879 lines
38 KiB
Python
879 lines
38 KiB
Python
"""
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Historical analysis service using Prometheus metrics
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"""
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import logging
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import asyncio
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from typing import List, Dict, Any, Optional, Tuple
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from datetime import datetime, timedelta
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import aiohttp
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import json
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from app.models.resource_models import PodResource, ResourceValidation
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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class HistoricalAnalysisService:
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"""Service for historical resource analysis using Prometheus"""
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def __init__(self):
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self.prometheus_url = settings.prometheus_url
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self.time_ranges = {
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'1h': 3600, # 1 hour
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'6h': 21600, # 6 hours
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'24h': 86400, # 24 hours
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'7d': 604800, # 7 days
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'30d': 2592000 # 30 days
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}
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async def analyze_pod_historical_usage(
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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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"""Analyze historical usage of a pod"""
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validations = []
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if time_range not in self.time_ranges:
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time_range = '24h'
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end_time = datetime.now()
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start_time = end_time - timedelta(seconds=self.time_ranges[time_range])
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try:
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# Analyze CPU
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cpu_analysis = await self._analyze_cpu_usage(
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pod, start_time, end_time, time_range
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)
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validations.extend(cpu_analysis)
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# Analyze memory
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memory_analysis = await self._analyze_memory_usage(
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pod, start_time, end_time, time_range
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)
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validations.extend(memory_analysis)
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except Exception as e:
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logger.error(f"Error in historical analysis for pod {pod.name}: {e}")
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validations.append(ResourceValidation(
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pod_name=pod.name,
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namespace=pod.namespace,
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container_name="all",
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validation_type="historical_analysis_error",
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severity="warning",
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message=f"Error in historical analysis: {str(e)}",
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recommendation="Check Prometheus connectivity"
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))
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return validations
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async def _analyze_cpu_usage(
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self,
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pod: PodResource,
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start_time: datetime,
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end_time: datetime,
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time_range: str
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) -> List[ResourceValidation]:
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"""Analyze historical CPU usage"""
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validations = []
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for container in pod.containers:
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container_name = container["name"]
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try:
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# Query for CPU usage rate
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cpu_query = f'''
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rate(container_cpu_usage_seconds_total{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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container="{container_name}",
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container!="POD",
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container!=""
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}}[{time_range}])
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'''
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# Query for CPU requests
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cpu_requests_query = f'''
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kube_pod_container_resource_requests{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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resource="cpu"
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}}
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'''
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# Query for CPU limits
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cpu_limits_query = f'''
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kube_pod_container_resource_limits{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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resource="cpu"
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}}
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'''
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# Execute queries
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cpu_usage = await self._query_prometheus(cpu_query, start_time, end_time)
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cpu_requests = await self._query_prometheus(cpu_requests_query, start_time, end_time)
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cpu_limits = await self._query_prometheus(cpu_limits_query, start_time, end_time)
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if cpu_usage and cpu_requests:
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analysis = self._analyze_cpu_metrics(
