Die systematische Überwachung von CI-Pipelines erfordert definierte Metriken, die Aufschluss über Performance, Zuverlässigkeit und Effizienz des Build-Prozesses geben. Build-Duration misst die Zeit von Commit bis zu deployable Artifacts und identifiziert Performance-Bottlenecks. Success-Rate quantifiziert die Stabilität der Pipeline durch das Verhältnis erfolgreicher zu fehlgeschlagenen Builds. Mean Time To Recovery (MTTR) dokumentiert, wie schnell fehlerhafte Builds repariert werden. Diese Basis-Metriken bilden das Fundament für datengetriebene Pipeline-Optimierung.
Die Granularität der Metriken ermöglicht präzise Analyse verschiedener Pipeline-Aspekte. Stage-Level-Metriken zeigen, welche Pipeline-Phasen die meiste Zeit konsumieren. Task-Level-Metriken aus Gradle identifizieren langsame Build-Komponenten. Resource-Utilization-Metriken revealing, ob CPU, Memory oder I/O zum Bottleneck werden. Queue-Time-Metriken messen Wartezeiten auf verfügbare Build-Agents. Diese mehrdimensionale Betrachtung ermöglicht gezielte Optimierungen statt generischer Performance-Verbesserungen.
Lead Time und Cycle Time messen die Geschwindigkeit der Software-Delivery. Lead Time trackt die Duration von Code-Commit bis Production-Deployment. Cycle Time misst die Zeit für einen kompletten Development-Cycle. Deployment Frequency dokumentiert, wie oft Code in Production deployed wird. Change Failure Rate zeigt den Prozentsatz von Deployments, die zu Production-Incidents führen. Diese DORA-Metriken (DevOps Research and Assessment) korrelieren mit Team-Performance und Business-Outcomes.
// Pipeline Metrics Collection
tasks.register("collectPipelineMetrics") {
description = "Collect and export CI pipeline metrics"
doLast {
val metrics = PipelineMetrics(
timestamp = Instant.now(),
buildId = System.getenv("BUILD_ID") ?: "local",
pipeline = System.getenv("JOB_NAME") ?: project.name,
branch = getCurrentBranch(),
// Duration metrics
totalDuration = measureTotalBuildTime(),
stageDurations = mapOf(
"compile" to measureStageTime("compile"),
"test" to measureStageTime("test"),
"package" to measureStageTime("package"),
"deploy" to measureStageTime("deploy")
),
// Quality metrics
testResults = TestMetrics(
total = countTotalTests(),
passed = countPassedTests(),
failed = countFailedTests(),
skipped = countSkippedTests(),
duration = measureTestDuration()
),
// Code metrics
codeMetrics = CodeMetrics(
linesOfCode = countLinesOfCode(),
coverage = calculateCodeCoverage(),
complexity = calculateCyclomaticComplexity(),
duplications = calculateCodeDuplication()
),
// Resource metrics
resourceUsage = ResourceMetrics(
peakMemory = Runtime.getRuntime().maxMemory() / 1024 / 1024,
cpuCores = Runtime.getRuntime().availableProcessors(),
buildCacheHitRate = calculateCacheHitRate()
)
)
// Export to monitoring systems
exportToPrometheus(metrics)
exportToInfluxDB(metrics)
exportToDatadog(metrics)
// Generate local report
val reportFile = file("${buildDir}/reports/pipeline-metrics.json")
reportFile.parentFile.mkdirs()
reportFile.writeText(groovy.json.JsonOutput.toJson(metrics))
}
}
// Prometheus metrics endpoint
tasks.register("prometheusMetrics") {
description = "Generate Prometheus-compatible metrics"
doLast {
val metricsFile = file("${buildDir}/metrics/prometheus.txt")
metricsFile.parentFile.mkdirs()
metricsFile.writeText("""
# HELP build_duration_seconds Total build duration
# TYPE build_duration_seconds gauge
build_duration_seconds{pipeline="${project.name}",branch="${getCurrentBranch()}"} ${getBuildDuration()}
# HELP build_success_total Number of successful builds
# TYPE build_success_total counter
build_success_total{pipeline="${project.name}"} ${getSuccessCount()}
# HELP build_failure_total Number of failed builds
# TYPE build_failure_total counter
build_failure_total{pipeline="${project.name}"} ${getFailureCount()}
