Kubernetes integrates with external logging tools through various methods including DaemonSets, sidecar containers, and direct application logging. These integrations enable centralised log collection, processing, and analysis from containerised applications across your cluster. External logging solutions like ELK Stack, Fluentd, and cloud-native tools provide the scalability and functionality that Kubernetes' built-in logging cannot match for production environments.
Understanding Kubernetes logging integration
Kubernetes logging integration addresses the fundamental challenge of collecting and managing logs from ephemeral containers across distributed clusters. Unlike traditional applications, containerised workloads generate logs that disappear when containers restart or terminate, making persistent log collection absolutely necessary.
Built-in Kubernetes logging captures stdout and stderr from containers, storing them locally on nodes. However, this approach falls short in production environments where you need log aggregation, searching, alerting, and long-term retention. When nodes fail or pods restart, these logs vanish permanently.
External logging tools solve these limitations by providing centralised collection, storage, and analysis capabilities. They ensure logs survive container lifecycles, offer powerful search and filtering features, and enable real-time monitoring and alerting across your entire infrastructure.
What external logging tools work with Kubernetes?
Several robust external logging solutions integrate seamlessly with Kubernetes, each offering unique strengths for different use cases. The ELK Stack (Elasticsearch, Logstash, Kibana) remains one of the most popular choices, providing powerful search capabilities and rich visualisation options.
Fluentd and Fluent Bit serve as lightweight, high-performance log processors and forwarders. Fluentd offers extensive plugin support and complex data processing capabilities, whilst Fluent Bit provides a smaller footprint ideal for resource-constrained environments.
| Tool | Best For | Resource Usage | Key Features |
|---|---|---|---|
| ELK Stack | Complex analytics | High | Advanced search, visualisation |
| Fluentd | Data processing | Medium | Plugin ecosystem, reliability |
| Fluent Bit | Edge environments | Low | Lightweight, fast processing |
| Grafana Loki | Prometheus users | Medium | Label-based indexing |
| Splunk | Enterprise compliance | High | Advanced analytics, security |
Grafana Loki integrates perfectly with existing Prometheus monitoring setups, using labels rather than full-text indexing to reduce storage costs. Splunk provides enterprise-grade features with advanced security analytics and compliance reporting capabilities.
How do you set up external logging in Kubernetes?
Setting up external logging in Kubernetes typically involves deploying a DaemonSet that runs logging agents on every node in your cluster. This ensures comprehensive log collection from all containers regardless of their placement or lifecycle.
The setup process begins with creating a DaemonSet configuration that mounts the Docker socket and log directories from each node. Your logging agent then monitors these locations, collecting logs from all running containers and forwarding them to your chosen external system.
Configuration involves several key steps:
- Deploy the logging agent DaemonSet with appropriate permissions
- Configure log sources and destinations in your agent's configuration file
- Set up parsing rules to structure your log data
- Define filtering rules to reduce noise and focus on relevant information
- Configure authentication and networking to reach your external logging system
Most logging tools provide Helm charts or Kubernetes manifests that simplify deployment. These pre-configured templates handle common setup patterns whilst allowing customisation for your specific requirements.
What are the different logging architectures for Kubernetes?
Kubernetes supports three primary logging architectures, each with distinct advantages and trade-offs. Node-level logging agents represent the most common approach, using DaemonSets to deploy agents that collect logs from all containers on each node.
Node-level agents offer excellent resource efficiency and centralised management. They handle log rotation, buffering, and forwarding automatically. However, they require cluster-wide permissions and may create single points of failure if not properly configured.
Sidecar container architecture deploys dedicated logging containers alongside your application pods. Each sidecar handles log collection for its specific application, providing isolation and customisation opportunities. This approach increases resource usage but offers better security boundaries and application-specific log processing.
Direct application logging allows applications to send logs directly to external systems using logging libraries. This method provides maximum flexibility and eliminates intermediate processing steps. However, it couples your applications to specific logging infrastructure and requires careful handling of credentials and network connectivity.
How do you configure log forwarding and filtering?
Log forwarding and filtering configuration determines which logs reach your external systems and how they're processed during transit. Effective filtering rules reduce storage costs, improve query performance, and focus attention on meaningful events.
Most logging agents support tag-based routing, allowing you to direct different log types to appropriate destinations. You might send application logs to one system whilst routing security events to another specialised platform.
Filtering mechanisms include:
- Namespace-based filtering to separate environments or teams
- Log level filtering to exclude debug messages from production logs
- Content-based filtering using regular expressions or keyword matching
- Rate limiting to prevent log flooding during incidents
- Data transformation to enrich logs with additional metadata
Parser configuration structures unstructured log data into searchable fields. JSON logs typically require minimal parsing, whilst plain text logs benefit from custom parsing rules that extract timestamps, severity levels, and relevant data fields.
Key takeaways for Kubernetes logging success
Successful Kubernetes logging implementation requires careful consideration of performance impact, security requirements, and operational complexity. Resource allocation for logging infrastructure should account for peak load scenarios and log volume spikes during incidents.
Security considerations include protecting log data in transit and at rest, managing access credentials, and ensuring compliance with data retention policies. Network policies should restrict logging agent communications to authorised destinations only.
Performance optimisation involves tuning buffer sizes, batch processing parameters, and compression settings to balance throughput with resource consumption. Monitor your logging infrastructure as carefully as your applications to prevent logging systems from becoming bottlenecks.
Regular testing of log collection, processing, and alerting ensures your logging system remains reliable when you need it most. Consider implementing log sampling during high-volume periods to maintain system stability whilst preserving visibility into critical events.
At Falconcloud, we understand the importance of robust logging infrastructure for containerised applications. Our managed Kubernetes solutions provide the foundation you need to implement these logging strategies effectively across multiple global data centres.