AI in Database Operations: Various Application Scenarios, Solutions, and Practice Guidelines

Overview

With the rapid development of artificial intelligence technology, AI is profoundly changing the way databases are managed and operated. From automated query generation and performance tuning to data quality monitoring and intelligent report analysis, AI has become an indispensable “intelligent assistant” in modern database systems.

This article systematically outlines  eight core application scenarios of AI in database operations , combining practical SQL examples and best practices to comprehensively demonstrate how AI can improve database development efficiency, optimize query performance, and enhance data insights.

1. Database exploration and structural analysis

Scene Description

When taking over an unfamiliar database or needing to quickly understand complex data models, traditional methods rely on documents or manually examining table structures. AI, however, can understand natural language, automatically generate structured queries, and quickly perform “reverse engineering” of the database.

AI-driven database exploration solutions

-- 1. Retrieve all table information (including comments)
SELECT 
    table_name,
    table_type,
    table_comment,
    create_time,
    update_time
FROM information_schema.tables 
WHERE table_schema = 'your_database'
  AND table_type = 'BASE TABLE'
ORDER BY table_name;

-- 2. Analyze the detailed structure of the specified table
SELECT 
    ordinal_position as pos,
    column_name,
    data_type,
    character_maximum_length as max_len,
    numeric_precision,
    numeric_scale,
    is_nullable,
    column_default,
    extra,
    column_comment
FROM information_schema.columns 
WHERE table_schema = 'your_database' 
  AND table_name = 'users'
ORDER BY ordinal_position;

-- 3. Automatically identify foreign key relationships and data dependencies
SELECT 
    kcu.table_name,
    kcu.column_name,
    kcu.referenced_table_name,
    kcu.referenced_column_name,
    rc.update_rule,
    rc.delete_rule
FROM information_schema.key_column_usage kcu
JOIN information_schema.referential_constraints rc
  ON kcu.constraint_name = rc.constraint_name
  AND kcu.constraint_schema = rc.constraint_schema
WHERE kcu.table_schema = 'your_database'
  AND kcu.referenced_table_name IS NOT NULL
ORDER BY kcu.table_name, kcu.ordinal_position;

AI advantages :

• Automatically generate ER diagram basic data
• Quickly identify primary and foreign key relationships
• Supports cross-database metadata comparison

2. Intelligent Report Generation

Scene Description

Traditional report development is time-consuming and costly. AI can automatically construct complex SQL queries based on natural language descriptions (such as “Please generate a sales trend report for each product category over the past year”), significantly improving BI efficiency.

AI-generated sales analysis reports

-- Sales Trend and Growth Analysis Report
WITH sales_summary AS (
    SELECT 
        DATE_FORMAT(order_date, '%Y-%m') as month,
        p.category as product_category,
        SUM(oi.quantity) as total_quantity,
        SUM(oi.quantity * oi.unit_price) as total_amount,
        COUNT(DISTINCT o.customer_id) as unique_customers,
        COUNT(o.order_id) as order_count
    FROM orders o
    JOIN order_items oi ON o.order_id = oi.order_id
    JOIN products p ON oi.product_id = p.product_id
    WHERE o.order_date >= DATE_SUB(NOW(), INTERVAL 12 MONTH)
      AND o.status IN ('completed', 'shipped')
    GROUP BY month, p.category
),
growth_analysis AS (
    SELECT 
        month,
        product_category,
        total_amount,
        LAG(total_amount, 1) OVER (PARTITION BY product_category ORDER BY month) as prev_month_amount,
        ROUND(
            (total_amount - LAG(total_amount, 1) OVER (PARTITION BY product_category ORDER BY month)) 
            / NULLIF(LAG(total_amount, 1) OVER (PARTITION BY product_category ORDER BY month), 0) * 100, 2
        ) as growth_rate_percent
    FROM sales_summary
)
SELECT 
    month,
    product_category,
    total_amount,
    prev_month_amount,
    growth_rate_percent,
    CASE 
        WHEN growth_rate_percent > 20 THEN 'Rapid growth'
        WHEN growth_rate_percent > 10 THEN 'stable growth'
        WHEN growth_rate_percent > 0 THEN 'slow growth'
        WHEN growth_rate_percent IS NULL THEN 'New Item'
        ELSE 'Need Attention'
    END as growth_status
FROM growth_analysis
WHERE month IS NOT NULL
ORDER BY month DESC, total_amount DESC;

AI capability expansion :

• Supports drill-down across multiple dimensions (time, region, channel).
• Automatic generation of year-on-year/month-on-month calculations
• Intelligent anomaly detection (such as sudden increases/decreases)

3. CRUD operation optimization

Scene Description

AI can generate efficient and secure CRUD templates based on table structure and business semantics, avoiding common errors (such as SQL injection, table locking, and full table scan).

