Observability is a fundamental key concept in the field of systems and software, which refers to the ability to effectively understand and monitor the internal behavior and performance of a system in real time. This practice enables engineers and developers to have a comprehensive and detailed view of what is happening within the system, facilitating the identification of issues, fault diagnosis, and performance optimization.
Observability goes beyond simple data collection and metrics. It encompasses the ability to gather meaningful information from different layers and components of the system, such as logs, request tracing, and events. This way, professionals can comprehend the interactions that occur among the system elements and detect abnormal patterns.
What is observability?
Observability is a property that refers to the ability to understand, monitor, and infer the internal behavior or state of a system based on externally accessible information. In other words, it is the ability to gain insights and comprehend the operation of a system from its outputs or external signals.
In the context of software engineering, observability is a fundamental practice to ensure the reliability, performance, and efficiency of applications and infrastructures. This includes collecting, analyzing, and interpreting relevant data and metrics to gain a comprehensive view of the system and identify issues or optimization opportunities.
Observability x monitoring: understand the difference
Observability and monitoring are related but distinct concepts within the context of systems and software engineering. Let’s understand the difference between them:
Observability
- Definition: It is the ability of a system to be understood and monitored based on externally accessible information.
- Focus: The emphasis of observability lies in understanding the internal behavior of a system through its outputs or external signals.
- Practice: Observability involves data collection, storage, and processing through clear information visualization and the ability to track and correlate events.
- Benefits: It enables users to identify complex issues, optimize system performance, and make decisions based on a comprehensive view of internal operations.
Monitoring
- Definition: It is a specific activity performed on a system to collect real-time data, metrics, and relevant information with the aim of verifying status and performance.
- Focus: The focus of monitoring is to track and analyze the real-time or periodic behavior and current state of the system.
- Practice: Monitoring typically centers around predefined specific metrics that are monitored at regular intervals using tools that alert administrators when certain thresholds are exceeded.
- Benefit: It is essential to ensure that the system is operating normally and to identify issues that require immediate attention.
In summary, observability is a broader and strategic concept that involves the ability to understand and comprehend the internal behavior of a system over time, while monitoring is a more specific operational activity that aims to track the current state of the system in real-time and detect immediate issues.
Observability provides the foundation for effective monitoring as it allows relevant metrics and data to be intelligently and meaningfully selected and monitored.
The pillars of observability
- Data collection: involves the capture of relevant information such as event logs, performance metrics, request tracing, among others;
- Storage and processing: the collected data is stored in an accessible location for later analysis and can be processed to generate useful metrics and indicators;
- Visualization: clear and intuitive data presentation is essential for professionals to understand and interpret the system’s behavior;
- Proactive monitoring: observability allows for the detection and resolution of issues before they become critical, reducing the impact on user operations;
- Tracking and diagnosis: the ability to trace the flow of a request or operation facilitates the identification of bottlenecks and performance issues;
- Data correlation: with observability, it is possible to relate seemingly disconnected events and understand how different parts of the system interact.
The practical use of observability
Observability is especially crucial in modern environments, such as distributed systems, microservices architectures, and cloud environments, where complexity is high, and there is a need to deal with various interconnected components.
Through observability, engineers and developers can ensure that the system is operating correctly, make more informed decisions, and provide a more reliable and efficient experience to end-users.
In this way, observability is an essential practice in various technology and software engineering fields, offering a clear and detailed view of the internal behavior of complex systems through some key practices:
- Infrastructure Monitoring: Observability plays a vital role in overseeing the health and performance of servers, networks, and infrastructure resources. Through the collection of metrics like memory usage, disk space availability, and network traffic, administrators can promptly identify issues, pinpoint bottlenecks, or detect hardware failures.
- Distributed Applications and Microservices: In distributed or microservices-based environments, observability becomes pivotal in comprehending the intricate interactions between diverse components and their impact on the overall system performance. Techniques such as request tracing, comprehensive log analysis, and monitoring latency metrics prove instrumental in identifying communication bottlenecks and enhancing inter-service communication.
