38 KiB
38 KiB
Enhanced Rollback Intelligence
Overview
Enhanced Rollback Intelligence transforms rollback from a reactive recovery mechanism into a proactive, intelligent system. It provides metric-driven automatic rollback, partial rollback for multi-component releases, rollback impact analysis, and predictive failure detection.
This is a best-in-class implementation that minimizes downtime, reduces blast radius, and provides clear decision transparency through comprehensive impact analysis.
Design Principles
- Proactive Detection: Detect degradation before users report issues
- Minimal Blast Radius: Rollback only what's necessary
- Predictive Analysis: Anticipate rollback needs from early signals
- Full Transparency: Every rollback decision is explainable
- Safe by Default: Automatic rollback with human override capability
- Evidence-Backed: All rollback decisions produce audit evidence
Architecture
Component Overview
┌────────────────────────────────────────────────────────────────────────┐
│ Rollback Intelligence System │
├────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────┐ ┌───────────────────┐ ┌─────────────────┐ │
│ │ MetricsCollector │───▶│ HealthAnalyzer │───▶│ RollbackDecider │ │
│ │ │ │ │ │ │ │
│ └──────────────────┘ └───────────────────┘ └─────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────┐ ┌───────────────────┐ ┌─────────────────┐ │
│ │ BaselineManager │ │ AnomalyDetector │ │ ImpactAnalyzer │ │
│ │ │ │ │ │ │ │
│ └──────────────────┘ └───────────────────┘ └─────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────┐ ┌───────────────────┐ ┌─────────────────┐ │
│ │ PartialRollback │ │ PredictiveEngine │ │ RollbackExecutor│ │
│ │ Planner │ │ │ │ │ │
│ └──────────────────┘ └───────────────────┘ └─────────────────┘ │
│ │
└────────────────────────────────────────────────────────────────────────┘
Key Components
1. MetricsCollector
Aggregates metrics from multiple sources for health analysis:
public sealed class MetricsCollector
{
private readonly ImmutableArray<IMetricsSource> _sources;
public async Task<MetricsSnapshot> CollectAsync(
Guid deploymentId,
MetricsCollectionConfig config,
CancellationToken ct)
{
var metrics = new ConcurrentDictionary<string, MetricSeries>();
await Parallel.ForEachAsync(_sources, ct, async (source, ct) =>
{
var sourceMetrics = await source.CollectAsync(deploymentId, config.TimeRange, ct);
foreach (var (name, series) in sourceMetrics)
{
metrics.TryAdd($"{source.Name}:{name}", series);
}
});
return new MetricsSnapshot
{
DeploymentId = deploymentId,
CollectedAt = _timeProvider.GetUtcNow(),
TimeRange = config.TimeRange,
Metrics = metrics.ToImmutableDictionary()
};
}
}
public interface IMetricsSource
{
string Name { get; }
Task<IReadOnlyDictionary<string, MetricSeries>> CollectAsync(
Guid deploymentId, TimeRange range, CancellationToken ct);
}
// Implementations
public sealed class PrometheusMetricsSource : IMetricsSource { }
public sealed class DatadogMetricsSource : IMetricsSource { }
public sealed class CloudWatchMetricsSource : IMetricsSource { }
public sealed class ApplicationInsightsMetricsSource : IMetricsSource { }
public sealed class CustomWebhookMetricsSource : IMetricsSource { }
2. BaselineManager
Maintains and compares deployment baselines:
public sealed class BaselineManager
{
public async Task<Baseline> CreateBaselineAsync(
Guid releaseId,
Guid environmentId,
TimeRange stableWindow,
CancellationToken ct)
{
var metrics = await _metricsCollector.CollectAsync(
releaseId, new MetricsCollectionConfig { TimeRange = stableWindow }, ct);
return new Baseline
{
Id = Guid.NewGuid(),
ReleaseId = releaseId,
EnvironmentId = environmentId,
CreatedAt = _timeProvider.GetUtcNow(),
StableWindow = stableWindow,
Metrics = CalculateBaselineMetrics(metrics)
