PRD: Monitoring & Analytics

Generated: 2025-07-23 00:00 UTC
Status: Complete
Verified:

Executive Summary

This PRD defines the monitoring, analytics, and observability requirements for the Document Conversion Service. It establishes comprehensive systems for tracking service health, user behavior, business metrics, and operational insights to ensure reliable service delivery, data-driven decision making, and proactive issue resolution.

Key Objectives

Implement real-time monitoring for service health and performance
Build comprehensive analytics for business intelligence
Create actionable alerting systems for incident response
Provide customer-facing status and transparency
Enable data-driven product development and optimization

User Stories

As a DevOps Engineer

I want real-time visibility into system health
I want alerts before users notice issues
I want detailed logs for troubleshooting
I want automated incident response

As a Product Manager

I want to understand user behavior patterns
I want conversion success metrics by format
I want feature adoption tracking
I want revenue analytics

As a Customer Success Manager

I want to monitor customer health scores
I want usage trends per customer
I want to identify at-risk accounts
I want success metrics for renewals

As an Executive

I want business KPI via API
I want growth trend analysis
I want competitive benchmarking
I want predictive analytics

Functional Requirements

System Monitoring

1. Infrastructure Monitoring

graph TB subgraph Metrics Collection A[Application Metrics] --> E[Prometheus] B[System Metrics] --> E C[Custom Metrics] --> E D[Cloud Metrics] --> E end subgraph Visualization E --> F[Grafana] E --> G[Custom Analytics API] end subgraph Alerting E --> H[Alert Manager] H --> I[PagerDuty] H --> J[Slack] H --> K[Email] end

Key Metrics:

CPU utilization per service
Memory usage and garbage collection
Network throughput and latency
Disk I/O and storage usage
Container health and restarts

2. Application Performance Monitoring (APM)

metrics:
  api_endpoints:
    - name: conversion_latency
      type: histogram
      labels: [endpoint, format, status]
      buckets: [0.1, 0.5, 1, 2, 5, 10, 30]
    
    - name: conversion_rate
      type: counter
      labels: [format_from, format_to, status]
    
    - name: file_size_processed
      type: histogram
      labels: [format]
      buckets: [1KB, 100KB, 1MB, 10MB, 50MB, 100MB]
    
    - name: queue_depth
      type: gauge
      labels: [queue_name, priority]

Performance Tracking:

Request rate (RPS) per endpoint
Response time percentiles (p50, p95, p99)
Error rates by error type
Throughput in MB/s
Concurrent request handling

3. Service Level Monitoring

SLI Definitions:

{
  "slis": {
    "availability": {
      "target": 99.9,
      "measurement": "successful_requests / total_requests"
    },
    "latency": {
      "target": {
        "p50": 1000,
        "p99": 5000
      },
      "measurement": "response_time_ms"
    },
    "error_rate": {
      "target": 0.1,
      "measurement": "(errors / total_requests) * 100"
    }
  }
}

Error Budget Tracking:

Monthly error budget: 43.2 minutes
Real-time budget consumption
Burn rate alerts
Historical trend analysis

Business Analytics

1. Conversion Analytics

-- Daily conversion metrics
SELECT 
  DATE(created_at) as date,
  source_format,
  target_format,
  COUNT(*) as total_conversions,
  AVG(file_size_bytes) as avg_file_size,
  AVG(processing_time_ms) as avg_processing_time,
  SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) / COUNT(*) as success_rate
FROM conversions
GROUP BY DATE(created_at), source_format, target_format;

Tracked Metrics:

Conversions by format pair
File size distribution
Processing time by format
Geographic distribution
Time-of-day patterns

2. User Behavior Analytics

graph LR A[User Events] --> B[Event Stream] B --> C[Real-time Processing] B --> D[Batch Processing] C --> E[Live Dashboards] C --> F[Alerts] D --> G[Data Warehouse] G --> H[BI Tools] G --> I[ML Models]

User Metrics:

API calls per user
Feature adoption rates
User retention curves
Conversion patterns
Error frequency

3. Revenue Analytics

Financial Tracking:

revenue_metrics:
  - metric: monthly_recurring_revenue
    calculation: sum(subscription_amount)
    dimensions: [plan_type, region]
  
  - metric: average_revenue_per_user
    calculation: total_revenue / active_users
    dimensions: [cohort, plan_type]
  
  - metric: customer_lifetime_value
    calculation: arpu * avg_customer_lifespan
    dimensions: [acquisition_channel, industry]
  
  - metric: churn_rate
    calculation: churned_customers / total_customers
    dimensions: [reason, plan_type, tenure]