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pod.name, pod.namespace, container_name,
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cpu_usage, cpu_requests, cpu_limits, time_range
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)
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validations.extend(analysis)
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except Exception as e:
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logger.warning(f"Error analyzing CPU for container {container_name}: {e}")
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return validations
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async def _analyze_memory_usage(
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self,
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pod: PodResource,
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start_time: datetime,
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end_time: datetime,
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time_range: str
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) -> List[ResourceValidation]:
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"""Analyze historical memory usage"""
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validations = []
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for container in pod.containers:
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container_name = container["name"]
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try:
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# Query for memory usage
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memory_query = f'''
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container_memory_working_set_bytes{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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container="{container_name}",
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container!="POD",
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container!=""
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}}
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'''
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# Query for memory requests
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memory_requests_query = f'''
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kube_pod_container_resource_requests{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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resource="memory"
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}}
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'''
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# Query for memory limits
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memory_limits_query = f'''
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kube_pod_container_resource_limits{{
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pod="{pod.name}",
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namespace="{pod.namespace}",
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resource="memory"
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}}
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'''
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# Execute queries
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memory_usage = await self._query_prometheus(memory_query, start_time, end_time)
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memory_requests = await self._query_prometheus(memory_requests_query, start_time, end_time)
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memory_limits = await self._query_prometheus(memory_limits_query, start_time, end_time)
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if memory_usage and memory_requests:
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analysis = self._analyze_memory_metrics(
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pod.name, pod.namespace, container_name,
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memory_usage, memory_requests, memory_limits, time_range
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)
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validations.extend(analysis)
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except Exception as e:
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logger.warning(f"Error analyzing memory for container {container_name}: {e}")
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return validations
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def _analyze_cpu_metrics(
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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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usage_data: List[Dict],
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requests_data: List[Dict],
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limits_data: List[Dict],
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time_range: str
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) -> List[ResourceValidation]:
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"""Analyze CPU metrics"""
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validations = []
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if not usage_data or not requests_data:
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return validations
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# Calculate usage statistics
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usage_values = [float(point[1]) for point in usage_data if point[1] != 'NaN']
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if not usage_values:
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return validations
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# Current values of requests/limits
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current_requests = float(requests_data[0][1]) if requests_data else 0
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current_limits = float(limits_data[0][1]) if limits_data else 0
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# Usage statistics
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avg_usage = sum(usage_values) / len(usage_values)
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max_usage = max(usage_values)
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p95_usage = sorted(usage_values)[int(len(usage_values) * 0.95)]
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p99_usage = sorted(usage_values)[int(len(usage_values) * 0.99)]
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# Request adequacy analysis
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if current_requests > 0:
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# Request too high (average usage < 50% of request)
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if avg_usage < current_requests * 0.5:
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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="historical_analysis",
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severity="warning",
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message=f"CPU request too high: average usage {avg_usage:.3f} cores vs request {current_requests:.3f} cores",
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recommendation=f"Consider reducing CPU request to ~{avg_usage * 1.2:.3f} cores (based on {time_range} of usage)"
