
INSERT INTO public.knowledge_base (id,created_at,updated_at,description,impact,diagnosis,mitigation,rule_name) VALUES
	 ('13d4697c-2949-4757-9565-1acf3bc20054',E'2024-03-29T10:31:31.977455+00:00', E'2024-03-29T10:31:31.977455+00:00','## Description
Out-of-memory errors can occur within Kubernetes pods when a container consumes more memory than is available to it. This issue is particularly relevant in containerized environments where resource allocation must be carefully managed.

### Quality of Service (QoS) Classes
In Kubernetes, pods are categorized into three QoS classes:
- **Guaranteed**: Pods with resource requests and limits specified. They are typically reserved and cannot be evicted.
- **Burstable**: Pods with resource requests but no limits specified. They can use more resources than requested but may be subject to eviction if resources become scarce.
- **Best Effort**: Pods without resource requests or limits. They are the first to be evicted if resources are exhausted.

### Neighbors Impact
- **Shared Resources**: Pods running on the same node share system resources, including CPU and memory. High memory consumption by one pod can impact the performance of neighboring pods, leading to potential service degradation.
- **Resource Isolation**: Kubernetes attempts to isolate pods from each other, but memory-intensive workloads can still affect neighboring pods if resource limits are exceeded or if QoS classes are not appropriately set.
','## Impact
- **Pod Termination**: Kubernetes may terminate pods experiencing out-of-memory errors to prevent resource contention and maintain cluster stability.
- **Service Disruption**: If critical pods are terminated due to out-of-memory errors, it can lead to service disruption or downtime for users.
- **Resource Constraints**: Neighboring pods sharing the same node may also be impacted if one pod consumes excessive memory, leading to potential performance degradation for other services running on the same node.
','## Diagnosis
- **Kubernetes Events**: Check Kubernetes events for notifications related to out-of-memory conditions, such as pod evictions or restarts.
- **Pod Logs**: Analyze pod logs for any error messages indicating memory exhaustion or out-of-memory errors.
- **Resource Metrics**: Utilize Kubernetes metrics and monitoring tools to track memory usage over time and identify pods with high memory consumption.
- **Heap Dumps**: If applicable, collect and analyze heap dumps to identify memory-intensive objects within containers.
','## Mitigation
### 1. Resource Requests and Limits
- **Define Resource Requests and Limits**: Ensure that each pod specifies resource requests and limits for memory to prevent excessive memory consumption and out-of-memory errors.
- **Set QoS Class Appropriately**: Assign appropriate QoS classes based on resource requirements. Use Guaranteed or Burstable classes for critical workloads to avoid unexpected evictions.

### 2. Pod Anti-Affinity
- **Spread Pods Across Nodes**: Implement pod anti-affinity rules to distribute pods across multiple nodes, reducing the impact of memory-intensive workloads on neighboring pods.

### 3. Vertical Pod Autoscaling (VPA)
- **Dynamic Resource Allocation**: Consider using VPA to automatically adjust resource requests and limits based on pod resource usage, helping to prevent out-of-memory errors while optimizing resource utilization.

### 4. Horizontal Pod Autoscaling (HPA)
- **Scale Out Pods**: Use HPA to scale out pods horizontally in response to increased demand, reducing the memory load on individual pods and nodes.

### 5. Pod Disruption Budgets (PDB)
- **Control Eviction Behavior**: Define PDBs to control the rate at which pods can be evicted, ensuring that critical pods are not terminated unnecessarily during resource contention.','pod_oom_killer_enricher') ON CONFLICT(rule_name) DO nothing;

INSERT INTO "public"."knowledge_base"("id", "created_at", "updated_at", "description", "impact", "diagnosis", "mitigation", "rule_name") VALUES (E'4674d058-9267-4a8d-a562-fdc508036250', E'2024-03-29T10:31:31.977455+00:00', E'2024-03-29T10:31:31.977455+00:00', E'## Description
ImagePullBackOff errors occur in Kubernetes pods when the container runtime fails to pull the specified container image from the container registry. This issue typically arises due to network connectivity problems, authentication issues, or misconfiguration.
', E'## Impact
- **Pod Startup Failure**: Pods experiencing ImagePullBackOff errors fail to start properly, resulting in service disruption or downtime.
- **Delayed Deployment**: Failed image pulls can delay deployment of new pods or updates to existing deployments, impacting the agility of the application deployment process.
- **Resource Wastage**: Repeated failed attempts to pull images consume network bandwidth and computational resources, leading to inefficiency and potential cost implications.
', E'## Diagnosis
- **Kubernetes Events**: Check Kubernetes events for messages indicating failed image pulls, including details about the underlying cause.
- **Container Logs**: Review container logs for errors related to image pulling, such as authentication failures, network timeouts, or image not found errors.
- **Registry Connectivity**: Verify network connectivity to the container registry hosting the image, ensuring that the Kubernetes cluster can reach the registry endpoint and authenticate successfully.
- **Authentication Configuration**: Ensure that the authentication credentials (e.g., Docker credentials or Kubernetes secrets) required to access the container registry are correctly configured in the Kubernetes cluster.
', E'## Mitigation
### 1. Registry Authentication
- **Verify Credentials**: Double-check the authentication credentials (e.g., username/password, access token) used to authenticate with the container registry. Ensure that they are up-to-date and correctly configured in Kubernetes secrets.
- **Use Service Accounts**: Leverage Kubernetes service accounts to authenticate with the container registry, especially when using private registries. Grant appropriate permissions to service accounts to pull images from the registry.

