Transform your database performance with AI-powered analytics. Get real-time insights, optimize queries, and improve database efficiency automatically.
Analytics Features
1. Performance Monitoring
SELECT
operation_type,
COUNT(*) as occurrence,
AVG(duration_ms) as avg_duration,
MAX(duration_ms) as max_duration,
SUM(rows_affected) as total_rows
FROM performance_log
WHERE timestamp >= NOW() - INTERVAL 1 HOUR
GROUP BY operation_type
ORDER BY avg_duration DESC
2. Query Analysis
WITH query_stats AS (
SELECT
query_pattern,
COUNT(*) as execution_count,
AVG(execution_time) as avg_time,
SUM(rows_examined) as total_rows
FROM query_log
GROUP BY query_pattern
)
SELECT
query_pattern,
execution_count,
avg_time,
ROUND(avg_time * execution_count, 2) as total_impact
FROM query_stats
ORDER BY total_impact DESC
3. Resource Utilization
SELECT
instance_name,
metric_name,
AVG(value) as avg_value,
MAX(value) as peak_value,
MIN(value) as min_value
FROM system_metrics
WHERE collect_time >= NOW() - INTERVAL 24 HOUR
GROUP BY instance_name,
Key Capabilities
1. Real-Time Analytics
Query performance tracking
Resource utilization
User activity monitoring
Error detection
Performance alerts
2. Historical Analysis
Performance trends
Usage patterns
Growth analysis
Capacity planning
Optimization tracking
3. AI-Powered Insights
Anomaly detection
Predictive analytics
Pattern recognition
Automated recommendations
Performance forecasting
Use Cases
1. E-commerce Platform
WITH customer_metrics AS (
SELECT
c.customer_segment,
COUNT(DISTINCT o.order_id) as orders,
SUM(o.total_amount) as revenue,
AVG(o.items_count) as avg_items
FROM customers c
JOIN orders o ON c.id = o.customer_id
WHERE o.order_date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
GROUP BY c.customer_segment
)
SELECT
customer_segment,
orders,
revenue,
avg_items,
revenue / orders as avg_order_value
FROM customer_metrics
ORDER BY revenue DESC
2. Financial Systems
SELECT
DATE_FORMAT(transaction_time, '%Y-%m-%d %H:00:00') as hour,
transaction_type,
COUNT(*) as transaction_count,
SUM(amount) as total_amount,
AVG(processing_time) as avg_processing_time
FROM transactions
WHERE transaction_time >= NOW() - INTERVAL 24 HOUR
GROUP BY hour, transaction_type
ORDER BY hour DESC
Analytics Dashboard
1. Performance Metrics
SELECT
metric_name,
current_value,
threshold_value,
CASE
WHEN current_value > threshold_value THEN 'Alert'
WHEN current_value > threshold_value * 0.8 THEN 'Warning'
ELSE 'Normal'
END as status
FROM performance_metrics
WHERE collect_time >= NOW() - INTERVAL 5 MINUTE
2. System Health
SELECT
component_name,
status,
last_check_time,
error_count,
warning_count
FROM system_health
WHERE last_check_time >= NOW() - INTERVAL 1 HOUR
ORDER BY error_count DESC, warning_count DESC
Optimization Features
1. Index Analysis
SELECT
table_name,
index_name,
usage_count,
last_used,
size_mb,
CASE
WHEN usage_count = 0 THEN 'Unused'
WHEN usage_count < 100 THEN 'Low Usage'
ELSE 'Active'
END as status
FROM index_statistics
ORDER BY usage_count DESC
2. Query Optimization
SELECT
query_hash,
execution_count,
avg_cpu_time,
avg_logical_reads,
suggested_indexes,
improvement_impact
FROM query_optimizer_recommendations
WHERE analysis_date = CURRENT_DATE
ORDER BY improvement_impact DESC
Best Practices
1. Performance Monitoring
Set up baseline metrics
Configure alerts
Track trends
Monitor resource usage
2. Query Optimization
Regular analysis
Index maintenance
Query tuning
Performance testing
3. Capacity Planning
Growth tracking
Resource forecasting
Scaling recommendations
Performance predictions
FAQs
Q: Can I integrate this with my existing monitoring tools? A: Yes, we provide APIs for integration with popular monitoring platforms.
Q: How does the AI optimization work? A: Our AI analyzes query patterns, performance metrics, and system resources to provide automated optimization recommendations.
Getting Started
Install analytics software
Configure monitoring
Set up dashboards
Enable alerts
Review recommendations