AI-optimized smart CRUD template

-- 1. Batch Insertion (UPSERT) Optimization
INSERT INTO users (username, email, created_at, updated_at) 
VALUES 
    ('alice', '[email protected]', NOW(), NOW()),
    ('bob', '[email protected]', NOW(), NOW()),
    ('charlie', '[email protected]', NOW(), NOW())
ON DUPLICATE KEY UPDATE 
    email = VALUES(email),
    updated_at = VALUES(updated_at);

-- 2. Security update (with conditional and audit fields)
UPDATE products 
SET 
    price = ?,
    stock_quantity = ?,
    updated_at = NOW(),
    updated_by = ?
WHERE product_id = ?
  AND status = 'active'
  AND version = ?; -- optimistic locking

-- 3. Soft deletion implementation (supports recovery)
UPDATE orders 
SET 
    status = 'deleted',
    deleted_at = NOW(),
    deleted_by = ?
WHERE order_id = ?
  AND deleted_at IS NULL;

-- 4. High performance pagination query (to avoid OFFSET performance issues)
-- Option 1: Based on cursor (recommended)
SELECT * FROM orders 
WHERE customer_id = ?
  AND (order_date < ? OR (order_date = ? AND order_id < ?))
ORDER BY order_date DESC, order_id DESC
LIMIT 20;

-- Option 2: Use Keyset pagination
SELECT * FROM orders 
WHERE id > ? 
ORDER BY id 
LIMIT 20;

AI suggestion :

• Automatically generate parameterized queries to prevent SQL injection.
• It is recommended to use  INSERT ... ON DUPLICATE KEY UPDATE the alternative query-then-insert method.
• Prompt to add   audit fields updated_by, etc.version

4. Query performance optimization

Scene Description

AI can analyze slow query logs, execution plans EXPLAIN, and table structures to automatically suggest indexes and query rewriting solutions.

AI-driven query optimization process

Before optimization (slow query)

SELECT * 
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_date BETWEEN '2023-01-01' AND '2023-12-31'
  AND c.country = 'USA';

AI optimization suggestions

1. Avoid SELECT *  → Select only the necessary fields
2. Optimize connection sequence  → Use  STRAIGHT_JOIN control driver table
3. Filter as early as possible  → Push  WHERE conditions forward
4. Pre-aggregation  → Reduce intermediate result sets
5. Use covering indexes  → Reduce table lookups

Optimized query

SELECT 
    o.order_id,
    o.order_date,
    c.customer_name,
    COUNT(oi.item_id) as item_count,
    SUM(oi.quantity * oi.unit_price) as order_total
FROM orders o
STRAIGHT_JOIN customers c ON o.customer_id = c.customer_id
STRAIGHT_JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_date >= '2023-01-01'
  AND o.order_date < '2024-01-01'
  AND c.country = 'USA'
GROUP BY o.order_id, o.order_date, c.customer_name
ORDER BY o.order_date DESC
LIMIT 1000;

AI-recommended indexing strategies

-- Analyze the usage of existing indexes
SHOW INDEX FROM orders;
EXPLAIN FORMAT=JSON SELECT ...;

-- AI suggests creating an index
CREATE INDEX idx_orders_date_customer_cover 
ON orders(order_date, customer_id, order_id); -- Coverage index

CREATE INDEX idx_customers_country 
ON customers(country, customer_id); -- Used for filtering and connecting

CREATE INDEX idx_order_items_order_cover 
ON order_items(order_id, item_id, quantity, unit_price); -- Aggregation coverage

AI tool recommendations :