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))
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# Request too low (P95 usage > 80% of request)
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elif p95_usage > current_requests * 0.8:
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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="historical_analysis",
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severity="warning",
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message=f"CPU request may be insufficient: P95 {p95_usage:.3f} cores vs request {current_requests:.3f} cores",
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recommendation=f"Consider increasing CPU request to ~{p95_usage * 1.2:.3f} cores (based on {time_range} of usage)"
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))
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# Limit adequacy analysis
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if current_limits > 0:
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# Limit too high (P99 usage < 50% of limit)
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if p99_usage < current_limits * 0.5:
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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="historical_analysis",
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severity="info",
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message=f"CPU limit too high: P99 {p99_usage:.3f} cores vs limit {current_limits:.3f} cores",
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recommendation=f"Consider reducing CPU limit to ~{p99_usage * 1.5:.3f} cores (based on {time_range} of usage)"
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))
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# Limit too low (maximum usage > 90% of limit)
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elif max_usage > current_limits * 0.9:
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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="historical_analysis",
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severity="warning",
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message=f"CPU limit may be insufficient: maximum usage {max_usage:.3f} cores vs limit {current_limits:.3f} cores",
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recommendation=f"Consider increasing CPU limit to ~{max_usage * 1.2:.3f} cores (based on {time_range} of usage)"
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))
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return validations
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def _analyze_memory_metrics(
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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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usage_data: List[Dict],
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requests_data: List[Dict],
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limits_data: List[Dict],
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time_range: str
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) -> List[ResourceValidation]:
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"""Analyze memory metrics"""
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validations = []
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if not usage_data or not requests_data:
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return validations
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# Calculate usage statistics
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usage_values = [float(point[1]) for point in usage_data if point[1] != 'NaN']
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if not usage_values:
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return validations
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# Current values of requests/limits (in bytes)
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current_requests = float(requests_data[0][1]) if requests_data else 0
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current_limits = float(limits_data[0][1]) if limits_data else 0
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# Usage statistics
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avg_usage = sum(usage_values) / len(usage_values)
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max_usage = max(usage_values)
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p95_usage = sorted(usage_values)[int(len(usage_values) * 0.95)]
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p99_usage = sorted(usage_values)[int(len(usage_values) * 0.99)]
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# Convert to MiB for better readability
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def bytes_to_mib(bytes_value):
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return bytes_value / (1024 * 1024)
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# Request adequacy analysis
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if current_requests > 0:
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# Request too high (average usage < 50% of request)
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if avg_usage < current_requests * 0.5:
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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="historical_analysis",
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severity="warning",
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message=f"Memory request too high: average usage {bytes_to_mib(avg_usage):.1f}Mi vs request {bytes_to_mib(current_requests):.1f}Mi",
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recommendation=f"Consider reducing memory request to ~{bytes_to_mib(avg_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
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))
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# Request too low (P95 usage > 80% of request)
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elif p95_usage > current_requests * 0.8:
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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="historical_analysis",
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severity="warning",
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message=f"Memory request may be insufficient: P95 {bytes_to_mib(p95_usage):.1f}Mi vs request {bytes_to_mib(current_requests):.1f}Mi",
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recommendation=f"Consider increasing memory request to ~{bytes_to_mib(p95_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
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))
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# Limit adequacy analysis
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if current_limits > 0:
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# Limit too high (P99 usage < 50% of limit)
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if p99_usage < current_limits * 0.5:
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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="historical_analysis",
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severity="info",
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message=f"Memory limit too high: P99 {bytes_to_mib(p99_usage):.1f}Mi vs limit {bytes_to_mib(current_limits):.1f}Mi",