- Fault Detection and Diagnosis: Leveraging observability, real-time system anomalies and failures can be swiftly identified. Configured alerts and notifications ensure that teams are promptly alerted to critical issues, enabling them to take corrective measures before end-users are adversely affected.
- Performance Optimization: Monitoring application and system performance metrics facilitate the detection of performance bottlenecks and inefficiencies. This empowers engineers to optimize code, enhance system efficiency, and deliver a quicker, smoother user experience.
- Behavior Analysis: Observability can be harnessed to track user behavior within an application or website. This encompasses in-depth event log analysis to gain insights into user interactions with the system, the utilization of specific features, and the prevalent issues they encounter.
- Security Monitoring: This tool is also invaluable for recognizing suspicious activities or thwarting attempted cyberattacks. By monitoring access logs, tracking failed login attempts and monitoring other security metrics, system integrity and protection can be maintained.
- Self-Service and Customer Support: Observability can be employed to create tailored dashboards and control panels, enabling customers to monitor the performance of services or applications hosted on a platform.
- Software Testing and Validation: Through the observation and analysis of performance metrics during load and stress tests, engineers can identify limitations and issues proactively, ensuring a smoother software launch into production.
These are just a few examples of how observability is applied in practice. Regardless of the case, it is a powerful tool to ensure that systems and applications operate reliably, efficiently, and securely.
Discover the main observability tools
The observability tools listed below are widely recognized and used in the industry for monitoring and analyzing systems and applications. Each of them has its specific features and focuses:
Dynatrace
Dynatrace is a performance monitoring and intelligence platform that provides a comprehensive approach to system and application observability. The tool leverages Artificial Intelligence and automation to deliver accurate real-time insights into the IT environment. It continuously monitors the entire technology stack, from infrastructure to applications, and provides detailed performance analysis, identifying issues and suggesting optimizations.
Key Features:
- Application, server, and cloud service monitoring;
- Root cause analysis to identify problems and their origins;
- Automatic detection of environments and components;
- Real user and experience analysis.
Datadog
Datadog is a real-time data monitoring and analysis platform designed for cloud environments and distributed systems. It consolidates metrics, tracing, logs, and infrastructure monitoring into a single platform. Datadog enables operations and development teams to collaborate in order to detect issues and optimize the performance of applications and services.
Key features:
- Monitoring of metrics and events;
- Distributed tracing for latency analysis and service performance;
- Real-time log collection and analysis;
- Infrastructure monitoring and integration with a wide range of cloud services and technologies.
Prometheus
Prometheus is an open-source monitoring and alerting tool, widely used in cloud and microservices environments. It collects metrics from configured targets at regular intervals and stores them in a time-series database, while also providing querying and alerting capabilities using the PromQL (Prometheus Query Language).
Key features:
- Collection of metrics with a pull-based collection model;
- Local storage of time series;
- PromQL query language for metric analysis;
- Alerting system for problem detection and notification.
Grafana
Grafana is a data visualization and analysis platform that can be integrated with various data sources, including Prometheus, Datadog, InfluxDB, Elasticsearch, among others. It provides advanced features for creating custom dashboards and panels, allowing real-time visualization of metrics and data.
Key features:
- Creation of interactive and custom dashboards;
- Integration with various data sources and observability tools;
- Flexible visualizations with line charts, bar charts, maps, etc.;
- Ability to share dashboards and collaborate with other teams.
These tools are just a few of the options available for observability practice. Each of them has its advantages and is suitable for different scenarios and needs. The choice depends on the environment in which it will be used, the technologies involved, and the specific requirements of the project or organization.
Conclusion
With the growing complexity of modern software architectures, especially with the emergence of cloud environments, microservices, and distributed applications, observability has become a crucial requirement to ensure the reliability of systems. Observability tools provide valuable insights for proactive monitoring, issue resolution, and continuous improvement of the services offered, resulting in better overall system performance and, consequently, an enhanced experience for end users
