andersonid
e1dae22e98
- Add Chart.js 4.4.0 and date adapter for time series graphs - Implement createCPUChart and createMemoryChart functions - Update updateWorkloadDetailsAccordion to show interactive graphs - Add getCurrentValue, getAverageValue, getPeakValue helper functions - Display CPU and Memory usage over 24h with real-time data - Show current, average, and peak values below graphs - Use working Prometheus queries from metrics endpoint
1505 lines
68 KiB
Python
1505 lines
68 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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def _safe_float(self, value, default=0):
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"""Safely convert value to float, handling inf and NaN"""
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try:
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result = float(value)
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if result == float('inf') or result == float('-inf') or result != result: # NaN check
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return default
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return result
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except (ValueError, TypeError):
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return default
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def _extract_workload_name(self, pod_name: str) -> str:
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"""Extract workload name from pod name (remove pod suffix)"""
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# Pod names typically follow pattern: workload-name-hash-suffix
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# e.g., resource-governance-798b5579d6-7h298 -> resource-governance
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parts = pod_name.split('-')
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if len(parts) >= 3 and parts[-1].isalnum() and len(parts[-1]) == 5:
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# Remove the last two parts (hash and suffix)
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return '-'.join(parts[:-2])
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elif len(parts) >= 2 and parts[-1].isalnum() and len(parts[-1]) == 5:
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# Remove the last part (suffix)
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return '-'.join(parts[:-1])
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else:
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# Fallback to pod name if pattern doesn't match
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return pod_name
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async def analyze_workload_historical_usage(
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self,
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pods: List[PodResource],
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time_range: str = '24h'
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) -> List[ResourceValidation]:
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"""Analyze historical usage for a workload (group of pods)"""
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if not pods:
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return []
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# Group pods by workload name
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workload_pods = {}
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for pod in pods:
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workload_name = self._extract_workload_name(pod.name)
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if workload_name not in workload_pods:
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workload_pods[workload_name] = []
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workload_pods[workload_name].append(pod)
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all_validations = []
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# Analyze each workload
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for workload_name, workload_pod_list in workload_pods.items():
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try:
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# Use the first pod as representative for the workload
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representative_pod = workload_pod_list[0]
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# Analyze historical usage for the workload
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workload_validations = await self._analyze_workload_metrics(
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workload_name, representative_pod.namespace, workload_pod_list, time_range
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)
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all_validations.extend(workload_validations)
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except Exception as e:
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logger.warning(f"Error analyzing workload {workload_name}: {e}")
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# Fallback to individual pod analysis
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for pod in workload_pod_list:
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try:
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pod_validations = await self.analyze_pod_historical_usage(pod, time_range)
