Performance Analytics and Capacity Planning

[rucka]

Story Statement

As an enterprise admin
I want performance analytics and capacity planning tools
So that I can optimize platform performance and plan for organizational growth

Where: Knowledge service — analytics layer on top of monitoring data

Epic Context

Parent Epic: Platform Hardening & Enterprise Readiness #68
Status: Refined
Priority: P1 (Should-Have)

Status Workflow

• Refined: Story is detailed, estimated, and ready for development
• In Progress: Story is actively being developed
• Done: Story delivered and accepted

Acceptance Criteria

Functional Requirements

1. Given an enterprise admin
   When they call GET /api/v1/organizations/acme/performance/trends?metric=latency_p95&period=30d&granularity=daily
   Then the service returns time-series data of p95 latency over the last 30 days with daily granularity

2. Given an enterprise admin
   When they call GET /api/v1/organizations/acme/performance/storage
   Then the service returns: current usage, quota, growth rate (bytes/day), projected full date based on linear extrapolation

3. Given an enterprise admin
   When they call GET /api/v1/organizations/acme/performance/endpoints
   Then the service returns per-endpoint breakdown: avg latency, request count, error rate, sorted by latency desc (slowest first)

4. Given historical performance data
   When the capacity projection runs
   Then it calculates: days until storage quota exceeded, days until connection pool saturation (based on growth trend), with confidence interval

5. Given an admin needs a performance report
   When they call GET /api/v1/organizations/acme/performance/report?period=2026-Q1&format=json
   Then the service returns a comprehensive report: latency trends, throughput trends, error rate trends, storage growth, capacity projections, top issues

Business Rules

• Metrics sourced from Prometheus/monitoring data (from Production Monitoring and Alerting #164)
• Time-series granularity: hourly, daily, weekly, monthly
• Storage tracking from S3 metadata and DB size queries
• Capacity projection: linear extrapolation with 95% confidence interval
• Performance baselines: computed from first 7 days of data, anomalies highlighted vs baseline
• Admin-only access
• Report exportable as JSON (CSV/PDF deferred to future)

Edge Cases and Error Handling

• Insufficient data for projection (<7 days): Return "Insufficient data for capacity projection. Need at least 7 days of data."
• Monitoring data unavailable: Return "Performance data temporarily unavailable" with last-known data timestamp
• Anomaly detection: Flag data points >2 standard deviations from baseline
• No storage growth: Projection returns "No growth detected — quota sufficient"

Definition of Done Checklist

Development Completion

• All 5 acceptance criteria implemented and verified
• Performance trends endpoint (latency, throughput, error rate)
• Storage analytics with growth projection
• Per-endpoint performance breakdown
• Capacity projection with confidence interval
• Performance report generation
• Unit tests for projection and aggregation logic
• Integration tests for analytics endpoints

Quality Assurance

• Analytics queries return in <500ms for 90-day range
• Projection accuracy validated against historical data
• Anomaly detection correctly flags outliers

Story Sizing and Sprint Readiness

Refined Story Points

Final Story Points: L(5)
Confidence Level: Medium
Sizing Justification: Builds on monitoring data from #164. Query Prometheus API, aggregate, project. Moderate analytics logic. No new data collection.

Sprint Capacity Validation

Sprint Fit Assessment: Fits in single sprint
Total Effort Assessment: Yes

Dependencies and Coordination

Story Dependencies

Prerequisite Stories: #164 (Monitoring — provides metrics data source)
Dependent Stories: #169 (SLA Reporting — shares analytics infrastructure)

Validation and Testing Strategy

Acceptance Testing Approach

Testing Methods: Unit tests for projection math and aggregation; integration tests with seeded Prometheus data
Test Data Requirements: Historical metrics data (at least 30 days simulated)
Environment Requirements: Prometheus test instance with seeded data

Notes

Refinement Insights: All data comes from existing monitoring stack — no new data collection needed. Focus is on analytics, aggregation, and presentation.

Technical Analysis

Implementation Approach

Technical Strategy: Query Prometheus HTTP API for metrics data. Aggregate and project in application layer. Cache computed results (TTL 1 hour). Linear regression for capacity projection.
Key Components: Performance analytics service, Prometheus query client, projection calculator, report generator, results cache
Data Flow: API request → query Prometheus API → aggregate → project → cache → response

Technical Requirements

• Prometheus query API: api/v1/query_range for time-series data
• Linear regression: simple least-squares for capacity projection (no external ML library needed)
• Cache: in-memory or Redis cache with 1-hour TTL for computed results
• Report: JSON template with sections for each metric category

Technical Risks and Mitigation

Risk	Impact	Probability	Mitigation Strategy
Prometheus query latency for large time ranges	Medium	Medium	Use recording rules for pre-aggregation; cache results

Spike Requirements

Required Spikes: None