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lamya1baidouri 2025-02-03 19:43:30 +01:00
parent fa459ba682
commit d48a81ce7a
1 changed files with 107 additions and 180 deletions
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287
.github/workflows/pipeline.yml vendored
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@ -32,14 +32,30 @@ jobs:
--health-timeout 5s
--health-retries 3
grafana:
image: grafana/grafana:latest
ports:
- 3000:3000
steps:
- uses: actions/checkout@v4
- name: Setup directories and tools
run: |
set -eo pipefail
# Créer la structure des répertoires
mkdir -p metrics/system
mkdir -p metrics/power
mkdir -p metrics/performance
# Installation des packages nécessaires
sudo apt-get update
sudo apt-get install -y powerstat linux-tools-common linux-tools-generic python3-pip
# Installation de PowerAPI et pandas
pip3 install powerapi pandas
# Vérifier les installations
python3 --version
pip3 list | grep powerapi
pip3 list | grep pandas
- name: Cache Maven packages
uses: actions/cache@v3
with:
@ -47,46 +63,23 @@ jobs:
key: ${{ runner.os }}-m2-${{ hashFiles('**/pom.xml') }}
restore-keys: ${{ runner.os }}-m2
- name: Setup monitoring tools
id: setup-monitoring
timeout-minutes: 5
run: |
set -eo pipefail
echo "::group::Installing system packages"
sudo apt-get update
sudo apt-get install -y powerstat linux-tools-common linux-tools-generic python3-pip stress-ng
echo "::endgroup::"
echo "::group::Installing PowerAPI"
pip3 install powerapi pandas
sudo powerapi --formula rapl
echo "::endgroup::"
echo "::group::Setting up node exporter"
curl -L --retry 3 https://github.com/prometheus/node_exporter/releases/download/v1.3.1/node_exporter-1.3.1.linux-amd64.tar.gz -o node_exporter.tar.gz
tar xvfz node_exporter.tar.gz
echo "::endgroup::"
- name: Start monitoring services
- name: Start monitoring
id: start-monitoring
timeout-minutes: 2
run: |
set -eo pipefail
# Démarrer node exporter
./node_exporter-*/node_exporter --web.listen-address=":9100" &
echo "NODE_EXPORTER_PID=$!" >> $GITHUB_ENV
# Démarrer PowerAPI
sudo powerapi daemon start --formula rapl
echo "POWERAPI_PID=$(pgrep -f powerapi)" >> $GITHUB_ENV
# Créer les répertoires pour les métriques
mkdir -p metrics/{power,system,performance}
# Marquer le début du pipeline
# Enregistrer le temps de début
date +%s%N > metrics/pipeline_start_time.txt
# Collecter les métriques initiales
echo "=== Initial System Resources ===" > metrics/system/initial_metrics.txt
top -b -n 1 >> metrics/system/initial_metrics.txt
echo "=== Initial Memory Usage ===" > metrics/system/initial_memory.txt
free -m >> metrics/system/initial_memory.txt
echo "=== Initial Disk Usage ===" > metrics/system/initial_disk.txt
df -h >> metrics/system/initial_disk.txt
- name: Set up JDK 17
uses: actions/setup-java@v4
@ -103,27 +96,26 @@ jobs:
run: |
set -eo pipefail
# Démarrer la mesure PowerAPI pour le build
sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/build_power.csv &
POWER_MONITOR_PID=$!
start_time=$(date +%s%N)
# Collecter les métriques avant build
free -m > metrics/system/pre_build_memory.txt
# Build optimisé
./mvnw -B verify \
-Dmaven.test.skip=true \
-Dcheckstyle.skip=true \
-T 1C \
-Dmaven.parallel.threads=4
-T 1C
build_status=$?
end_time=$(date +%s%N)
# Arrêter la mesure PowerAPI
kill $POWER_MONITOR_PID
# Collecter les métriques post-build
free -m > metrics/system/post_build_memory.txt
# Exporter les métriques de build
# Enregistrer le temps de build
echo "BUILD_TIME=$((($end_time - $start_time)/1000000))" >> $GITHUB_ENV
echo "$((($end_time - $start_time)/1000000))" > metrics/performance/build_time.txt
exit $build_status
@ -134,25 +126,24 @@ jobs:
run: |
set -eo pipefail
# Démarrer la mesure PowerAPI pour les tests
sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/test_power.csv &
POWER_MONITOR_PID=$!