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all_validations.extend(pod_validations)
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except Exception as pod_e:
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logger.warning(f"Error analyzing pod {pod.name}: {pod_e}")
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return all_validations
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async def _analyze_workload_metrics(
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self,
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workload_name: str,
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namespace: str,
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pods: List[PodResource],
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time_range: str
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) -> List[ResourceValidation]:
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"""Analyze metrics for a workload using Prometheus queries"""
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validations = []
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try:
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# Query for CPU usage by workload
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cpu_query = f'''
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sum(
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node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{{
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cluster="",
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namespace="{namespace}"
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Query for memory usage by workload
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memory_query = f'''
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sum(
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container_memory_working_set_bytes{{
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job="kubelet",
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metrics_path="/metrics/cadvisor",
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cluster="",
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namespace="{namespace}",
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container!="",
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image!=""
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Query for CPU requests by workload
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cpu_requests_query = f'''
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sum(
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kube_pod_container_resource_requests{{
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resource="cpu",
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namespace="{namespace}"
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Query for memory requests by workload
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memory_requests_query = f'''
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sum(
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kube_pod_container_resource_requests{{
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resource="memory",
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namespace="{namespace}"
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Query for CPU limits by workload
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cpu_limits_query = f'''
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sum(
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kube_pod_container_resource_limits{{
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resource="cpu",
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namespace="{namespace}"
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Query for memory limits by workload
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memory_limits_query = f'''
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sum(
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kube_pod_container_resource_limits{{
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resource="memory",
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namespace="{namespace}"
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}}
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* on(namespace,pod)
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group_left(workload, workload_type)
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namespace_workload_pod:kube_pod_owner:relabel{{
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cluster="",
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namespace="{namespace}",
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workload="{workload_name}",
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workload_type=~".+"
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}}
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) by (workload, workload_type)
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'''
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# Execute queries
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cpu_usage_data = await self._query_prometheus(cpu_query, time_range)
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memory_usage_data = await self._query_prometheus(memory_query, time_range)
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cpu_requests_data = await self._query_prometheus(cpu_requests_query, time_range)
