Huawei Cloud Cce Metric Analyzer

Huawei Cloud CCE Metric Analyzer

Overview

Query and analyze metrics for CCE clusters (Pod/Node CPU/memory/disk) and cloud resources (ECS, ELB, EIP, NAT). Supports threshold-based anomaly detection, status classification (critical/warning/normal), and full-cluster monitoring aggregation.

Architecture: python3 scripts/huawei-cloud.py dispatcher → Huawei Cloud Python SDK + AOM Prometheus → Pod/Node metrics, ECS/ELB/EIP/NAT metrics → Threshold classification → Anomaly detection

Related Skills:

• huawei-cloud-cce-pod-failure-diagnoser - Pod CrashLoopBackOff, OOMKilled, restart storms
• huawei-cloud-cce-node-failure-diagnoser - Node health, resource pressure diagnosis
• huawei-cloud-cce-kubernetes-event-analyzer - Warning events, failure patterns
• huawei-cloud-cce-capacity-trend-forecaster - Capacity planning and trend forecasting
• huawei-cloud-cce-cost-optimization-advisor - Resource cost optimization
• huawei-cloud-cce-auto-remediation-runner - Remediation actions (scale, resize, drain)

Capabilities:

• Pod CPU/memory TopN ranking and single Pod time-series metrics
• Node CPU/memory/disk TopN ranking and single Node time-series metrics
• ECS instance CPU/memory/disk/network metrics
• ELB connection, bandwidth, QPS metrics
• EIP bandwidth, traffic, packet loss metrics
• NAT Gateway SNAT connection metrics
• Full-cluster monitoring aggregation with anomaly detection (80% threshold)
• Threshold-based status classification (critical/warning/normal/unknown)

Typical Use Cases:

• "Show Pods with the highest CPU usage in my cluster"
• "Get Node memory usage ranking"
• "Check ECS instance resource metrics"
• "What is the ELB QPS for my load balancer?"
• "Show EIP bandwidth usage"
• "Aggregate all monitoring data for the cluster"
• "Which resources have exceeded critical thresholds?"
• "Detect resource anomalies in the last hour"

Prerequisites

1. Python Dependencies

• Python 3.8+ with huaweicloudsdkcce, huaweicloudsdkcore, huaweicloudsdkaom, huaweicloudsdkces packages
• Run environment check before first use (see Verification section)

2. Credential Configuration

• Valid Huawei Cloud credentials (AK/SK mode)
• Security Rules:
  • 🚫 Never expose AK/SK values in code, conversation, or commands
  • 🚫 Never use echo $HUAWEI_AK or echo $HUAWEI_SK to check credentials
  • ✅ Use environment variables: HUAWEI_AK, HUAWEI_SK, HUAWEI_REGION
  • ✅ Prefer IAM users over root account for cloud operations
  • ✅ Enable MFA for sensitive operations

Configuration Method (Environment Variables Only):

export HUAWEI_AK=
export HUAWEI_SK=
export HUAWEI_REGION=cn-north-4

⚠️ Important Security Notes:

• Never commit credentials to version control
• Use IAM users with minimal required permissions
• Enable MFA for sensitive operations
• Rotate AK/SK regularly

3. IAM Permission Requirements

Permission Failure Handling:

1. When any command fails due to IAM permission errors, display the required permission list
2. Guide the user to create a custom policy in the IAM console and grant authorization
3. Pause execution and wait for user confirmation that permissions have been granted

Core Commands

All commands use the Python dispatcher script: python3 scripts/huawei-cloud.py ...

1. CCE Pod Metrics

# Pod TopN — cluster-wide CPU/memory ranking
python3 scripts/huawei-cloud.py huawei_get_cce_pod_metrics_topN \
  region=cn-north-4 cluster_id= \
  namespace=default top_n=10 hours=1

# Pod TopN with label selector
python3 scripts/huawei-cloud.py huawei_get_cce_pod_metrics_topN \
  region=cn-north-4 cluster_id= \
  namespace=default label_selector="app=nginx,version=v1" top_n=10 hours=1

# Single Pod time-series
python3 scripts/huawei-cloud.py huawei_get_cce_pod_metrics \
  region=cn-north-4 cluster_id= \
  pod_name=my-app-xxx namespace=default hours=1

2. CCE Node Metrics

# Node TopN — cluster-wide CPU/memory/disk ranking
python3 scripts/huawei-cloud.py huawei_get_cce_node_metrics_topN \
  region=cn-north-4 cluster_id= \
  top_n=10 hours=1

# Single Node time-series
python3 scripts/huawei-cloud.py huawei_get_cce_node_metrics \
  region=cn-north-4 cluster_id= \
  node_ip=10.0.0.1 hours=1