};
}
private BaselineMetrics CalculateBaselineMetrics(MetricsSnapshot snapshot)
{
return new BaselineMetrics
{
// Error rate baseline (P50, P95, P99)
ErrorRateP50 = CalculatePercentile(snapshot.GetMetric("error_rate"), 50),
ErrorRateP95 = CalculatePercentile(snapshot.GetMetric("error_rate"), 95),
ErrorRateP99 = CalculatePercentile(snapshot.GetMetric("error_rate"), 99),
// Latency baseline
LatencyP50 = CalculatePercentile(snapshot.GetMetric("latency_ms"), 50),
LatencyP95 = CalculatePercentile(snapshot.GetMetric("latency_ms"), 95),
LatencyP99 = CalculatePercentile(snapshot.GetMetric("latency_ms"), 99),
// Throughput baseline
ThroughputMean = CalculateMean(snapshot.GetMetric("requests_per_second")),
ThroughputStdDev = CalculateStdDev(snapshot.GetMetric("requests_per_second")),
// Resource baseline
CpuMean = CalculateMean(snapshot.GetMetric("cpu_percent")),
MemoryMean = CalculateMean(snapshot.GetMetric("memory_percent")),
// Custom metrics
CustomMetrics = snapshot.Metrics
.Where(m => m.Key.StartsWith("custom:"))
.ToDictionary(m => m.Key, m => CalculateMetricBaseline(m.Value))
.ToImmutableDictionary()
};
}
}
public sealed record Baseline
{
public Guid Id { get; init; }
public Guid ReleaseId { get; init; }
public Guid EnvironmentId { get; init; }
public DateTimeOffset CreatedAt { get; init; }
public TimeRange StableWindow { get; init; }
public BaselineMetrics Metrics { get; init; }
}
3. HealthAnalyzer
Analyzes current health against baseline:
public sealed class HealthAnalyzer
{
public async Task<HealthAnalysis> AnalyzeAsync(
Guid deploymentId,
Baseline baseline,
HealthAnalysisConfig config,
CancellationToken ct)
{
var currentMetrics = await _metricsCollector.CollectAsync(
deploymentId,
new MetricsCollectionConfig { TimeRange = config.AnalysisWindow },
ct);
var analysis = new HealthAnalysis
{
DeploymentId = deploymentId,
BaselineId = baseline.Id,
AnalyzedAt = _timeProvider.GetUtcNow(),
OverallHealth = HealthStatus.Healthy,
Signals = new List<HealthSignal>()
};
// Error rate analysis
var errorSignal = AnalyzeErrorRate(currentMetrics, baseline, config);
analysis.Signals.Add(errorSignal);
// Latency analysis
var latencySignal = AnalyzeLatency(currentMetrics, baseline, config);
analysis.Signals.Add(latencySignal);
// Throughput analysis
var throughputSignal = AnalyzeThroughput(currentMetrics, baseline, config);
analysis.Signals.Add(throughputSignal);
// Resource analysis
var resourceSignal = AnalyzeResources(currentMetrics, baseline, config);
analysis.Signals.Add(resourceSignal);
// Custom metrics analysis
foreach (var (name, baselineMetric) in baseline.Metrics.CustomMetrics)
{
var customSignal = AnalyzeCustomMetric(name, currentMetrics, baselineMetric, config);
analysis.Signals.Add(customSignal);
}
// Calculate overall health
analysis.OverallHealth = CalculateOverallHealth(analysis.Signals);
analysis.RollbackRecommended = ShouldRecommendRollback(analysis);
analysis.Confidence = CalculateConfidence(analysis);
return analysis;
}
private HealthSignal AnalyzeErrorRate(
MetricsSnapshot current,
Baseline baseline,
HealthAnalysisConfig config)
{
var currentP95 = CalculatePercentile(current.GetMetric("error_rate"), 95);
var baselineP95 = baseline.Metrics.ErrorRateP95;
var deviation = (currentP95 - baselineP95) / Math.Max(baselineP95, 0.001);
var status = deviation switch
{
> 2.0 => SignalStatus.Critical, // 200% above baseline
> 1.0 => SignalStatus.Warning, // 100% above baseline
> 0.5 => SignalStatus.Degraded, // 50% above baseline
_ => SignalStatus.Healthy
};
return new HealthSignal
{
Name = "error_rate",
Status = status,
CurrentValue = currentP95,
BaselineValue = baselineP95,
DeviationPercent = deviation * 100,
Threshold = config.ErrorRateThreshold,
Message = status switch
{