# HELP test_execution_duration_seconds Test execution time
# TYPE test_execution_duration_seconds histogram
test_execution_duration_seconds_bucket{le="1.0"} ${countTestsUnder(1)}
test_execution_duration_seconds_bucket{le="5.0"} ${countTestsUnder(5)}
test_execution_duration_seconds_bucket{le="10.0"} ${countTestsUnder(10)}
test_execution_duration_seconds_bucket{le="+Inf"} ${countAllTests()}
# HELP code_coverage_ratio Code coverage percentage
# TYPE code_coverage_ratio gauge
code_coverage_ratio{pipeline="${project.name}"} ${getCodeCoverage()}
# HELP build_cache_hit_ratio Build cache hit rate
# TYPE build_cache_hit_ratio gauge
build_cache_hit_ratio{pipeline="${project.name}"} ${getCacheHitRate()}
""".trimIndent())
}
}Real-Time-Monitoring von CI-Pipelines ermöglicht proaktive Intervention bei Problemen. Streaming-Metriken werden während der Build-Execution an Monitoring-Systeme gesendet, nicht erst nach Completion. Diese Live-Visibility ermöglicht Early-Termination von offensichtlich fehlerhaften Builds und reduziert Resource-Waste. WebSocket-Connections oder Server-Sent-Events streamen Build-Logs zu Dashboards, wo Teams Build-Progress in Echtzeit verfolgen können.
Alert-Konfigurationen definieren Schwellwerte und Bedingungen für Notifications. Build-Failures triggern immediate Alerts an verantwortliche Teams. Performance-Degradation über definierte Thresholds generiert Warnings. Stuck-Builds, die erwartete Duration überschreiten, produzieren Timeout-Alerts. Diese Multi-Level-Alerting-Strategy balanciert zwischen Information-Overload und Missing-Critical-Issues. Die Alert-Routing basiert auf Severity, betroffenen Components und Team-Responsibilities.
// Real-time monitoring integration
abstract class BuildMonitor : BuildService<BuildMonitor.Params>, OperationCompletionListener {
interface Params : BuildServiceParameters {
val monitoringEndpoint: Property<String>
val apiKey: Property<String>
}
private val metricsBuffer = mutableListOf<BuildEvent>()
private var buildStartTime = Instant.now()
override fun onFinish(event: FinishEvent) {
val metric = BuildEvent(
timestamp = Instant.now(),
eventType = event.descriptor.name,
duration = event.result.duration,
status = if (event.result.failure.isPresent) "FAILED" else "SUCCESS",
details = extractEventDetails(event)
)
metricsBuffer.add(metric)
// Stream to monitoring system
if (metricsBuffer.size >= 10 || event.descriptor.name.contains("build")) {
streamMetrics()
}
// Check alert conditions
checkAlertConditions(metric)
}
private fun streamMetrics() {
val endpoint = parameters.monitoringEndpoint.get()
val payload = groovy.json.JsonOutput.toJson(metricsBuffer)
// Send metrics via HTTP
URL(endpoint).openConnection().apply {
requestMethod = "POST"
setRequestProperty("Authorization", "Bearer ${parameters.apiKey.get()}")
setRequestProperty("Content-Type", "application/json")
doOutput = true
outputStream.write(payload.toByteArray())
if (responseCode != 200) {
logger.warn("Failed to send metrics: $responseCode")
}
}
metricsBuffer.clear()
}
private fun checkAlertConditions(event: BuildEvent) {
// Duration threshold alerting
if (event.duration > 600_000) { // 10 minutes
sendAlert(AlertLevel.WARNING,
"Long running task: ${event.eventType} (${event.duration / 1000}s)")
}
// Failure alerting
if (event.status == "FAILED") {
sendAlert(AlertLevel.ERROR,
"Build failure in ${event.eventType}: ${event.details}")
}
// Memory threshold alerting
val usedMemory = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory()
val maxMemory = Runtime.getRuntime().maxMemory()
if (usedMemory > maxMemory * 0.9) {
sendAlert(AlertLevel.WARNING,
"High memory usage: ${usedMemory / 1024 / 1024}MB / ${maxMemory / 1024 / 1024}MB")
}
}
}
// Register monitoring service
val buildMonitor = gradle.sharedServices.registerIfAbsent("buildMonitor", BuildMonitor::class) {
parameters {
monitoringEndpoint.set("https://monitoring.company.com/api/metrics")
