Managing cloud-native applications requires comprehensive visibility into real-time activities due to the distributed and dynamic nature of these applications, often deployed through ephemeral technologies like containers and serverless functions.
To ensure effective control amid the complexity of cloud-native systems, robust monitoring and logging practices are crucial. This article outlines some recommended practices and standards for logging and monitoring cloud-native applications:
Opt for Managed Logging Solutions
First off, log monitoring should reflect your applications. In a world of cloud-native applications, logging solutions should be built on the same principles as high availability, distributed processing, and intelligent failover that consequently lay the foundation for the applications themselves. This is what differentiates modern cloud-native apps from legacy monolithic apps.
Discern What to Monitor
Avoid logging unnecessary data to prevent information overload and streamline log management processes. Focus on logging critical production data for compliance, auditing, troubleshooting, user experience, and security monitoring. Exclude data from non-essential environments or sensitive data that could compromise security.
Implement Log Security and Retention Policies
Logs may contain sensitive information, necessitating a security policy for data protection. Anonymize or encrypt sensitive data before shipping logs to third parties. Tailor retention times based on log source relevance, ensuring a flexible policy. Design scalable and reliable log storage to prevent downtime during high load peaks.
Prioritize Log Storage Planning
Plan log storage capacity considering potential high load peaks and critical system errors. Design storage as a scalable and reliable cyclic buffer to avoid losing essential logs during storage limits. Implement separate security policies for log storage to safeguard against potential attacks.
Regularly Review and Maintain Logs
Unmaintained logs can lead to longer troubleshooting times, security risks, and increased storage costs. Regularly review and adjust log outputs for usability, operational efficiency, and security. Create meaningful log messages, use structured log formats, and make log levels configurable for efficient log management.
Correlate Data Sources for Holistic Analysis
Avoid analyzing logs in isolation. Correlate logging with other monitoring strategies, such as APM, network monitoring, and infrastructure monitoring. A comprehensive monitoring solution should provide a unified view of the entire system or integrate seamlessly with other tools for holistic analysis.
View Logging as a GitOps Enabler
Consider logging as an essential component in the DevOps and CI/CD pipeline. Logging provides visibility into issues introduced during development, enabling GitOps by identifying faulty code, dependency issues, external attacks, and more. It is not just an add-on but a crucial enabler for automation and continuous integration.
Obtain Real-Time Feedback on Events
Leverage logging to gain real-time feedback on every code change, especially as automated testing becomes more prevalent. Logging plays a vital role in providing visibility and enabling GitOps by preventing runaway releases and deployment challenges.
Identify Automation Opportunities and Trends
Use logging to detect issues early in the pipeline and identify automation opportunities. Set up custom alerts and automated actions to streamline processes. Consider logging as a driver of GitOps, contributing to efficient automation and deployment.
Conclusion
Logging is integral to building and managing successful cloud-native applications. To maximize its effectiveness, logging should align with application states, scale seamlessly, and be part of a broader monitoring strategy. Rather than an afterthought, logging should be viewed as a vital enabler for observability and GitOps in cloud-native environments.
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