SignalStatus.Critical => $"Error rate {currentP95:P2} is {deviation:P0} above baseline",
SignalStatus.Warning => $"Error rate elevated: {currentP95:P2} vs {baselineP95:P2} baseline",
_ => $"Error rate normal: {currentP95:P2}"
}
};
}
}
public sealed record HealthAnalysis
{
public Guid DeploymentId { get; init; }
public Guid BaselineId { get; init; }
public DateTimeOffset AnalyzedAt { get; init; }
public HealthStatus OverallHealth { get; init; }
public ImmutableArray<HealthSignal> Signals { get; init; }
public bool RollbackRecommended { get; init; }
public double Confidence { get; init; } // 0.0 - 1.0
public string? RecommendationReason { get; init; }
}
public enum HealthStatus
{
Healthy,
Degraded,
Warning,
Critical,
Unknown
}
4. AnomalyDetector
Detects anomalies in real-time metrics:
public sealed class AnomalyDetector
{
private readonly ImmutableArray<IAnomalyAlgorithm> _algorithms;
public async Task<AnomalyReport> DetectAsync(
MetricSeries series,
AnomalyDetectionConfig config,
CancellationToken ct)
{
var anomalies = new List<Anomaly>();
foreach (var algorithm in _algorithms)
{
var detected = await algorithm.DetectAsync(series, config, ct);
anomalies.AddRange(detected);
}
// Deduplicate and rank
var ranked = anomalies
.GroupBy(a => a.Timestamp.Ticks / TimeSpan.FromMinutes(1).Ticks)
.Select(g => g.OrderByDescending(a => a.Severity).First())
.OrderByDescending(a => a.Severity)
.ToImmutableArray();
return new AnomalyReport
{
Series = series.Name,
DetectedAt = _timeProvider.GetUtcNow(),
Anomalies = ranked,
OverallSeverity = ranked.Any() ? ranked.Max(a => a.Severity) : AnomalySeverity.None
};
}
}
// Anomaly detection algorithms
public interface IAnomalyAlgorithm
{
Task<IReadOnlyList<Anomaly>> DetectAsync(
MetricSeries series, AnomalyDetectionConfig config, CancellationToken ct);
}
public sealed class ZScoreAlgorithm : IAnomalyAlgorithm
{
// Detects values > N standard deviations from mean
public async Task<IReadOnlyList<Anomaly>> DetectAsync(
MetricSeries series, AnomalyDetectionConfig config, CancellationToken ct)
{
var mean = series.Values.Average();
var stdDev = CalculateStdDev(series.Values, mean);
var threshold = config.ZScoreThreshold;
return series.DataPoints
.Where(dp => Math.Abs((dp.Value - mean) / stdDev) > threshold)
.Select(dp => new Anomaly
{
Timestamp = dp.Timestamp,
Value = dp.Value,
ExpectedValue = mean,
Deviation = (dp.Value - mean) / stdDev,
Algorithm = "z_score",
Severity = CalculateSeverity((dp.Value - mean) / stdDev, threshold)
})
.ToList();
}
}
public sealed class SlidingWindowAlgorithm : IAnomalyAlgorithm
{
// Detects sudden changes in moving average
}
public sealed class SeasonalDecompositionAlgorithm : IAnomalyAlgorithm
{
// Detects anomalies accounting for daily/weekly patterns
}
public sealed class IsolationForestAlgorithm : IAnomalyAlgorithm
{
// ML-based multivariate anomaly detection
}
5. PredictiveEngine
Predicts potential failures from early warning signals:
public sealed class PredictiveEngine
{
public async Task<FailurePrediction> PredictAsync(
Guid deploymentId,
HealthAnalysis currentAnalysis,
IReadOnlyList<HealthAnalysis> historicalAnalyses,
CancellationToken ct)
{
var prediction = new FailurePrediction
{
DeploymentId = deploymentId,
PredictedAt = _timeProvider.GetUtcNow()
};
// Trend analysis
var errorTrend = AnalyzeTrend(
historicalAnalyses.Select(a => a.GetSignal("error_rate")));
var latencyTrend = AnalyzeTrend(
historicalAnalyses.Select(a => a.GetSignal("latency")));
// Pattern matching against known failure patterns
var patterns = await _patternStore.GetKnownFailurePatternsAsync(ct);
var matchedPatterns = patterns
.Where(p => MatchesPattern(currentAnalysis, historicalAnalyses, p))
.ToList();
if (matchedPatterns.Any())
{
var bestMatch = matchedPatterns.OrderByDescending(p => p.Confidence).First();
