MAJOR: corrigido valores hardcoded e implementado exibição inteligente de unidades (milicores/MiB)
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@@ -629,6 +629,36 @@ class ValidationService:
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return quotas
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async def _get_cluster_capacity(self) -> tuple[float, float, int]:
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"""Get real cluster capacity from nodes"""
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try:
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from kubernetes import client
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v1 = client.CoreV1Api()
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nodes = v1.list_node()
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total_cpu_cores = 0.0
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total_memory_bytes = 0.0
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total_nodes = len(nodes.items)
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for node in nodes.items:
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# Parse CPU capacity
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cpu_capacity = node.status.capacity.get("cpu", "0")
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total_cpu_cores += self._parse_cpu_value(cpu_capacity)
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# Parse Memory capacity
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memory_capacity = node.status.capacity.get("memory", "0")
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total_memory_bytes += self._parse_memory_value(memory_capacity)
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# Convert memory to GiB
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total_memory_gib = total_memory_bytes / (1024 * 1024 * 1024)
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return total_cpu_cores, total_memory_gib, total_nodes
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except Exception as e:
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logger.warning(f"Could not get real cluster capacity: {e}. Using fallback values.")
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# Fallback values based on typical OpenShift cluster
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return 24.0, 70.0, 6
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async def get_cluster_health(self, pods: List[PodResource]) -> ClusterHealth:
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"""Get cluster health overview with overcommit analysis"""
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total_pods = len(pods)
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@@ -640,9 +670,8 @@ class ValidationService:
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cluster_cpu_limits = sum(pod.cpu_limits for pod in pods)
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cluster_memory_limits = sum(pod.memory_limits for pod in pods)
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# Simulate cluster capacity (would come from node metrics)
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cluster_cpu_capacity = 100.0 # 100 CPU cores
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cluster_memory_capacity = 400.0 # 400 GiB
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# Get real cluster capacity
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cluster_cpu_capacity, cluster_memory_capacity, total_nodes = await self._get_cluster_capacity()
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# Calculate overcommit percentages
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cpu_overcommit = (cluster_cpu_requests / cluster_cpu_capacity) * 100
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@@ -677,7 +706,7 @@ class ValidationService:
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return ClusterHealth(
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total_pods=total_pods,
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total_namespaces=total_namespaces,
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total_nodes=10, # Simulated
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total_nodes=total_nodes,
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cluster_cpu_capacity=cluster_cpu_capacity,
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cluster_memory_capacity=cluster_memory_capacity,
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cluster_cpu_requests=cluster_cpu_requests,
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@@ -688,7 +717,7 @@ class ValidationService:
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memory_overcommit_percentage=memory_overcommit,
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overall_health=overall_health,
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critical_issues=critical_issues,
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namespaces_in_overcommit=3, # Simulated
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namespaces_in_overcommit=len([ns for ns in set(pod.namespace for pod in pods) if self._is_namespace_in_overcommit(ns, pods)]),
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top_resource_consumers=[
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{
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"name": pod.name,
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@@ -700,5 +729,27 @@ class ValidationService:
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for pod in top_consumers
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],
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qos_distribution=qos_distribution,
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resource_quota_coverage=0.6 # Simulated
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resource_quota_coverage=self._calculate_resource_quota_coverage(pods)
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)
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def _is_namespace_in_overcommit(self, namespace: str, pods: List[PodResource]) -> bool:
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"""Check if namespace is in overcommit"""
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namespace_pods = [pod for pod in pods if pod.namespace == namespace]
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if not namespace_pods:
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return False
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# Simple overcommit check: if any pod has limits > requests
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for pod in namespace_pods:
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if pod.cpu_limits > pod.cpu_requests or pod.memory_limits > pod.memory_requests:
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return True
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return False
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def _calculate_resource_quota_coverage(self, pods: List[PodResource]) -> float:
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"""Calculate resource quota coverage percentage"""
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namespaces = set(pod.namespace for pod in pods)
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if not namespaces:
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return 0.0
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# For now, return a simple calculation based on namespace count
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# In a real implementation, this would check actual ResourceQuota objects
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return min(len(namespaces) * 0.2, 1.0) # 20% per namespace, max 100%
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@@ -3001,23 +3001,41 @@
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updateQoSDistribution(qosData.distribution);
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}
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function formatCpuValue(value) {
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if (value >= 1.0) {
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return `${value.toFixed(1)} cores`;
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} else {
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return `${(value * 1000).toFixed(0)}m`;
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}
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}
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function formatMemoryValue(valueBytes) {
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const valueGiB = valueBytes / (1024 * 1024 * 1024);
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if (valueGiB >= 1.0) {
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return `${valueGiB.toFixed(1)} GiB`;
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} else {
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const valueMiB = valueBytes / (1024 * 1024);
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return `${valueMiB.toFixed(0)} MiB`;
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}
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}
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function updateResourceConsumption(healthData) {
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// CPU
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const cpuUsagePercent = (healthData.cluster_cpu_requests / healthData.cluster_cpu_capacity) * 100;
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document.getElementById('cpuUsageBar').style.width = Math.min(cpuUsagePercent, 100) + '%';
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document.getElementById('cpuUsageText').textContent =
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`${healthData.cluster_cpu_requests.toFixed(1)} / ${healthData.cluster_cpu_capacity.toFixed(1)} cores`;
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`${formatCpuValue(healthData.cluster_cpu_requests)} / ${formatCpuValue(healthData.cluster_cpu_capacity)}`;
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document.getElementById('cpuOvercommitText').textContent =
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`${healthData.cpu_overcommit_percentage.toFixed(1)}% overcommit`;
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// Memory - Convert bytes to GiB
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// Memory - Convert bytes to appropriate unit
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const memoryRequestsGiB = healthData.cluster_memory_requests / (1024 * 1024 * 1024);
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const memoryCapacityGiB = healthData.cluster_memory_capacity; // Already in GiB from API
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const memoryUsagePercent = (memoryRequestsGiB / memoryCapacityGiB) * 100;
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document.getElementById('memoryUsageBar').style.width = Math.min(memoryUsagePercent, 100) + '%';
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document.getElementById('memoryUsageText').textContent =
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`${memoryRequestsGiB.toFixed(1)} / ${memoryCapacityGiB.toFixed(1)} GiB`;
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`${formatMemoryValue(healthData.cluster_memory_requests)} / ${formatMemoryValue(healthData.cluster_memory_capacity * 1024 * 1024 * 1024)}`;
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document.getElementById('memoryOvercommitText').textContent =
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`${healthData.memory_overcommit_percentage.toFixed(1)}% overcommit`;
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}
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@@ -3041,8 +3059,8 @@
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</div>
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</div>
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<div class="consumer-resources">
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<div>CPU: ${consumer.cpu_requests.toFixed(1)} cores</div>
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<div>Memory: ${(consumer.memory_requests / (1024 * 1024 * 1024)).toFixed(1)} GiB</div>
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<div>CPU: ${formatCpuValue(consumer.cpu_requests)}</div>
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<div>Memory: ${formatMemoryValue(consumer.memory_requests)}</div>
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<div class="qos-badge qos-${consumer.qos_class.toLowerCase()}">${consumer.qos_class}</div>
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</div>
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`;
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