Observability Platform

1. Distributed Tracing

{
  "trace": {
    "trace_id": "abc123def456",
    "spans": [
      {
        "span_id": "span1",
        "operation": "http_request",
        "duration_ms": 245,
        "tags": {
          "http.method": "POST",
          "http.url": "/api/v1/convert",
          "user.id": "user123"
        }
      },
      {
        "span_id": "span2",
        "parent_id": "span1",
        "operation": "file_validation",
        "duration_ms": 32
      },
      {
        "span_id": "span3",
        "parent_id": "span1",
        "operation": "conversion",
        "duration_ms": 189,
        "tags": {
          "format.from": "xlsx",
          "format.to": "json",
          "file.size": 1048576
        }
      }
    ]
  }
}

2. Centralized Logging

Log Structure:

{
  "timestamp": "2025-07-23T10:15:30.123Z",
  "level": "INFO",
  "service": "xlsx-converter",
  "trace_id": "abc123def456",
  "user_id": "user123",
  "message": "Conversion completed successfully",
  "context": {
    "file_size": 1048576,
    "processing_time_ms": 189,
    "sheets_processed": 3,
    "output_size": 524288
  }
}

Log Aggregation:

ElasticSearch for storage
Logstash for processing
Kibana for visualization
Retention: 30 days hot, 1 year cold

3. Custom Metrics

# Custom metric collection
from prometheus_client import Counter, Histogram, Gauge

conversion_counter = Counter(
    'document_conversions_total',
    'Total number of document conversions',
    ['source_format', 'target_format', 'status']
)

processing_time = Histogram(
    'conversion_processing_seconds',
    'Time spent processing conversions',
    ['format'],
    buckets=[0.1, 0.5, 1.0, 2.5, 5.0, 10.0]
)

active_conversions = Gauge(
    'active_conversions',
    'Number of conversions currently processing'
)

Alerting System

1. Alert Configuration

alerts:
  - name: HighErrorRate
    expr: |
      rate(http_requests_total{status=~"5.."}[5m]) > 0.05
    for: 5m
    severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is {{ $value }} (threshold: 0.05)"
    
  - name: LowConversionSuccess
    expr: |
      rate(conversions_total{status="success"}[15m]) < 0.95
    for: 10m
    severity: warning
    annotations:
      summary: "Conversion success rate below threshold"
    
  - name: HighMemoryUsage
    expr: |
      container_memory_usage_bytes / container_spec_memory_limit_bytes > 0.9
    for: 5m
    severity: warning

2. Alert Routing

graph TD A[Alert Triggered] --> B{Severity} B -->|Critical| C[Page On-Call] B -->|High| D[Slack #incidents] B -->|Medium| E[Email Team] B -->|Low| F[Dashboard Only] C --> G[Incident Response] D --> G G --> H[Post-Mortem]

3. Incident Management

Runbook Template:

## Alert: High Error Rate

### Symptoms
- API error rate > 5%
- Customer complaints
- Increased support tickets

### Diagnosis Steps
1. Check Grafana dashboard
2. Review error logs in Kibana
3. Check recent deployments
4. Verify external dependencies

### Mitigation
1. Enable circuit breaker
2. Scale up instances
3. Roll back if deployment-related
4. Engage escalation team if needed

### Resolution
- Fix root cause
- Deploy patch
- Verify metrics return to normal
- Update runbook if needed

Customer-Facing Analytics

1. Usage API Response Structure

interface UsageAPIResponse {
  summary: {
    totalConversions: number;
    successRate: number;
    creditsRemaining: number;
    currentPlan: string;
  };
  
  charts: {
    dailyUsage: TimeSeriesData[];
    formatDistribution: PieChartData[];
    successRatetrend: LineChartData[];
    creditConsumption: AreaChartData[];
  };
  
  details: {
    recentConversions: Conversion[];
    topFormats: FormatPair[];
    averageFileSize: number;
    peakUsageTime: string;
  };
}

2. API Analytics

Endpoint Metrics:

Requests per endpoint
Average response time
Error distribution
Rate limit usage
Geographic origin

3. Status Page

components:
  - name: API
    status: operational
    uptime_90d: 99.95%
    response_time: 142ms
  
  - name: Conversion Service
    status: operational
    uptime_90d: 99.92%
    queue_depth: 23
  
  - name: File Storage
    status: operational
    uptime_90d: 100%
    
incidents:
  - title: "Elevated API Latency"
    status: resolved
    duration: 15m
    impact: minor
    postmortem_url: "/incidents/2024-07-15"

