Logging in Kubernetes: What to Collect, What to Ignore
Learn best practices for Kubernetes logging: what logs to collect for effective monitoring and debugging, what to ignore to reduce noise, and how to implement efficient log management strategies in your Kubernetes clusters.
LogNroll Team
DevOps & Infrastructure
Effective logging in Kubernetes is crucial for maintaining observability, debugging issues, and ensuring system reliability. However, collecting too many logs can lead to increased costs, storage overhead, and difficulty finding critical information. This guide will help you make informed decisions about what to log and what to ignore.
Understanding Kubernetes Logging Architecture
Kubernetes logging involves multiple layers: container logs, node logs, cluster-level logs, and application logs. Each layer serves a different purpose and requires different collection strategies. Container logs are written to stdout and stderr, which are then captured by container runtimes and made available through the Kubernetes API.
The Kubernetes logging architecture includes several components: the kubelet collects logs from containers, log aggregation tools like Fluentd or Fluent Bit forward logs to centralized storage, and monitoring systems analyze logs for insights. Understanding this architecture helps you make better decisions about what to collect and how to structure your logging strategy.
Key Components:
- Container runtime logs (stdout/stderr)
- Kubernetes system component logs (kubelet, API server, etc.)
- Application-level structured logs
- Audit logs for security and compliance
What to Collect: Essential Logs for Kubernetes
Application Errors and Exceptions
All application-level errors, exceptions, and stack traces are critical for debugging production issues and understanding failure patterns.
Resource Metrics and Health Checks
CPU, memory usage, pod restarts, and health check failures provide essential insights into application performance and stability.
Authentication and Authorization Events
Login attempts, permission denials, and security-related events help identify potential security issues and access problems.
Application-Level Logs
Application logs are the most valuable for debugging and understanding user behavior. Collect error logs, warning messages, and critical business events. Structured logging in JSON format makes it easier to query and analyze logs later. Include contextual information like request IDs, user IDs, and timestamps to enable effective log correlation and debugging.
Infrastructure and System Logs
Kubernetes system component logs provide insights into cluster health and operations. Collect logs from the API server, kubelet, controller manager, and scheduler. These logs help diagnose cluster-level issues, resource constraints, and scheduling problems. However, be selective—only collect logs at INFO level or higher to avoid overwhelming your log storage.
Security and Audit Logs
Security-related logs are critical for compliance and threat detection. Collect authentication attempts, authorization failures, API access logs, and policy violations. Kubernetes audit logs provide a complete record of all API requests, which is essential for security auditing and compliance requirements. These logs should be retained for extended periods as required by your compliance standards.
What to Ignore: Reducing Log Noise
Verbose Debug Logs in Production
High-frequency debug logs that don't provide actionable information should be disabled or filtered out to reduce log volume and costs.
Redundant Health Check Logs
Successful health check pings that occur every few seconds create unnecessary noise and should be aggregated or filtered.
Noisy Third-Party Library Logs
Verbose logging from dependencies and libraries that don't contribute to debugging should be suppressed or filtered.
High-Frequency Debug Logs
Debug-level logs that fire frequently (multiple times per second) should be disabled in production environments. These logs include detailed function entry/exit points, variable dumps, and verbose tracing information. While valuable during development, they create excessive log volume in production and increase storage costs without providing actionable insights.
Successful Routine Operations
Logs for routine operations that complete successfully don't need to be logged at the same level as errors. For example, successful health checks, cache hits, or routine data synchronization can be aggregated into metrics instead of individual log entries. This approach reduces log volume while maintaining observability through metrics dashboards.
Best Practices for Kubernetes Logging
Logging Best Practices
Use Structured Logging
Format logs as JSON to enable efficient querying and analysis in log aggregation systems
Implement Log Levels
Use appropriate log levels (ERROR, WARN, INFO, DEBUG) and configure collection based on environment
Set Retention Policies
Define log retention periods based on compliance requirements and storage costs
Use Log Aggregation
Centralize logs using tools like ELK stack, Loki, or cloud-native solutions for better analysis
Implementing Log Collection in Kubernetes
To implement effective log collection in Kubernetes, you'll need to deploy a log aggregation solution. Popular options include Fluentd, Fluent Bit, or Loki. These tools run as DaemonSets on each node, collecting logs from containers and forwarding them to centralized storage. Configure log filters to exclude unnecessary logs at the collection level to reduce storage and processing costs.
Implementation Checklist:
Cost Optimization Strategies
Log storage and processing can become expensive, especially in large Kubernetes clusters. To optimize costs, implement log sampling for high-volume, low-value logs, use log compression, and archive older logs to cheaper storage tiers. Consider using log aggregation services that offer tiered storage and automatic archival to reduce long-term storage costs.
Monitor your log volume and costs regularly. Set up alerts for unusual log volume spikes, which might indicate a misconfiguration or an issue that needs attention. Use log sampling for non-critical logs while maintaining full logging for errors and security events.
Conclusion
Effective Kubernetes logging requires a balanced approach: collect essential logs for debugging and monitoring while filtering out noise to reduce costs and improve signal-to-noise ratio. By following these best practices, you can maintain excellent observability without overwhelming your log storage systems or breaking your budget.
Ready to improve your Kubernetes logging strategy? Start by auditing your current log collection, identifying high-volume low-value logs, and implementing structured logging across your applications.