An Introduction To Observability And Its Pillars
Cloud-native infrastructure services such as microservice, serverless and container technologies have become the de facto standard for applications today. As these systems become more distributed and complex, tracking and observing how the application and code are performing is critical. Hence, observability should be considered to monitor, troubleshoot, and debug a distributed system more efficiently.
In this article, we'll examine observability in more detail, including its definition, significance, advantages, and top observability tools to take into account.
What is Observability?
Observability is the ability to measure and track the status of a software's internal state using its output. It is the collection, correlation, alert and analysis of a continual stream of performance data from a distributed system hardware it runs on. In an observable system, this data is collected from various locations and analyzed to produce meaningful insights based on the output.
Observability allows you to determine how particular occurrences affect your system, operations, application security, software development life cycles, and end-user experiences. This system management strategy relies on telemetry data to provide visibility to your distributed systems. Organizations use observability to help in data disclosure and easy access to the information they need to identify potential root causes for a failure in a system.
Monitoring VS Observability?
Although they are related and can work well together, observability and monitoring are two distinct ideas.
Monitoring is a set of tools or technical solutions telling you something is wrong. The outcome of monitoring is based on predefined sets of metrics or logs. In a monitoring solution, you will see preconfigured dashboards that alert you of specific performance issues. These dashboards rely on predefined metrics or logs to identify particular problems.
However, due to the distributed, dynamic and complex nature of most container, microservice and serverless technologies, it becomes impossible to predict all failures that may arise.
On the other side, observability gives you the liberty to identify what's happening and quickly pinpoint the root of issues. It integrates the facts and data generated by monitoring to offer you a thorough view of your system’s performance and health. When a system is instrumented to provide complete observability data, you can monitor and troubleshoot issues system failure without being able to predict.
The Three Pillars of Observability
Observability relies on some primary data points — logs, metrics and traces for collecting measurements. These source data types are called "the three pillars of observability."
- Logs
Logs are written records of system events that happened at a specific time. Logs include payloads that give context and timestamps that indicate when an event occurred. Logs can either be binary, structural (which consists of text and metadata and is often simpler to retrieve), or plain format. The first place to check when an error occurs is the system logs.
- Metrics
Metrics are numeric values with specific attributes measured over some time. It encompassed a range of KPIs used to gain insight into a system's performance. Metrics can measure a system's health or performance by tracking anomalous activities in timestamp, name, CPU capacity, memory, or other value. Metrics, unlike logs, come with a default format that makes it simpler to query and optimize for storage, allowing you to keep them around for extended periods.
- Traces
A trace depicts a request's complete path across a distributed system. It is a method for following a user request from the user interface through the system and returning to the user after they learn that their request has been fulfilled. In a complex system, a single request may go through dozens of microservices. Essential details about the microservice responsible for this activity are encoded in "Span" (operations carried out as requests go through the host system).
Traces are essential for observability because they help discover system constraints and identify where a system process has failed in a distributed system.
Implementing these data classes individually doesn't guarantee observability to your distributed system. This is because Logs are discrete data, whereas metrics are continuous data, and traces employ specific data "tags" to monitor data across an application's processes. As a result, combining all three data types will enable you to see, analyze, and query data that provides you with accurate insights into the performance and health of your system.
Why is Observability Important?
Observability gives you greater control over complex systems. A common challenge in a most heavily distributed system is that they are often changing and unpredictable. Hence it might be impossible to know what problems might arise. Through observability, teams can receive prompt alerts about issues and pro-actively resolve them before they impact users. Observability enables you to understand and answer specific questions about your system's behaviour when failures occur. You track how slow or fast, what is broken and what should be done to enhance system performance.
Using observability, you can automatically analyze your data and improve user experiences based on rapid, accurate input.
What are the Benefits of Observability?
- Rapid deployment
In an observable system, debugging is no longer necessary in a production setting. Developers have access to pertinent information regarding individual problems and the end-to-end path of a request. By accelerating the process of application troubleshooting and debugging, observability aids in improving system performance.
- Application performance monitoring:
An advanced observability solution helps identify the root cause of performance issues in heavily distributed systems. It offers more system visibility to find issues and address them before it escalates. DevOps teams can leverage observability to automate more processes, increasing efficiency and developing secure, more resilient applications.
- End-user experience
Observability solutions empower engineers and developers to create better customer experiences despite the increasing complexity of the digital enterprise. By analyzing and troubleshooting your system proactively, observability helps you uncover problems before your users do, therefore improving user experiences.
- Lower cost, less effort
There are many intuitive dashboards to observe what is happening in Real-Time in a system. They help identify the root causes of issues and help in monitoring and troubleshooting any bottlenecks. Thereby making delivery times faster and assisting the developers in spending more time on innovative solutions than debugging.
Observability Best Practices
- Be user-friendly
Your observability tool should be easy to use and integrate into the application workflow. This will make it easy to adopt and be used by DevOps and developer teams.
- Give context
It will be considerably more challenging to identify the problem and discover a solution if you do not have thorough information about your application's performance. As a result, your observability tool should give sufficient context to track how your system's performance has changed over time—more context to the cause of the changes in the system and the gravity of the problem.
- Focus on real-time data
For teams to understand, analyze and debug a system failure, your observability technologies should deliver critical information in real-time using dashboards, reports, and queries. It should track issues critical to resolving when they fail, i.e., relevant data from across your stacks, technologies, and operating environments.
- Offer business value
While improving your application performance, you should also use observability tools that measure metrics critical to your organization, such as system stability, deployment speed, and customer experience.
- Automate processes
Ensure your observability tool supports the frameworks and languages in your environment, messaging platform, container platform and other essential software. They should also allow for automatic data processing and curation, allowing you to get warnings and respond to security threats.
Tools For Observability
Conclusion
The increased adoption of Clouds native, containerization, microservices, and other technologies have added unprecedented complexity to systems today. So troubleshooting and debugging have also become the primary friction point for developers and DevOps. However, observability bridges this gap as a more practical technique to gain insight into your entire infrastructure. Therefore, developers can leverage it to understand why incidents occur and proactively identify and resolve performance issues.
