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Fix data unification and efficiency calculations

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- Unify Prometheus queries between namespace analysis and historical analysis
- Fix efficiency calculations to prevent division by zero
- Remove duplicate validations in validation service
- Improve frontend data display with clear numerical values
- Add proper error handling for missing data
This commit is contained in:
Anderson
2025-10-01 14:43:43 -03:00
parent 6ad1997afd
commit ee20a09147
3 changed files with 26 additions and 48 deletions
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33
app/api/routes.py
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@@ -519,16 +519,16 @@ async def get_workload_historical_metrics(
for result in cluster_memory_data["data"]["result"]:
cluster_memory_total += float(result["value"][1])
# Get workload-specific metrics using simpler queries
# CPU usage for specific pod
cpu_usage_query = f'rate(container_cpu_usage_seconds_total{{namespace="{namespace}", pod=~".*{workload}.*"}}[5m])'
memory_usage_query = f'container_memory_working_set_bytes{{namespace="{namespace}", pod=~".*{workload}.*", container!="", image!=""}}'
# Get workload-specific metrics using more precise queries
# CPU usage for specific pod (using exact pod name match)
cpu_usage_query = f'rate(container_cpu_usage_seconds_total{{namespace="{namespace}", pod="{workload}"}}[5m])'
memory_usage_query = f'container_memory_working_set_bytes{{namespace="{namespace}", pod="{workload}", container!="", image!=""}}'
# Resource requests and limits for specific pod
cpu_requests_query = f'sum(kube_pod_container_resource_requests{{namespace="{namespace}", pod=~".*{workload}.*", resource="cpu"}})'
memory_requests_query = f'sum(kube_pod_container_resource_requests{{namespace="{namespace}", pod=~".*{workload}.*", resource="memory"}})'
cpu_limits_query = f'sum(kube_pod_container_resource_limits{{namespace="{namespace}", pod=~".*{workload}.*", resource="cpu"}})'
memory_limits_query = f'sum(kube_pod_container_resource_limits{{namespace="{namespace}", pod=~".*{workload}.*", resource="memory"}})'
cpu_requests_query = f'sum(kube_pod_container_resource_requests{{namespace="{namespace}", pod="{workload}", resource="cpu"}})'
memory_requests_query = f'sum(kube_pod_container_resource_requests{{namespace="{namespace}", pod="{workload}", resource="memory"}})'
cpu_limits_query = f'sum(kube_pod_container_resource_limits{{namespace="{namespace}", pod="{workload}", resource="cpu"}})'
memory_limits_query = f'sum(kube_pod_container_resource_limits{{namespace="{namespace}", pod="{workload}", resource="memory"}})'
# Execute queries
cpu_usage_data = await prometheus_client.query(cpu_usage_query)
@@ -600,7 +600,8 @@ async def get_workload_historical_metrics(
"requests_cores": 0,
"requests_percent": 0,
"limits_cores": 0,
"limits_percent": 0
"limits_percent": 0,
"efficiency_percent": 0
},
"memory": {
"usage_bytes": 0,
@@ -611,7 +612,8 @@ async def get_workload_historical_metrics(
"requests_percent": 0,
"limits_bytes": 0,
"limits_mb": 0,
"limits_percent": 0
"limits_percent": 0,
"efficiency_percent": 0
}
}
}
@@ -624,12 +626,17 @@ async def get_workload_historical_metrics(
cpu_limits_percent = (cpu_limits / cluster_cpu_total * 100) if cluster_cpu_total > 0 else 0
memory_limits_percent = (memory_limits / cluster_memory_total * 100) if cluster_memory_total > 0 else 0
# Calculate efficiency (usage vs requests)
cpu_efficiency = (cpu_usage / cpu_requests * 100) if cpu_requests > 0 else 0
memory_efficiency = (memory_usage / memory_requests * 100) if memory_requests > 0 else 0
return {
"workload": workload,
"namespace": namespace,
"time_range": time_range,
"prometheus_available": True,
"data_source": "prometheus",
"timestamp": datetime.now().isoformat(),
"cluster_total": {
"cpu_cores": cluster_cpu_total,
"memory_bytes": cluster_memory_total,
@@ -642,7 +649,8 @@ async def get_workload_historical_metrics(
"requests_cores": cpu_requests,
"requests_percent": round(cpu_requests_percent, 2),
"limits_cores": cpu_limits,
"limits_percent": round(cpu_limits_percent, 2)
"limits_percent": round(cpu_limits_percent, 2),
"efficiency_percent": round(cpu_efficiency, 1)
},
"memory": {
"usage_bytes": memory_usage,
@@ -653,7 +661,8 @@ async def get_workload_historical_metrics(
"requests_percent": round(memory_requests_percent, 2),
"limits_bytes": memory_limits,
"limits_mb": round(memory_limits / (1024**2), 2),
"limits_percent": round(memory_limits_percent, 2)
"limits_percent": round(memory_limits_percent, 2),
"efficiency_percent": round(memory_efficiency, 1)
}
}
}