prediction.FailureLikelihood = bestMatch.Confidence;
prediction.PredictedFailureType = bestMatch.FailureType;
prediction.EstimatedTimeToFailure = bestMatch.TypicalTimeToFailure;
prediction.EarlyWarningSignals = bestMatch.MatchedSignals;
prediction.RecommendedAction = bestMatch.RecommendedAction;
}
else
{
// Extrapolation-based prediction
if (errorTrend.Slope > 0 && errorTrend.Confidence > 0.8)
{
var timeToThreshold = EstimateTimeToThreshold(
errorTrend, currentAnalysis.GetSignal("error_rate").Threshold);
prediction.FailureLikelihood = errorTrend.Confidence * 0.7;
prediction.PredictedFailureType = FailureType.ErrorRateExceeded;
prediction.EstimatedTimeToFailure = timeToThreshold;
prediction.EarlyWarningSignals = new[] { "error_rate_trending_up" }.ToImmutableArray();
}
}
return prediction;
}
private TrendAnalysis AnalyzeTrend(IEnumerable<HealthSignal> signals)
{
var values = signals.Select(s => (s.Timestamp, s.CurrentValue)).ToList();
if (values.Count < 3)
return TrendAnalysis.Insufficient;
// Linear regression
var (slope, intercept, rSquared) = LinearRegression(values);
return new TrendAnalysis
{
Slope = slope,
Intercept = intercept,
Confidence = rSquared,
Direction = slope > 0.01 ? TrendDirection.Increasing :
slope < -0.01 ? TrendDirection.Decreasing :
TrendDirection.Stable
};
}
}
public sealed record FailurePrediction
{
public Guid DeploymentId { get; init; }
public DateTimeOffset PredictedAt { get; init; }
public double FailureLikelihood { get; init; } // 0.0 - 1.0
public FailureType? PredictedFailureType { get; init; }
public TimeSpan? EstimatedTimeToFailure { get; init; }
public ImmutableArray<string> EarlyWarningSignals { get; init; }
public RecommendedAction? RecommendedAction { get; init; }
}
public enum FailureType
{
ErrorRateExceeded,
LatencyDegraded,
ThroughputDrop,
ResourceExhaustion,
MemoryLeak,
ConnectionPoolExhaustion,
CascadingFailure
}
6. ImpactAnalyzer
Analyzes rollback impact before execution:
public sealed class ImpactAnalyzer
{
public async Task<RollbackImpactAnalysis> AnalyzeAsync(
RollbackRequest request,
CancellationToken ct)
{
var analysis = new RollbackImpactAnalysis
{
RequestId = request.Id,
AnalyzedAt = _timeProvider.GetUtcNow()
};
// 1. Identify affected components
var currentRelease = await _releaseStore.GetAsync(request.CurrentReleaseId, ct);
var targetRelease = await _releaseStore.GetAsync(request.TargetReleaseId, ct);
analysis.AffectedComponents = currentRelease.Components
.Where(c => targetRelease.Components.Any(tc =>
tc.Name == c.Name && tc.Digest != c.Digest))
.Select(c => new AffectedComponent
{
Name = c.Name,
CurrentDigest = c.Digest,
TargetDigest = targetRelease.Components.First(tc => tc.Name == c.Name).Digest,
ChangeType = DetermineChangeType(c, targetRelease)
})
.ToImmutableArray();
// 2. Analyze downstream dependencies
var dependencyGraph = await _dependencyStore.GetGraphAsync(
request.EnvironmentId, ct);
foreach (var component in analysis.AffectedComponents)
{
var dependents = dependencyGraph.GetDependents(component.Name);
analysis.DownstreamImpact.Add(component.Name, new DependencyImpact
{
DirectDependents = dependents.Direct.Count,
TransitiveDependents = dependents.Transitive.Count,
CriticalPathComponents = dependents.OnCriticalPath.ToImmutableArray()
});
}
// 3. Estimate downtime
analysis.EstimatedDowntime = EstimateDowntime(analysis.AffectedComponents, request.Strategy);
// 4. Risk assessment
analysis.RiskLevel = AssessRisk(analysis);
analysis.RiskFactors = IdentifyRiskFactors(analysis);
// 5. Data migration considerations
analysis.DataMigrationRequired = await CheckDataMigrationAsync(
currentRelease, targetRelease, ct);
// 6. Feature flag impact
analysis.FeatureFlagImpact = await AnalyzeFeatureFlagImpactAsync(
currentRelease, targetRelease, ct);
