703 lines
29 KiB
Python
703 lines
29 KiB
Python
"""
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Resource validation service following Red Hat best practices
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"""
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import logging
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from typing import List, Dict, Any, Optional
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from decimal import Decimal, InvalidOperation
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import re
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from app.models.resource_models import (
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PodResource,
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ResourceValidation,
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NamespaceResources,
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QoSClassification,
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ResourceQuota,
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ClusterHealth
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)
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from app.core.config import settings
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from app.services.historical_analysis import HistoricalAnalysisService
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from app.services.smart_recommendations import SmartRecommendationsService
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logger = logging.getLogger(__name__)
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class ValidationService:
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"""Service for resource validation"""
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def __init__(self):
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self.cpu_ratio = settings.cpu_limit_ratio
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self.memory_ratio = settings.memory_limit_ratio
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self.min_cpu_request = settings.min_cpu_request
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self.min_memory_request = settings.min_memory_request
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self.historical_analysis = HistoricalAnalysisService()
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self.smart_recommendations = SmartRecommendationsService()
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def validate_pod_resources(self, pod: PodResource) -> List[ResourceValidation]:
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"""Validate pod resources"""
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validations = []
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for container in pod.containers:
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container_validations = self._validate_container_resources(
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pod.name, pod.namespace, container
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)
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validations.extend(container_validations)
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return validations
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async def validate_pod_resources_with_historical_analysis(
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self,
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pod: PodResource,
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time_range: str = '24h'
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) -> List[ResourceValidation]:
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"""Validate pod resources including historical analysis"""
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# Static validations
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static_validations = self.validate_pod_resources(pod)
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# Historical analysis
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try:
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historical_validations = await self.historical_analysis.analyze_pod_historical_usage(
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pod, time_range
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)
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static_validations.extend(historical_validations)
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except Exception as e:
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logger.warning(f"Error in historical analysis for pod {pod.name}: {e}")
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return static_validations
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def _validate_container_resources(
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self,
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pod_name: str,
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namespace: str,
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container: Any
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) -> List[ResourceValidation]:
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"""Validate container resources"""
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validations = []
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resources = container.resources
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requests = resources.get("requests", {})
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limits = resources.get("limits", {})
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# Determine QoS class based on Red Hat best practices
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qos_class = self._determine_qos_class(requests, limits)
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# 1. Check if requests are defined
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if not requests:
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validations.append(ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container["name"],
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validation_type="missing_requests",
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severity="error",
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message="Container without defined requests",
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recommendation="Define CPU and memory requests to guarantee QoS (currently BestEffort class)"
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))
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# 2. Check if limits are defined
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if not limits:
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validations.append(ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container["name"],
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validation_type="missing_limits",
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severity="warning",
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message="Container without defined limits",
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recommendation="Define limits to avoid excessive resource consumption"
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))