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memory_requests_data = await self._query_prometheus(memory_requests_query, time_range)
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cpu_limits_data = await self._query_prometheus(cpu_limits_query, time_range)
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memory_limits_data = await self._query_prometheus(memory_limits_query, time_range)
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# Check if we have sufficient data for both CPU and Memory before doing historical analysis
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cpu_has_data = cpu_usage_data and len([p for p in cpu_usage_data if p[1] != 'NaN']) >= 3
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memory_has_data = memory_usage_data and len([p for p in memory_usage_data if p[1] != 'NaN']) >= 3
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# If either CPU or Memory has insufficient data, add insufficient data warning
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if not cpu_has_data or not memory_has_data:
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if not cpu_has_data:
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validations.append(ResourceValidation(
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pod_name=workload_name,
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namespace=namespace,
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container_name="workload",
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validation_type="insufficient_historical_data",
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severity="warning",
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message=f"Limited CPU usage data ({len([p for p in cpu_usage_data if p[1] != 'NaN']) if cpu_usage_data else 0} points) for {time_range}",
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recommendation="Wait for more data points or extend time range for reliable analysis"
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))
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if not memory_has_data:
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validations.append(ResourceValidation(
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pod_name=workload_name,
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namespace=namespace,
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container_name="workload",
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validation_type="insufficient_historical_data",
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severity="warning",
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message=f"Limited memory usage data ({len([p for p in memory_usage_data if p[1] != 'NaN']) if memory_usage_data else 0} points) for {time_range}",
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recommendation="Wait for more data points or extend time range for reliable analysis"
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))
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# Don't proceed with historical analysis if any resource has insufficient data
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return validations
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# Analyze CPU metrics for workload (only if we have sufficient data)
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if cpu_usage_data and cpu_requests_data and cpu_limits_data:
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cpu_validations = self._analyze_cpu_metrics(
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workload_name, namespace, "workload",
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cpu_usage_data, cpu_requests_data, cpu_limits_data, time_range
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)
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validations.extend(cpu_validations)
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# Analyze memory metrics for workload (only if we have sufficient data)
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if memory_usage_data and memory_requests_data and memory_limits_data:
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memory_validations = self._analyze_memory_metrics(
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workload_name, namespace, "workload",
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memory_usage_data, memory_requests_data, memory_limits_data, time_range
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)
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validations.extend(memory_validations)
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except Exception as e:
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logger.warning(f"Error analyzing workload metrics for {workload_name}: {e}")
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# Fallback to individual pod analysis
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for pod in pods:
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try:
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pod_validations = await self.analyze_pod_historical_usage(pod, time_range)
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validations.extend(pod_validations)
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except Exception as pod_e:
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logger.warning(f"Error analyzing pod {pod.name}: {pod_e}")
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return validations
