---name: dt-obs-kubernetesdescription: >-Trigger: Kubernetes pods", "K8s cluster health", "OOMKill", "pod restarts", "container CPU",license: Apache-2.0---

Infrastructure Kubernetes

Monitor and analyze Kubernetes infrastructure using Dynatrace DQL. Query cluster resources, monitor workload health, analyze pod placement, optimize costs, and assess security posture.

When to Use This Skill

• Monitoring Kubernetes cluster health and capacity
• Analyzing pod and container resource utilization
• Investigating pod failures, OOMKills, evictions, or crash loops
• Debugging degraded deployments, stuck rollouts, or node pressure
• Optimizing Kubernetes resource costs
• Assessing security posture and compliance
• Troubleshooting workload scheduling and placement
• Auditing ingress routing and network policies

Knowledge Base Structure

Core Monitoring (Start Here)

1. Cluster Inventory → references/cluster-inventory.md - Clusters, namespaces, resource distribution
2. Node Monitoring - Node capacity, CPU/memory usage, pod density
3. Pod Monitoring - Pod CPU, memory, lifecycle events
4. Workload Monitoring - Deployment, StatefulSet, DaemonSet resources

Advanced Topics

1. Configuration Analysis → references/labels-annotations.md - Parse k8s.object, labels, annotations
2. Scheduling & Placement → references/pod-node-placement.md - Node selectors, affinity, taints, HA
3. Cost Optimization - Right-sizing, waste detection, efficiency scoring
4. Security & Compliance - Privileged containers, security contexts

Key Concepts

Entity Types

Workloads: K8S_DEPLOYMENT, K8S_STATEFULSET, K8S_DAEMONSET, K8S_JOB, K8S_CRONJOB, K8S_HORIZONTALPODAUTOSCALER
Infrastructure: K8S_CLUSTER, K8S_NAMESPACE, K8S_NODE, K8S_POD
Configuration: K8S_SERVICE, K8S_CONFIGMAP, K8S_SECRET, K8S_PERSISTENTVOLUMECLAIM, K8S_PERSISTENTVOLUME, K8S_INGRESS, K8S_NETWORKPOLICY

Note: HPA, Job, CronJob, and configuration entity types are listed for inventory queries only. For HPA scaling analysis see references/workload-health.md; for PVC/PV see references/pv-pvc.md; for Ingress/NetworkPolicy see the corresponding reference files.

Query Types

smartscapeNodes - Query K8s entities (current state, no time range needed):

smartscapeNodes K8S_POD
| filter k8s.namespace.name == "production"
| fields k8s.cluster.name, k8s.pod.name

timeseries - Monitor metrics over time (always specify a from: range):

timeseries cpu = sum(dt.kubernetes.container.cpu_usage),
  by: {k8s.pod.name, k8s.namespace.name},
  from: now()-1h
| fieldsAdd avg_cpu = arrayAvg(cpu)

timeseries returns each metric as a time-bucket array. To collapse it to a scalar in a downstream fieldsAdd, use arrayAvg(series) or arraySum(series) — calling avg() or sum() on a series field outside the timeseries {} block is not supported.

fetch logs - Analyze log events:

fetch logs
| filter k8s.namespace.name == "production" and loglevel == "ERROR"

Core Fields

• k8s.cluster.name, k8s.namespace.name, k8s.pod.name, k8s.node.name
• k8s.workload.name, k8s.workload.kind, k8s.container.name
• k8s.object - Full JSON configuration for deep inspection
• tags[label] - Access labels and annotations

k8s.workload.kind values (always lowercase in DT — not Pascal-case as in the K8s API): "deployment", "statefulset", "daemonset", "replicaset", "job", "cronjob"

k8s.object availability:

Entity type	k8s.object available?
K8S_POD	Yes
K8S_NAMESPACE	Yes
K8S_NODE	Yes
Workload types (K8S_DEPLOYMENT, etc.)	Yes
K8S_CLUSTER	No

