Observability: How to monitor your systems for performance and predict potential issues

Behind the scenes, software applications have a complex ecosystem to manage, with numerous opportunities for unexpected failures to occur. Hence, it is crucial to monitor your application’s health and receive alerts immediately to fix a problem.

Traditional monitoring includes pushing metrics and logs to a dashboard for analysis by a dedicated team. However, as application complexity increases, this is no longer ideal. Here’s why:

• Traditional monitoring provides a limited view, often only showing an abnormal metric for the application but not why the abnormal behavior exists.
• Understanding a system's behavior is essential for debugging issues. For instance, while a spike in usage may be routine, detecting abnormalities requires deeper familiarity with system patterns to avoid oversight.

What is observability?

Observability allows organizations to collect and analyze the data a system generates to understand its present state. The practice is usually concerned with three types of data:

• Logs: Unstructured data that are ingested in a log analytics platform; can include event timestamps along with details such as which user performed an action on which endpoint in a service
• Metrics: Any time series values collected at regular polling frequencies. This data can include CPU and memory usage
• Traces: Track the endpoint request workflow throughout the application, pinpointing failures along with error details including potential code-level issues

Observability vs. traditional monitoring

Observability platforms score higher than traditional monitoring platforms in their ability to understand and correlate data from multiple applications and microservices. This offers a comprehensive view of the state of system performance.

Observability can additionally:

• Predict potential issues since it delivers a continuous stream of contextual system data that helps provide a clear picture of internal systems.
• Enhance debugging as engineers gain more contextual data on which they can draw better conclusions.
• Improve capacity planning since it supports better predictions for future capacity needs by tracking resource utilization and performance trends over time.

The ideal observability platform

When selecting an observability platform, organizations should make sure it includes the following must-have features.

Coverage

An observability platform must be comprehensive—covering all the layers in the IT stack from the end-user layer to the application, infrastructure, and network layers and everything in between.

Agent-based monitoring helps keep an eye on the entire IT infrastructure, including on-premises, multi-cloud, and especially containerized microservices deployments. This helps the observability platform correlate data from different services and present a clear picture of the application landscape.

In-depth logging

An ideal observability platform offers centralized log consolidation across servers and application instances, facilitating comprehensive analysis from a single interface. The platform should also provide easy ways to search, filter, and correlate all the logs collected.

Alerting

Without alerts, none of the above matters. Organizations need to ensure the platform they choose for observability comes with robust alerting mechanisms to notify users about anomalies, performance degradations, or system failures. Alerts should be based on predefined thresholds or anomaly detection algorithms. Alerting can include multiple notification mediums such as SMS, voice calls, and application notifications.

As an example, voice calls are typically used when there is a very critical failure in the system, requiring a war-room situation to make decisions and fix it. They are usually configured for escalations or critical infrastructure and applications that experience outages due to unexpected failures.

Integration

Making sure the platform can integrate seamlessly with existing monitoring and ITSM tools is key. This must also be the case for popular cloud platforms in use, container orchestration systems, and other ecosystem components.

How to implement observability

Observability involves a systematic approach to understanding, monitoring, and optimizing complex systems. Below, we cover the key steps an organization must take to achieve full observability and predict issues promptly.

Define goals and end state

Implementing observability begins with defining clear objectives and a desired end state for a system. This entails articulating goals such as improving system reliability, reducing mean time to resolution (MTTR) for incidents, enhancing performance, and optimizing resource utilization.

Additionally, it is critical to identify the key metrics and indicators needed to facilitate these goals. Error rates, latency, throughput, resource utilization, and other relevant parameters will provide insights into system behavior and performance.

Implement centralized logging and tracing

After an organization establishes its objective, the next step is to create a robust foundation for data collection and analysis.

Centralized logging aggregates logs from various infrastructure and application components. A centralized logging system should be chosen based on its scalability, real-time processing capabilities, and search functionalities.

Simultaneously, integrating distributed tracing enables the capture of request flows across microservices or distributed systems. You can leverage open-source tracing frameworks, such as OpenTelemetry or Jaeger, or cloud-based tracing services for this purpose.

Set up alerts for failures

Configuring alerting mechanisms for failures is another crucial aspect of implementing observability and detecting potential issues.

Organizations must define alerting rules according to established thresholds and anomaly detection algorithms. These rules must also cover a wide range of scenarios, from infrastructure-level issues, such as high CPU usage or disk space exhaustion, to application-level anomalies, including increased error rates or degraded performance.

Alerts should be sent to relevant stakeholders and teams via email, SMS, or chat platforms; this ensures they are actionable and provide sufficient context to facilitate quick issue diagnosis and resolution.

Review and reiterate to improve

Finally, companies must review their observability system and iterate on a regular basis. It is essential to monitor key performance indicators related to system reliability, incident response time, and overall system health. Collecting feedback from users and stakeholders additionally helps organizations uncover pain points and needed improvements.

Continuous iteration based on feedback and monitoring insights will allow the observability system to evolve and adapt to changing needs effectively. By following this iterative process, organizations can build and maintain a robust observability system that enables them to gain insights to optimize performance effectively.

Additionally, organizations should implement auto-remediation for minor issues. Leveraging predefined rules and workflows, it identifies anomalies and proactively triggers actions to rectify them, reducing manual intervention and minimizing downtime. By continuously monitoring and automatically addressing issues, auto-remediation enhances system reliability and ensures uninterrupted operations.

Conclusion

Observability in today’s software environment is crucial. It provides insights into complex systems, allowing organizations to understand, troubleshoot, and optimize performance, ultimately enhancing reliability and user experience.

Some organizations may think they’re better off building their own observability platform from a suite of varied tools. However, aside from the time and resources this entails, it is preferable to find an all-in-one observability platform for streamlined management and simplified workflows. The perfect observability platform gives complete visibility across all infrastructure layers, providing organizations with unmatched insights into their systems' performance and health.

Site24x7 offers an AI-driven, comprehensive observability solution, enabling complete visibility across your entire software infrastructure via a unified dashboard.

To learn more about Site24x7 and master continuous monitoring and issue detection in your ecosystem, click here.