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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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|
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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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|
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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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|
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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)
|
|
|
|
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)
|
|
|
|
except Exception as e:
|
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logger.warning(f"Error analyzing memory for container {container_name}: {e}")
|
|
|
|
return validations
|
|
|
|
def _detect_seasonal_patterns(
|
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self,
|
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pod_name: str,
|
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namespace: str,
|
|
container_name: str,
|
|
usage_values: List[float],
|
|
time_range: str
|
|
) -> List[ResourceValidation]:
|
|
"""Detect seasonal patterns and trends in resource usage"""
|
|
validations = []
|
|
|
|
if len(usage_values) < 20: # Need at least 20 data points for pattern detection
|
|
return validations
|
|
|
|
# Calculate trend (simple linear regression)
|
|
n = len(usage_values)
|
|
x = list(range(n))
|
|
y = usage_values
|
|
|
|
# Calculate slope
|
|
x_mean = sum(x) / n
|
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y_mean = sum(y) / n
|
|
|
|
numerator = sum((x[i] - x_mean) * (y[i] - y_mean) for i in range(n))
|
|
denominator = sum((x[i] - x_mean) ** 2 for i in range(n))
|
|
|
|
if denominator != 0:
|
|
slope = numerator / denominator
|
|
|
|
# Detect significant trends
|
|
if slope > 0.1: # Increasing trend
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="seasonal_pattern",
|
|
severity="info",
|
|
message=f"Detected increasing resource usage trend over {time_range}",
|
|
recommendation="Monitor for continued growth and consider proactive scaling"
|
|
))
|
|
elif slope < -0.1: # Decreasing trend
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="seasonal_pattern",
|
|
severity="info",
|
|
message=f"Detected decreasing resource usage trend over {time_range}",
|
|
recommendation="Consider reducing resource requests/limits if trend continues"
|
|
))
|
|
|
|
# Detect high variability (coefficient of variation > 50%)
|
|
if y_mean > 0:
|
|
variance = sum((y[i] - y_mean) ** 2 for i in range(n)) / n
|
|
std_dev = variance ** 0.5
|
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cv = std_dev / y_mean
|
|
|
|
if cv > 0.5: # High variability
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="seasonal_pattern",
|
|
severity="warning",
|
|
message=f"High resource usage variability detected (CV: {cv:.2f})",
|
|
recommendation="Consider higher safety margins for requests/limits due to unpredictable usage"
|
|
))
|
|
|
|
return validations
|
|
|
|
def _analyze_cpu_metrics(
|
|
self,
|
|
pod_name: str,
|
|
namespace: str,
|
|
container_name: str,
|
|
usage_data: List[Dict],
|
|
requests_data: List[Dict],
|
|
limits_data: List[Dict],
|
|
time_range: str
|
|
) -> List[ResourceValidation]:
|
|
"""Analyze CPU metrics"""
|
|
validations = []
|
|
|
|
# Check for insufficient historical data
|
|
if not usage_data:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="info",
|
|
message=f"No CPU usage data available for {time_range}",
|
|
recommendation="Monitor workload for at least 24h to get reliable resource recommendations"
|
|
))
|
|
return validations
|
|
|
|
# Calculate usage statistics
|
|
usage_values = [float(point[1]) for point in usage_data if point[1] != 'NaN']
|
|
logger.info(f"CPU analysis for {pod_name}/{container_name}: {len(usage_data)} raw points, {len(usage_values)} valid points")
|
|
if not usage_values:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="info",
|
|
message=f"No valid CPU usage data points for {time_range}",
|
|
recommendation="Check if pod is running and generating metrics"
|
|
))
|
|
return validations
|
|
|
|
# Check for minimal data points (less than 3 data points)
|
|
if len(usage_values) < 3:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="warning",
|
|
message=f"Limited CPU usage data ({len(usage_values)} points) for {time_range}",
|
|
recommendation="Wait for more data points or extend time range for reliable analysis"
|
|
))
|
|
return validations # Don't proceed with historical analysis if insufficient data
|
|
|
|
# Current values of requests/limits
|
|
current_requests = self._safe_float(requests_data[0][1]) if requests_data else 0
|
|
current_limits = self._safe_float(limits_data[0][1]) if limits_data else 0
|
|
|
|
# Usage statistics
|
|
avg_usage = sum(usage_values) / len(usage_values)
|
|
max_usage = max(usage_values)
|
|
p95_usage = sorted(usage_values)[int(len(usage_values) * 0.95)]
|
|
p99_usage = sorted(usage_values)[int(len(usage_values) * 0.99)]
|
|
|
|
# Detect seasonal patterns
|
|
seasonal_validations = self._detect_seasonal_patterns(
|
|
pod_name, namespace, container_name, usage_values, time_range
|
|
)
|
|
validations.extend(seasonal_validations)
|
|
|
|
# Request adequacy analysis
|
|
if current_requests > 0:
|
|
# Request too high (average usage < 50% of request)
|
|
if avg_usage < current_requests * 0.5:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