apiKey.set(providers.environmentVariable("MONITORING_API_KEY"))
}
}
// Alerting configuration
tasks.register("configureAlerts") {
description = "Configure pipeline alerting rules"
doLast {
val alertConfig = AlertConfiguration(
rules = listOf(
AlertRule(
name = "Build Failure",
condition = "build.status == 'FAILED'",
severity = AlertLevel.ERROR,
channels = listOf("slack", "email"),
recipients = listOf("#ci-alerts", "devops@company.com")
),
AlertRule(
name = "Slow Build",
condition = "build.duration > 1800000", // 30 minutes
severity = AlertLevel.WARNING,
channels = listOf("slack"),
recipients = listOf("#ci-performance")
),
AlertRule(
name = "Low Test Coverage",
condition = "test.coverage < 0.7",
severity = AlertLevel.WARNING,
channels = listOf("email"),
recipients = listOf("qa@company.com")
),
AlertRule(
name = "High Failure Rate",
condition = "build.failureRate > 0.3", // 30% failure rate
severity = AlertLevel.CRITICAL,
channels = listOf("pagerduty"),
recipients = listOf("oncall")
)
)
)
// Deploy alert configuration
deployAlertConfig(alertConfig)
}
}Anomaly-Detection nutzt Machine-Learning zur Identifikation ungewöhnlicher Pipeline-Behavior. Baseline-Models lernen normale Build-Duration-Patterns und flaggen signifikante Deviations. Sudden Increases in Test-Failures oder Memory-Usage triggern Investigations. Diese predictive Monitoring identifiziert Problems bevor sie zu Failures eskalieren. Die Integration mit AIOps-Platforms automatisiert Root-Cause-Analysis und suggests Remediation-Actions.
Pipeline-Dashboards transformieren rohe Metriken in actionable Visualisierungen. Grafana, Kibana oder custom Web-Dashboards aggregieren Daten aus verschiedenen Sources zu unified Views. Real-Time-Widgets zeigen current Build-Status, Queue-Lengths und Resource-Utilization. Historical Charts visualisieren Trends über Zeit. Heatmaps identifizieren Patterns in Build-Failures. Diese Visualisierungen machen abstrakte Metriken konkret und verständlich für alle Stakeholder.
Executive Dashboards fokussieren auf High-Level-Metriken wie Delivery-Speed und Quality-Trends. Team Dashboards zeigen granulare Details zu individual Builds und Tasks. Developer Dashboards highlighting personal Build-Success-Rates und Impact auf Team-Velocity. Diese role-based Views ensuring relevant Information ohne Information-Overload. Die Dashboard-Hierarchie ermöglicht Drill-Down von Overview zu Details bei Bedarf.
// Dashboard data generation
tasks.register("generateDashboardData") {
description = "Generate data for pipeline dashboards"
doLast {
val dashboardData = DashboardData(
generated = Instant.now(),
// Overview metrics
overview = OverviewMetrics(
totalBuilds = countTotalBuilds(),
successRate = calculateSuccessRate(),
averageDuration = calculateAverageDuration(),
leadTime = calculateLeadTime(),
deploymentFrequency = calculateDeploymentFrequency()
),
// Trend data
trends = TrendData(
daily = generateDailyTrends(30),
weekly = generateWeeklyTrends(12),
monthly = generateMonthlyTrends(6)
),
// Pipeline breakdown
pipelineBreakdown = PipelineBreakdown(
stages = analyzeStagePerformance(),
bottlenecks = identifyBottlenecks(),
failures = categorizeFailures()
),
// Team metrics
teamMetrics = TeamMetrics(
velocity = calculateTeamVelocity(),
throughput = calculateThroughput(),
cycleTime = calculateCycleTime(),
workInProgress = countWorkInProgress()
)
)
// Generate HTML dashboard
val dashboardHtml = file("${buildDir}/reports/dashboard.html")
dashboardHtml.parentFile.mkdirs()
dashboardHtml.writeText(generateDashboardHtml(dashboardData))
// Export for external dashboards
exportToGrafana(dashboardData)
exportToKibana(dashboardData)
exportToTableau(dashboardData)
}
}
fun generateDashboardHtml(data: DashboardData): String = """
<!DOCTYPE html>
<html>
<head>
<title>CI Pipeline Dashboard</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
margin: 0;
padding: 20px;
background: #f5f5f5;
}
