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Refactor: group smart recommendations by type and remove redundant View Details button

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Anderson
2025-10-02 10:15:51 -03:00
parent 91e68b79c7
commit 943fe4fcac
3 changed files with 186 additions and 77 deletions
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171
app/services/smart_recommendations.py
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@@ -63,7 +63,10 @@ class SmartRecommendationsService:
categories: List[WorkloadCategory]
) -> List[SmartRecommendation]:
"""Generate smart recommendations based on workload analysis"""
recommendations = []
# Group workloads by recommendation type
vpa_workloads = []
resource_config_workloads = []
ratio_adjustment_workloads = []
for category in categories:
workload_pods = [p for p in pods if self._extract_workload_name(p.name) == category.workload_name and p.namespace == category.namespace]
@@ -71,17 +74,173 @@ class SmartRecommendationsService:
if not workload_pods:
continue
# Generate recommendations based on category
workload_recommendations = await self._generate_workload_recommendations(
category, workload_pods
)
recommendations.extend(workload_recommendations)
# Categorize workloads by recommendation type
if category.vpa_candidate:
vpa_workloads.append((category, workload_pods))
elif category.resource_config_status == "missing_requests":
resource_config_workloads.append((category, workload_pods))
elif category.resource_config_status == "missing_limits":
resource_config_workloads.append((category, workload_pods))
elif category.resource_config_status == "suboptimal_ratio":
ratio_adjustment_workloads.append((category, workload_pods))
# Generate grouped recommendations
recommendations = []
# VPA Activation group
if vpa_workloads:
recommendations.append(self._create_grouped_vpa_recommendation(vpa_workloads))
# Resource Config group
if resource_config_workloads:
recommendations.append(self._create_grouped_resource_config_recommendation(resource_config_workloads))
# Ratio Adjustment group
if ratio_adjustment_workloads:
recommendations.append(self._create_grouped_ratio_adjustment_recommendation(ratio_adjustment_workloads))
# Sort by priority
recommendations.sort(key=lambda x: self._get_priority_score(x.priority), reverse=True)
return recommendations
def _create_grouped_vpa_recommendation(self, vpa_workloads: List[tuple]) -> SmartRecommendation:
"""Create grouped VPA activation recommendation"""
# Sort workloads by priority
vpa_workloads.sort(key=lambda x: self._get_priority_score(x[0].estimated_impact), reverse=True)
# Get highest priority
highest_priority = vpa_workloads[0][0].estimated_impact
# Create workload list
workload_list = []
for category, _ in vpa_workloads:
workload_list.append(f"{category.workload_name} ({category.namespace}) - {category.estimated_impact.upper()}")
# Generate consolidated VPA YAML
vpa_yaml = self._generate_consolidated_vpa_yaml(vpa_workloads)
return SmartRecommendation(
workload_name="Multiple Workloads",
namespace="Multiple",
recommendation_type="vpa_activation",
priority=highest_priority,
title="Activate VPA for Workloads",
description=f"Enable VPA for {len(vpa_workloads)} workloads to get automatic resource recommendations based on usage patterns.",
confidence_level=0.8,
estimated_impact=highest_priority,
implementation_steps=[
"Create VPA resources for all workloads",
"Set updateMode to 'Off' for recommendation-only mode",
"Monitor VPA recommendations for 24-48 hours",
"Apply recommended values when confident"
],
kubectl_commands=[
"oc apply -f vpa-workloads-batch.yaml"
],
vpa_yaml=vpa_yaml,
workload_list=workload_list
)
def _create_grouped_resource_config_recommendation(self, resource_workloads: List[tuple]) -> SmartRecommendation:
"""Create grouped resource configuration recommendation"""
# Sort workloads by priority
resource_workloads.sort(key=lambda x: self._get_priority_score(x[0].estimated_impact), reverse=True)
# Get highest priority
highest_priority = resource_workloads[0][0].estimated_impact
# Create workload list
workload_list = []
for category, _ in resource_workloads:
workload_list.append(f"{category.workload_name} ({category.namespace}) - {category.estimated_impact.upper()}")
return SmartRecommendation(
workload_name="Multiple Workloads",
namespace="Multiple",
recommendation_type="resource_config",
priority=highest_priority,
title="Configure Resource Requests/Limits",
description=f"Define proper resource requests and limits for {len(resource_workloads)} workloads to ensure QoS and prevent resource conflicts.",
confidence_level=0.9,
estimated_impact=highest_priority,
implementation_steps=[
"Analyze current resource usage for all workloads",
"Set CPU requests based on P95 usage + 20% buffer",
"Set memory requests based on P95 usage + 20% buffer",
"Set limits with 3:1 ratio to requests",
"Update deployments with new resource configuration"
],
kubectl_commands=[
"oc patch deployment <workload-name> -n <namespace> -p '{\"spec\":{\"template\":{\"spec\":{\"containers\":[{\"name\":\"<container-name>\",\"resources\":{\"requests\":{\"cpu\":\"200m\",\"memory\":\"512Mi\"},\"limits\":{\"cpu\":\"600m\",\"memory\":\"1536Mi\"}}}]}}}}'"
],
workload_list=workload_list
)
def _create_grouped_ratio_adjustment_recommendation(self, ratio_workloads: List[tuple]) -> SmartRecommendation:
"""Create grouped ratio adjustment recommendation"""
# Sort workloads by priority
ratio_workloads.sort(key=lambda x: self._get_priority_score(x[0].estimated_impact), reverse=True)
# Get highest priority
highest_priority = ratio_workloads[0][0].estimated_impact
# Create workload list
workload_list = []
for category, _ in ratio_workloads:
workload_list.append(f"{category.workload_name} ({category.namespace}) - {category.estimated_impact.upper()}")
return SmartRecommendation(
workload_name="Multiple Workloads",
namespace="Multiple",
recommendation_type="ratio_adjustment",
priority=highest_priority,
title="Optimize Resource Ratios",
description=f"Optimize CPU and memory limit:request ratios for {len(ratio_workloads)} workloads to follow best practices (3:1 ratio).",
confidence_level=0.8,
estimated_impact=highest_priority,
implementation_steps=[
"Analyze current resource ratios for all workloads",
"Adjust limits to maintain 3:1 ratio with requests",
"Test with updated ratios in staging environment",
"Apply changes to production"
],
kubectl_commands=[
"oc patch deployment <workload-name> -n <namespace> -p '{\"spec\":{\"template\":{\"spec\":{\"containers\":[{\"name\":\"<container-name>\",\"resources\":{\"requests\":{\"cpu\":\"200m\",\"memory\":\"512Mi\"},\"limits\":{\"cpu\":\"600m\",\"memory\":\"1536Mi\"}}}]}}}}'"
],
workload_list=workload_list
)
def _generate_consolidated_vpa_yaml(self, vpa_workloads: List[tuple]) -> str:
"""Generate consolidated VPA YAML for multiple workloads"""
yaml_parts = []
for category, _ in vpa_workloads:
yaml_parts.append(f"""---
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: {category.workload_name}-vpa
namespace: {category.namespace}
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: {category.workload_name}
updatePolicy:
updateMode: "Off" # Recommendation only
resourcePolicy:
containerPolicies:
- containerName: {category.workload_name}
maxAllowed:
cpu: 2
memory: 4Gi
minAllowed:
cpu: 100m
memory: 128Mi""")
return "\n".join(yaml_parts)
def _group_pods_by_workload(self, pods: List[PodResource]) -> Dict[str, List[PodResource]]:
"""Group pods by workload (deployment) name"""
workloads = {}