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recommendation=f"Consider reducing memory limit to ~{bytes_to_mib(p99_usage * 1.5):.1f}Mi (based on {time_range} of usage)"
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))
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# Limit too low (maximum usage > 90% of limit)
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elif max_usage > current_limits * 0.9:
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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="historical_analysis",
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severity="warning",
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message=f"Memory limit may be insufficient: maximum usage {bytes_to_mib(max_usage):.1f}Mi vs limit {bytes_to_mib(current_limits):.1f}Mi",
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recommendation=f"Consider increasing memory limit to ~{bytes_to_mib(max_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
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))
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return validations
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async def _query_prometheus(self, query: str, start_time: datetime, end_time: datetime) -> List[Dict]:
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"""Execute query in Prometheus"""
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try:
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async with aiohttp.ClientSession() as session:
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params = {
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'query': query,
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'start': start_time.timestamp(),
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'end': end_time.timestamp(),
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'step': '60s' # 1 minute resolution
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}
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async with session.get(
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f"{self.prometheus_url}/api/v1/query_range",
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params=params,
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timeout=aiohttp.ClientTimeout(total=30)
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) as response:
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if response.status == 200:
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data = await response.json()
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if data['status'] == 'success' and data['data']['result']:
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return data['data']['result'][0]['values']
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else:
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logger.warning(f"Prometheus query failed: {response.status}")
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return []
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except Exception as e:
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logger.error(f"Error querying Prometheus: {e}")
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return []
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async def get_cluster_historical_summary(self, time_range: str = '24h') -> Dict[str, Any]:
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"""Get cluster historical summary"""
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try:
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# Query for total cluster CPU
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cpu_query = f'''
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sum(rate(container_cpu_usage_seconds_total{{
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container!="POD",
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container!=""
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}}[{time_range}]))
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'''
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# Query for total cluster memory
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memory_query = f'''
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sum(container_memory_working_set_bytes{{
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container!="POD",
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container!=""
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}})
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'''
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# Query for total requests
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cpu_requests_query = f'''
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sum(kube_pod_container_resource_requests{{resource="cpu"}})
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'''
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memory_requests_query = f'''
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sum(kube_pod_container_resource_requests{{resource="memory"}})
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'''
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# Execute queries
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cpu_usage = await self._query_prometheus(cpu_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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memory_usage = await self._query_prometheus(memory_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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cpu_requests = await self._query_prometheus(cpu_requests_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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memory_requests = await self._query_prometheus(memory_requests_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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return {
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'time_range': time_range,
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'cpu_usage': float(cpu_usage[0][1]) if cpu_usage else 0,
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'memory_usage': float(memory_usage[0][1]) if memory_usage else 0,
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'cpu_requests': float(cpu_requests[0][1]) if cpu_requests else 0,
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'memory_requests': float(memory_requests[0][1]) if memory_requests else 0,
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'cpu_utilization': (float(cpu_usage[0][1]) / float(cpu_requests[0][1]) * 100) if cpu_usage and cpu_requests and cpu_requests[0][1] != '0' else 0,
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'memory_utilization': (float(memory_usage[0][1]) / float(memory_requests[0][1]) * 100) if memory_usage and memory_requests and memory_requests[0][1] != '0' else 0
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}
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except Exception as e:
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logger.error(f"Error getting historical summary: {e}")
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return {}
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async def get_namespace_historical_analysis(self, namespace: str, time_range: str, prometheus_client):
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"""Get historical analysis for a specific namespace"""
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try:
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logger.info(f"Getting historical analysis for namespace: {namespace}")
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# Query for CPU usage by namespace
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cpu_query = f'''
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sum(rate(container_cpu_usage_seconds_total{{
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namespace="{namespace}",
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container!="POD",
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container!=""
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}}[{time_range}]))
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'''
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# Query for memory usage by namespace
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memory_query = f'''
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sum(container_memory_working_set_bytes{{
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namespace="{namespace}",
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container!="POD",
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container!=""
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}})
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'''
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# Query for CPU requests by namespace
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cpu_requests_query = f'''
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sum(kube_pod_container_resource_requests{{
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namespace="{namespace}",
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resource="cpu"
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}})
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'''
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# Query for memory requests by namespace
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memory_requests_query = f'''
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sum(kube_pod_container_resource_requests{{
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namespace="{namespace}",
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resource="memory"
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}})
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'''
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# Query for pod count by namespace
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pod_count_query = f'''
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count(kube_pod_info{{namespace="{namespace}"}})
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'''
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# Execute queries
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cpu_usage = await self._query_prometheus(cpu_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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memory_usage = await self._query_prometheus(memory_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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cpu_requests = await self._query_prometheus(cpu_requests_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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memory_requests = await self._query_prometheus(memory_requests_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
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datetime.now())
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pod_count = await self._query_prometheus(pod_count_query,
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datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
|
|
# Calculate utilization percentages
|
|
cpu_utilization = 0
|
|
memory_utilization = 0
|
|
|
|
if cpu_usage and cpu_requests and cpu_requests[0][1] != '0':
|
|
cpu_utilization = (float(cpu_usage[0][1]) / float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and memory_requests[0][1] != '0':
|
|
memory_utilization = (float(memory_usage[0][1]) / float(memory_requests[0][1])) * 100
|
|
|
|
# Generate recommendations based on utilization
|
|
recommendations = []
|
|
|
|
if cpu_utilization > 80:
|
|
recommendations.append({
|
|
"type": "cpu_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider increasing CPU requests or optimizing application performance"
|
|
})
|
|
elif cpu_utilization < 20:
|
|
recommendations.append({
|
|
"type": "cpu_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider reducing CPU requests to optimize resource allocation"
|
|
})
|
|
|
|
if memory_utilization > 80:
|
|
recommendations.append({
|
|
"type": "memory_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider increasing memory requests or optimizing memory usage"
|
|
})
|
|
elif memory_utilization < 20:
|
|
recommendations.append({
|
|
"type": "memory_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider reducing memory requests to optimize resource allocation"
|
|
})
|
|
|
|
return {
|
|
'namespace': namespace,
|
|
'time_range': time_range,
|
|
'cpu_usage': float(cpu_usage[0][1]) if cpu_usage else 0,
|
|
'memory_usage': float(memory_usage[0][1]) if memory_usage else 0,
|
|
'cpu_requests': float(cpu_requests[0][1]) if cpu_requests else 0,
|
|
'memory_requests': float(memory_requests[0][1]) if memory_requests else 0,
|
|
'cpu_utilization': cpu_utilization,
|
|
'memory_utilization': memory_utilization,
|
|
'pod_count': int(pod_count[0][1]) if pod_count else 0,
|
|
'recommendations': recommendations
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting historical analysis for namespace {namespace}: {e}")
|
|
return {
|
|
'namespace': namespace,
|
|
'time_range': time_range,
|
|
'error': str(e),
|
|
'recommendations': []
|
|
}
|
|
|
|
async def get_workload_historical_analysis(self, namespace: str, workload: str, time_range: str, prometheus_client):
|
|
"""Get historical analysis for a specific workload/deployment"""
|
|
try:
|
|
logger.info(f"Getting historical analysis for workload: {workload} in namespace: {namespace}")
|
|
|
|
# Query for CPU usage by workload (aggregated by workload)
|
|
cpu_query = f'''
|
|
sum(
|
|
node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{{
|
|
cluster="",
|
|
namespace="{namespace}"
|
|
}}
|
|
* on(namespace,pod)
|
|
group_left(workload, workload_type)
|
|
namespace_workload_pod:kube_pod_owner:relabel{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
workload="{workload}",
|
|
workload_type=~".+"
|
|
}}
|
|
) by (workload, workload_type)
|
|
'''
|
|
|
|
# Query for memory usage by workload (aggregated by workload)
|
|
memory_query = f'''
|
|
sum(
|
|
container_memory_working_set_bytes{{
|
|
namespace="{namespace}",
|
|
container!="POD",
|
|
container!=""
|
|
}}
|
|
* on(namespace,pod)
|
|
group_left(workload, workload_type)
|
|
namespace_workload_pod:kube_pod_owner:relabel{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
workload="{workload}",
|
|
workload_type=~".+"
|
|
}}
|
|
) by (workload, workload_type)
|
|
'''
|
|
|
|
# Query for CPU requests by namespace (using resource quota)
|
|
cpu_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory requests by namespace (using resource quota)
|
|
memory_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.memory"
|
|
}})
|
|
'''
|
|
|
|
# Query for CPU limits by namespace (using resource quota)
|
|
cpu_limits_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="limits.cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory limits by namespace (using resource quota)
|
|