// 7. Generate recommendation
analysis.Recommendation = GenerateRecommendation(analysis);
return analysis;
}
private RollbackRisk AssessRisk(RollbackImpactAnalysis analysis)
{
var riskScore = 0;
// Component count
riskScore += analysis.AffectedComponents.Length * 10;
// Downstream impact
var totalDependents = analysis.DownstreamImpact.Values.Sum(d => d.TransitiveDependents);
riskScore += totalDependents * 5;
// Data migration
if (analysis.DataMigrationRequired)
riskScore += 50;
// Critical path
var criticalPathCount = analysis.DownstreamImpact.Values
.Sum(d => d.CriticalPathComponents.Length);
riskScore += criticalPathCount * 20;
return riskScore switch
{
< 20 => RollbackRisk.Low,
< 50 => RollbackRisk.Medium,
< 100 => RollbackRisk.High,
_ => RollbackRisk.Critical
};
}
}
public sealed record RollbackImpactAnalysis
{
public Guid RequestId { get; init; }
public DateTimeOffset AnalyzedAt { get; init; }
// What's changing
public ImmutableArray<AffectedComponent> AffectedComponents { get; init; }
// Who's affected
public ImmutableDictionary<string, DependencyImpact> DownstreamImpact { get; init; }
// How long
public TimeSpan EstimatedDowntime { get; init; }
// How risky
public RollbackRisk RiskLevel { get; init; }
public ImmutableArray<RiskFactor> RiskFactors { get; init; }
// Special considerations
public bool DataMigrationRequired { get; init; }
public DataMigrationAnalysis? DataMigration { get; init; }
public FeatureFlagImpact? FeatureFlagImpact { get; init; }
// Recommendation
public RollbackRecommendation Recommendation { get; init; }
}
public sealed record RollbackRecommendation
{
public RollbackDecision Decision { get; init; }
public string Rationale { get; init; }
public ImmutableArray<string> Warnings { get; init; }
public ImmutableArray<string> Prerequisites { get; init; }
public RollbackStrategy SuggestedStrategy { get; init; }
}
7. PartialRollbackPlanner
Plans rollback of specific components:
public sealed class PartialRollbackPlanner
{
public async Task<PartialRollbackPlan> PlanAsync(
PartialRollbackRequest request,
CancellationToken ct)
{
var currentRelease = await _releaseStore.GetAsync(request.CurrentReleaseId, ct);
var dependencyGraph = await _dependencyStore.GetGraphAsync(request.EnvironmentId, ct);
var plan = new PartialRollbackPlan
{
Id = Guid.NewGuid(),
CreatedAt = _timeProvider.GetUtcNow(),
RequestedComponents = request.ComponentsToRollback,
TargetDigests = new Dictionary<string, string>()
};
// 1. Determine which components to actually rollback
var componentsToRollback = new HashSet<string>(request.ComponentsToRollback);
// 2. Check for required co-rollbacks (tight coupling)
foreach (var component in request.ComponentsToRollback)
{
var requiredCoRollbacks = dependencyGraph.GetRequiredCoRollbacks(component);
foreach (var required in requiredCoRollbacks)
{
if (!componentsToRollback.Contains(required))
{
componentsToRollback.Add(required);
plan.AutoIncludedComponents.Add(required, $"Required by {component}");
}
}
}
// 3. Find target digests for each component
foreach (var component in componentsToRollback)
{
var history = await _deploymentHistoryStore.GetComponentHistoryAsync(
request.EnvironmentId, component, ct);
// Find last known good version
var targetVersion = history
.Where(h => h.Status == DeploymentStatus.Succeeded)
.Where(h => !request.ExcludeDigests.Contains(h.Digest))
.OrderByDescending(h => h.DeployedAt)
.Skip(request.VersionsBack - 1) // Default: 1 (previous)
.FirstOrDefault();
if (targetVersion == null)
{
plan.CannotRollback.Add(component, "No previous good version found");
continue;
}
plan.TargetDigests[component] = targetVersion.Digest;
plan.RollbackDetails.Add(new ComponentRollbackDetail
{
ComponentName = component,