Available Metrics

CPU: dt.kubernetes.container.cpu_usage, cpu_throttled, limits_cpu, requests_cpu
Memory: dt.kubernetes.container.memory_working_set, limits_memory, requests_memory
Operations: dt.kubernetes.container.restarts, oom_kills
Node: dt.kubernetes.node.pods_allocatable, cpu_allocatable, memory_allocatable, dt.kubernetes.pods

Aggregation rule for requests/limits: Always use sum() — never avg() — when aggregating requests_cpu, limits_cpu, requests_memory, or limits_memory. avg() returns a per-container average and silently undercounts multi-container pods and multi-replica workloads. For DaemonSets (one pod per node), this error is proportional to cluster size.

Entity Disambiguation

K8S_POD vs CONTAINER: these are different entity types in Dynatrace.

• K8S_POD — K8s-native entities with k8s.object JSON, scheduling state, conditions, and K8s metrics. Use this skill.
• CONTAINER — Host-level container inventory (image, lifetime, host assignment). Use dt-obs-hosts skill instead.

The smartscape edge is CONTAINER --(is_part_of)--> K8S_POD. To reach containers from a pod, traverse backward:

smartscapeNodes K8S_POD
| filter k8s.namespace.name == "<namespace>"
| traverse edgeTypes: {is_part_of}, targetTypes: {CONTAINER}, direction: backward, fieldsKeep: {id}
| fields k8s.cluster.name, k8s.namespace.name, k8s.pod.name, container.id=id

Service → K8S_POD Correlation

No direct smartscape edge exists between SERVICE and K8S_POD. The correlation key is the shared dimension k8s.workload.name. See Service → Pod Drill-Down in references/pod-debugging.md for the full two-step pattern.

Common Workflows

1. Cluster Health Check

List all clusters:

smartscapeNodes K8S_CLUSTER
| fields k8s.cluster.name, k8s.cluster.version, k8s.cluster.distribution

Check node capacity:

timeseries {
  current_pods = avg(dt.kubernetes.pods),
  max_pods = avg(dt.kubernetes.node.pods_allocatable)
}, by: {k8s.node.name, k8s.cluster.name},
from: now()-1h
| fieldsAdd pod_capacity_pct = (arrayAvg(current_pods) / arrayAvg(max_pods)) * 100
| filter pod_capacity_pct > 80

Identify pods in non-Running state:

smartscapeNodes K8S_POD
| parse k8s.object, "JSON:config"
| fieldsAdd phase = config[status][phase]
| filter not(in(phase, {"Running", "Succeeded"}))
| fields k8s.cluster.name, k8s.namespace.name, k8s.pod.name, phase

Succeeded is a healthy terminal phase for completed Job pods — excluding it avoids false positives. Adjust if you specifically want to audit completed Jobs.

2. Resource Optimization

Pod-level — find over-provisioned pods (usage < 30%):

timeseries {
  cpu_usage = sum(dt.kubernetes.container.cpu_usage),
  cpu_requests = sum(dt.kubernetes.container.requests_cpu)
}, by: {k8s.pod.name, k8s.namespace.name, k8s.cluster.name},
from: now()-7d
| fieldsAdd usage_pct = (arrayAvg(cpu_usage) / arrayAvg(cpu_requests)) * 100
| filter usage_pct < 30 and arrayAvg(cpu_requests) > 0

Workload-level — aggregate across all replicas (required for correct DaemonSet accounting):

timeseries {
  cpu_usage = sum(dt.kubernetes.container.cpu_usage),
  cpu_requests = sum(dt.kubernetes.container.requests_cpu)
}, by: {k8s.workload.name, k8s.workload.kind, k8s.namespace.name, k8s.cluster.name},
from: now()-7d
| fieldsAdd
    avg_usage = arrayAvg(cpu_usage),
    avg_requests = arrayAvg(cpu_requests)
| fieldsAdd usage_pct = (avg_usage / avg_requests) * 100
| filter usage_pct < 30 and avg_requests > 0
| sort usage_pct asc

Use sum() for both cpu_usage and cpu_requests at every aggregation level. A DaemonSet running on 50 nodes has 50× the per-pod request total; avg() would report 1/50th of the true reserved capacity.

