Running production workloads on Amazon EKS demands more than basic health checks. After managing dozens of Kubernetes clusters across various industries, I’ve learned that the difference between a resilient system and a fragile one often comes down to how deeply you can see into your infrastructure. This guide shares the observability patterns and CDK-based automation that have proven invaluable in my production environments.
The Three Pillars of Observability
Observability in Kubernetes environments rests on three fundamental pillars: metrics, logs, and traces. Each provides a different lens through which to understand system behavior, and together they form a complete picture of what’s happening inside your clusters.
Metrics tell you what is happening at any given moment. CPU utilization, memory consumption, request rates, and error counts provide quantitative measurements that can trigger alerts and inform scaling decisions. In EKS, metrics flow from multiple sources: the Kubernetes API server, node-level exporters, and application-specific endpoints.
Logs capture the narrative of your system’s behavior. Container stdout/stderr, application logs, and audit logs from the control plane create a detailed record of events. The challenge in Kubernetes is aggregating logs from ephemeral containers that may be rescheduled across nodes at any time.
Traces reveal the journey of individual requests through your distributed system. When a user action triggers calls across multiple microservices, traces connect those disparate events into a coherent story, making it possible to identify bottlenecks and failures in complex architectures.
Building the Metrics Pipeline with CDK
AWS CDK transforms infrastructure provisioning from manual configuration into repeatable, version-controlled code. For EKS monitoring, this means we can define our entire observability stack as Python constructs that deploy consistently across environments.
The foundation starts with Container Insights, which provides out-of-the-box metrics for EKS clusters. However, production systems typically need custom metrics that reflect business-specific concerns. Here’s how I structure the monitoring stack:
from aws_cdk import (
Stack,
aws_cloudwatch as cloudwatch,
aws_sns as sns,
aws_cloudwatch_actions as cw_actions,
Duration,
)
from constructs import Construct
class EKSObservabilityStack(Stack):
def __init__(self, scope: Construct, id: str, cluster_name: str, **kwargs):
super().__init__(scope, id, **kwargs)
# Create SNS topic for alerts
alert_topic = sns.Topic(
self, "EKSAlertTopic",
display_name=f"{cluster_name}-alerts"
)
# CPU utilization alarm with stabilization
cpu_alarm = cloudwatch.Alarm(
self, "HighCPUAlarm",
metric=cloudwatch.Metric(
namespace="ContainerInsights",
metric_name="pod_cpu_utilization",
dimensions_map={"ClusterName": cluster_name},
statistic="Average",
period=Duration.minutes(5)
),
threshold=80,
evaluation_periods=3,
comparison_operator=cloudwatch.ComparisonOperator.GREATER_THAN_THRESHOLD,
alarm_description="Pod CPU utilization exceeds 80% for 15 minutes"
)
cpu_alarm.add_alarm_action(cw_actions.SnsAction(alert_topic))
# Memory pressure alarm
memory_alarm = cloudwatch.Alarm(
self, "MemoryPressureAlarm",
metric=cloudwatch.Metric(
namespace="ContainerInsights",
metric_name="pod_memory_utilization",
dimensions_map={"ClusterName": cluster_name},
statistic="Average",
period=Duration.minutes(5)
),
threshold=85,
evaluation_periods=2,
comparison_operator=cloudwatch.ComparisonOperator.GREATER_THAN_THRESHOLD
)
memory_alarm.add_alarm_action(cw_actions.SnsAction(alert_topic))Log Aggregation Architecture
Container logs in EKS present unique challenges. Pods are ephemeral, potentially running on any node in the cluster, and their logs disappear when containers terminate. A robust log aggregation strategy must capture logs in real-time and persist them to durable storage.
Fluent Bit has become my preferred choice for log collection in EKS. Deployed as a DaemonSet, it runs on every node and captures container logs before forwarding them to CloudWatch Logs or OpenSearch. The key configuration decisions involve filtering, parsing, and routing logs based on their source and content.
For production clusters, I implement a tiered retention strategy: recent logs (7-14 days) remain in CloudWatch Logs for quick access, while older logs transition to S3 for cost-effective long-term storage. This approach balances operational needs with cost management.
Distributed Tracing with AWS X-Ray
Tracing becomes essential when debugging issues that span multiple services. AWS X-Ray integrates natively with EKS through the OpenTelemetry Collector, which can be deployed as a sidecar or DaemonSet depending on your instrumentation strategy.
The OpenTelemetry approach offers flexibility: applications instrumented with OpenTelemetry SDKs can export traces to X-Ray, Jaeger, or other backends without code changes. This vendor-neutral approach has saved significant refactoring effort when requirements changed.
Production Alert Design Principles
Effective alerting requires careful thought about what truly warrants human attention. Alert fatigue is real, and teams that receive too many notifications quickly learn to ignore them. My approach follows several principles developed through years of on-call experience.
First, alert on symptoms rather than causes. Users care whether the service is responding, not whether CPU is high. High CPU that doesn’t affect user experience shouldn’t page anyone at 3 AM. Second, include stabilization periods to avoid alerting on transient spikes. A brief CPU spike during deployment is normal; sustained high utilization indicates a problem.
Third, provide actionable context in alert messages. An alert should tell the responder what’s wrong, where to look, and ideally suggest initial investigation steps. Finally, implement escalation paths so that unacknowledged alerts reach additional responders.
Cost-Effective Observability
Observability costs can spiral quickly in large clusters. CloudWatch charges for metrics ingestion, storage, and queries. Log storage costs accumulate with retention. Tracing adds overhead to every request. Managing these costs requires intentional decisions about what to collect and how long to retain it.
Sampling strategies help control tracing costs without sacrificing visibility. Collecting 10% of traces still provides statistical significance for identifying patterns while reducing costs by 90%. For logs, aggressive filtering at the collection layer prevents low-value data from reaching storage.
The Path Forward
EKS observability continues evolving with new AWS services and open-source tools. Amazon Managed Service for Prometheus offers a managed Prometheus-compatible metrics backend. Amazon Managed Grafana provides visualization without operational overhead. These managed services reduce the complexity of running observability infrastructure while maintaining compatibility with existing tooling.
The investment in comprehensive observability pays dividends during incidents. When something goes wrong, and it will, the ability to quickly understand system state, trace request flows, and correlate events across services determines how fast you can restore service. Build your observability foundation before you need it.