support and managers to better understand and address problems in their system that could negatively impact the business.
- The volume, velocity and the variety of logs, metrics, traces generated is fundamentally unmanageable by humans and therefore observability requires tools and practices which leverages AI and ML for sophisticated analysis.
- Different companies implement observability differently. Some track dozens of metrics and some track only a few; some keep all their logs and some downsample them aggressively. Which solution works for you depends heavily on your company, your system, and your resources.
- Observability tools and platforms are still in their early stages and it is likely to go through many iterations of redesign, adding more value creation at each iteration but observability is a real thing, it’s important, and systems that implement it from the get-go will be uniquely positioned to spring back quickly from failure when it happens.
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