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Understanding a Log Monitoring in Kubernetes

September 29, 2023 Leave a Comment

Log monitoring in Kubernetes is the process of collecting, storing, and analyzing logs from Kubernetes nodes, pods, and applications. This can be done to identify potential problems, such as errors, performance issues, and security threats.

Log monitoring is important for Kubernetes because it can help you to:

  • Troubleshoot problems: When something goes wrong with your Kubernetes applications, you can look at the logs to see what happened. This can help you to identify the root cause of the problem and fix it.
  • Identify performance bottlenecks: By monitoring your logs, you can identify areas where your Kubernetes applications are performing poorly. This can help you to optimize your applications and improve their performance.
  • Detect security threats: Log monitoring can help you to detect security threats, such as unauthorized access to your Kubernetes cluster or your applications. This can help you to protect your cluster and your applications from attack.

Collecting cluster logs and application logs in Kubernetes is crucial for troubleshooting, monitoring, and maintaining the health of your applications and infrastructure. Kubernetes provides various mechanisms and tools to collect logs effectively. Here’s a step-by-step guide on how to collect both cluster and application logs:

  • Application Logs: These logs are generated by the applications running in containers within your Kubernetes pods. Applications typically write logs to stdout and stderr, which can be collected and monitored.
  • Kubernetes System Logs: Kubernetes itself generates logs for its control plane components (e.g., API server, kube-controller-manager) and worker nodes (e.g., kubelet).
  • Node-Level Logs: These logs include system-level information and may encompass kernel logs, system service logs (e.g., syslog), and container runtime logs (e.g., Docker logs).

Collecting Cluster Logs:

  1. Kubernetes Logging Architecture: Kubernetes itself generates cluster-level logs for its components, including the control plane (e.g., API server, kube-controller-manager) and worker nodes (e.g., kubelet). These logs are typically stored in a default location on each node (e.g., /var/log/containers).
  2. Use a Logging Driver: To centralize these logs, you can use a logging driver like Fluentd or Fluent Bit, which can collect logs from various sources, format them, and send them to a centralized location such as Elasticsearch or a cloud-based logging service.
    • For Fluentd, you can use the fluentd-elasticsearch Helm chart to deploy Fluentd and Elasticsearch in your cluster.
    • For Fluent Bit, you can deploy it as a DaemonSet in your cluster and configure it to send logs to your preferred destination.
  3. Configure Node-Level Logging: To capture node-level logs (e.g., system logs, kernel logs), you can use tools like rsyslog or journald to redirect logs to your chosen logging solution.

Collecting Application Logs:

  1. Use a Logging Library or Framework: Applications running in Kubernetes should be designed to log their output to stdout and stderr. Many programming languages have logging libraries or frameworks (e.g., Winston for Node.js, log4j for Java) that can be configured to log in this format.
  2. Container Logging Configuration: Ensure that your container images are configured to send logs to stdout and stderr. For example, in a Dockerfile, you can use the following instructions:Dockerfile
# Redirect application logs to stdout
CMD ["./your-application", "--logtostderr"]
  1. Kubernetes Logging Sidecars: You can deploy logging sidecar containers alongside your application containers to collect logs. For example, the fluentd sidecar can collect logs from your application and forward them to a centralized logging system.
  2. Logging Agents and Collectors: Many Kubernetes-native logging solutions exist, such as Loki, Fluentd, Fluent Bit, and Filebeat. These agents can be deployed as DaemonSets or sidecars to collect and ship application logs to centralized storage or analysis tools.
  3. Centralized Logging Storage: Choose a centralized log storage solution, such as Elasticsearch, Logstash, Kibana (ELK Stack), or cloud-based services like AWS CloudWatch, Google Cloud Logging, or Azure Monitor. Configure your logging agents to send logs to this storage.
  4. Log Analysis and Visualization: Use tools like Kibana, Grafana, or a cloud-based logging service’s dashboard to analyze and visualize your logs, create alerts, and troubleshoot issues.