CurrentDigest = currentRelease.Components.First(c => c.Name == component).Digest,
TargetDigest = targetVersion.Digest,
TargetDeployedAt = targetVersion.DeployedAt,
VersionsBack = request.VersionsBack
});
}
// 4. Validate compatibility
var compatibility = await ValidateCompatibilityAsync(plan, ct);
plan.CompatibilityValidation = compatibility;
// 5. Determine execution order
plan.ExecutionOrder = DetermineExecutionOrder(plan, dependencyGraph);
return plan;
}
private ImmutableArray<string> DetermineExecutionOrder(
PartialRollbackPlan plan,
DependencyGraph graph)
{
// Topological sort based on dependencies
// Rollback dependents before dependencies
var sorted = new List<string>();
var visited = new HashSet<string>();
void Visit(string component)
{
if (visited.Contains(component))
return;
visited.Add(component);
var dependents = graph.GetDependents(component).Direct;
foreach (var dependent in dependents.Where(d => plan.TargetDigests.ContainsKey(d)))
{
Visit(dependent);
}
sorted.Add(component);
}
foreach (var component in plan.TargetDigests.Keys)
{
Visit(component);
}
return sorted.ToImmutableArray();
}
}
public sealed record PartialRollbackPlan
{
public Guid Id { get; init; }
public DateTimeOffset CreatedAt { get; init; }
// Input
public ImmutableArray<string> RequestedComponents { get; init; }
// Analysis
public ImmutableDictionary<string, string> AutoIncludedComponents { get; init; }
public ImmutableDictionary<string, string> CannotRollback { get; init; }
// Plan
public ImmutableDictionary<string, string> TargetDigests { get; init; }
public ImmutableArray<ComponentRollbackDetail> RollbackDetails { get; init; }
public ImmutableArray<string> ExecutionOrder { get; init; }
// Validation
public CompatibilityValidation CompatibilityValidation { get; init; }
}
8. RollbackDecider
Makes automated rollback decisions:
public sealed class RollbackDecider
{
public async Task<RollbackDecision> DecideAsync(
Guid deploymentId,
HealthAnalysis healthAnalysis,
FailurePrediction? prediction,
AutoRollbackPolicy policy,
CancellationToken ct)
{
var decision = new RollbackDecision
{
DeploymentId = deploymentId,
DecidedAt = _timeProvider.GetUtcNow(),
HealthAnalysis = healthAnalysis,
Prediction = prediction,
Policy = policy
};
// Check if auto-rollback is enabled
if (!policy.Enabled)
{
decision.Action = RollbackAction.NotifyOnly;
decision.Reason = "Auto-rollback disabled by policy";
return decision;
}
// Check maintenance window
if (!IsWithinRollbackWindow(policy))
{
decision.Action = RollbackAction.DeferToWindow;
decision.Reason = "Outside auto-rollback window";
decision.DeferredUntil = GetNextRollbackWindowStart(policy);
return decision;
}
// Evaluate health signals against policy thresholds
var criticalSignals = healthAnalysis.Signals
.Where(s => s.Status == SignalStatus.Critical)
.ToList();
var warningSignals = healthAnalysis.Signals
.Where(s => s.Status == SignalStatus.Warning)
.ToList();
// Critical signals: immediate rollback
if (criticalSignals.Any())
{
decision.Action = RollbackAction.ImmediateRollback;
decision.Reason = $"Critical health signals: {string.Join(", ", criticalSignals.Select(s => s.Name))}";
decision.TriggeringSignals = criticalSignals.ToImmutableArray();
decision.SuggestedStrategy = RollbackStrategy.AllAtOnce;
return decision;
}
// Predictive rollback
if (prediction != null &&
prediction.FailureLikelihood >= policy.PredictiveThreshold &&
prediction.EstimatedTimeToFailure < policy.PredictiveWindow)
{
decision.Action = RollbackAction.PreemptiveRollback;
decision.Reason = $"Predicted failure ({prediction.FailureLikelihood:P0} confidence) " +
$"within {prediction.EstimatedTimeToFailure}";
decision.SuggestedStrategy = RollbackStrategy.Rolling;
return decision;
}
// Warning signals: check duration
if (warningSignals.Any())
{