Identify containers without limits:

smartscapeNodes K8S_POD
| parse k8s.object, "JSON:config"
| expand container = config[spec][containers]
| fieldsAdd
    container_name = container[name],
    cpu_limit = container[resources][limits][cpu],
    memory_limit = container[resources][limits][memory]
| filter isNull(cpu_limit) or isNull(memory_limit)

3. Troubleshooting Pod Issues

Pod troubleshooting benefits from combining metrics (timeseries) with Kubernetes events (event stream) for a complete picture.

Metrics-Based Troubleshooting

Find pods with OOMKills:

timeseries oom_kills = sum(dt.kubernetes.container.oom_kills),
  by: {k8s.pod.name, k8s.namespace.name, k8s.cluster.name},
  from: now()-1h
| filter arraySum(oom_kills) > 0
| fieldsAdd total_oom_kills = arraySum(oom_kills)
| sort total_oom_kills desc

Analyze pod restart patterns:

timeseries restarts = sum(dt.kubernetes.container.restarts),
  by: {k8s.pod.name, k8s.namespace.name, k8s.cluster.name},
  from: now()-1h
| fieldsAdd total_restarts = arraySum(restarts)
| filter total_restarts > 5

Event-Based Troubleshooting

For operational events (pod restarts, OOM kills, evictions, scheduling failures), Kubernetes events provide richer context than metrics alone — including event reasons, messages, and timestamps.

When to use Kubernetes events over metrics:

• User asks about recent operational events ("show me pod restart events")
• User wants event details like reasons and messages
• User asks about events in a specific time window ("last 48 hours")
• User wants to correlate events with root causes

Kubernetes events are available through the get-events-for-kubernetes-cluster tool (a Dynatrace MCP tool). Call it with findAllK8Events: true to get events for all clusters, or findAllK8Events: false with either clusterId (k8s.cluster.uid) or kubernetesEntityId (dt.entity.kubernetes_cluster) to scope to one cluster. Use history to set the lookback window (e.g. "1h", "24h", "7d"; max "60d"). Prefer this tool when the user asks about OOM events, pod restarts, evictions, or cluster-wide event history. Fall back to the fetch events DQL pattern below if the tool is unavailable.

Important: distinguish event types when filtering results. Kubernetes events cover many categories. When the user asks about a specific event type, filter the results accordingly — do not report unrelated events:

User Asks About	Relevant Event Reasons	NOT Related
Pod restarts	BackOff, CrashLoopBackOff, Killing	Readiness probe failures, CPU throttling
OOM events	OOMKilling, OOMKilled	Memory pressure warnings
Evictions	Evicted, Preempting	Node pressure
Scheduling failures	FailedScheduling, Unschedulable	Resource quotas

For a complete answer, combine both approaches:

1. Use the events tool to get the event details (what happened, when, why)
2. Use timeseries metrics to show the quantitative impact (how many restarts, OOM kill counts over time)

Fetch Kubernetes Events via DQL

Pod restart and operational events can also be queried via DQL from the events table:

fetch events
| filter event.kind == "K8S_EVENT"
| filter event.type == "Warning"
| fields timestamp, k8s.cluster.name, k8s.namespace.name, k8s.pod.name,
    event.reason, event.message
| sort timestamp desc
| limit 50

Filter for specific event reasons:

fetch events
| filter event.kind == "K8S_EVENT"
| filter in(event.reason, {"OOMKilling", "BackOff", "Evicted", "FailedScheduling"})
| fields timestamp, k8s.cluster.name, k8s.namespace.name, k8s.pod.name,
    event.reason, event.message
| sort timestamp desc

Field names in fetch events: Use event.reason and event.message — not dt.kubernetes.event.reason. The dt.kubernetes.* prefix is for timeseries metrics, not the events table. Queries using the wrong prefix return zero results.

