Predictive Analytics
Forecast future database needs and proactively prevent performance issues with AI-powered predictive insights.
Overview
Predictive Analytics leverages machine learning to forecast database resource needs, performance trends, and potential issues before they occur. By analyzing historical patterns and current growth trajectories, it helps you make informed decisions about capacity planning, scaling, and optimization.
Capacity Planning
Predict when you'll need to scale resources based on growth trends
Trend Analysis
Identify performance degradation patterns before they become critical
Smart Recommendations
Receive actionable scaling and optimization suggestions
How Predictions Are Made
Historical Data Collection
The system aggregates historical metrics across multiple time horizons:
- Last 30 days: Short-term patterns and recent trends
- Last 90 days: Medium-term growth and seasonal patterns
- Last 12 months: Long-term trends and annual cycles
- Special events: Traffic spikes, deployments, incidents
Pattern Recognition
Machine learning models identify various patterns in your data:
Linear Growth
Steady, predictable increase
Exponential Growth
Accelerating growth rate
Seasonal Patterns
Recurring cyclical trends
Step Changes
Sudden level shifts
Time Series Forecasting
Advanced forecasting algorithms (ARIMA, Prophet, LSTM neural networks) project future values with confidence intervals. Multiple models are used and ensembled for accuracy.
Threshold Analysis
Compares predictions against defined capacity limits and performance thresholds to identify when action is needed. Calculates "time to critical" estimates for each resource.
Recommendation Engine
Generates actionable recommendations based on predictions, including optimal timing for scaling, resource allocation suggestions, and cost-benefit analysis.
Capacity Planning Predictions
Capacity planning predictions help you stay ahead of resource constraints by forecasting when you'll need to scale up your infrastructure:
Storage Capacity
RECOMMENDATION:
Expand storage to 2 TB before March 1st to maintain 30-day buffer. Estimated cost: $250/month.
Memory Usage
RECOMMENDATION:
Current capacity sufficient for next 6 months. Review in Q3 2026 for potential optimization opportunities.
Connection Pool
RECOMMENDATION:
Increase pool size to 300 by April 1st. Also investigate connection optimization to reduce overall usage.
CPU Usage
RECOMMENDATION:
CPU capacity is well-optimized. No action needed. Continue monitoring for workload changes.
Capacity Planning Timeline
Visual timeline showing predicted resource exhaustion dates:
Performance Trend Analysis
Track and predict performance metrics over time to identify degradation patterns before they impact users:
Query Performance Degradation
Current Analysis
30-Day Forecast
IDENTIFIED CAUSES:
- Table `orders` growing 15% monthly without index optimization
- Query complexity increased from JOIN depth 2 → 3 in recent releases
- Cache hit rate declining from 92% to 87%
RECOMMENDATIONS:
- Add composite index on orders(customer_id, created_at, status)
- Review and optimize new queries with deep joins
- Increase query cache size from 256MB to 512MB
Table Size Growth Projection
Current Size
42.3 GB
15.2M rows
30-Day Forecast
51.8 GB
+22.5% growth
90-Day Forecast
72.1 GB
+70.4% growth
GROWTH ANALYSIS:
The `events` table is experiencing exponential growth due to increased user activity. At current rate, it will reach 100 GB in approximately 120 days.
RECOMMENDATIONS:
- Implement data retention policy: archive events older than 90 days
- Enable table partitioning by month for better performance
- Consider separate analytics database for historical data
Index Usage Efficiency
Recent index additions on high-traffic tables have improved efficiency. Continue monitoring and expect to reach 96% target by end of month.
Scaling Recommendations
Immediate Action Required
Storage capacity will reach critical levels in 36 days
Recommended Actions:
- Expand storage from 1TB to 2TB before March 1st
- Estimated cost impact: +$250/month
- Zero downtime migration available
- Expected to provide 12-month capacity buffer
Planned Scaling (Q2 2026)
Connection pool will need expansion in 74 days
Recommended Actions:
- Increase connection pool from 200 to 300 by April 1st
- Estimated cost impact: +$120/month
- Consider implementing connection pooling optimizations
- Review application connection handling patterns
Optimization Opportunities
Potential to reduce scaling needs through optimization
Optimization Recommendations:
- Implement data archival policy - could reduce storage needs by 25%
- Optimize connection pooling - could delay connection pool expansion by 3 months
- Add missing indexes - could improve query performance by 40%
- Estimated annual savings: $3,600 if implemented
Configuration Options
| Setting | Description | Default |
|---|---|---|
| Forecast Horizon | How far into the future to predict (30, 60, 90 days) | 90 days |
| Historical Window | Amount of historical data to use for predictions | 90 days |
| Confidence Level | Statistical confidence for predictions (80%, 90%, 95%) | 90% |
| Alert Threshold | Days before predicted issue to send alerts | 30 days |
| Growth Buffer | Target buffer after scaling (e.g., 6 months capacity) | 6 months |
| Model Selection | Auto-select best model or use specific algorithm | Auto (ensemble) |
Note: Predictions automatically update daily. For critical systems, consider enabling real-time prediction updates for faster response to changing patterns.
Plan Ahead with Predictive Insights
Make informed decisions about scaling and optimization with AI-powered forecasts