var oldestWarning = warningSignals.Min(s => s.Timestamp);
var warningDuration = _timeProvider.GetUtcNow() - oldestWarning;
if (warningDuration >= policy.WarningGracePeriod)
{
decision.Action = RollbackAction.GracefulRollback;
decision.Reason = $"Warning signals persisted for {warningDuration}";
decision.TriggeringSignals = warningSignals.ToImmutableArray();
decision.SuggestedStrategy = RollbackStrategy.Rolling;
return decision;
}
else
{
decision.Action = RollbackAction.Monitor;
decision.Reason = $"Warning signals detected, monitoring for {policy.WarningGracePeriod - warningDuration}";
return decision;
}
}
// All healthy
decision.Action = RollbackAction.None;
decision.Reason = "All health signals within acceptable thresholds";
return decision;
}
}
public sealed record RollbackDecision
{
public Guid DeploymentId { get; init; }
public DateTimeOffset DecidedAt { get; init; }
public RollbackAction Action { get; init; }
public string Reason { get; init; }
public HealthAnalysis HealthAnalysis { get; init; }
public FailurePrediction? Prediction { get; init; }
public ImmutableArray<HealthSignal>? TriggeringSignals { get; init; }
public RollbackStrategy? SuggestedStrategy { get; init; }
public DateTimeOffset? DeferredUntil { get; init; }
public AutoRollbackPolicy Policy { get; init; }
}
public enum RollbackAction
{
None, // No action needed
Monitor, // Continue monitoring
NotifyOnly, // Alert but don't rollback
DeferToWindow, // Wait for rollback window
GracefulRollback, // Rolling rollback
PreemptiveRollback, // Rollback before predicted failure
ImmediateRollback // Emergency rollback
}
Auto-Rollback Policy
public sealed record AutoRollbackPolicy
{
public Guid Id { get; init; }
public string Name { get; init; }
public Guid EnvironmentId { get; init; }
// Enable/disable
public bool Enabled { get; init; }
// Thresholds
public double ErrorRateCriticalThreshold { get; init; } // e.g., 0.10 (10%)
public double ErrorRateWarningThreshold { get; init; } // e.g., 0.05 (5%)
public double LatencyP95CriticalThreshold { get; init; } // e.g., 5000ms
public double LatencyP95WarningThreshold { get; init; } // e.g., 2000ms
// Grace periods
public TimeSpan WarningGracePeriod { get; init; } // e.g., 5 minutes
// Predictive settings
public double PredictiveThreshold { get; init; } // e.g., 0.80 (80% confidence)
public TimeSpan PredictiveWindow { get; init; } // e.g., 10 minutes
// Rollback window
public TimeOnly RollbackWindowStart { get; init; } // e.g., 00:00
public TimeOnly RollbackWindowEnd { get; init; } // e.g., 23:59
public ImmutableArray<DayOfWeek> RollbackDays { get; init; }
// Notifications
public NotificationConfig Notifications { get; init; }
// Manual override
public bool RequireApprovalForProduction { get; init; }
}
API Design
REST Endpoints
# Health Analysis
GET /api/v1/deployments/{id}/health # Get current health
GET /api/v1/deployments/{id}/health/history # Health history
GET /api/v1/deployments/{id}/baselines # List baselines
POST /api/v1/deployments/{id}/baselines # Create baseline
# Predictions
GET /api/v1/deployments/{id}/predictions # Get failure predictions
# Impact Analysis
POST /api/v1/rollback/analyze # Analyze rollback impact
POST /api/v1/rollback/partial/analyze # Analyze partial rollback
# Auto-Rollback Policies
POST /api/v1/rollback/policies # Create policy
GET /api/v1/rollback/policies # List policies
PUT /api/v1/rollback/policies/{id} # Update policy
DELETE /api/v1/rollback/policies/{id} # Delete policy
# Rollback Execution
POST /api/v1/rollback/execute # Execute full rollback
POST /api/v1/rollback/partial/execute # Execute partial rollback
POST /api/v1/rollback/{id}/approve # Approve pending rollback
POST /api/v1/rollback/{id}/cancel # Cancel rollback
# History
GET /api/v1/rollback/history # Rollback history
