Analytics and Visualization

VIJ provides comprehensive analytics and visualization tools to help you understand error patterns, track trends, and make data-driven decisions.

Dashboard Analytics

The dashboard home provides key metrics and visualizations.

Overview Metrics

Total Errors

Total number of errors across all time periods

Error Rate

Errors per hour/day trend over time

Unique Groups

Number of distinct error groups

Key Metrics Explained

Total Errors

Total Count: 15,247 errors
Last 24 hours: 1,234 errors
Previous 24 hours: 987 errors
Change: +25% ↑

Use Cases:

Monitor overall application health
Identify error spikes
Track improvement over time

Error Rate

Current Rate: 51 errors/hour
Average Rate: 42 errors/hour
Peak Rate: 127 errors/hour (at 2024-01-01 14:00)

Alerts:

Rate > 2x average: Warning
Rate > 5x average: Critical

Active Error Groups

Total Groups: 347
New (last 24h): 23
Resolved: 12
Active: 335

Metrics:

New groups indicate new bugs
Resolved groups show progress
Active groups need attention

Time-Series Visualizations

Error Trends Chart

Interactive chart showing error frequency over time. Features:

Multiple time ranges (1h, 24h, 7d, 30d)
Severity breakdown (stacked)
Hover for details
Zoom and pan
Export as image

Data Structure:

{
  "timestamp": "2024-01-01T12:00:00Z",
  "errors": 45,
  "warnings": 12,
  "info": 3,
  "total": 60
}

Severity Distribution

Pie chart showing error breakdown by severity.

{
  "error": 78.5%,    // 12,000 errors
  "warning": 18.2%,  // 2,800 warnings
  "info": 3.3%       // 500 info logs
}

Insights:

High error percentage: Critical issues need attention
Balanced distribution: Good error handling
Low warning percentage: Under-reporting potential issues

Error Groups Over Time

Track new vs. resolved error groups.

{
  "date": "2024-01-01",
  "newGroups": 15,
  "resolvedGroups": 8,
  "activeGroups": 347
}

Health Indicator:

newGroups < resolvedGroups: Improving ✓
newGroups > resolvedGroups: Degrading ✗
newGroups ≈ resolvedGroups: Stable ~

Application Analytics

Per-application metrics and comparisons.

Application Comparison

Compare error rates across applications:

Application	Errors	Rate	Change	Status
frontend	8,234	34/h	+12%	🔴
backend-api	5,123	21/h	-5%	🟢
mobile-app	1,890	8/h	+45%	🔴

Insights:

Mobile app error rate increased significantly
Backend API improving
Frontend needs attention

Environment Breakdown

Error distribution by environment:

{
  "production": { "count": 12000, "percentage": 78.7% },
  "staging": { "count": 2500, "percentage": 16.4% },
  "development": { "count": 747, "percentage": 4.9% }
}

Expectations:

Production should be lowest (well-tested code)
Staging catches most issues
Development has experimental code

Top Error-Prone Features

Identify features with most errors:

[
  { "feature": "checkout", "errors": 3456, "percentage": 22.7% },
  { "feature": "authentication", "errors": 2134, "percentage": 14.0% },
  { "feature": "search", "errors": 1678, "percentage": 11.0% }
]

Actions:

Prioritize fixing high-error features
Add more tests for error-prone areas
Review code quality

User Impact Analytics

Understand how errors affect users.

Affected Users

Track unique users impacted by errors:

{
  "totalUsers": 50000,
  "affectedUsers": 2345,
  "affectedPercentage": 4.69%,
  "usersWithMultipleErrors": 456
}

Metrics:

< 1% affected: Isolated issues
1-5% affected: Moderate impact
5% affected: Widespread problem

User Journey Analysis

See where errors occur in user flows:

Home → Product → Cart → Checkout → Payment
100%   95%      92%     78%      45%  ← Completion rate

Errors by step:
1. Home: 234 errors (2%)
2. Product: 567 errors (5%)
3. Cart: 891 errors (8%)
4. Checkout: 1234 errors (22%)  ← Problem area
5. Payment: 2345 errors (55%)   ← Critical

Insights:

Checkout and payment have high error rates
Users drop off due to errors
Fix payment errors first (highest impact)

Error Recurrence

Track how often users encounter the same error:

{
  "oneTime": 1500,      // 64% of users
  "recurring": 845,     // 36% of users
  "averageRecurrence": 2.3
}

High recurrence indicates:

Persistent bugs not fixed
Poor error recovery
User frustration

Performance Analytics

Correlate errors with performance metrics.