Log Levels

  • DEBUG: Detailed information typically used for debugging purposes. These logs are usually verbose and provide a granular view of an application’s internal state.
  • INFO: General informational messages that confirm that things are working as expected. These logs can be useful for tracking the normal operation of an application.
  • WARNING: Messages that indicate potential issues or situations that might require attention but don’t necessarily indicate an error. These logs help in identifying warnings and potential problems.
  • ERROR: Messages that indicate that something has gone wrong, but the application can still continue to operate. These logs are crucial for troubleshooting and identifying errors.
  • FATAL: Critical errors that usually lead to the termination of an application or a significant failure in the system. These logs signal severe issues that require immediate attention.

How to collect Cluster Logs & Application Logs in Kubernetes

There are two main ways to collect cluster logs and application logs in Kubernetes:

  • Using a node-level logging agent: A node-level logging agent is a dedicated tool that runs on each node in your Kubernetes cluster and collects logs from all of the pods on that node. The logging agent can then push the logs to a central location for storage and analysis.
  • Using a sidecar container: A sidecar container is a container that is deployed alongside your application container. The sidecar container can be used to collect logs from your application container and then push the logs to a central location for storage and analysis.

Using a node-level logging agent

To collect cluster logs and application logs using a node-level logging agent, you can use a tool such as Fluentd or Elasticsearch. Fluentd is a popular choice because it is easy to use and configure.

To deploy Fluentd, you can use a Helm chart or a Kubernetes deployment. Once Fluentd is deployed, you need to configure it to collect logs from your Kubernetes nodes and pods. You can do this by editing the Fluentd configuration file and adding the appropriate inputs and outputs.

Once Fluentd is configured, it will start collecting logs from your Kubernetes nodes and pods and pushing them to the central location that you specified.

Using a sidecar container

To collect cluster logs and application logs using a sidecar container, you can use a tool such as Fluentd or the Kubernetes logging driver. The Kubernetes logging driver is a built-in Kubernetes feature that can be used to collect logs from containers.

To deploy a sidecar container, you need to create a Kubernetes deployment that includes both your application container and the sidecar container. The sidecar container should be configured to collect logs from your application container and then push the logs to a central location for storage and analysis.

Once the Kubernetes deployment is created, the sidecar container will start collecting logs from your application container and pushing them to the central location that you specified.

List of Options for Log Collection in Kubernetes?


In Kubernetes, there are several options and tools available for log collection from various components and applications running within the cluster. These log collection solutions help aggregate, store, and analyze log data for troubleshooting, monitoring, and analysis. Here is a list of some popular log collection options for Kubernetes:

  1. Fluentd:
    • Fluentd is a versatile open-source log collector and forwarder.
    • It is commonly used in Kubernetes for log collection and forwarding to various destinations, including Elasticsearch, Kafka, and cloud-based logging services.
    • Fluentd provides a wide range of plugins to collect logs from various sources and formats.
  2. Fluent Bit:
    • Fluent Bit is a lightweight and high-performance log collector and forwarder.
    • It is suitable for Kubernetes environments where resource efficiency is critical.
    • Fluent Bit can collect logs from container runtimes, standard output (stdout), and more.
  3. Filebeat:
    • Filebeat is part of the Elastic Stack (ELK Stack) and specializes in log shipping.
    • It’s often used to collect logs from Kubernetes nodes and forward them to Elasticsearch or Logstash for further processing and analysis.
  4. Loki:
    • Loki is a cloud-native log aggregation system designed for Kubernetes and Docker environments.
    • It’s a part of the Grafana observability stack and can be used for log collection, storage, and querying.
    • Loki’s unique approach to log storage helps reduce storage costs.
  5. Kubernetes Logging Sidecars:
    • Many Kubernetes logging solutions involve deploying sidecar containers alongside application containers to capture logs and forward them to a centralized logging system.
    • These sidecars can use tools like Fluentd, Fluent Bit, or custom scripts for log collection.
  6. Prometheus with Grafana:
    • While Prometheus is primarily a monitoring tool, it can be configured to collect and store logs using the Prometheus Remote Write feature.
    • Grafana can then be used to query and visualize log data stored in Prometheus.
  7. AWS CloudWatch Logs:
    • If you’re running Kubernetes on AWS, you can use AWS CloudWatch Logs for log collection and storage.
    • You can configure the Fluentd or Fluent Bit agents to send logs to CloudWatch Logs.
  8. Google Cloud Logging (formerly Stackdriver):
    • If you’re using Google Kubernetes Engine (GKE), Google Cloud Logging can be integrated to collect and analyze logs from your cluster.
    • It supports various logging sources, including Kubernetes pods and Google Cloud Platform services.
  9. Azure Monitor Logs:
    • For Azure Kubernetes Service (AKS) clusters, Azure Monitor Logs can be used to collect, store, and analyze logs.
    • Fluentd can be configured to send logs to Azure Monitor Logs.
  10. Syslog Servers:
    • You can configure Kubernetes nodes to send logs to remote syslog servers for centralized collection.
    • Syslog-ng and rsyslog are commonly used syslog servers.