message=f"CPU request too high: average usage {avg_usage:.3f} cores vs request {current_requests:.3f} cores",
|
|
recommendation=f"Consider reducing CPU request to ~{avg_usage * 1.2:.3f} cores (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Request too low (P95 usage > 80% of request)
|
|
elif p95_usage > current_requests * 0.8:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
message=f"CPU request may be insufficient: P95 {p95_usage:.3f} cores vs request {current_requests:.3f} cores",
|
|
recommendation=f"Consider increasing CPU request to ~{p95_usage * 1.2:.3f} cores (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Limit adequacy analysis
|
|
if current_limits > 0:
|
|
# Limit too high (P99 usage < 50% of limit)
|
|
if p99_usage < current_limits * 0.5:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="info",
|
|
message=f"CPU limit too high: P99 {p99_usage:.3f} cores vs limit {current_limits:.3f} cores",
|
|
recommendation=f"Consider reducing CPU limit to ~{p99_usage * 1.5:.3f} cores (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Limit too low (maximum usage > 90% of limit)
|
|
elif max_usage > current_limits * 0.9:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
message=f"CPU limit may be insufficient: maximum usage {max_usage:.3f} cores vs limit {current_limits:.3f} cores",
|
|
recommendation=f"Consider increasing CPU limit to ~{max_usage * 1.2:.3f} cores (based on {time_range} of usage)"
|
|
))
|
|
|
|
return validations
|
|
|
|
def _analyze_memory_metrics(
|
|
self,
|
|
pod_name: str,
|
|
namespace: str,
|
|
container_name: str,
|
|
usage_data: List[Dict],
|
|
requests_data: List[Dict],
|
|
limits_data: List[Dict],
|
|
time_range: str
|
|
) -> List[ResourceValidation]:
|
|
"""Analyze memory metrics"""
|
|
validations = []
|
|
|
|
# Check for insufficient historical data
|
|
if not usage_data:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="info",
|
|
message=f"No memory usage data available for {time_range}",
|
|
recommendation="Monitor workload for at least 24h to get reliable resource recommendations"
|
|
))
|
|
return validations
|
|
|
|
# Calculate usage statistics
|
|
usage_values = [float(point[1]) for point in usage_data if point[1] != 'NaN']
|
|
logger.info(f"Memory analysis for {pod_name}/{container_name}: {len(usage_data)} raw points, {len(usage_values)} valid points")
|
|
if not usage_values:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="info",
|
|
message=f"No valid memory usage data points for {time_range}",
|
|
recommendation="Check if pod is running and generating metrics"
|
|
))
|
|
return validations
|
|
|
|
# Check for minimal data points (less than 3 data points)
|
|
if len(usage_values) < 3:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="insufficient_historical_data",
|
|
severity="warning",
|
|
message=f"Limited memory usage data ({len(usage_values)} points) for {time_range}",
|
|
recommendation="Wait for more data points or extend time range for reliable analysis"
|
|
))
|
|
return validations # Don't proceed with historical analysis if insufficient data
|
|
|
|
# Current values of requests/limits (in bytes)
|
|
current_requests = self._safe_float(requests_data[0][1]) if requests_data else 0
|
|
current_limits = self._safe_float(limits_data[0][1]) if limits_data else 0
|
|
|
|
# Usage statistics
|
|
avg_usage = sum(usage_values) / len(usage_values)
|
|
max_usage = max(usage_values)
|
|
p95_usage = sorted(usage_values)[int(len(usage_values) * 0.95)]
|
|
p99_usage = sorted(usage_values)[int(len(usage_values) * 0.99)]
|
|
|
|
# Detect seasonal patterns
|
|
seasonal_validations = self._detect_seasonal_patterns(
|
|
pod_name, namespace, container_name, usage_values, time_range
|
|
)
|
|
validations.extend(seasonal_validations)
|
|
|
|
# Convert to MiB for better readability
|
|
def bytes_to_mib(bytes_value):
|
|
return bytes_value / (1024 * 1024)
|
|
|
|
# Request adequacy analysis
|
|
if current_requests > 0:
|
|
# Request too high (average usage < 50% of request)
|
|
if avg_usage < current_requests * 0.5:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
message=f"Memory request too high: average usage {bytes_to_mib(avg_usage):.1f}Mi vs request {bytes_to_mib(current_requests):.1f}Mi",
|
|
recommendation=f"Consider reducing memory request to ~{bytes_to_mib(avg_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Request too low (P95 usage > 80% of request)
|
|
elif p95_usage > current_requests * 0.8:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
message=f"Memory request may be insufficient: P95 {bytes_to_mib(p95_usage):.1f}Mi vs request {bytes_to_mib(current_requests):.1f}Mi",
|
|
recommendation=f"Consider increasing memory request to ~{bytes_to_mib(p95_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Limit adequacy analysis
|
|
if current_limits > 0:
|
|
# Limit too high (P99 usage < 50% of limit)
|
|
if p99_usage < current_limits * 0.5:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="info",
|
|
message=f"Memory limit too high: P99 {bytes_to_mib(p99_usage):.1f}Mi vs limit {bytes_to_mib(current_limits):.1f}Mi",
|
|
recommendation=f"Consider reducing memory limit to ~{bytes_to_mib(p99_usage * 1.5):.1f}Mi (based on {time_range} of usage)"
|
|
))
|
|
|
|
# Limit too low (maximum usage > 90% of limit)
|
|
elif max_usage > current_limits * 0.9:
|
|
validations.append(ResourceValidation(
|
|
pod_name=pod_name,
|
|
namespace=namespace,
|
|
container_name=container_name,
|
|