.dashboard-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 20px;
}
.metric-card {
background: white;
border-radius: 8px;
padding: 20px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.metric-value {
font-size: 3em;
font-weight: bold;
color: #2196F3;
}
.metric-label {
color: #666;
margin-top: 10px;
}
.chart-container {
background: white;
border-radius: 8px;
padding: 20px;
margin: 20px 0;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.status-good { color: #4CAF50; }
.status-warning { color: #FF9800; }
.status-critical { color: #F44336; }
</style>
</head>
<body>
<h1>CI Pipeline Dashboard</h1>
<p>Last updated: ${data.generated}</p>
<div class="dashboard-grid">
<div class="metric-card">
<div class="metric-value">${data.overview.successRate}%</div>
<div class="metric-label">Success Rate</div>
</div>
<div class="metric-card">
<div class="metric-value">${data.overview.averageDuration}</div>
<div class="metric-label">Avg Build Time</div>
</div>
<div class="metric-card">
<div class="metric-value">${data.overview.leadTime}</div>
<div class="metric-label">Lead Time</div>
</div>
<div class="metric-card">
<div class="metric-value">${data.overview.deploymentFrequency}</div>
<div class="metric-label">Deploys/Day</div>
</div>
</div>
<div class="chart-container">
<h2>Build Trend (Last 30 Days)</h2>
<canvas id="trendChart"></canvas>
</div>
<div class="chart-container">
<h2>Pipeline Stage Performance</h2>
<div id="stageChart"></div>
</div>
<div class="chart-container">
<h2>Failure Analysis</h2>
<div id="failureChart"></div>
</div>
<script>
// Trend chart
const trendCtx = document.getElementById('trendChart').getContext('2d');
new Chart(trendCtx, {
type: 'line',
data: {
labels: ${data.trends.daily.map { it.date }.toJson()},
datasets: [{
label: 'Build Duration',
data: ${data.trends.daily.map { it.duration }.toJson()},
borderColor: '#2196F3',
tension: 0.1
}, {
label: 'Success Rate',
data: ${data.trends.daily.map { it.successRate }.toJson()},
borderColor: '#4CAF50',
tension: 0.1,
yAxisID: 'y1'
}]
},
options: {
responsive: true,
scales: {
y: {
type: 'linear',
display: true,
position: 'left'
},
y1: {
type: 'linear',
display: true,
position: 'right',
grid: {
drawOnChartArea: false
}
}
}
}
});
// Stage performance chart
Plotly.newPlot('stageChart', [{
type: 'waterfall',
x: ${data.pipelineBreakdown.stages.map { it.name }.toJson()},
y: ${data.pipelineBreakdown.stages.map { it.duration }.toJson()},
connector: {line: {color: 'rgb(63, 63, 63)'}},
decreasing: {marker: {color: 'red'}},
increasing: {marker: {color: 'green'}},
totals: {marker: {color: 'blue'}}
}], {
title: 'Pipeline Stage Duration',
xaxis: {title: 'Stage'},
yaxis: {title: 'Duration (seconds)'}
});
// Failure analysis chart
Plotly.newPlot('failureChart', [{
type: 'pie',
labels: ${data.pipelineBreakdown.failures.map { it.category }.toJson()},
values: ${data.pipelineBreakdown.failures.map { it.count }.toJson()},
hole: 0.4
}], {
title: 'Failure Categories'
});
</script>
</body>
</html>
""".trimIndent()Automated Reporting generiert periodische Summaries für verschiedene Audiences. Daily Stand-Up Reports highlighting Yesterday’s Failures und Today’s Priorities. Weekly Management Reports summarizing Velocity-Trends und Quality-Metrics. Monthly Executive Reports focusing auf Strategic KPIs und ROI. Diese Reports werden automatisch generiert und via Email, Slack oder Confluence distributed. Die Automation ensuring consistent Communication ohne manual Effort.
Historical Pipeline-Data ermöglicht Trend-Analysis und Pattern-Recognition. Time-Series-Analysis identifiziert Seasonal Patterns, wie increased Failure-Rates vor Releases. Regression-Analysis correlating Code-Changes mit Build-Duration-Increases. Correlation-Analysis zwischen verschiedenen Metriken revealing hidden Dependencies. Diese statistischen Analysen transformieren Pipeline-Data in strategische Insights für Process-Improvement.
