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Logging in Kubernetes - Best Practices

Logging in Kubernetes can be a challenge, especially when dealing with data across a distributed, containerized system. To keep your logs efficient and actionable, it’s essential to understand logging best practices, from handling diverse formats to managing back pressure effectively. Here are the best practices for Kubernetes logging, focused on tools, performance, and scalability.

The main goal of logging isn’t just to collect data—it’s to analyze it and gain insights into your system’s health and performance. Collecting excess logs can create noise, so start by defining clear goals. Consider which data is essential for troubleshooting, compliance, and performance monitoring, and focus on collecting only that information. This targeted approach helps reduce unnecessary log volume and keeps analysis efficient.

Kubernetes applications can produce logs in various formats and locations. Some applications write to standard output, while others log to specific files. In addition, different applications, such as NGINX, syslog, or even Windows services, may log in entirely different formats. Use a centralized logging system to consolidate all logs into a single view.

A common tool for this purpose is Fluent Bit, which is lightweight, supports various inputs and outputs, and has a plugin system that allows it to integrate with numerous backends. Fluent Bit’s flexibility makes it a solid choice for aggregating logs from multiple sources and delivering them in a standardized format, such as JSON.

Unstructured logs (raw text logs) can be difficult to parse and analyze, especially at scale. Structuring logs, ideally in a key-value format, makes it easier to query and process them. Consider using structured logging formats like JSON to provide clear, consistent fields (e.g., timestamp, log level, message). Fluent Bit can handle log parsing and transformation to convert unstructured logs into a structured format.

For example, rather than storing a plain log message, structure it to include relevant metadata:

{
  "timestamp": "2024-05-01T12:00:00Z",
  "level": "error",
  "service": "user-auth",
  "message": "Authentication failed",
  "user_id": "12345",
  "ip_address": "192.168.1.1"
}

In distributed systems, it’s often hard to pinpoint where an issue occurred based on logs alone. Enrich your logs with additional metadata, such as the Kubernetes namespace, pod name, and container ID. This metadata provides valuable context that makes debugging easier. Fluent Bit can automatically add Kubernetes metadata to logs, providing instant visibility into which parts of the system are generating specific logs.

Logging everything can quickly lead to storage and performance issues, especially with high log volumes. It’s essential to filter out non-critical logs, such as verbose debug logs, to reduce unnecessary data. Implement filters to retain only the logs you need, based on severity (e.g., only errors and warnings in production environments).

In Fluent Bit, you can apply filters to drop debug-level logs in production. For instance:

[FILTER]
    Name    grep
    Match   *
    Exclude log_level debug

Logs can sometimes contain sensitive information, such as user IDs or payment data. To prevent exposing sensitive data, redact or mask it in logs. Fluent Bit supports redaction and can modify logs to obscure sensitive data before it’s stored or transmitted.

Example: Use a Lua script or regular expressions in Fluent Bit to mask sensitive information like credit card numbers.

[FILTER]
    Name    modify
    Match   *
    Add     card_number   "xxxx-xxxx-xxxx-1234"

Some logs contain high-frequency, repetitive events that might be more useful as metrics rather than logs. Converting these logs into metrics can improve performance and reduce storage needs. For instance, tracking the number of failed authentication attempts is often more efficient as a metric.

Fluent Bit can convert log entries to metrics, which can then be visualized and analyzed in tools like Prometheus. This approach reduces storage costs and enhances visibility for events that are better tracked over time.

In high-throughput environments, the risk of back pressure increases, especially when logs are sent to remote endpoints with limited processing capacity. Fluent Bit provides buffering options, including in-memory and file-based buffers, to handle spikes in log traffic. Using a file-based buffer is generally more reliable for production, as it prevents data loss if in-memory buffers are exhausted.

Configure Fluent Bit with file-based buffering for resilience:

[OUTPUT]
    Name  es
    Match *
    Host  elasticsearch.example.com
    Port  9200
    Buffer_Chunk_Size 1M
    Buffer_Max_Size   5M

Once your logging infrastructure is in place, monitor its health to ensure consistent performance. Metrics like input and output rates, dropped logs, and processing latency can reveal bottlenecks and prevent data loss.

You can use Prometheus to monitor Fluent Bit’s internal metrics and set up alerts for issues like increased log drop rates or high memory usage. Regular monitoring helps you catch problems early and ensures that your logging pipeline remains effective.

Kubernetes clusters grow over time, so it’s essential to design your logging solution to scale with your application. Ensure your log storage backend can handle increased volumes, and consider using Fluent Bit’s load-balancing capabilities if you have multiple log destinations.

You can scale Fluent Bit horizontally by running multiple instances in Kubernetes and balancing log traffic across them. This architecture helps maintain stability and reduces the risk of data loss during peak loads.

Logging in Kubernetes involves more than just collecting logs; it requires a thoughtful approach to structuring, filtering, and managing data at scale. With the right tools and practices, you can create a robust logging pipeline that supports your troubleshooting and monitoring needs without overwhelming your infrastructure. Following these best practices can lead to a more effective, scalable, and cost-efficient logging system in Kubernetes.