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# 3. QoS Class validation based on Red Hat recommendations
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qos_validation = self._validate_qos_class(pod_name, namespace, container["name"], qos_class, requests, limits)
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if qos_validation:
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validations.append(qos_validation)
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# 3. Validate limit:request ratio
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if requests and limits:
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cpu_validation = self._validate_cpu_ratio(
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pod_name, namespace, container["name"], requests, limits
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)
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if cpu_validation:
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validations.append(cpu_validation)
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memory_validation = self._validate_memory_ratio(
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pod_name, namespace, container["name"], requests, limits
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)
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if memory_validation:
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validations.append(memory_validation)
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# 4. Validate minimum values
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if requests:
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min_validation = self._validate_minimum_values(
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pod_name, namespace, container["name"], requests
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)
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validations.extend(min_validation)
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return validations
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def _validate_cpu_ratio(
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self,
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pod_name: str,
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namespace: str,
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container_name: str,
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requests: Dict[str, str],
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limits: Dict[str, str]
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) -> ResourceValidation:
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"""Validate CPU limit:request ratio"""
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if "cpu" not in requests or "cpu" not in limits:
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return None
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try:
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request_value = self._parse_cpu_value(requests["cpu"])
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limit_value = self._parse_cpu_value(limits["cpu"])
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if request_value > 0:
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ratio = limit_value / request_value
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if ratio > self.cpu_ratio * 1.5: # 50% de tolerância
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="invalid_ratio",
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severity="warning",
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message=f"CPU limit:request ratio too high ({ratio:.2f}:1) - Request: {requests['cpu']}, Limit: {limits['cpu']}",
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recommendation=f"Consider reducing limits or increasing requests (recommended ratio: {self.cpu_ratio}:1)"
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)
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elif ratio < 1.0:
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="invalid_ratio",
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severity="error",
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message=f"CPU limit less than request ({ratio:.2f}:1) - Request: {requests['cpu']}, Limit: {limits['cpu']}",
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recommendation="CPU limit should be greater than or equal to request"
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)
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except (ValueError, InvalidOperation) as e:
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logger.warning(f"Error validating CPU ratio: {e}")
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return None
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def _validate_memory_ratio(
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self,
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pod_name: str,
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namespace: str,
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container_name: str,
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requests: Dict[str, str],
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limits: Dict[str, str]
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) -> ResourceValidation:
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"""Validate memory limit:request ratio"""
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if "memory" not in requests or "memory" not in limits:
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return None
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try:
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request_value = self._parse_memory_value(requests["memory"])
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limit_value = self._parse_memory_value(limits["memory"])
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if request_value > 0:
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ratio = limit_value / request_value
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if ratio > self.memory_ratio * 1.5: # 50% de tolerância
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="invalid_ratio",
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severity="warning",
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message=f"Memory limit:request ratio too high ({ratio:.2f}:1) - Request: {requests['memory']}, Limit: {limits['memory']}",
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recommendation=f"Consider reducing limits or increasing requests (recommended ratio: {self.memory_ratio}:1)"
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)
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elif ratio < 1.0:
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="invalid_ratio",
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severity="error",
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message=f"Memory limit less than request ({ratio:.2f}:1) - Request: {requests['memory']}, Limit: {limits['memory']}",
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recommendation="Memory limit should be greater than or equal to request"
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)
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except (ValueError, InvalidOperation) as e:
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logger.warning(f"Error validating memory ratio: {e}")
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return None
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def _validate_minimum_values(
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self,
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pod_name: str,
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namespace: str,
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container_name: str,
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requests: Dict[str, str]
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) -> List[ResourceValidation]:
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"""Validate minimum request values"""
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validations = []
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# Validate minimum CPU
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if "cpu" in requests:
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try:
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request_value = self._parse_cpu_value(requests["cpu"])
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min_value = self._parse_cpu_value(self.min_cpu_request)
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if request_value < min_value:
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validations.append(ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="minimum_value",
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severity="warning",
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message=f"CPU request too low ({requests['cpu']})",
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recommendation=f"Consider increasing to at least {self.min_cpu_request}"
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))
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except (ValueError, InvalidOperation):
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pass
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# Validate minimum memory
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if "memory" in requests:
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try:
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request_value = self._parse_memory_value(requests["memory"])
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min_value = self._parse_memory_value(self.min_memory_request)
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if request_value < min_value:
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validations.append(ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="minimum_value",
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severity="warning",
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message=f"Memory request too low ({requests['memory']})",
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recommendation=f"Consider increasing to at least {self.min_memory_request}"
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))
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except (ValueError, InvalidOperation):
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pass
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return validations
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def _parse_cpu_value(self, value: str) -> float:
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"""Convert CPU value to float (cores)"""
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if value.endswith('m'):
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return float(value[:-1]) / 1000
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elif value.endswith('n'):
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return float(value[:-1]) / 1000000000
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else:
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return float(value)
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def _parse_memory_value(self, value: str) -> int:
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"""Convert memory value to bytes"""
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value = value.upper()
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if value.endswith('KI'):
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return int(float(value[:-2]) * 1024)
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elif value.endswith('MI'):
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return int(float(value[:-2]) * 1024 * 1024)
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elif value.endswith('GI'):
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return int(float(value[:-2]) * 1024 * 1024 * 1024)
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elif value.endswith('K'):
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return int(float(value[:-1]) * 1000)
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elif value.endswith('M'):
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return int(float(value[:-1]) * 1000 * 1000)
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elif value.endswith('G'):
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return int(float(value[:-1]) * 1000 * 1000 * 1000)
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else:
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return int(value)
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def _determine_qos_class(self, requests: Dict[str, str], limits: Dict[str, str]) -> str:
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"""Determine QoS class based on requests and limits"""
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cpu_requests = self._parse_cpu_value(requests.get("cpu", "0"))
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memory_requests = self._parse_memory_value(requests.get("memory", "0")) / (1024 * 1024 * 1024) # Convert to GB
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cpu_limits = self._parse_cpu_value(limits.get("cpu", "0"))
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memory_limits = self._parse_memory_value(limits.get("memory", "0")) / (1024 * 1024 * 1024) # Convert to GB
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# Guaranteed: both CPU and memory requests and limits are set and equal
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if (cpu_requests > 0 and memory_requests > 0 and
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cpu_requests == cpu_limits and memory_requests == memory_limits):
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return "Guaranteed"
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# Burstable: at least one request is set
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elif cpu_requests > 0 or memory_requests > 0:
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return "Burstable"
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# BestEffort: no requests set
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else:
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return "BestEffort"
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def _validate_qos_class(self, pod_name: str, namespace: str, container_name: str, qos_class: str, requests: Dict[str, str], limits: Dict[str, str]) -> Optional[ResourceValidation]:
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"""Validate QoS class and provide recommendations"""
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cpu_requests = self._parse_cpu_value(requests.get("cpu", "0"))
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memory_requests = self._parse_memory_value(requests.get("memory", "0")) / (1024 * 1024 * 1024) # Convert to GB
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cpu_limits = self._parse_cpu_value(limits.get("cpu", "0"))
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memory_limits = self._parse_memory_value(limits.get("memory", "0")) / (1024 * 1024 * 1024) # Convert to GB
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# Check for missing requests (BestEffort pods)
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if qos_class == "BestEffort":
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="missing_requests",
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severity="warning",
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message="Pod has no resource requests defined",
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recommendation="Define CPU and memory requests for better resource management",
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priority_score=7,
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workload_category="new",
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estimated_impact="medium"
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)
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# Check for missing limits (Burstable pods)
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elif qos_class == "Burstable" and (cpu_limits == 0 or memory_limits == 0):
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return ResourceValidation(
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pod_name=pod_name,
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namespace=namespace,
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container_name=container_name,
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validation_type="missing_limits",
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severity="warning",
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message="Pod has requests but no limits defined",
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recommendation="Define resource limits to prevent resource starvation",
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priority_score=5,
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workload_category="established",
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estimated_impact="low"
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)
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return None
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def validate_namespace_overcommit(
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self,
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namespace_resources: NamespaceResources,
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node_capacity: Dict[str, str]
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) -> List[ResourceValidation]:
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"""Validate overcommit in a namespace"""
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validations = []
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# Calculate total namespace requests
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total_cpu_requests = self._parse_cpu_value(namespace_resources.total_cpu_requests)
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total_memory_requests = self._parse_memory_value(namespace_resources.total_memory_requests)
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# Calculate total node capacity
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total_cpu_capacity = self._parse_cpu_value(node_capacity.get("cpu", "0"))
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total_memory_capacity = self._parse_memory_value(node_capacity.get("memory", "0"))
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# Check CPU overcommit
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if total_cpu_capacity > 0:
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cpu_utilization = (total_cpu_requests / total_cpu_capacity) * 100
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if cpu_utilization > 100:
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validations.append(ResourceValidation(
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pod_name="namespace",
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namespace=namespace_resources.name,
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container_name="all",
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validation_type="overcommit",
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severity="critical",
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message=f"CPU overcommit in namespace: {cpu_utilization:.1f}%",
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recommendation="Reduce CPU requests or add more nodes to the cluster"
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))
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# Check memory overcommit
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if total_memory_capacity > 0:
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memory_utilization = (total_memory_requests / total_memory_capacity) * 100
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if memory_utilization > 100:
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validations.append(ResourceValidation(
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pod_name="namespace",
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namespace=namespace_resources.name,
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container_name="all",
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validation_type="overcommit",
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severity="critical",
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message=f"Memory overcommit in namespace: {memory_utilization:.1f}%",
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recommendation="Reduce memory requests or add more nodes to the cluster"
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))
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return validations
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def generate_recommendations(self, validations: List[ResourceValidation]) -> List[str]:
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"""Generate recommendations based on validations"""
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recommendations = []
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# Group validations by type
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validation_counts = {}
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for validation in validations:
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validation_type = validation.validation_type
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if validation_type not in validation_counts:
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validation_counts[validation_type] = 0
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validation_counts[validation_type] += 1
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# Generate recommendations based on found issues
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if validation_counts.get("missing_requests", 0) > 0:
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recommendations.append(
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f"Implement LimitRange in namespace to define default requests "
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f"({validation_counts['missing_requests']} containers without requests)"
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)
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if validation_counts.get("missing_limits", 0) > 0:
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recommendations.append(
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f"Define limits for {validation_counts['missing_limits']} containers "
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"to avoid excessive resource consumption"
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)
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if validation_counts.get("invalid_ratio", 0) > 0:
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recommendations.append(
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f"Adjust limit:request ratio for {validation_counts['invalid_ratio']} containers "
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f"(recommended: {self.cpu_ratio}:1)"
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)
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if validation_counts.get("overcommit", 0) > 0:
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recommendations.append(
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f"Resolve overcommit in {validation_counts['overcommit']} namespaces "
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"to avoid performance issues"
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)
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return recommendations
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async def validate_pod_resources_with_categorization(
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self,
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pod: PodResource,
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workload_category: str = None,
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priority_score: int = None
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) -> List[ResourceValidation]:
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"""Validate pod resources with enhanced categorization and scoring"""
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validations = self.validate_pod_resources(pod)
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# Add categorization and scoring to validations
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for validation in validations:
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validation.workload_category = workload_category
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validation.priority_score = priority_score or self._calculate_priority_score(validation)
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validation.estimated_impact = self._determine_impact(validation.priority_score)
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return validations
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async def validate_pod_resources_with_smart_analysis(
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self,
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pod: PodResource,
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time_range: str = '24h'
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) -> List[ResourceValidation]:
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"""Validate pod resources with smart analysis including historical data"""
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# Static validations
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static_validations = self.validate_pod_resources(pod)
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# Get workload category
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workload_category = await self._categorize_workload(pod)
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# Get smart recommendations
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smart_recommendations = await self.smart_recommendations.generate_smart_recommendations([pod], [workload_category])
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# Enhance validations with smart analysis
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enhanced_validations = []
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for validation in static_validations:
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validation.workload_category = workload_category.category
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validation.priority_score = self._calculate_priority_score(validation)
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validation.estimated_impact = self._determine_impact(validation.priority_score)
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enhanced_validations.append(validation)
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# Add smart recommendations as validations
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for recommendation in smart_recommendations:
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smart_validation = ResourceValidation(
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pod_name=pod.name,
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namespace=pod.namespace,
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container_name="workload",
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validation_type="smart_recommendation",
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severity=recommendation.priority,
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message=recommendation.title,
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recommendation=recommendation.description,
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priority_score=self._get_priority_score_from_string(recommendation.priority),
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workload_category=workload_category.category,
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estimated_impact=recommendation.estimated_impact
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)
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enhanced_validations.append(smart_validation)
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return enhanced_validations
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async def _categorize_workload(self, pod: PodResource) -> Any:
|
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"""Categorize a single workload"""
|
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categories = await self.smart_recommendations.categorize_workloads([pod])
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return categories[0] if categories else None
|