Predictive Analytics forecasted future Pipeline-Performance basierend auf Historical Trends. Machine-Learning-Models trainieren auf Past Build-Data und predicten Failure-Probability für neue Commits. Duration-Predictions helfen bei Capacity-Planning und Release-Scheduling. Queue-Time-Predictions optimieren Agent-Allocation. Diese Predictions ermöglichen proactive Optimization statt reactive Problem-Solving.
// Trend analysis and prediction
tasks.register("analyzePipelineTrends") {
description = "Analyze pipeline trends and generate predictions"
doLast {
val historicalData = loadHistoricalPipelineData(days = 90)
val trendAnalysis = TrendAnalyzer.analyze(historicalData)
val predictions = PipelinePredictor.predict(historicalData, horizon = 7)
val analysisReport = file("${buildDir}/reports/trend-analysis.json")
analysisReport.parentFile.mkdirs()
val report = mapOf(
"generated" to Instant.now().toString(),
"dataPoints" to historicalData.size,
"trends" to mapOf(
"buildDuration" to trendAnalysis.durationTrend,
"successRate" to trendAnalysis.successTrend,
"testCoverage" to trendAnalysis.coverageTrend
),
"patterns" to mapOf(
"dailyPattern" to trendAnalysis.dailyPattern,
"weeklyPattern" to trendAnalysis.weeklyPattern,
"releaseImpact" to trendAnalysis.releaseImpact
),
"anomalies" to trendAnalysis.anomalies.map { anomaly ->
mapOf(
"date" to anomaly.date,
"metric" to anomaly.metric,
"expected" to anomaly.expected,
"actual" to anomaly.actual,
"severity" to anomaly.severity
)
},
"predictions" to mapOf(
"nextWeekSuccessRate" to predictions.successRate,
"nextWeekAvgDuration" to predictions.avgDuration,
"failureRisk" to predictions.failureRisk,
"bottlenecks" to predictions.likelyBottlenecks
),
"recommendations" to generateRecommendations(trendAnalysis, predictions)
)
analysisReport.writeText(groovy.json.JsonOutput.toJson(report))
// Alert on concerning trends
if (trendAnalysis.durationTrend.slope > 0.1) {
logger.warn("Build duration increasing: ${trendAnalysis.durationTrend.slope * 100}% per week")
}
if (trendAnalysis.successTrend.slope < -0.05) {
logger.warn("Success rate declining: ${trendAnalysis.successTrend.slope * 100}% per week")
}
// Generate predictive alerts
if (predictions.failureRisk > 0.7) {
sendAlert("High failure risk predicted for next week: ${predictions.failureRisk * 100}%")
}
}
}
// Machine learning model integration
class PipelinePredictor {
companion object {
fun predict(data: List<PipelineData>, horizon: Int): Predictions {
// Feature extraction
val features = extractFeatures(data)
// Load trained model
val model = loadModel("pipeline-predictor-v2.pkl")
// Generate predictions
val predictions = model.predict(features, horizon)
return Predictions(
successRate = predictions["success_rate"],
avgDuration = predictions["avg_duration"],
failureRisk = calculateFailureRisk(predictions),
likelyBottlenecks = identifyFutureBottlenecks(predictions)
)
}
private fun extractFeatures(data: List<PipelineData>): FeatureMatrix {
return FeatureMatrix(
// Time-based features
hourOfDay = data.map { it.timestamp.hour },
dayOfWeek = data.map { it.timestamp.dayOfWeek.value },
// Performance features
durations = data.map { it.duration },
queueTimes = data.map { it.queueTime },
// Code change features
filesChanged = data.map { it.filesChanged },
linesAdded = data.map { it.linesAdded },
linesRemoved = data.map { it.linesRemoved },
// Historical features
rollingAvgDuration = calculateRollingAverage(data.map { it.duration }, 7),
rollingSuccessRate = calculateRollingAverage(data.map { it.success }, 7)
)
}
}
}Capacity Planning nutzt Pipeline-Analytics zur Resource-Optimization. Build-Agent-Utilization-Patterns informieren Scaling-Decisions. Peak-Load-Analysis dimensioning Queue-Sizes und Parallel-Execution-Limits. Cost-per-Build-Calculations justifying Infrastructure-Investments. Diese data-driven Capacity-Planning ensuring adequate Resources ohne Over-Provisioning.
