-- Example analysis output
Table: customers
├── Columns: 12
├── Indexes: 3
├── Foreign Keys: 2
└── Recommendations:
    ├── Add index on (last_login, status)
    ├── Optimize data types for email, phone
    └── Consider partitioning by

-- Query performance analysis
SELECT
    table_name,
    avg_row_length,
    data_length/1024/1024 as size_mb,
    index_length/1024/1024 as index_size_mb,
    table_rows
FROM information_schema.tables
WHERE table_schema = DATABASE()
ORDER BY data_length DESC;

-- Recommendations:
-- 1. Optimize large tables
-- 2. Review index usage
-- 3. Consider partitioning

-- Slow query identification
SELECT
    query_time,
    rows_examined,
    rows_sent,
    sql_text
FROM mysql.slow_log
WHERE start_time > DATE_SUB(NOW(), INTERVAL 24 HOUR)
ORDER BY query_time DESC
LIMIT 10;

-- AI-Generated Optimization:
-- 1. Add covering indexes
-- 2. Rewrite subqueries
-- 3. Optimize JOIN order

-- Analysis findings
Tables requiring attention:
1. products
   - High update frequency
   - Missing index on (category_id, price)
   - Consider partitioning by date

2. orders
   - Large table (500GB)
   - Suboptimal JOIN

-- Performance bottlenecks
1. user_events table
   - High write volume
   - Inefficient indexes
   - Recommend:
     └── Partitioning by

-- Current indexes
SHOW INDEX FROM orders;

-- Recommended new indexes
CREATE INDEX idx_order_status
    ON orders(status, created_at);
CREATE INDEX idx_product_category
    ON products(category_id, status)

-- Before optimization
SELECT
    c.customer_name,
    COUNT(o.order_id) as total_orders
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id
WHERE o.created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY c.id, c.customer_name;

-- After optimization
SELECT
    c.customer_name,
    COUNT(o.order_id) as total_orders
FROM customers c
LEFT JOIN orders o
    ON c.id = o.customer_id
    AND o.created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY c.id,