Performance
Engineering

<!-- Elemente liegen in der hashTree der ThreadGroup -->
<CSVDataSet guiclass="TestBeanGUI" testclass="CSVDataSet"
  testname="User data" enabled="true">
  <stringProp name="filename">${__P(csv,users.csv)}</stringProp>
  <stringProp name="variableNames">username,password,customer_id</stringProp>
  <stringProp name="delimiter">,</stringProp>
  <boolProp name="ignoreFirstLine">true</boolProp>
  <boolProp name="quotedData">true</boolProp>
  <boolProp name="recycle">false</boolProp>
  <boolProp name="stopThread">true</boolProp>
  <stringProp name="shareMode">shareMode.all</stringProp>
</CSVDataSet>
<hashTree/>

<TransactionController guiclass="TransactionControllerGui"
  testclass="TransactionController" testname="Checkout transaction"
  enabled="true">
  <boolProp name="TransactionController.parent">true</boolProp>
</TransactionController>
<hashTree>
  <HTTPSamplerProxy testname="Login"/>
  <hashTree/>
  <HTTPSamplerProxy testname="Add item to cart"/>
  <hashTree/>
  <HTTPSamplerProxy testname="Submit checkout"/>
  <hashTree/>
</hashTree>

import csv
import math
import sys
from collections import defaultdict


def percentile(values, quantile):
    ordered = sorted(values)
    index = max(0, math.ceil(quantile * len(ordered)) - 1)
    return ordered[index]


def summarize(path):
    durations = defaultdict(list)
    failures = defaultdict(int)
    with open(path, newline="", encoding="utf-8") as source:
        for row in csv.DictReader(source):
            label = row["label"]
            durations[label].append(int(row["elapsed"]))
            failures[label] += row["success"].lower() != "true"

    for label, samples in sorted(durations.items()):
        error_rate = failures[label] / len(samples)
        print(
            f"{label}: n={len(samples)}, "
            f"p95={percentile(samples, .95)}ms, "
            f"p99={percentile(samples, .99)}ms, "
            f"errors={error_rate:.2%}"
        )


if __name__ == "__main__":
    summarize(sys.argv[1] if len(sys.argv) > 1 else "results.jtl")

# P99 je Service im rollierenden 5-Minuten-Fenster
histogram_quantile(
  0.99,
  sum by (le, service) (
    rate(http_request_duration_seconds_bucket{
      environment=~"$environment", service=~"$service"
    }[5m])
  )
)

# Prozess-CPU-Kerne je Instanz (1.0 = ein voll genutzter Kern)
sum by (instance) (
  rate(process_cpu_seconds_total{
    job="$job", environment=~"$environment"
  }[5m])
)

# Host-CPU-Auslastung in Prozent, aus idle-Zeit abgeleitet
100 * (1 - avg by (instance) (
  rate(node_cpu_seconds_total{mode="idle", instance=~"$instance"}[5m])
))

name: performance-smoke
on:
  pull_request:
  workflow_dispatch:

jobs:
  smoke:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: grafana/setup-k6-action@v1
      - name: Run performance smoke test
        env:
          BASE_URL: ${{ secrets.STAGING_BASE_URL }}
          TEST_TOKEN: ${{ secrets.PERF_TEST_TOKEN }}
        run: |
          k6 run --summary-export=summary.json \
            -e BASE_URL="$BASE_URL" \
            -e TEST_TOKEN="$TEST_TOKEN" \
            tests/checkout-smoke.js
      - name: Archive result
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: performance-${{ github.sha }}
          path: summary.json
          if-no-files-found: ignore