GET /api/v1/rollback/history/{id} # Rollback details
GET /api/v1/rollback/history/{id}/evidence # Rollback evidence
Metrics & Observability
Prometheus Metrics
# Health Analysis
stella_deployment_health_status{deployment_id, environment, status}
stella_deployment_health_signal{deployment_id, signal_name, status}
stella_deployment_health_analysis_duration_seconds
# Predictions
stella_failure_prediction_likelihood{deployment_id, failure_type}
stella_failure_prediction_time_to_failure_seconds{deployment_id}
stella_failure_predictions_total{outcome} # correct, false_positive, missed
# Rollback Decisions
stella_rollback_decisions_total{action, environment}
stella_rollback_decision_confidence{deployment_id}
# Rollback Execution
stella_rollback_executions_total{type, strategy, status}
stella_rollback_duration_seconds{type, strategy}
stella_rollback_components_total{type, status}
# Impact
stella_rollback_impact_components{deployment_id}
stella_rollback_impact_dependents{deployment_id}
stella_rollback_impact_risk_level{deployment_id, level}
Evidence Generation
Every rollback decision and execution produces evidence:
public sealed record RollbackEvidence
{
// Decision context
public HealthAnalysis HealthAnalysis { get; init; }
public FailurePrediction? Prediction { get; init; }
public RollbackDecision Decision { get; init; }
// Impact analysis
public RollbackImpactAnalysis ImpactAnalysis { get; init; }
// Execution
public RollbackPlan Plan { get; init; }
public RollbackResult Result { get; init; }
// Audit
public string InitiatedBy { get; init; } // "system:auto" or user ID
public string? ApprovedBy { get; init; }
public DateTimeOffset InitiatedAt { get; init; }
public DateTimeOffset CompletedAt { get; init; }
}
Configuration Example
auto_rollback_policy:
name: "production-auto-rollback"
environment_id: "prod-001"
enabled: true
thresholds:
error_rate:
critical: 0.10 # 10% error rate
warning: 0.05 # 5% error rate
latency_p95:
critical: 5000 # 5 seconds
warning: 2000 # 2 seconds
throughput_drop:
critical: 0.50 # 50% drop
warning: 0.25 # 25% drop
grace_periods:
warning: "00:05:00" # 5 minutes
predictive:
enabled: true
threshold: 0.80 # 80% confidence
window: "00:10:00" # 10 minute lookahead
rollback_window:
enabled: false # Allow 24/7 for production
days: [monday, tuesday, wednesday, thursday, friday, saturday, sunday]
notifications:
on_warning: true
on_rollback_initiated: true
on_rollback_completed: true
channels:
- type: slack
channel: "#prod-alerts"
- type: pagerduty
severity: critical
approval:
require_for_production: false # Auto-rollback without approval
Test Strategy
Unit Tests
- Severity calculation for various health signals
- Baseline comparison logic
- Anomaly detection algorithms
- Impact analysis calculations
Integration Tests
- Full health analysis pipeline
- Predictive engine with historical data
- Partial rollback planning
- Auto-rollback decision flow
Chaos Tests
- Metrics source failures during analysis
- Database unavailability
- Concurrent rollback requests
Golden Tests
- Deterministic health scoring
- Deterministic impact analysis
- Evidence packet structure
Migration Path
Phase 1: Metrics Collection (Week 1-2)
- Metrics collector implementation
- Prometheus/Datadog sources
- Baseline manager
Phase 2: Health Analysis (Week 3-4)
- Health analyzer
- Signal evaluation
- Anomaly detection
Phase 3: Impact Analysis (Week 5-6)
- Impact analyzer
- Dependency graph integration
- Risk assessment
Phase 4: Partial Rollback (Week 7-8)
- Partial rollback planner
- Compatibility validation
- Execution order
Phase 5: Predictive Engine (Week 9-10)
- Trend analysis
- Pattern matching
- Failure prediction
Phase 6: Auto-Rollback (Week 11-12)
- Rollback decider
- Policy management
- Automated execution