memory_limits_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="limits.memory"
|
|
}})
|
|
'''
|
|
|
|
# Execute queries
|
|
cpu_usage = await self._query_prometheus(cpu_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
memory_usage = await self._query_prometheus(memory_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
cpu_requests = await self._query_prometheus(cpu_requests_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
memory_requests = await self._query_prometheus(memory_requests_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
cpu_limits = await self._query_prometheus(cpu_limits_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
memory_limits = await self._query_prometheus(memory_limits_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
|
|
# Calculate utilization percentages
|
|
cpu_utilization = 0
|
|
memory_utilization = 0
|
|
|
|
if cpu_usage and cpu_requests and cpu_requests[0][1] != '0':
|
|
cpu_utilization = (float(cpu_usage[0][1]) / float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and memory_requests[0][1] != '0':
|
|
memory_utilization = (float(memory_usage[0][1]) / float(memory_requests[0][1])) * 100
|
|
|
|
# Generate recommendations based on utilization
|
|
recommendations = []
|
|
|
|
if cpu_utilization > 80:
|
|
recommendations.append({
|
|
"type": "cpu_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider increasing CPU requests or optimizing application performance"
|
|
})
|
|
elif cpu_utilization < 20 and cpu_utilization > 0:
|
|
recommendations.append({
|
|
"type": "cpu_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider reducing CPU requests to optimize resource allocation"
|
|
})
|
|
|
|
if memory_utilization > 80:
|
|
recommendations.append({
|
|
"type": "memory_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider increasing memory requests or optimizing memory usage"
|
|
})
|
|
elif memory_utilization < 20 and memory_utilization > 0:
|
|
recommendations.append({
|
|
"type": "memory_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider reducing memory requests to optimize resource allocation"
|
|
})
|
|
|
|
return {
|
|
'namespace': namespace,
|
|
'workload': workload,
|
|
'time_range': time_range,
|
|
'cpu_usage': float(cpu_usage[0][1]) if cpu_usage else 0,
|
|
'memory_usage': float(memory_usage[0][1]) if memory_usage else 0,
|
|
'cpu_requests': float(cpu_requests[0][1]) if cpu_requests else 0,
|
|
'memory_requests': float(memory_requests[0][1]) if memory_requests else 0,
|
|
'cpu_limits': float(cpu_limits[0][1]) if cpu_limits else 0,
|
|
'memory_limits': float(memory_limits[0][1]) if memory_limits else 0,
|
|
'cpu_utilization': cpu_utilization,
|
|
'memory_utilization': memory_utilization,
|
|
'recommendations': recommendations
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting historical analysis for workload {workload} in namespace {namespace}: {e}")
|
|
return {
|
|
'namespace': namespace,
|
|
'workload': workload,
|
|
'time_range': time_range,
|
|
'error': str(e),
|
|
'recommendations': []
|
|
}
|
|
|
|
async def get_pod_historical_analysis(self, namespace: str, pod_name: str, time_range: str, prometheus_client):
|
|
"""Get historical analysis for a specific pod"""
|
|
try:
|
|
logger.info(f"Getting historical analysis for pod: {pod_name} in namespace: {namespace}")
|
|
|
|
# Query for CPU usage by pod (more generic query)
|
|
cpu_query = f'''
|
|
sum(rate(container_cpu_usage_seconds_total{{
|
|
namespace="{namespace}",
|
|
pod=~"{pod_name}.*",
|
|
container!="POD",
|
|
container!=""
|
|
}}[{time_range}]))
|
|
'''
|
|
|
|
# Query for memory usage by pod (more generic query)
|
|
memory_query = f'''
|
|
sum(container_memory_working_set_bytes{{
|
|
namespace="{namespace}",
|
|
pod=~"{pod_name}.*",
|
|
container!="POD",
|
|
container!=""
|
|
}})
|
|
'''
|
|
|
|
# Query for CPU requests by pod (more generic query)
|
|
cpu_requests_query = f'''
|
|
sum(kube_pod_container_resource_requests{{
|
|
namespace="{namespace}",
|
|
pod=~"{pod_name}.*",
|
|
resource="cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory requests by pod (more generic query)
|
|
memory_requests_query = f'''
|
|
sum(kube_pod_container_resource_requests{{
|
|
namespace="{namespace}",
|
|
pod=~"{pod_name}.*",
|
|
resource="memory"
|
|
}})
|
|
'''
|
|
|
|
# Query for container count by pod (more generic query)
|
|
container_count_query = f'''
|
|
count(container_memory_working_set_bytes{{
|
|
namespace="{namespace}",
|
|
pod=~"{pod_name}.*",
|
|
container!="POD",
|
|
container!=""
|
|
}})
|
|
'''
|
|
|
|
# Execute queries
|
|
cpu_usage = await self._query_prometheus(cpu_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
memory_usage = await self._query_prometheus(memory_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
cpu_requests = await self._query_prometheus(cpu_requests_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
memory_requests = await self._query_prometheus(memory_requests_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
container_count = await self._query_prometheus(container_count_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
|
|
# Calculate utilization percentages
|
|
cpu_utilization = 0
|
|
memory_utilization = 0
|
|
|
|
if cpu_usage and cpu_requests and cpu_requests[0][1] != '0':
|
|
cpu_utilization = (float(cpu_usage[0][1]) / float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and memory_requests[0][1] != '0':
|
|
memory_utilization = (float(memory_usage[0][1]) / float(memory_requests[0][1])) * 100
|
|
|
|
# Generate recommendations based on utilization
|
|
recommendations = []
|
|
|
|
if cpu_utilization > 80:
|
|
recommendations.append({
|
|
"type": "cpu_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider increasing CPU requests or optimizing application performance"
|
|
})
|
|
elif cpu_utilization < 20:
|
|
recommendations.append({
|
|
"type": "cpu_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low CPU utilization: {cpu_utilization:.1f}%",
|
|
"recommendation": "Consider reducing CPU requests to optimize resource allocation"
|
|
})
|
|
|
|
if memory_utilization > 80:
|
|
recommendations.append({
|
|
"type": "memory_high_utilization",
|
|
"severity": "warning",
|
|
"message": f"High memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider increasing memory requests or optimizing memory usage"
|
|
})
|
|
elif memory_utilization < 20:
|
|
recommendations.append({
|
|
"type": "memory_low_utilization",
|
|
"severity": "info",
|
|
"message": f"Low memory utilization: {memory_utilization:.1f}%",
|
|
"recommendation": "Consider reducing memory requests to optimize resource allocation"
|
|
})
|
|
|
|
return {
|
|
'namespace': namespace,
|
|
'pod_name': pod_name,
|
|
'time_range': time_range,
|
|
'cpu_usage': float(cpu_usage[0][1]) if cpu_usage else 0,
|
|
'memory_usage': float(memory_usage[0][1]) if memory_usage else 0,
|
|
'cpu_requests': float(cpu_requests[0][1]) if cpu_requests else 0,
|
|
'memory_requests': float(memory_requests[0][1]) if memory_requests else 0,
|
|
'cpu_utilization': cpu_utilization,
|
|
'memory_utilization': memory_utilization,
|
|
'container_count': int(container_count[0][1]) if container_count else 0,
|
|
'recommendations': recommendations
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting historical analysis for pod {pod_name} in namespace {namespace}: {e}")
|
|
return {
|
|
'namespace': namespace,
|
|
'pod_name': pod_name,
|
|
'time_range': time_range,
|
|
'error': str(e),
|
|
'recommendations': []
|
|
}
|