mirror of
https://github.com/spring-projects/spring-petclinic.git
synced 2025-07-15 12:25:50 +00:00
debbug
This commit is contained in:
lamya1baidouri
parent
f5d2b0b0aa
commit
3e9be926fc
1 changed files with 115 additions and 69 deletions
184
.github/workflows/pipeline.yml
vendored
184
.github/workflows/pipeline.yml
vendored
|
|
@ -206,89 +206,135 @@ jobs:
|
|||
if: always()
|
||||
run: |
|
||||
set -eo pipefail
|
||||
|
||||
|
||||
# Collecter les métriques système finales
|
||||
echo "=== Final System Resources ===" > metrics/system/final_metrics.txt
|
||||
top -b -n 1 >> metrics/system/final_metrics.txt
|
||||
|
||||
top -b -n 1 >> metrics/system/final_metrics.txt || echo "Failed to collect top metrics"
|
||||
|
||||
echo "=== Final Memory Usage ===" > metrics/system/final_memory.txt
|
||||
free -m >> metrics/system/final_memory.txt
|
||||
|
||||
free -m >> metrics/system/final_memory.txt || echo "Failed to collect memory metrics"
|
||||
|
||||
echo "=== Final Disk Usage ===" > metrics/system/final_disk.txt
|
||||
df -h >> metrics/system/final_disk.txt
|
||||
|
||||
df -h >> metrics/system/final_disk.txt || echo "Failed to collect disk metrics"
|
||||
|
||||
# Marquer la fin du pipeline
|
||||
date +%s%N > metrics/pipeline_end_time.txt
|
||||
|
||||
|
||||
python3 << EOF
|
||||
import pandas as pd
|
||||
import glob
|
||||
import os
|
||||
|
||||
def read_time_file(filename, default=0):
|
||||
"""Lit un fichier de temps avec gestion d'erreur"""
|
||||
try:
|
||||
with open(filename) as f:
|
||||
return float(f.read().strip())
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not read {filename}: {e}")
|
||||
return default
|
||||
|
||||
def analyze_power_metrics():
|
||||
power_files = glob.glob('metrics/power/*.csv')
|
||||
if not power_files:
|
||||
print("No power metrics found")
|
||||
return
|
||||
|
||||
power_data = []
|
||||
for file in power_files:
|
||||
stage = os.path.basename(file).replace('_power.csv', '')
|
||||
try:
|
||||
df = pd.read_csv(file)
|
||||
stats = {
|
||||
'stage': stage,
|
||||
'avg_power': df['power'].mean(),
|
||||
'max_power': df['power'].max(),
|
||||
'total_energy': df['power'].sum() * df['power'].count() * 0.1,
|
||||
'duration': len(df) * 0.1
|
||||
}
|
||||
power_data.append(stats)
|
||||
except Exception as e:
|
||||
print(f"Error processing {file}: {e}")
|
||||
|
||||
if power_data:
|
||||
power_df = pd.DataFrame(power_data)
|
||||
power_df.to_csv('metrics/power/power_analysis.csv', index=False)
|
||||
|
||||
with open('metrics/power/power_summary.txt', 'w') as f:
|
||||
f.write("Energy Consumption Summary\n")
|
||||
f.write("=========================\n\n")
|
||||
|
||||
for _, row in power_df.iterrows():
|
||||
f.write(f"Stage: {row['stage']}\n")
|
||||
f.write(f"Average Power: {row['avg_power']:.2f} W\n")
|
||||
f.write(f"Maximum Power: {row['max_power']:.2f} W\n")
|
||||
f.write(f"Total Energy: {row['total_energy']:.2f} J\n")
|
||||
f.write(f"Duration: {row['duration']:.2f} s\n\n")
|
||||
"""Analyse les données de PowerAPI avec gestion d'erreur"""
|
||||
try:
|
||||
power_files = glob.glob('metrics/power/*.csv')
|
||||
if not power_files:
|
||||
print("No power metrics found")
|
||||
return {}
|
||||
|
||||
power_data = []
|
||||
for file in power_files:
|
||||
stage = os.path.basename(file).replace('_power.csv', '')
|
||||
try:
|
||||
df = pd.read_csv(file)
|
||||
stats = {
|
||||
'stage': stage,
|
||||
'avg_power': df['power'].mean(),
|
||||
'max_power': df['power'].max(),
|
||||
'total_energy': df['power'].sum() * df['power'].count() * 0.1,
|
||||
'duration': len(df) * 0.1
|
||||
}
|
||||
power_data.append(stats)
|
||||
except Exception as e:
|
||||
print(f"Error processing {file}: {e}")
|
||||
|
||||
if power_data:
|
||||
power_df = pd.DataFrame(power_data)
|
||||
power_df.to_csv('metrics/power/power_analysis.csv', index=False)
