Collect container stdout and stderr
Capture logs emitted to stdout and stderr by containers running in Kubernetes pods, including application output and runtime errors.
Kubernetes Logs Monitoring & Observability
Explore the nuances of Kubernetes Logging, offering a comprehensive approach to efficiently troubleshoot Kubernetes, trace issues, and optimize cluster performance.
Challenges
Monitor Kubernetes logs across clusters, nodes, and workloads
Aggregate pod and namespace logs
Organize logs by pod, namespace, workload, and label selectors to simplify troubleshooting in multi-tenant Kubernetes clusters.
Track pod lifecycle events
Ingest logs related to pod creation, restarts, crashes, and termination to analyze deployment and stability issues.
Monitor node-level system logs
Collect kubelet and container runtime logs from worker nodes to diagnose scheduling failures, image pull errors, and resource pressure.
Debug CrashLoopBackOff scenarios
Analyze container startup logs and failure messages to identify configuration errors and repeated pod restarts.
Correlate logs with Kubernetes metadata
Enrich log entries with Kubernetes context such as pod name, node, namespace, and deployment to speed up root cause analysis.
Observe control plane log signals
Capture logs from Kubernetes control plane components to investigate cluster-level issues and API server errors.
Handle high-volume cluster logging
Centralize Kubernetes logs from large clusters while maintaining query performance during traffic spikes and rolling deployments.
Pod and Container Lifecycle Visibility
- Track log generation across pod creation, container start and stop events, and workload execution to understand lifecycle behavior in Kubernetes environments.
- Correlate logs with deployment changes, scaling events, and scheduling decisions across cluster workloads.
- Identify pod restarts, crash loops, and container failures affecting workload stability.
- Detect disruptions in workload orchestration and container lifecycle management impacting service availability.
Node, Scheduler, and Control Plane Diagnostics
- Capture logs from kubelet, scheduler, and control plane components to monitor cluster operations and workload placement.
- Correlate node-level events with pod scheduling failures, resource constraints, and infrastructure issues.
- Identify recurring failures in cluster coordination, node health, and workload orchestration.
- Detect hidden infrastructure issues affecting cluster stability and workload execution.
Workload Performance and Resource Signals
- Analyze execution logs, resource warnings, and workload timing signals across pods and containers.
- Correlate log activity with CPU usage, memory consumption, and network behavior during workload execution.
- Identify excessive logging from containers increasing storage usage and processing overhead.
- Detect performance degradation through abnormal workload behavior and irregular log volume patterns.
Security and Cluster Activity Monitoring
- Track unauthorized access attempts, RBAC violations, and suspicious cluster activity captured in logs.
- Identify abnormal traffic behavior and misuse affecting Kubernetes environments.
- Correlate application and infrastructure logs for incident investigation across distributed services.
- Detect operational and security incidents affecting cluster reliability using centralized log insights.
Why Atatus
Why choose Atatus for Kubernetes logs monitoring?
Production-ready visibility into Kubernetes workloads, pod lifecycle events, and cluster operations
01
Kubernetes-native log collection
Designed to collect logs directly from Kubernetes pods and nodes without requiring changes to application containers.
02
Deep workload context
Automatically associates logs with Kubernetes metadata such as namespaces, deployments, nodes, and labels.
03
Faster incident investigation
Correlates pod restarts, container crashes, and deployment events across logs to reduce mean time to resolution.
04
Scales with growing clusters
Handles high log volumes generated by large Kubernetes clusters and dynamic workloads without performance degradation.
05
Supports modern Kubernetes environments
Works across managed Kubernetes services, self-hosted clusters, and hybrid infrastructure setups.
06
Built for SRE and platform teams
Provides the log visibility required by platform engineers to operate and maintain reliable Kubernetes infrastructure.
Teams
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Debug errors and slow paths with the full context of what happened.
Atatus for developersDevOps
Track deployments and see how each release affects stability.
Atatus for DevOpsRelease engineers
Watch latency and error rates to ship with less production risk.
Atatus for release engineers
FAQ
Frequently Asked Questions
Pricing, deployment, data and the agent. If something is missing, a person will answer it on a demo call.
Kubernetes Logs Monitoring in Atatus is a comprehensive solution that allows users to track, analyze, and troubleshoot logs generated by Kubernetes applications. It provides real-time visibility into log data, helping to identify issues, improve performance, and ensure the health of your Kubernetes applications.
Atatus's Kubernetes Logs Monitoring integrates seamlessly with your Kubernetes applications, capturing log data and centralizing it for easy analysis. The solution offers powerful search and filter capabilities, making it simple to pinpoint specific events or issues within your logs. It also provides alerting features to notify you of critical events.
By offering real-time log streaming and advanced search capabilities, Kubernetes Logs Monitoring enables quicker issue identification and resolution. The ability to correlate logs with performance metrics provides a contextual understanding, streamlining the troubleshooting process.
Yes, Atatus provides powerful log filtering and searching capabilities. Users can filter logs based on specific criteria, such as log level, timestamp, or custom keywords. The search functionality allows for efficient navigation through large log datasets.
Yes, Kubernetes Logs Monitoring seamlessly integrates with other Atatus products, such as APM (Application Performance Monitoring). This integration allows for a holistic view of your Kubernetes applications, combining logs with performance metrics such as Distributed Traces to provide a comprehensive understanding of your application's health.
Log data parsing prioritizes customer security by meticulously anonymizing Personally Identifiable Information (PII) data. Sensitive fields can be completely excluded during the parsing process, ensuring the privacy and compliance of customer information.
In Kubernetes Logs Monitoring, log pipelines are customizable workflows that allow users to filter, parse, and fine-tune log data based on specific criteria.
Log pipelines can be customized by using diverse filters and defining custom parsing rules. Users have the flexibility to configure pipelines to meet their specific monitoring requirements, ensuring that log data is processed and organized according to their preferences.
Yes, log pipelines can be configured to filter log data based on various attributes such as log source, severity, timestamp, or any custom field. This customization allows users to focus on specific aspects of log data that are most relevant to their monitoring goals.
Saved Views in Kubernetes Logs Monitoring enable you to create and save custom configurations for different troubleshooting scenarios. This feature simplifies collaboration, allows for quick context switching, and automates repetitive tasks, significantly improving overall efficiency.
If you exceed your log ingestion limits, we would contact you to discuss on stopping further processing new log data or upgrade your subscription.
You can choose to store logs in Atatus for a limited time (e.g., 7 days) or export them to external storage solutions like Amazon S3 for long-term retention.
To access historical log data beyond the retention period, you can rely on log data exports from Amazon S3, where you can push the logs into Atatus for further analysis.
Yes, Atatus provides users with the flexibility to customize log retention settings. Users can adjust retention periods based on their specific needs, aligning with compliance standards or internal data management policies.