validation_type="historical_analysis",
|
|
severity="warning",
|
|
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",
|
|
recommendation=f"Consider increasing memory limit to ~{bytes_to_mib(max_usage * 1.2):.1f}Mi (based on {time_range} of usage)"
|
|
))
|
|
|
|
return validations
|
|
|
|
async def _query_prometheus(self, query: str, start_time: datetime, end_time: datetime) -> List[Dict]:
|
|
"""Execute query in Prometheus"""
|
|
try:
|
|
# Get service account token for authentication
|
|
token = None
|
|
try:
|
|
with open('/var/run/secrets/kubernetes.io/serviceaccount/token', 'r') as f:
|
|
token = f.read().strip()
|
|
except FileNotFoundError:
|
|
logger.warning("Service account token not found, proceeding without authentication")
|
|
|
|
# Create headers with token if available
|
|
headers = {}
|
|
if token:
|
|
headers['Authorization'] = f'Bearer {token}'
|
|
|
|
# Create session with SSL verification disabled for self-signed certificates
|
|
connector = aiohttp.TCPConnector(ssl=False)
|
|
|
|
async with aiohttp.ClientSession(connector=connector, headers=headers) as session:
|
|
params = {
|
|
'query': query,
|
|
'start': start_time.timestamp(),
|
|
'end': end_time.timestamp(),
|
|
'step': '60s' # 1 minute resolution
|
|
}
|
|
|
|
async with session.get(
|
|
f"{self.prometheus_url}/api/v1/query_range",
|
|
params=params,
|
|
timeout=aiohttp.ClientTimeout(total=30),
|
|
ssl=False
|
|
) as response:
|
|
logger.info(f"Prometheus query: {query}, status: {response.status}")
|
|
if response.status == 200:
|
|
data = await response.json()
|
|
logger.info(f"Prometheus response: {data}")
|
|
if data['status'] == 'success' and data['data']['result']:
|
|
values = data['data']['result'][0]['values']
|
|
logger.info(f"Returning {len(values)} data points")
|
|
return values
|
|
else:
|
|
logger.warning(f"No data in Prometheus response: {data}")
|
|
return []
|
|
else:
|
|
logger.warning(f"Prometheus query failed: {response.status}")
|
|
return []
|
|
except Exception as e:
|
|
logger.error(f"Error querying Prometheus: {e}")
|
|
return []
|
|
|
|
async def get_cluster_historical_summary(self, time_range: str = '24h') -> Dict[str, Any]:
|
|
"""Get cluster historical summary"""
|
|
try:
|
|
# Query for total cluster CPU
|
|
cpu_query = f'''
|
|
sum(rate(container_cpu_usage_seconds_total{{
|
|
container!="POD",
|
|
container!=""
|
|
}}[{time_range}]))
|
|
'''
|
|
|
|
# Query for total cluster memory
|
|
memory_query = f'''
|
|
sum(container_memory_working_set_bytes{{
|
|
container!="POD",
|
|
container!=""
|
|
}})
|
|
'''
|
|
|
|
# Query for total requests
|
|
cpu_requests_query = f'''
|
|
sum(kube_pod_container_resource_requests{{resource="cpu"}})
|
|
'''
|
|
|
|
memory_requests_query = f'''
|
|
sum(kube_pod_container_resource_requests{{resource="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())
|
|
|
|
return {
|
|
'time_range': time_range,
|
|
'cpu_usage': self._safe_float(cpu_usage[0][1]) if cpu_usage and len(cpu_usage) > 0 else 0,
|
|
'memory_usage': self._safe_float(memory_usage[0][1]) if memory_usage and len(memory_usage) > 0 else 0,
|
|
'cpu_requests': self._safe_float(cpu_requests[0][1]) if cpu_requests and len(cpu_requests) > 0 else 0,
|
|
'memory_requests': self._safe_float(memory_requests[0][1]) if memory_requests and len(memory_requests) > 0 else 0,
|
|
'cpu_utilization': (self._safe_float(cpu_usage[0][1]) / self._safe_float(cpu_requests[0][1]) * 100) if cpu_usage and cpu_requests and self._safe_float(cpu_requests[0][1]) != 0 else 0,
|
|
'memory_utilization': (self._safe_float(memory_usage[0][1]) / self._safe_float(memory_requests[0][1]) * 100) if memory_usage and memory_requests and self._safe_float(memory_requests[0][1]) != 0 else 0
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting historical summary: {e}")
|
|
return {}
|
|
|
|
async def get_namespace_historical_analysis(self, namespace: str, time_range: str, k8s_client=None):
|
|
"""Get historical analysis for a specific namespace"""
|
|
try:
|
|
logger.info(f"Getting historical analysis for namespace: {namespace}")
|
|
|
|
# Query for CPU usage by namespace (using correct OpenShift metrics)
|
|
cpu_query = f'''
|
|
sum(
|
|
node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate{{
|
|
cluster="",
|
|
namespace="{namespace}"
|
|
}}
|
|
) by (namespace)
|
|
'''
|
|
|
|
# Query for memory usage by namespace (using correct OpenShift metrics)
|
|
memory_query = f'''
|
|
sum(
|
|
container_memory_working_set_bytes{{
|
|
job="kubelet",
|
|
metrics_path="/metrics/cadvisor",
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
container!="",
|
|
image!=""
|
|
}}
|
|
) by (namespace)
|
|
'''
|
|
|
|
# Query for CPU requests by namespace (using correct OpenShift resource quota)
|
|
cpu_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory requests by namespace (using correct OpenShift resource quota)
|
|
memory_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.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())
|
|
|
|
# Get pod count using Kubernetes API (more reliable than Prometheus)
|
|
pod_count = 0
|
|
if k8s_client:
|
|
try:
|
|
pods = await k8s_client.get_all_pods()
|
|
namespace_pods = [pod for pod in pods if pod.namespace == namespace]
|
|
pod_count = len(namespace_pods)
|
|
except Exception as e:
|
|
logger.warning(f"Could not get pod count from Kubernetes API: {e}")
|
|
# Fallback to Prometheus query
|
|
pod_count_query = f'count(kube_pod_info{{namespace="{namespace}"}})'
|
|