Die Integration von CI-Pipeline-Monitoring in Enterprise-Observability-Platforms schafft holistische System-Views. Application Performance Monitoring (APM) Tools wie Datadog, New Relic oder Dynatrace correlating Deployment-Events mit Production-Performance. Log-Aggregation-Platforms wie ELK-Stack oder Splunk collecting Build-Logs für Centralized Analysis. Diese Integration enabling End-to-End-Traceability von Code-Commit bis Production-Impact.
OpenTelemetry standardisiert Telemetry-Collection über Tool-Boundaries. Traces dokumentieren complete Build-Executions mit Spans für individual Stages. Metrics exportieren Performance-Indicators zu common Backends. Logs werden mit Trace-Context enriched für Correlation. Diese standardisierte Observability-Infrastructure reduziert Integration-Complexity und ermöglicht Tool-Agnostic Monitoring.
// OpenTelemetry integration
dependencies {
implementation("io.opentelemetry:opentelemetry-api:1.30.0")
implementation("io.opentelemetry:opentelemetry-sdk:1.30.0")
implementation("io.opentelemetry:opentelemetry-exporter-otlp:1.30.0")
}
// Configure OpenTelemetry
val otelConfig = """
otel.service.name = ${project.name}-pipeline
otel.exporter.otlp.endpoint = https://otel.company.com:4317
otel.exporter.otlp.headers = Authorization=Bearer ${System.getenv("OTEL_TOKEN")}
otel.traces.exporter = otlp
otel.metrics.exporter = otlp
otel.logs.exporter = otlp
""".trimIndent()
// Instrumented build task
abstract class InstrumentedBuildTask : DefaultTask() {
private val tracer = GlobalOpenTelemetry.getTracer("gradle-build")
private val meter = GlobalOpenTelemetry.getMeter("gradle-build")
@TaskAction
fun execute() {
val span = tracer.spanBuilder("${project.name}.${name}")
.setAttribute("build.task", name)
.setAttribute("build.project", project.name)
.setAttribute("build.version", project.version.toString())
.startSpan()
val scope = span.makeCurrent()
val startTime = System.currentTimeMillis()
try {
// Execute actual task logic
executeTask()
span.setStatus(StatusCode.OK)
// Record metrics
val duration = System.currentTimeMillis() - startTime
meter.histogramBuilder("build.task.duration")
.setUnit("ms")
.build()
.record(duration.toDouble())
meter.counterBuilder("build.task.success")
.build()
.add(1)
} catch (e: Exception) {
span.setStatus(StatusCode.ERROR, e.message ?: "Unknown error")
span.recordException(e)
meter.counterBuilder("build.task.failure")
.build()
.add(1)
throw e
} finally {
span.end()
scope.close()
}
}
abstract fun executeTask()
}
// Observability dashboard configuration
tasks.register("configureObservability") {
description = "Configure observability platform integration"
doLast {
// Datadog integration
val datadogConfig = """
{
"api_key": "${System.getenv("DD_API_KEY")}",
"app_key": "${System.getenv("DD_APP_KEY")}",
"tags": [
"team:platform",
"service:ci-pipeline",
"env:${System.getenv("ENV") ?: "dev"}"
],
"dashboards": [
{
"name": "CI Pipeline Overview",
"widgets": [
{
"type": "timeseries",
"query": "avg:ci.pipeline.duration{*} by {branch}"
},
{
"type": "query_value",
"query": "sum:ci.pipeline.success{*}.as_rate()"
}
]
}
]
}
""".trimIndent()
// Deploy configuration to monitoring platforms
deployDatadogConfig(datadogConfig)
deployGrafanaConfig(generateGrafanaConfig())
deployPrometheusRules(generatePrometheusAlertRules())
}
}Service Level Objectives (SLOs) definieren Reliability-Targets für CI-Pipelines. Build-Success-Rate SLOs ensuring Pipeline-Reliability meets Team-Expectations. Build-Duration SLOs guaranteeing Fast-Feedback-Cycles. Deployment-Frequency SLOs supporting Business-Agility-Goals. Diese SLOs werden kontinuierlich monitored mit Error-Budgets tracking acceptable Failure-Rates. SLO-Violations triggering Priority-Incidents und Post-Mortem-Analysis zur Process-Improvement.