3. Cloud Resource Metrics

# ECS instance metrics
python3 scripts/huawei-cloud.py huawei_get_ecs_metrics \
  region=cn-north-4 instance_id=

# ELB metrics
python3 scripts/huawei-cloud.py huawei_get_elb_metrics \
  region=cn-north-4 elb_id= hours=1

# EIP metrics
python3 scripts/huawei-cloud.py huawei_get_eip_metrics \
  region=cn-north-4 eip_id= hours=1

# NAT Gateway metrics
python3 scripts/huawei-cloud.py huawei_get_nat_gateway_metrics \
  region=cn-north-4 nat_gateway_id= hours=1

4. Cluster Monitoring Aggregation

# Aggregate all monitoring data with anomaly detection
python3 scripts/huawei-cloud.py huawei_cce_cluster_monitoring_aggregation \
  region=cn-north-4 cluster_id= \
  start_time="2026-05-30 00:00:00" end_time="2026-05-30 23:59:59" \
  namespace=default top_n=10

This tool aggregates: Pod TopN CPU/memory, Node TopN CPU/memory/disk, ELB metrics (with LoadBalancer service association), NAT Gateway metrics, EIP metrics (bandwidth, packet loss), and anomaly detection using 80% threshold.

Parameter Reference

Common Parameters

huawei_get_cce_pod_metrics_topN Parameters

huawei_get_cce_pod_metrics Parameters

huawei_get_cce_node_metrics_topN Parameters

huawei_get_cce_node_metrics Parameters

huawei_get_ecs_metrics Parameters

huawei_get_elb_metrics Parameters

huawei_get_eip_metrics Parameters

huawei_get_nat_gateway_metrics Parameters

huawei_cce_cluster_monitoring_aggregation Parameters

Output Format

See Output Schema for the complete JSON response structure.

Key output fields:

• success — boolean, true if query completed
• region — Huawei Cloud region
• cluster_id / cluster_name — CCE cluster identity
• aom_instance_id — AOM Prometheus instance used for metric queries
• metrics — Dict with cpu/memory/disk data per resource, including status classification
• time_series — Historical data points with timestamp, time, average, min, max
• status — Threshold classification: critical (>80% CPU, >85% memory/disk), warning (>50% CPU/memory, >70% disk), normal (below warning), unknown (no data)

Cloud resource metric fields (ECS/ELB/EIP/NAT):

• ECS: cpu_util, mem_util, disk_util, network_incoming/outgoing_bytes_rate, disk_read/write_bytes_rate
• ELB: m1_cps, m14_l7_rt, mb_l7_qps, mc-me-mf_l7_http_2xx-5xx
• EIP: upstream/downstream_bandwidth, upstream/downstream_bandwidth_usage, upstream/downstream_traffic, packet_loss_rate
• NAT: snat_connection, inbound/outbound_bandwidth, snat_connection_ratio

Verification

1. Run python3 scripts/huawei-cloud.py huawei_get_cce_pod_metrics_topN region=cn-north-4 cluster_id= namespace=default top_n=5 to verify Pod metric queries
2. Run python3 scripts/huawei-cloud.py huawei_get_cce_node_metrics_topN region=cn-north-4 cluster_id= top_n=5 to verify Node metric queries
3. Run python3 scripts/huawei-cloud.py huawei_get_ecs_metrics region=cn-north-4 instance_id= to verify CES metric connectivity

Best Practices

1. Start with TopN for cluster-wide overview — use Pod/Node TopN before drilling into individual resources
2. Time-bound queries — keep hours small (1-4) for recent analysis; cap at 24 hours for historical reviews
3. Use namespace filtering — always provide namespace to reduce noise in Pod TopN results
4. Check status classification — focus on critical and warning resources first; normal resources can be skipped
5. Use aggregation for full-cluster health checks — huawei_cce_cluster_monitoring_aggregation gives a one-shot overview of all resource metrics with anomaly detection
6. Correlate with events — if metrics show anomalies, check huawei-cloud-cce-kubernetes-event-analyzer for related warning events
7. Hand off, don't remediate — this skill is read-only; hand off to diagnosis skills for root cause analysis
8. Sanitize output — do not expose production pod names, node IPs, or cluster IDs in public summaries; use redacted examples

Reference Documents

Notes

• This skill is strictly read-only — it only queries and analyzes metrics; no modifications are made to resources or configurations
• Thresholds (CPU >80%, Memory >85%, Disk >85%) are predefined baselines — actual thresholds may vary by workload SLO; recommend users customize thresholds based on their specific requirements
• AK/SK must never be hardcoded — use environment variables only
• The Python dispatcher script (scripts/huawei-cloud.py) is the only execution method — do not use hcloud CLI or direct API calls for metric queries
• AOM Prometheus instance is auto-discovered — no need to manually specify aom_instance_id
• Cloud resource metrics (ECS/ELB/EIP/NAT) use CES (Cloud Eye Service), not AOM
• Do not make automatic scaling or remediation decisions based solely on metric analysis — forward to huawei-cloud-cce-auto-remediation-runner only if explicitly requested and validated

Common Pitfalls