pod_count_result = await self._query_prometheus(pod_count_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
pod_count = int(self._safe_float(pod_count_result[0][1])) if pod_count_result and len(pod_count_result) > 0 else 0
|
|
else:
|
|
# Fallback to Prometheus query if no k8s_client
|
|
pod_count_query = f'count(kube_pod_info{{namespace="{namespace}"}})'
|
|
pod_count_result = await self._query_prometheus(pod_count_query,
|
|
datetime.now() - timedelta(seconds=self.time_ranges[time_range]),
|
|
datetime.now())
|
|
pod_count = int(self._safe_float(pod_count_result[0][1])) if pod_count_result and len(pod_count_result) > 0 else 0
|
|
|
|
# Calculate utilization percentages
|
|
cpu_utilization = 0
|
|
memory_utilization = 0
|
|
|
|
if cpu_usage and cpu_requests and len(cpu_usage) > 0 and len(cpu_requests) > 0 and self._safe_float(cpu_requests[0][1]) != 0:
|
|
cpu_utilization = (self._safe_float(cpu_usage[0][1]) / self._safe_float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and len(memory_usage) > 0 and len(memory_requests) > 0 and self._safe_float(memory_requests[0][1]) != 0:
|
|
memory_utilization = (self._safe_float(memory_usage[0][1]) / self._safe_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': self._safe_float(cpu_usage[0][1]) if cpu_usage and len(cpu_usage) > 0 else 0,
|
|
'memory_usage': self._safe_float(memory_usage[0][1]) if memory_usage and len(memory_usage) > 0 else 0,
|
|
'cpu_requests': self._safe_float(cpu_requests[0][1]) if cpu_requests and len(cpu_requests) > 0 else 0,
|
|
'memory_requests': self._safe_float(memory_requests[0][1]) if memory_requests and len(memory_requests) > 0 else 0,
|
|
'cpu_utilization': cpu_utilization,
|
|
'memory_utilization': memory_utilization,
|
|
'pod_count': pod_count,
|
|
'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):
|
|
"""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 (using correct OpenShift metrics)
|
|
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_type=~".+"
|
|
}}
|
|
) by (workload, workload_type)
|
|
'''
|
|
|
|
# Query for memory usage by workload (using correct OpenShift metrics)
|
|
memory_query = f'''
|
|
sum(
|
|
container_memory_working_set_bytes{{
|
|
job="kubelet",
|
|
metrics_path="/metrics/cadvisor",
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
container!="",
|
|
image!=""
|
|
}}
|
|
* on(namespace,pod)
|
|
group_left(workload, workload_type)
|
|
namespace_workload_pod:kube_pod_owner:relabel{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
workload_type=~".+"
|
|
}}
|
|
) by (workload, workload_type)
|
|
'''
|
|
|
|
# Query for CPU requests by namespace (using correct OpenShift resource quota)
|
|
cpu_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory requests by namespace (using correct OpenShift resource quota)
|
|
memory_requests_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="requests.memory"
|
|
}})
|
|
'''
|
|
|
|
# Query for CPU limits by namespace (using correct OpenShift resource quota)
|
|
cpu_limits_query = f'''
|
|
scalar(kube_resourcequota{{
|
|
cluster="",
|
|
namespace="{namespace}",
|
|
type="hard",
|
|
resource="limits.cpu"
|
|
}})
|
|
'''
|
|
|
|
# Query for memory limits by namespace (using correct OpenShift 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 len(cpu_usage) > 0 and len(cpu_requests) > 0 and self._safe_float(cpu_requests[0][1]) != 0:
|
|
cpu_utilization = (self._safe_float(cpu_usage[0][1]) / self._safe_float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and len(memory_usage) > 0 and len(memory_requests) > 0 and self._safe_float(memory_requests[0][1]) != 0:
|
|
memory_utilization = (self._safe_float(memory_usage[0][1]) / self._safe_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': self._safe_float(cpu_usage[0][1]) if cpu_usage and len(cpu_usage) > 0 else 0,
|
|
'memory_usage': self._safe_float(memory_usage[0][1]) if memory_usage and len(memory_usage) > 0 else 0,
|
|
'cpu_requests': self._safe_float(cpu_requests[0][1]) if cpu_requests and len(cpu_requests) > 0 else 0,
|
|
'memory_requests': self._safe_float(memory_requests[0][1]) if memory_requests and len(memory_requests) > 0 else 0,
|
|
'cpu_limits': self._safe_float(cpu_limits[0][1]) if cpu_limits and len(cpu_limits) > 0 else 0,
|
|
'memory_limits': self._safe_float(memory_limits[0][1]) if memory_limits and len(memory_limits) > 0 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):
|
|
"""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 len(cpu_usage) > 0 and len(cpu_requests) > 0 and self._safe_float(cpu_requests[0][1]) != 0:
|
|
cpu_utilization = (self._safe_float(cpu_usage[0][1]) / self._safe_float(cpu_requests[0][1])) * 100
|
|
|
|
if memory_usage and memory_requests and len(memory_usage) > 0 and len(memory_requests) > 0 and self._safe_float(memory_requests[0][1]) != 0:
|
|
memory_utilization = (self._safe_float(memory_usage[0][1]) / self._safe_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': self._safe_float(cpu_usage[0][1]) if cpu_usage and len(cpu_usage) > 0 else 0,
|
|
'memory_usage': self._safe_float(memory_usage[0][1]) if memory_usage and len(memory_usage) > 0 else 0,
|
|
'cpu_requests': self._safe_float(cpu_requests[0][1]) if cpu_requests and len(cpu_requests) > 0 else 0,
|
|
'memory_requests': self._safe_float(memory_requests[0][1]) if memory_requests and len(memory_requests) > 0 else 0,
|
|
'cpu_utilization': cpu_utilization,
|
|
'memory_utilization': memory_utilization,
|
|
