Overview of Observability and Monitoring
In software delivery, observability and monitoring have become relevant terms, especially when discussing software development. No matter how diligently you work to create high-quality software applications, errors and bugs are inevitable. As user numbers grow and software complexity increases, having an observable system becomes even more crucial. The software delivery culture is changing, and it is shifting from it.
Although applications in both on-premises and cloud-native environments are expected to be highly available and resilient to failure, the methods used to achieve those goals differ. Monitoring has many benefits, such as improving productivity and performance; you can efficiently allocate resources according to the users' needs and easily detect and solve problems before they affect your business. Thus, you can better allocate time and upgrade to new projects.
Assembling all fragments from logs, monitoring tools and organize them in such a way which gives actionable knowledge of the whole environment, thus creating an insight. Taken from Article, It's Working Architecture and Benefits
What is Observability?
It is the degree to which organizations can understand a complex system's internal state or condition based only on knowledge of its external outputs. The more observable a system is, the faster and more accurately you can navigate from a recognized problem to its root cause without additional effort.
In DevOps
The CI-CD pipeline requires each part of the pipe to be observable. Each piece of the pipeline must produce appropriate data to support automated problem detection and alerting, manual debugging when necessary, and system health analysis (historical trends and analytics).
These are the types of data that a system should produce to be observable:
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Health checks: These are typically customized HTTP endpoints that assist orchestrators such as Kubernetes or Cloud Foundry maintain the system's optimal health and performance.
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Metrics: Metrics are numerical representations of data collected regularly and organized into a time series. This numerical data is easily stored and can be quickly queried, making it valuable when analyzing historical trends. Additionally, numerical data can be condensed over longer periods into shorter intervals, such as monthly, weekly, or daily, for more concise analysis.
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Log entries: Log entries serve as records of specific events. They are crucial in the debugging process as they often contain detailed information, such as stack traces and contextual data, that can assist in pinpointing the underlying cause of observed failures.
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Distributed, request, or end-to-end tracing: Tracing allows for the comprehensive tracking of an application's journey through the system, capturing not only the services it interacts with but also the intricate structure of its workflow. This includes the analysis of synchronous or asynchronous processing and the relationships between different components, such as child-of or follows-from relations.
Gather, organize, and analyze data for instant business insights. Build tools to dive into infrastructure logs, turning data into actionable real-time results. Know more about Monitoring and Data Observability Solutions.
What is Monitoring?
The development lifecycle includes planning, development, integration and testing, deployment, and operations. It involves a complete and real-time view of applications, services, and system infrastructure status. In the software delivery process, continuous monitoring activity increases productivity and performance, reduces downtime, and allows us to allocate time and resources better to quickly plan our upgrades and new projects. It also alerts us whenever there is any failure in the system before it affects our business.
Monitoring in DevOps
DevOps is performed by tools such as Nagios, Tensible, Snort, etc. It provides feedback from the production environment and produces information about an application’s performance and usage patterns. The main aim is to achieve high availability by minimizing time to detect and time to mitigate. The benefit of constant is to Automate work that used to be manual, repetitive, and error-prone, resulting in faster speed, productivity, and scalability — and the assurance of standardized configurations across test, dev, and production environments. Eliminating errors and bugs reduces the wastage of time and lets you deploy software faster and more reliably. Why does continuous Monitoring do? In general, it does the following activities.
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Problem Detection: It lets you know the problems by alerting or seeing issues on dashboards.
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Problem Resolution: After knowing the problem, the root cause, and troubleshooting.
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Continuous Improvement: We can improve the software delivery process by combining capacity planning, financial planning, trending, performance engineering, and reporting.
What is the difference between Observability and Monitoring?
Let’s take an example of a large and complex scalable data centre’s infrastructure system, monitored using log analysis and ITSM tools. Analyzing too many data points continuously will generate large volumes of unnecessary alerts, data, and false flags. The software delivery process may have fewer characteristics unless the correct metrics are calculated and the unwanted noise is carefully removed using Artificial Intelligence and continuous monitoring solutions.
On the other hand, an application with a single server can be easily monitored using health checks, metrics, logs, and parameters such as response time, throughput, and efficiency. These parameters are widely interrelated with the health of internal system components. Therefore, the system has demonstrated extensive observability. Using essential tools, such as energy and temperature measurement instruments, software-based tools, or web-based real-time monitoring, the performance, life cycle, and risk of potential performance incidents can be calculated before any negative impact on the business.
The system’s simplicity, the insightful representation of the performance metrics, and the capability of the tools to identify the correct parameters are responsible for the system. This combination yields the necessary insights to accurately represent the internal states despite a system’s inherent complexity.
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Observability
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Monitoring
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Actively, the information is gained |
Information is consumed passively |
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Questions are asked based on hypotheses |
Questions or queries are based on the data dashboards |
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In use for complex and dynamic Environments |
In use for static with a little variation environment |
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Preferred by developers with variability and unknown permutations |
Used for developers of systems with little change and no permutation |
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A system has to be designed to be observable |
Any System can be monitored |
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Why did my system fail? |
What is the state of the system? |
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Generate Metrics |
Collect Metrics |
We can conclude that monitoring is similar to taking your vehicle to a mechanic for routine maintenance and checking the engine light when it comes on. You want to keep your vehicle running smoothly and avoid breakdowns. On the server, you want to know whether users can access your application and services and if they are performing within appropriate limitations. Notifications are sent to teams when something is not performing accurately, is about to break, or is broken. Before a notification is sent, it is crucial to create a baseline and understand the current state of the environment, and here, observability first comes in. Without observing the architecture, knowing what is wrong or incorrect is impossible. We can only determine what is wrong and what is right by observing application, architecture, system, and service.
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