start_time=$(date +%s%N)
# Collecter les métriques pré-tests
free -m > metrics/system/pre_test_memory.txt
# Tests optimisés
./mvnw test \
-T 1C \
-Dmaven.parallel.threads=4 \
-Dsurefire.useFile=false
./mvnw test -T 1C
test_status=$?
end_time=$(date +%s%N)
# Arrêter la mesure PowerAPI
kill $POWER_MONITOR_PID
# Collecter les métriques post-tests
free -m > metrics/system/post_test_memory.txt
# Enregistrer le temps des tests
echo "TEST_TIME=$((($end_time - $start_time)/1000000))" >> $GITHUB_ENV
echo "$((($end_time - $start_time)/1000000))" > metrics/performance/test_time.txt
exit $test_status
- name: Build Docker image
@ -162,129 +153,85 @@ jobs:
run: |
set -eo pipefail
# Démarrer la mesure PowerAPI pour le build Docker
sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/docker_power.csv &
POWER_MONITOR_PID=$!
start_time=$(date +%s%N)
# Collecter les métriques pré-docker
free -m > metrics/system/pre_docker_memory.txt
df -h > metrics/system/pre_docker_disk.txt
# Build Docker optimisé
DOCKER_BUILDKIT=1 docker build \
--no-cache \
--build-arg JAVA_VERSION=17 \
--build-arg JAVA_DISTRIBUTION=adoptopenjdk \
-t app:latest \
-f Dockerfile .
DOCKER_BUILDKIT=1 docker build -t app:latest .
build_status=$?
end_time=$(date +%s%N)
# Arrêter la mesure PowerAPI
kill $POWER_MONITOR_PID
# Collecter les métriques post-docker
free -m > metrics/system/post_docker_memory.txt
df -h > metrics/system/post_docker_disk.txt
# Enregistrer le temps de build Docker
echo "DOCKER_BUILD_TIME=$((($end_time - $start_time)/1000000))" >> $GITHUB_ENV
echo "$((($end_time - $start_time)/1000000))" > metrics/performance/docker_time.txt
# Collecter la taille de l'image
docker images app:latest --format "{{.Size}}" > metrics/performance/docker_image_size.txt
exit $build_status
- name: Setup Kubernetes
id: k8s-setup
if: success()
uses: helm/kind-action@v1
with:
wait: 120s
config: |
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
- role: control-plane
kubeadmConfigPatches:
- |
kind: InitConfiguration
nodeRegistration:
kubeletExtraArgs:
system-reserved: memory=1Gi
eviction-hard: memory.available<500Mi
- name: Deploy to Kubernetes
id: deploy
if: success()
timeout-minutes: 10
run: |
set -eo pipefail
# Démarrer la mesure PowerAPI pour le déploiement
sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/deploy_power.csv &
POWER_MONITOR_PID=$!
