Best Practices for Kubernetes Logging
Logging is crucial for managing and maintaining Kubernetes applications. Proper logging helps in monitoring, troubleshooting, and optimizing your system. Inadequate logging can result in challenges in identifying issues quickly and optimizing system performance. Here’s a comprehensive guide to best practices for logging in Kubernetes applications.
Logging Formats
Unstructured Logs
Unstructured logs are primarily human-readable strings with embedded variables. These logs often require complex regular expressions to parse and lack consistency for automated tools.
Example of Unstructured Logs:
Connection to database failed
Structured Logs
Structured logging helps organize event data upon creation, facilitating easier parsing, searching, analysis, and monitoring.
Example of Structured Logs:
{
"level": "error",
"timestamp": "2024-06-13T07:12:46.743Z",
"db": "database-1",
"msg": "Connection to database failed"
}
Structured logs help create dashboards in tools like Grafana, ELK to monitor various metrics efficiently.
Important Fields in Logs
Log Level
Log levels define the severity or urgency of a log entry. They help distinguish between routine operations and critical errors, enabling efficient automated alerts or manual reviews.
Examples:
{"level": "info","timestamp": "2024-06-13T10:15:45.123Z","msg": "Data inserted successfully"}
{"level": "error","timestamp": "2024-06-13T10:30:15.103Z","msg": "Connection to database failed"}
Some commonly used log levels include: Info, Warn, Error, and Fatal.
Timestamp
Ensure each log entry is accompanied by a timestamp for contextualizing and organizing your logs. Record timestamps in ISO-8601 format. This consistency aids in correlating events across distributed systems.
Descriptive Log Messages
Include thorough details about the event in each log entry without unnecessary or redundant information. Descriptive messages provide meaningful insights.
Logging Unexpected Errors with a Stack Trace
Log exceptions with a stack trace to determine the root cause of issues quickly. A stack trace pinpoints the exact location of an error, aiding faster diagnosis.
Example:
{
"level": "fatal",
"time": "2022-06-15T21:23:04.436Z",
"err": {
"type": "Error",
"message": "runtime error",
"stack": "runtime error: invalid memory address or nil pointer dereference\n goroutine 1 [running]:\n main.main()\n /home/ayo/dev/demo/snippets/main.go:10 +0x40\n runtime.main()\n /usr/local/go/src/runtime/proc.go:225 +0x256\n runtime.goexit()\n /usr/local/go/src/runtime/asm_amd64.s:1371 +0x1"
},
"msg": "runtime error"
}
Add Contextual Fields to Log Entries
Incorporate relevant details into log entries to enable a holistic understanding of events and easier tracing of transactions or requests across various systems. Adding unique identifiers like transaction IDs, request IDs, or user IDs provides clarity and allows for easier correlation of log entries.
Include Build Version or Commit Hash (Optional)
Incorporate the build version number or commit hash into your log entries to associate logs with a specific version of your application. This connection is crucial for reproducing and troubleshooting issues.
Correlate Request Logs with an ID
Include a correlation ID to tie together all log entries related to a specific request. This ensures each related log message carries this identifier, enabling you to group and analyze logs from a single request effectively. This helps to correlate results between multiple microservices.
Ensure Sensitive Data Stays Out of Logs
Never capture sensitive data like passwords or authorization tokens in your application logs. Ensuring the exclusion of such data helps maintain security and compliance.
By following these best practices, developers can create comprehensive, structured, and secure logging mechanisms within their Kubernetes applications, greatly enhancing the ability to monitor, troubleshoot, and optimize system performance.