Data Pipeline

1. Real-time Analytics

graph LR A[API Events] --> B[Kafka] B --> C[Stream Processing] C --> D[Real-time DB] D --> E[Live Dashboards] B --> F[Data Lake] F --> G[Batch Processing] G --> H[Data Warehouse] H --> I[BI Tools]

2. Data Warehouse Schema

-- Fact table
CREATE TABLE fact_conversions (
    conversion_id UUID PRIMARY KEY,
    user_id UUID,
    timestamp TIMESTAMP,
    source_format VARCHAR(10),
    target_format VARCHAR(10),
    file_size_bytes BIGINT,
    processing_time_ms INTEGER,
    status VARCHAR(20),
    error_code VARCHAR(50),
    api_version VARCHAR(10),
    sdk_version VARCHAR(20),
    ip_country VARCHAR(2)
);

-- Dimension tables
CREATE TABLE dim_users (
    user_id UUID PRIMARY KEY,
    account_type VARCHAR(20),
    created_date DATE,
    industry VARCHAR(50),
    company_size VARCHAR(20)
);

Non-Functional Requirements

Performance Requirements

Metric ingestion: 1M points/second
Query response: < 2 seconds
Dashboard load: < 3 seconds
Alert evaluation: < 30 seconds

Retention Requirements

Real-time metrics: 7 days
Aggregated metrics: 2 years
Raw logs: 30 days
Compressed logs: 1 year
Traces: 7 days

Reliability Requirements

Monitoring uptime: 99.99%
No data loss for metrics
Alert delivery: 99.9%
Dashboard availability: 99.9%

Technical Specifications

Monitoring Stack

1. Metrics Collection

# Prometheus configuration
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'api-servers'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

2. Visualization

{
  "dashboard": {
    "title": "Conversion Service Overview",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(http_requests_total[5m])"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "stat",
        "targets": [
          {
            "expr": "rate(http_requests_total{status=~'5..'}[5m])"
          }
        ]
      }
    ]
  }
}

Analytics Infrastructure

1. Event Streaming

# Event producer
from confluent_kafka import Producer

producer = Producer({
    'bootstrap.servers': 'kafka:9092',
    'compression.type': 'snappy'
})

def track_conversion(event_data):
    producer.produce(
        'conversion-events',
        key=event_data['user_id'],
        value=json.dumps(event_data)
    )

2. Data Processing

-- Streaming SQL for real-time aggregation
CREATE STREAM conversion_stats AS
SELECT 
    TUMBLE_START(rowtime, INTERVAL '1' MINUTE) as window_start,
    source_format,
    target_format,
    COUNT(*) as conversion_count,
    AVG(processing_time_ms) as avg_processing_time,
    MAX(file_size_bytes) as max_file_size
FROM conversion_events
GROUP BY 
    TUMBLE(rowtime, INTERVAL '1' MINUTE),
    source_format,
    target_format;

Success Metrics

Monitoring Effectiveness

Mean time to detection < 2 minutes
False positive rate < 5%
Alert acknowledgment < 5 minutes
Incident resolution < 1 hour

Analytics Value

Dashboard adoption > 80%
Data-driven decisions > 10/month
Predictive accuracy > 85%
ROI from insights > $100K/year

Operational Excellence

Runbook coverage > 95%
Automated remediation > 50%
Post-mortem completion > 95%
SLO achievement > 99%

Dependencies

External Services

Monitoring platforms
Time-series databases
Log aggregation services
Analytics warehouses

Internal Systems

API services
Event streaming
Data pipelines
Authentication

Timeline & Milestones

Phase 1: Core Monitoring (Month 1)

Basic metrics collection
Essential dashboards
Critical alerts
Status page

Phase 2: Advanced Analytics (Month 2)

User behavior tracking
Business dashboards
Predictive models
A/B testing

Phase 3: Automation (Month 3)

Auto-remediation
Anomaly detection
Capacity planning
Cost optimization

Phase 4: Intelligence (Month 4)

ML-powered insights
Predictive alerts
Customer health scores
Revenue optimization

Risk Mitigation

Monitoring Risks

Data loss: Multiple collection points
Alert fatigue: Smart deduplication
Blind spots: Comprehensive coverage

Analytics Risks

Data privacy: Anonymization
Incorrect insights: Data validation
Performance impact: Sampling strategies

Future Considerations

Advanced Capabilities

AI-powered root cause analysis
Predictive failure detection
Automated capacity planning
Real-time cost optimization

Platform Evolution

Edge monitoring
IoT integration
Blockchain audit trails
Quantum-safe metrics