4. Security Assessment

Identify privileged containers:

smartscapeNodes K8S_POD
| parse k8s.object, "JSON:config"
| expand container = config[spec][containers]
| fieldsAdd
    container_name = container[name],
    privileged = container[securityContext][privileged]
| filter privileged == true

Find containers running as root:

smartscapeNodes K8S_POD
| parse k8s.object, "JSON:config"
| expand container = config[spec][containers]
| fieldsAdd
    container_name = container[name],
    run_as_user = container[securityContext][runAsUser],
    run_as_non_root = container[securityContext][runAsNonRoot]
| filter (isNull(run_as_user) or run_as_user == 0) and run_as_non_root != true

5. Scheduling Analysis

Verify pod distribution (HA compliance) for Deployments and StatefulSets:

smartscapeNodes K8S_POD
| filter in(k8s.workload.kind, {"deployment", "statefulset"})
| summarize pod_count = count(),
            node_count = countDistinct(k8s.node.name),
            by: {k8s.cluster.name, k8s.namespace.name, k8s.workload.name, k8s.workload.kind}
| fieldsAdd ha_compliant = node_count > 1
| filter pod_count >= 2 and not ha_compliant

DaemonSets are intentionally excluded — each pod runs on exactly one node by design, so single-node placement is not an HA violation for them.

6. DAVIS Problems affecting K8s Entities

Find active DAVIS problems affecting K8s entities:

fetch dt.davis.problems, from:now() - 2h
| filter not(dt.davis.is_duplicate) and event.status == "ACTIVE"
| filter iAny(startsWith(smartscape.affected_entities[][type], "K8S_"))
| fields display_id, event.name, event.category, affected_entity_ids = smartscape.affected_entities[][id]

smartscape.affected_entities is a record array; each record has id, type, and name. Use [][id] to get the array of Smartscape IDs to look up the affected entity, or [id] after expand smartscape.affected_entities. Without a preceding expand, [id] returns null silently. A filter cannot take a bare iterative expression, so wrap it in iAny(...).

Best Practices

Choosing the Right Data Source

User Question	Best Approach	Why
"Show me OOM events"	Events tool + metrics	Events give reasons/messages; metrics show trends
"Show me pod restart events"	Events tool + timeseries metrics	Events reveal the reason (BackOff, Killing, CrashLoopBackOff); dt.kubernetes.container.restarts metric gives the actual restart counts
"How many pod restarts?"	Timeseries metrics	Quantitative data over time
"What happened to my pods in the last 48h?"	Events tool	Operational event history with context
"Which pods are using the most CPU?"	Timeseries metrics	Resource utilization analysis
"List all clusters/namespaces"	smartscapeNodes	Entity discovery and inventory
"Are there scheduling failures?"	Events tool	Event reasons explain why
"Which workloads are over-provisioned?"	Timeseries metrics, workload-level	Must use sum() for requests; group by k8s.workload.name

Time Ranges

• smartscapeNodes — no time range; always returns current entity state.
• timeseries — always add from: now()-<window>. Default window is system-determined and often too short for trend analysis. Recommended defaults: now()-1h for recent spikes, now()-24h for daily patterns, now()-7d for weekly trends.
• fetch events — adding from: is recommended for reproducible results; without it the system default window applies.
• fetch dt.davis.problems — use from: now()-2h for active problems; extend to now()-7d to include recently closed problems.