start_time=$(date +%s%N)
# Déploiement optimisé
kubectl apply -f k8s/
# Attendre que les pods soient prêts
kubectl wait --for=condition=ready pod -l app=petclinic --timeout=180s
end_time=$(date +%s%N)
# Arrêter la mesure PowerAPI
kill $POWER_MONITOR_PID
echo "DEPLOY_TIME=$((($end_time - $start_time)/1000000))" >> $GITHUB_ENV
- name: Collect and analyze metrics
if: always()
run: |
set -eo pipefail
# Collecter les métriques système finales
echo "=== System Resources ===" > metrics/system/system_metrics.txt
top -b -n 1 >> metrics/system/system_metrics.txt
echo "=== Final System Resources ===" > metrics/system/final_metrics.txt
top -b -n 1 >> metrics/system/final_metrics.txt || echo "Failed to collect top metrics"
echo "=== Memory Usage ===" > metrics/system/memory_metrics.txt
free -m >> metrics/system/memory_metrics.txt
echo "=== Final Memory Usage ===" > metrics/system/final_memory.txt
free -m >> metrics/system/final_memory.txt || echo "Failed to collect memory metrics"
echo "=== Disk Usage ===" > metrics/system/disk_metrics.txt
df -h >> metrics/system/disk_metrics.txt
echo "=== Final Disk Usage ===" > 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
# Analyser les métriques de puissance
# Analyser les temps d'exécution
python3 << EOF
import pandas as pd
import glob
import os
def analyze_power_metrics():
power_files = glob.glob('metrics/power/*.csv')
metrics = []
def read_time_file(filename):
try:
with open(filename, 'r') as f:
return float(f.read().strip())
except:
return 0
for file in power_files:
stage = os.path.basename(file).replace('_power.csv', '')
df = pd.read_csv(file)
stats = {
'stage': stage,
'avg_power': df['power'].mean(),
'max_power': df['power'].max(),
'total_energy': df['energy'].sum(),
'duration': len(df) * df['power'].iloc[0] # Assuming fixed sampling rate
}
metrics.append(stats)
# Collecter les temps
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')
}
results = pd.DataFrame(metrics)
results.to_csv('metrics/power/power_analysis.csv', index=False)
# Créer le rapport de performance
with open('metrics/performance/summary.txt', 'w') as f:
f.write("Pipeline Performance Summary\n")
f.write("==========================\n\n")
# Créer un rapport sommaire
with open('metrics/performance/summary.txt', 'w') as f:
f.write("Pipeline Performance Summary\n")
f.write("==========================\n\n")
total_time = sum(times.values())
for _, row in results.iterrows():
f.write(f"Stage: {row['stage']}\n")
f.write(f"Average Power: {row['avg_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")
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")
analyze_power_metrics()
f.write(f"Total Pipeline Duration: {total_time/1000:.2f} seconds\n")
# Créer un CSV avec les métriques
pd.DataFrame([times]).to_csv('metrics/performance/times.csv', index=False)
EOF
- name: Export metrics to Prometheus
@ -299,41 +246,21 @@ jobs:
local stage=$3
if [ -n "$metric_value" ]; then
echo "${metric_name}{stage=\"${stage}\",project=\"petclinic\"} ${metric_value}" | \
curl --retry 3 --retry-delay 2 --max-time 10 --silent --show-error \
--data-binary @- http://localhost:9091/metrics/job/petclinic-pipeline
echo "${metric_name}{stage=\"${stage}\",project=\"java-app\"} ${metric_value}" | \
curl --retry 3 --max-time 10 --silent --show-error \
--data-binary @- http://localhost:9091/metrics/job/pipeline-metrics || \
echo "Failed to export metric ${metric_name}"
fi
}
# Exporter les durées
export_metric "pipeline_build_duration_ms" "${BUILD_TIME}" "build"
export_metric "pipeline_test_duration_ms" "${TEST_TIME}" "test"
export_metric "pipeline_docker_build_duration_ms" "${DOCKER_BUILD_TIME}" "docker-build"
export_metric "pipeline_deploy_duration_ms" "${DEPLOY_TIME}" "deploy"
# Exporter les temps d'exécution
export_metric "pipeline_duration_ms" "${BUILD_TIME}" "build"
export_metric "pipeline_duration_ms" "${TEST_TIME}" "test"
export_metric "pipeline_duration_ms" "${DOCKER_BUILD_TIME}" "docker"
# Exporter les métriques de ressources
# Exporter l'utilisation mémoire finale
mem_usage=$(free -b | grep Mem: | awk '{print $3}')
export_metric "pipeline_memory_usage_bytes" "$mem_usage" "memory"
cpu_usage=$(top -bn1 | grep "Cpu(s)" | awk '{print $2}')
export_metric "pipeline_cpu_usage_percent" "$cpu_usage" "cpu"
# Exporter les métriques de puissance
while IFS=, read -r stage avg_power total_energy; do
export_metric "pipeline_power_usage_watts" "$avg_power" "$stage"
export_metric "pipeline_energy_consumption_joules" "$total_energy" "$stage"
done < <(tail -n +2 metrics/power/power_analysis.csv)
- name: Stop monitoring services
if: always()
run: |
# Arrêter PowerAPI
sudo powerapi daemon stop
# Arrêter node exporter
if [ -n "$NODE_EXPORTER_PID" ]; then
kill $NODE_EXPORTER_PID
fi
export_metric "memory_usage_bytes" "$mem_usage" "final"
- name: Save metrics
if: always()