|
||||
return power_df
|
||||
return {}
|
||||
except Exception as e:
|
||||
print(f"Error in power analysis: {e}")
|
||||
return {}
|
||||
|
||||
def analyze_times():
|
||||
"""Analyse les temps d'exécution avec gestion d'erreur"""
|
||||
try:
|
||||
times = {
|
||||
'build': read_time_file('metrics/performance/build_time.txt'),
|
||||
'test': read_time_file('metrics/performance/test_time.txt'),
|
||||
'docker': read_time_file('metrics/performance/docker_time.txt')
|
||||
}
|
||||
|
||||
total_time = sum(times.values())
|
||||
if total_time > 0:
|
||||
os.makedirs('metrics/performance', exist_ok=True)
|
||||
|
||||
with open('metrics/performance/summary.txt', 'w') as f:
|
||||
f.write("Pipeline Performance Summary\n")
|
||||
f.write("==========================\n\n")
|
||||
|
||||
for stage, duration in times.items():
|
||||
percentage = (duration / total_time * 100) if total_time > 0 else 0
|
||||
f.write(f"{stage.capitalize()} Stage:\n")
|
||||
f.write(f"Duration: {duration/1000:.2f} seconds\n")
|
||||
f.write(f"Percentage of total time: {percentage:.1f}%\n\n")
|
||||
|
||||
f.write(f"Total Pipeline Duration: {total_time/1000:.2f} seconds\n")
|
||||
|
||||
pd.DataFrame([times]).to_csv('metrics/performance/times.csv', index=False)
|
||||
return times
|
||||
except Exception as e:
|
||||
print(f"Error in time analysis: {e}")
|
||||
return {}
|
||||
|
||||
# Créer les répertoires si nécessaire
|
||||
os.makedirs('metrics/power', exist_ok=True)
|
||||
os.makedirs('metrics/performance', exist_ok=True)
|
||||
os.makedirs('metrics/system', exist_ok=True)
|
||||
|
||||
# Analyser les données
|
||||
power_metrics = analyze_power_metrics()
|
||||
time_metrics = analyze_times()
|
||||
|
||||
# Créer un rapport final
|
||||
try:
|
||||
with open('metrics/summary_report.txt', 'w') as f:
|
||||
f.write("Pipeline Analysis Report\n")
|
||||
f.write("======================\n\n")
|
||||
|
||||
if time_metrics:
|
||||
f.write("Performance Metrics:\n")
|
||||
f.write("-----------------\n")
|
||||
total_time = sum(time_metrics.values())
|
||||
f.write(f"Total Duration: {total_time/1000:.2f} seconds\n")
|
||||
for stage, duration in time_metrics.items():
|
||||
f.write(f"{stage}: {duration/1000:.2f}s\n")
|
||||
f.write("\n")
|
||||
|
||||
if power_metrics:
|
||||
f.write("Energy Metrics:\n")
|
||||
f.write("--------------\n")
|
||||
for stage, metrics in power_metrics.items():
|
||||
f.write(f"Stage: {stage}\n")
|
||||
for metric, value in metrics.items():
|
||||
if metric != 'stage':
|
||||
f.write(f"{metric}: {value:.2f}\n")
|
||||
f.write("\n")
|
||||
except Exception as e:
|
||||
print(f"Error creating final report: {e}")
|
||||
|
||||
def analyze_times():
|
||||
times = {
|
||||
'build': float(open('metrics/performance/build_time.txt').read().strip()),
|
||||
'test': float(open('metrics/performance/test_time.txt').read().strip()),
|
||||
'docker': float(open('metrics/performance/docker_time.txt').read().strip())
|
||||
}
|
||||
|
||||
total_time = sum(times.values())
|
||||
|
||||
with open('metrics/performance/summary.txt', 'w') as f:
|
||||
f.write("Pipeline Performance Summary\n")
|
||||
f.write("==========================\n\n")
|
||||
|
||||
for stage, duration in times.items():
|
||||
percentage = (duration / total_time * 100)
|
||||
f.write(f"{stage.capitalize()} Stage:\n")
|
||||
f.write(f"Duration: {duration/1000:.2f} seconds\n")
|
||||
f.write(f"Percentage of total time: {percentage:.1f}%\n\n")
|
||||
|
||||
f.write(f"Total Pipeline Duration: {total_time/1000:.2f} seconds\n")
|
||||
|
||||
pd.DataFrame([times]).to_csv('metrics/performance/times.csv', index=False)
|
||||
|
||||
analyze_power_metrics()
|
||||
analyze_times()
|
||||
EOF
|
||||
|
||||
- name: Export metrics to Prometheus
|
||||
if: always()
|
||||
timeout-minutes: 5
|
||||
|
|
|
|||
Loading…
Reference in a new issue