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def _get_priority_score_from_string(self, priority: str) -> int:
|
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"""Convert priority string to numeric score"""
|
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priority_map = {
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"critical": 10,
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"high": 8,
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"medium": 5,
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"low": 2
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}
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return priority_map.get(priority, 5)
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|
|
def _calculate_priority_score(self, validation: ResourceValidation) -> int:
|
|
"""Calculate priority score for validation (1-10)"""
|
|
score = 1
|
|
|
|
# Base score by severity
|
|
if validation.severity == "critical":
|
|
score += 4
|
|
elif validation.severity == "error":
|
|
score += 3
|
|
elif validation.severity == "warning":
|
|
score += 1
|
|
|
|
# Add score by validation type
|
|
if validation.validation_type == "missing_requests":
|
|
score += 3
|
|
elif validation.validation_type == "missing_limits":
|
|
score += 2
|
|
elif validation.validation_type == "invalid_ratio":
|
|
score += 1
|
|
elif validation.validation_type == "overcommit":
|
|
score += 4
|
|
|
|
# Add score for production namespaces
|
|
if validation.namespace in ["default", "production", "prod"]:
|
|
score += 2
|
|
|
|
return min(score, 10)
|
|
|
|
def _determine_impact(self, priority_score: int) -> str:
|
|
"""Determine estimated impact based on priority score"""
|
|
if priority_score >= 8:
|
|
return "critical"
|
|
elif priority_score >= 6:
|
|
return "high"
|
|
elif priority_score >= 4:
|
|
return "medium"
|
|
else:
|
|
return "low"
|
|
|
|
async def get_workload_categories(self, pods: List[PodResource]) -> List[Any]:
|
|
"""Get workload categories for all pods"""
|
|
return await self.smart_recommendations.categorize_workloads(pods)
|
|
|
|
async def get_smart_recommendations(self, pods: List[PodResource]) -> List[Any]:
|
|
"""Get smart recommendations for all workloads"""
|
|
categories = await self.get_workload_categories(pods)
|
|
return await self.smart_recommendations.generate_smart_recommendations(pods, categories)
|
|
|
|
def classify_qos(self, pod: PodResource) -> QoSClassification:
|
|
"""Classify pod QoS based on Red Hat best practices"""
|
|
cpu_requests = pod.cpu_requests
|
|
memory_requests = pod.memory_requests
|
|
cpu_limits = pod.cpu_limits
|
|
memory_limits = pod.memory_limits
|
|
|
|
# Determine QoS class
|
|
if (cpu_requests > 0 and memory_requests > 0 and
|
|
cpu_limits > 0 and memory_limits > 0 and
|
|
cpu_requests == cpu_limits and memory_requests == memory_limits):
|
|
qos_class = "Guaranteed"
|
|
efficiency_score = 1.0
|
|
elif (cpu_requests > 0 or memory_requests > 0):
|
|
qos_class = "Burstable"
|
|
# Calculate efficiency based on request/limit ratio
|
|
cpu_efficiency = cpu_requests / cpu_limits if cpu_limits > 0 else 0.5
|
|
memory_efficiency = memory_requests / memory_limits if memory_limits > 0 else 0.5
|
|
efficiency_score = (cpu_efficiency + memory_efficiency) / 2
|
|
else:
|
|
qos_class = "BestEffort"
|
|
efficiency_score = 0.0
|
|
|
|
# Generate recommendation
|
|
recommendation = None
|
|
if qos_class == "BestEffort":
|
|
recommendation = "Define CPU and memory requests for better resource management"
|
|
elif qos_class == "Burstable" and efficiency_score < 0.3:
|
|
recommendation = "Consider setting limits closer to requests for better predictability"
|
|
elif qos_class == "Guaranteed":
|
|
recommendation = "Optimal QoS configuration for production workloads"
|
|
|
|
return QoSClassification(
|
|
pod_name=pod.name,
|
|
namespace=pod.namespace,
|
|
qos_class=qos_class,
|
|
cpu_requests=cpu_requests,
|
|
memory_requests=memory_requests,
|
|
cpu_limits=cpu_limits,
|
|
memory_limits=memory_limits,
|
|
efficiency_score=efficiency_score,
|
|
recommendation=recommendation
|
|
)
|
|
|
|
async def analyze_resource_quotas(self, namespaces: List[str]) -> List[ResourceQuota]:
|
|
"""Analyze Resource Quotas for namespaces"""
|
|
quotas = []
|
|
|
|
for namespace in namespaces:
|
|
# This would typically query the Kubernetes API
|
|
# For now, we'll simulate the analysis
|
|
quota = ResourceQuota(
|
|
namespace=namespace,
|
|
name=f"quota-{namespace}",
|
|
status="Missing", # Would be determined by API call
|
|
usage_percentage=0.0,
|
|
recommended_quota={
|
|
"cpu": "2000m",
|
|
"memory": "8Gi",
|
|
"pods": "20"
|
|
}
|
|
)
|
|
quotas.append(quota)
|
|
|
|
return quotas
|
|
|
|
async def get_cluster_health(self, pods: List[PodResource]) -> ClusterHealth:
|
|
"""Get cluster health overview with overcommit analysis"""
|
|
total_pods = len(pods)
|
|
total_namespaces = len(set(pod.namespace for pod in pods))
|
|
|
|
# Calculate cluster resource totals
|
|
cluster_cpu_requests = sum(pod.cpu_requests for pod in pods)
|
|
cluster_memory_requests = sum(pod.memory_requests for pod in pods)
|
|
cluster_cpu_limits = sum(pod.cpu_limits for pod in pods)
|
|
cluster_memory_limits = sum(pod.memory_limits for pod in pods)
|
|
|
|
# Simulate cluster capacity (would come from node metrics)
|
|
cluster_cpu_capacity = 100.0 # 100 CPU cores
|
|
cluster_memory_capacity = 400.0 # 400 GiB
|
|
|
|
# Calculate overcommit percentages
|
|
cpu_overcommit = (cluster_cpu_requests / cluster_cpu_capacity) * 100
|
|
memory_overcommit = (cluster_memory_requests / cluster_memory_capacity) * 100
|
|
|
|
# Determine overall health
|
|
if cpu_overcommit > 150 or memory_overcommit > 150:
|
|
overall_health = "Critical"
|
|
elif cpu_overcommit > 120 or memory_overcommit > 120:
|
|
overall_health = "Warning"
|
|
else:
|
|
overall_health = "Healthy"
|
|
|
|
# Count critical issues
|
|
critical_issues = sum(1 for pod in pods if pod.cpu_requests == 0 or pod.memory_requests == 0)
|
|
|
|
# Get top resource consumers
|
|
top_consumers = sorted(
|
|
pods,
|
|
key=lambda p: p.cpu_requests + p.memory_requests,
|
|
reverse=True
|
|
)[:10]
|
|
|
|
# QoS distribution
|
|
qos_distribution = {"Guaranteed": 0, "Burstable": 0, "BestEffort": 0}
|
|
for pod in pods:
|
|
qos = self.classify_qos(pod)
|
|
qos_distribution[qos.qos_class] += 1
|
|
|
|
return ClusterHealth(
|
|
total_pods=total_pods,
|
|
total_namespaces=total_namespaces,
|
|
total_nodes=10, # Simulated
|
|
cluster_cpu_capacity=cluster_cpu_capacity,
|
|
cluster_memory_capacity=cluster_memory_capacity,
|
|
cluster_cpu_requests=cluster_cpu_requests,
|
|
cluster_memory_requests=cluster_memory_requests,
|
|
cluster_cpu_limits=cluster_cpu_limits,
|
|
cluster_memory_limits=cluster_memory_limits,
|
|
cpu_overcommit_percentage=cpu_overcommit,
|
|
memory_overcommit_percentage=memory_overcommit,
|
|
overall_health=overall_health,
|
|
critical_issues=critical_issues,
|
|
namespaces_in_overcommit=3, # Simulated
|
|
top_resource_consumers=[
|
|
{
|
|
"name": pod.name,
|
|
"namespace": pod.namespace,
|
|
"cpu_requests": pod.cpu_requests,
|
|
"memory_requests": pod.memory_requests,
|
|
"qos_class": self.classify_qos(pod).qos_class
|
|
}
|
|
for pod in top_consumers
|
|
],
|
|
qos_distribution=qos_distribution,
|
|
resource_quota_coverage=0.6 # Simulated
|
|
)
|