Error Rate vs. Response Time

{
  "timestamp": "2024-01-01T12:00:00Z",
  "errorRate": 45,
  "avgResponseTime": 1234,
  "correlation": 0.78  // Strong correlation
}

Correlation values:

0.7: Errors likely caused by performance issues
0.3-0.7: Moderate correlation
< 0.3: Independent factors

Memory Usage Correlation

{
  "heapUsed": 850000000,  // 850 MB
  "errorCount": 67,
  "memoryThreshold": 900000000  // 900 MB threshold
}

Pattern detection:

Errors spike when memory > 90% threshold
Likely memory leaks or OOM errors
Action: Optimize memory usage

Custom Analytics

Create custom analytics dashboards.

Custom Metrics

Define custom metrics to track:

// Example: Track payment errors by gateway
const paymentAnalytics = await db.logs.aggregate([
  {
    $match: {
      "metadata.feature": "payment",
      severity: "error"
    }
  },
  {
    $group: {
      _id: "$metadata.paymentGateway",
      count: { $sum: 1 },
      avgResponseTime: { $avg: "$metadata.responseTime" }
    }
  },
  {
    $sort: { count: -1 }
  }
]);

Result:

[
  { "gateway": "stripe", "count": 234, "avgResponseTime": 1234 },
  { "gateway": "paypal", "count": 123, "avgResponseTime": 2345 }
]

Funnels

Track error rates through conversion funnels:

const checkoutFunnel = [
  { step: "cart", errors: 234, users: 10000 },
  { step: "shipping", errors: 156, users: 9500 },
  { step: "payment", errors: 890, users: 9200 },
  { step: "confirmation", errors: 45, users: 8500 }
];

// Calculate error impact on conversion
const errorImpact = checkoutFunnel.map(step => ({
  step: step.step,
  errorRate: (step.errors / step.users * 100).toFixed(2) + '%',
  completionRate: ((step.users / checkoutFunnel[0].users) * 100).toFixed(2) + '%'
}));

Cohort Analysis

Compare error rates across user cohorts:

const cohortAnalysis = await db.logs.aggregate([
  {
    $match: { "metadata.userId": { $exists: true } }
  },
  {
    $group: {
      _id: {
        cohort: "$metadata.userCohort",
        severity: "$severity"
      },
      count: { $sum: 1 }
    }
  }
]);

Insights:

New users encounter more errors (onboarding issues)
Power users have different error patterns
Mobile users vs. desktop users

Alerting and Monitoring

Set up alerts based on analytics.

Threshold Alerts

const alerts = [
  {
    name: "High Error Rate",
    condition: "errorRate > 100/hour",
    action: "Send email to team@example.com"
  },
  {
    name: "New Critical Error",
    condition: "newGroup && severity=error",
    action: "Send Slack notification"
  },
  {
    name: "Error Spike",
    condition: "errorRate > 2x average (last 1h)",
    action: "Create PagerDuty incident"
  }
];

Anomaly Detection

Detect unusual patterns:

function detectAnomalies(errorRates: number[]) {
  const mean = average(errorRates);
  const stdDev = standardDeviation(errorRates);

  return errorRates.map((rate, i) => {
    const zScore = (rate - mean) / stdDev;
    return {
      timestamp: timestamps[i],
      rate,
      isAnomaly: Math.abs(zScore) > 3,  // 3 sigma rule
      severity: Math.abs(zScore) > 5 ? 'critical' : 'warning'
    };
  });
}

Trend Analysis

Predict future error rates:

// Simple linear regression
function predictErrorRate(historicalData: number[]): number {
  const n = historicalData.length;
  const xMean = (n - 1) / 2;
  const yMean = average(historicalData);

  let numerator = 0;
  let denominator = 0;

  historicalData.forEach((y, x) => {
    numerator += (x - xMean) * (y - yMean);
    denominator += (x - xMean) ** 2;
  });

  const slope = numerator / denominator;
  const intercept = yMean - slope * xMean;

  // Predict next value
  return slope * n + intercept;
}

Export and Reporting

Generate reports from analytics data.