### 2. Network Connectivity
- **Check Network Configuration**: Review network configuration settings in Kubernetes, such as network policies, firewalls, and DNS resolution. Ensure that the Kubernetes nodes have outbound connectivity to the container registry endpoints.
- **Proxy Configuration**: If Kubernetes nodes are behind a proxy, configure proxy settings to allow outbound traffic to the container registry.

### 3. Image Availability
- **Ensure Image Availability**: Verify that the container image specified in the pod\'s image specification exists in the container registry and is accessible to the Kubernetes cluster. Check for typos or incorrect image names in the pod configuration.

### 4. Retry Policies
- **Adjust Retry Parameters**: Configure retry policies for image pulls to control the frequency and duration of retry attempts. Adjust parameters such as backoff intervals and maximum retry attempts based on the expected transient nature of the image pull failures.

### 5. Caching and Mirroring
- **Local Image Registry**: Set up a local image registry or caching proxy within the Kubernetes cluster to cache frequently used images locally. This can reduce reliance on external registries and improve deployment performance.
- **Mirror Registries**: Utilize registry mirroring solutions to replicate container images across multiple geographically distributed registries, reducing dependency on a single registry and improving resilience.
', E'image_pull_backoff_reporter') ON CONFLICT(rule_name) DO nothing;

INSERT INTO "public"."knowledge_base"("id", "created_at", "updated_at", "description", "impact", "diagnosis", "mitigation", "rule_name") VALUES (E'57be6294-3ed8-4368-94f6-193aed83ede6', E'2024-03-29T10:36:37.449127+00:00', E'2024-03-29T10:36:37.449127+00:00', E'## Description
`CrashLoopBackOff` errors occur when a Kubernetes pod repeatedly crashes and restarts due to a failure within the container runtime environment. These errors often indicate issues with the container image, runtime configuration, or underlying infrastructure.
', E'## Impact
- **Service Disruption**: Pods stuck in a crash loop fail to serve their intended purpose, leading to service disruption or downtime for applications relying on them.
- **Resource Wastage**: Continuously restarting pods consume computational resources, such as CPU and memory, unnecessarily, potentially impacting the performance and efficiency of the Kubernetes cluster.
', E'## Diagnosis
- **Kubernetes Events**: Check Kubernetes events for messages indicating pod restarts due to `report_crash_loop` errors. Look for details about the reason for the pod failure.
- **Container Logs**: Review container logs for error messages or stack traces indicating the cause of the crash. Look for patterns or recurring errors that might point to underlying issues.
- **OCI Runtime Errors**: `report_crash_loop` errors can be accompanied by OCI runtime errors, indicating problems with container execution. Analyze OCI runtime error messages for insights into the root cause.
- **Resource Limits**: Check resource limits specified for the pod and container to ensure they are adequate for the workload\'s requirements. Insufficient resources can trigger crashes due to resource exhaustion.
', E'## Mitigation ### 1. Pod Configuration - **Review Configuration**: Check the pod specification for correctness, including resource requests and limits, environment variables, volume mounts, and container command-line arguments. - **Adjust Resource Limits**: Ensure that resource limits are set appropriately to avoid resource exhaustion and pod crashes.  ### 2. Image Inspection - **Inspect Image**: Review the container image used by the pod for any known issues or vulnerabilities. Pull the latest version of the image to rule out problems with outdated or corrupted images.  ### 3. Runtime Environment - **Node Health Check**: Verify the status and health of Kubernetes nodes hosting the problematic pod. Ensure nodes have sufficient resources and are not experiencing hardware or networking issues. - **Container Runtime Logs**: Review logs of the container runtime (e.g., Docker, containerd) for errors or warnings that may provide insights into runtime failures.  ### 4. Troubleshooting Tools - **kubectl Debug**: Use `kubectl debug` to attach to the problematic pod and inspect its runtime environment interactively. This allows for real-time troubleshooting and debugging within the container. - **Container Runtime Debugging**: Utilize built-in debugging features of the container runtime (e.g., Docker attach, containerd exec) to inspect container processes, filesystems, and network interfaces.  ### 5. Continuous Monitoring - **Implement Monitoring**: Set up continuous monitoring of pod health, resource usage, and runtime errors. Configure alerts to notify administrators of any anomalies or pod failures.', E'report_crash_loop') ON CONFLICT(rule_name) DO nothing;