Query Performance

1. Filter early - Apply cluster/namespace filters immediately
2. Use specific entity types - Avoid wildcards
3. Limit result sets - Use limit for exploration
4. Cache cluster lists - Store in variables
5. Omit k8s.object unless needed - Parsing it increases query cost significantly

Monitoring Recommendations

1. Set resource limits on all containers
2. Monitor OOMKills and adjust memory limits
3. Track CPU throttling and adjust CPU limits
4. Review resource efficiency regularly (target 70-80%)
5. Implement security best practices (non-root, read-only filesystem)
6. Use specific image tags (avoid :latest)

Configuration Standards

1. Use labels for organization (app, environment, team)
2. Set resource requests and limits
3. Configure health checks (liveness/readiness probes)
4. Use TLS for all ingress resources
5. Document with annotations

Troubleshooting

Problem	Cause	Solution
No pod data returned	Wrong entity type or missing cluster filter	Use K8S_POD (not POD); add k8s.cluster.name filter
k8s.object parsing errors	Complex JSON structure	Use parse k8s.object, "JSON:config" then access nested fields
Pod network metrics unavailable	Not available in Grail	Use service mesh metrics or host-level network metrics
Large result sets	No time range or cluster filter	Add time range and filter by cluster/namespace early
Missing labels in output	Labels accessed incorrectly	Use tags[label_name] to access labels
k8s.workload.kind filter returns no results	Value is Pascal-case (e.g. "Deployment")	Values are lowercase in DT: "deployment", "statefulset", "daemonset"

Limitations

Unavailable Metrics:

• Pod network metrics (rx_bytes, tx_bytes) are NOT available in Grail
• Workaround: Use service mesh metrics or host-level network metrics

Query Considerations:

• Minimize result set size: Do not include the k8s.object field if not necessary
• Keep result set as simple as possible: Parsing k8s.object increases query complexity
• Large clusters may require pagination or time-range limits
• Some K8s status fields update asynchronously

When to Load References

Load cluster-inventory.md when:

• Performing cluster, namespace, or resource distribution analysis
• Auditing workload counts across clusters

→ references/cluster-inventory.md

Load labels-annotations.md when:

• Filtering by labels or annotations
• Parsing k8s.object for detailed configuration inspection

→ references/labels-annotations.md

Load pod-node-placement.md when:

• Analyzing scheduling constraints (affinity, taints, tolerations)
• Verifying HA compliance and pod distribution

→ references/pod-node-placement.md

Load pod-debugging.md when:

• Investigating pod exit codes, crash loops, or init container failures
• Diagnosing image pull errors or service-to-pod connectivity issues
• Drilling down from a service problem to pod-level details

→ references/pod-debugging.md

Load workload-health.md when:

• Investigating degraded deployments or stuck rollouts
• Checking node conditions, CPU throttling, or HPA scaling
• Analyzing StatefulSet ordering or DaemonSet coverage

→ references/workload-health.md

Load pv-pvc.md when:

• Working with persistent storage (PVC/PV lifecycle, orphaned volumes)
• Checking StorageClass configurations

→ references/pv-pvc.md

Load ingress.md when:

• Analyzing ingress routing rules or TLS certificates
• Auditing ingress controller configurations

→ references/ingress.md

Load network-policies.md when:

• Listing or auditing network policies
• Checking namespace isolation configurations

→ references/network-policies.md

References

• cluster-inventory.md — Cluster, namespace, and resource distribution analysis
• labels-annotations.md — Label/annotation filtering and k8s.object parsing
• pod-node-placement.md — Scheduling, affinity, taints, and HA patterns
• pod-debugging.md — Exit codes, pod conditions, init containers, image pull errors, logs, service-to-pod drill-down
• workload-health.md — Degraded deployments, stuck rollouts, node conditions, CPU throttling, HPA, StatefulSet ordering
• pv-pvc.md — PVC/PV lifecycle, phase reference, orphaned volumes, StorageClass
• ingress.md — Routing rule parsing, TLS audit
• network-policies.md — Policy listing, namespace isolation audit

Related Skills

• dt-obs-problems — For problems associated with Kubernetes clusters (use dt.smartscape_source.id with K8S_ prefix filters)
• dt-dql-essentials — Core DQL syntax and query structure
• dt-obs-hosts — Host-level metrics for K8s nodes