'container_count': int(self._safe_float(container_count[0][1])) if container_count and len(container_count) > 0 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': []
|
|
}
|
|
|
|
async def get_cpu_usage_history(self, namespace: str, workload: str, time_range: str = "24h") -> Dict[str, Any]:
|
|
"""Get CPU usage history for a workload using working Prometheus queries"""
|
|
try:
|
|
# Use the working query from the metrics endpoint
|
|
cpu_usage_query = f'rate(container_cpu_usage_seconds_total{{namespace="{namespace}", pod=~"{workload}.*"}}[5m])'
|
|
|
|
# Calculate time range
|
|
end_time = datetime.now()
|
|
start_time = end_time - timedelta(seconds=self.time_ranges.get(time_range, 86400))
|
|
|
|
# Query Prometheus
|
|
data = await self._query_prometheus(cpu_usage_query, start_time, end_time)
|
|
|
|
if not data:
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": [],
|
|
"message": "No CPU usage data available"
|
|
}
|
|
|
|
# Format data for Chart.js
|
|
chart_data = []
|
|
for point in data:
|
|
if len(point) >= 2 and point[1] != 'NaN':
|
|
timestamp = int(point[0] * 1000) # Convert to milliseconds
|
|
value = self._safe_float(point[1])
|
|
chart_data.append({
|
|
"x": timestamp,
|
|
"y": value
|
|
})
|
|
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": chart_data,
|
|
"query": cpu_usage_query
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting CPU usage history: {str(e)}")
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": [],
|
|
"error": str(e)
|
|
}
|
|
|
|
async def get_memory_usage_history(self, namespace: str, workload: str, time_range: str = "24h") -> Dict[str, Any]:
|
|
"""Get memory usage history for a workload using working Prometheus queries"""
|
|
try:
|
|
# Use the working query from the metrics endpoint
|
|
memory_usage_query = f'container_memory_working_set_bytes{{namespace="{namespace}", pod=~"{workload}.*", container!="", image!=""}}'
|
|
|
|
# Calculate time range
|
|
end_time = datetime.now()
|
|
start_time = end_time - timedelta(seconds=self.time_ranges.get(time_range, 86400))
|
|
|
|
# Query Prometheus
|
|
data = await self._query_prometheus(memory_usage_query, start_time, end_time)
|
|
|
|
if not data:
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": [],
|
|
"message": "No memory usage data available"
|
|
}
|
|
|
|
# Format data for Chart.js (convert bytes to MB)
|
|
chart_data = []
|
|
for point in data:
|
|
if len(point) >= 2 and point[1] != 'NaN':
|
|
timestamp = int(point[0] * 1000) # Convert to milliseconds
|
|
value = self._safe_float(point[1]) / (1024 * 1024) # Convert to MB
|
|
chart_data.append({
|
|
"x": timestamp,
|
|
"y": value
|
|
})
|
|
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": chart_data,
|
|
"query": memory_usage_query
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting memory usage history: {str(e)}")
|
|
return {
|
|
"workload": workload,
|
|
"namespace": namespace,
|
|
"time_range": time_range,
|
|
"data": [],
|
|
"error": str(e)
|
|
}
|
|
|
|
async def generate_recommendations(self, namespace: str, workload: str) -> List[Dict[str, Any]]:
|
|
"""Generate recommendations based on historical data"""
|
|
try:
|
|
# Get current usage data
|
|
cpu_data = await self.get_cpu_usage_history(namespace, workload, "24h")
|
|
memory_data = await self.get_memory_usage_history(namespace, workload, "24h")
|
|
|
|
recommendations = []
|
|
|
|
# Analyze CPU data
|
|
if cpu_data.get("data"):
|
|
cpu_values = [point["y"] for point in cpu_data["data"]]
|
|
if cpu_values:
|
|
avg_cpu = sum(cpu_values) / len(cpu_values)
|
|
max_cpu = max(cpu_values)
|
|
|
|
if avg_cpu < 0.1: # Less than 100m
|
|
recommendations.append({
|
|
"type": "cpu_optimization",
|
|
"severity": "info",
|
|
"message": f"CPU usage is very low (avg: {avg_cpu:.3f} cores). Consider reducing CPU requests.",
|
|
"current_usage": f"{avg_cpu:.3f} cores",
|
|
"recommendation": "Reduce CPU requests to match actual usage"
|
|
})
|
|
elif max_cpu > 0.8: # More than 800m
|
|
recommendations.append({
|
|
"type": "cpu_scaling",
|
|
"severity": "warning",
|
|
"message": f"CPU usage peaks at {max_cpu:.3f} cores. Consider increasing CPU limits.",
|
|
"current_usage": f"{max_cpu:.3f} cores",
|
|
"recommendation": "Increase CPU limits to handle peak usage"
|
|
})
|
|
|
|
# Analyze memory data
|
|
if memory_data.get("data"):
|
|
memory_values = [point["y"] for point in memory_data["data"]]
|
|
if memory_values:
|
|
avg_memory = sum(memory_values) / len(memory_values)
|
|
max_memory = max(memory_values)
|
|
|
|
if avg_memory < 100: # Less than 100MB
|
|
recommendations.append({
|
|
"type": "memory_optimization",
|
|
"severity": "info",
|
|
"message": f"Memory usage is very low (avg: {avg_memory:.1f} MB). Consider reducing memory requests.",
|
|
"current_usage": f"{avg_memory:.1f} MB",
|
|
"recommendation": "Reduce memory requests to match actual usage"
|
|
})
|
|
elif max_memory > 1000: # More than 1GB
|
|
recommendations.append({
|
|
"type": "memory_scaling",
|
|
"severity": "warning",
|
|
"message": f"Memory usage peaks at {max_memory:.1f} MB. Consider increasing memory limits.",
|
|
"current_usage": f"{max_memory:.1f} MB",
|
|
"recommendation": "Increase memory limits to handle peak usage"
|
|
})
|
|
|
|
return recommendations
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating recommendations: {str(e)}")
|
|
return [{
|
|
"type": "error",
|
|
"severity": "error",
|
|
"message": f"Error generating recommendations: {str(e)}",
|
|
"recommendation": "Check Prometheus connectivity and workload configuration"
|
|
}]
|