Report Types

Daily Report
Weekly Report
Monthly Report

Email sent at 9 AM daily

VIJ Daily Report - 2024-01-01

Summary:
- Total Errors: 1,234 (+12% vs yesterday)
- New Error Groups: 15
- Resolved Groups: 8
- Top Error: "TypeError: Cannot read property 'map'"

Top Applications:
1. frontend: 567 errors
2. backend-api: 345 errors
3. mobile-app: 234 errors

Action Items:
- Fix checkout errors (234 occurrences)
- Investigate payment gateway timeout

Custom Reports

Generate custom reports via API:

GET /api/reports/generate
  ?type=custom
  &startDate=2024-01-01
  &endDate=2024-01-31
  &groupBy=appId,severity
  &format=pdf

Export Formats

GET /api/reports/export?format=pdf&report=weekly

Real-Time Analytics

Live analytics dashboard with streaming updates.

WebSocket Connection

const ws = new WebSocket('wss://vij.example.com/analytics');

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);

  if (data.type === 'errorRate') {
    updateErrorRateChart(data.value);
  }

  if (data.type === 'newError') {
    incrementCounter();
    addToRecentErrors(data.error);
  }
};

Live Metrics

interface LiveMetrics {
  errorsLastMinute: number;
  errorsLastHour: number;
  currentRate: number;      // Errors per minute
  activeUsers: number;
  topErrors: Array<{
    message: string;
    count: number;
  }>;
}

Best Practices

Monitor trends, not just counts

Focus on:

Error rate over time (not just total)
Week-over-week changes
Correlations with deployments
Seasonal patterns

Set meaningful thresholds

const thresholds = {
  errorRate: {
    warning: 50,     // errors per hour
    critical: 100
  },
  affectedUsers: {
    warning: 1,      // percentage
    critical: 5
  },
  responseTime: {
    warning: 2000,   // milliseconds
    critical: 5000
  }
};

Correlate with deployments

Track errors against deploy times:

const deployments = [
  { time: "2024-01-01T10:00:00Z", version: "v2.1.0" },
  { time: "2024-01-01T14:00:00Z", version: "v2.1.1" }
];

// Overlay on error chart to see impact

Use percentages, not absolutes

Better: "5% of users affected"
Worse: "2,500 errors occurred"

Better: "Error rate increased 45%"
Worse: "567 more errors than yesterday"

Set up automated alerts

Don’t rely on manual checks:

Email for critical errors
Slack for warnings
PagerDuty for emergencies
Weekly summary reports

Next Steps

Dashboard Features

Explore analytics in the dashboard

Filtering & Search

Filter data for specific analytics

Error Grouping

Analyze error groups and patterns

API Reference

Use the stats API for custom analytics

Getting started

SDK

Dashboard

Advanced

​Dashboard Analytics

​Overview Metrics

Total Errors

Error Rate

Unique Groups

​Key Metrics Explained

​Total Errors

​Error Rate

​Active Error Groups

​Time-Series Visualizations

​Error Trends Chart

​Severity Distribution

​Error Groups Over Time

​Application Analytics

​Application Comparison

​Environment Breakdown

​Top Error-Prone Features

​User Impact Analytics

​Affected Users

​User Journey Analysis

​Error Recurrence

​Performance Analytics

​Error Rate vs. Response Time

​Memory Usage Correlation

​Custom Analytics

​Custom Metrics

​Funnels

​Cohort Analysis

​Alerting and Monitoring

​Threshold Alerts

​Anomaly Detection

​Trend Analysis

​Export and Reporting

​Report Types

​Custom Reports

​Export Formats

​Real-Time Analytics

​WebSocket Connection

​Live Metrics

​Best Practices

​Next Steps

Dashboard Features

Filtering & Search

Error Grouping

API Reference

Dashboard Analytics

Overview Metrics

Key Metrics Explained

Total Errors

Error Rate

Active Error Groups

Time-Series Visualizations

Error Trends Chart

Severity Distribution

Error Groups Over Time

Application Analytics

Application Comparison

Environment Breakdown

Top Error-Prone Features

User Impact Analytics

Affected Users

User Journey Analysis

Error Recurrence

Performance Analytics

Error Rate vs. Response Time

Memory Usage Correlation

Custom Analytics

Custom Metrics

Funnels

Cohort Analysis

Alerting and Monitoring

Threshold Alerts

Anomaly Detection

Trend Analysis

Export and Reporting

Report Types

Custom Reports

Export Formats

Real-Time Analytics

WebSocket Connection

Live Metrics

Best Practices

Next Steps