List of Options for Log Storage in Kubernetes?

Storing logs in Kubernetes involves selecting a storage solution that can handle the volume of log data generated by your cluster and applications. Here is a list of options for log storage in Kubernetes:

  1. Elasticsearch:
    • Elasticsearch is a popular open-source search and analytics engine often used as the backend for log storage and retrieval.
    • When combined with Kibana for visualization and Logstash for log processing, it forms the ELK Stack (Elasticsearch, Logstash, Kibana).
  2. Fluentd + Elasticsearch:
    • Fluentd can be configured to collect logs and forward them to Elasticsearch for storage.
    • This combination is commonly used for log aggregation in Kubernetes environments.
  3. Fluent Bit + Elasticsearch:
    • Fluent Bit can also forward logs to Elasticsearch, making it a lightweight alternative to Fluentd for log collection in Kubernetes.
  4. Loki:
    • Loki is a horizontally scalable, highly available, and multi-tenant log aggregation system designed for Kubernetes.
    • It is part of the Grafana observability stack and can be used for log storage, querying, and visualization.
  5. AWS CloudWatch Logs:
    • If you are running Kubernetes on AWS, you can use AWS CloudWatch Logs for log storage.
    • Logs collected by Fluentd or Fluent Bit can be sent to CloudWatch Logs.
  6. Google Cloud Storage:
    • Google Kubernetes Engine (GKE) clusters can use Google Cloud Storage as a log storage solution.
    • Logs can be exported to Cloud Storage for archival and analysis.
  7. Azure Blob Storage:
    • For Azure Kubernetes Service (AKS) clusters, Azure Blob Storage can be used to store log data.
    • Fluentd can be configured to send logs to Azure Blob Storage.
  8. S3 Bucket (Amazon S3):
    • You can configure Fluentd or Fluent Bit to send logs to an Amazon S3 bucket for storage.
    • Amazon S3 provides a scalable and durable storage solution for logs.
  9. NFS (Network File System):
    • You can mount an NFS volume to store logs in a centralized location outside of the cluster.
    • This approach is useful when you want to keep logs accessible from multiple clusters or locations.
  10. Local Disk:
    • Logs can be stored on local disks within nodes, but this approach is not recommended for production clusters because it lacks redundancy and scalability.
  11. Custom Databases:
    • Depending on your needs, you can store logs in custom databases like MySQL, PostgreSQL, or NoSQL databases.
    • This approach may require custom log processing and data retention policies.
  12. Managed Log Storage Services:
    • Cloud providers offer managed log storage services, such as AWS CloudWatch Logs, Google Cloud Logging, and Azure Monitor Logs, which can simplify log storage and retention in Kubernetes environments.
Rajesh Kumar
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