• MySQL：Performance Schema + sys schema
• PostgreSQL：pg_stat_statements
• Third-party tools: Percona Toolkit, SolarWinds DPA

5. Solutions for Complex Problems

Option 1: Recursive query processing of hierarchical data

-- Organizational structure/classification tree hierarchical query
WITH RECURSIVE org_hierarchy AS (
    -- Anchor point query: root node
    SELECT 
        employee_id,
        employee_name,
        manager_id,
        1 as level,
        CAST(employee_name AS CHAR(1000)) as path
    FROM employees 
    WHERE manager_id IS NULL
    
    UNION ALL
    
    -- recursive part
    SELECT 
        e.employee_id,
        e.employee_name,
        e.manager_id,
        oh.level + 1,
        CONCAT(oh.path, ' → ', e.employee_name)
    FROM employees e
    INNER JOIN org_hierarchy oh ON e.manager_id = oh.employee_id
    WHERE oh.level < 10 -- Prevent infinite recursion
)
SELECT 
    employee_id,
    employee_name,
    level,
    path
FROM org_hierarchy
ORDER BY path;

Option 2: Automated Data Quality Check

-- AI generated data quality monitoring report
SELECT 
    'orders' as table_name,
    COUNT(*) as total_records,
    SUM(CASE WHEN order_date IS NULL THEN 1 ELSE 0 END) as null_dates,
    SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) as null_customers,
    SUM(CASE WHEN amount < 0 THEN 1 ELSE 0 END) as negative_amounts,
    SUM(CASE WHEN order_id IS NULL THEN 1 ELSE 0 END) as null_ids,
    COUNT(*) - COUNT(DISTINCT order_id) as duplicate_ids,
    ROUND(
        (SUM(CASE WHEN order_date IS NULL THEN 1 ELSE 0 END) * 100.0 / NULLIF(COUNT(*), 0)), 2
    ) as null_rate_percent
FROM orders

UNION ALL

SELECT 
    'customers' as table_name,
    COUNT(*) as total_records,
    SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) as null_emails,
    SUM(CASE WHEN email NOT REGEXP '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$' THEN 1 ELSE 0 END) as invalid_emails,
    SUM(CASE WHEN created_at > NOW() THEN 1 ELSE 0 END) as future_dates,
    SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) as null_ids,
    COUNT(*) - COUNT(DISTINCT customer_id) as duplicate_ids,
    ROUND(
        (SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) * 100.0 / NULLIF(COUNT(*), 0)), 2
    ) as null_rate_percent
FROM customers;

AI scalability :

• Automatically generate data quality scorecards
• Predicting abnormal trends in data
• Recommended cleaning rules (such as regular expression standardization)

6. AI-assisted database maintenance

Scene Description

AI can generate database health reports regularly and automatically identify problems such as index redundancy and tablespace fragmentation.

-- Table Space and Fragmentation Analysis
SELECT 
    table_name,
    engine,
    table_rows,
    round(data_length / 1024 / 1024, 2) as data_size_mb,
    round(index_length / 1024 / 1024, 2) as index_size_mb,
    round((data_length + index_length) / 1024 / 1024, 2) as total_size_mb,
    round(data_free / 1024 / 1024, 2) as free_space_mb,
    round(data_free * 100.0 / (data_length + index_length), 2) as fragmentation_percent
FROM information_schema.tables 
WHERE table_schema = DATABASE()
  AND data_length > 0
ORDER BY data_length DESC;

-- Index usage statistics（MySQL 8.0+）
SELECT 
    object_schema,
    object_name,
    index_name,
    count_read,
    count_fetch,
    count_insert,
    count_update,
    count_delete,
    -- Read write ratio
    ROUND(count_read * 1.0 / NULLIF(count_insert + count_update + count_delete, 0), 2) as read_write_ratio
FROM performance_schema.table_io_waits_summary_by_index_usage 
WHERE index_name IS NOT NULL
  AND object_schema = DATABASE()
ORDER BY count_read DESC;

AI suggestion :

• It is recommended to delete indexes marked as “never read”.
• Merging inefficient indexes is recommended.
• Forecasting storage growth trends for the next 3 months

7. Practical Application Example: E-commerce Data Analysis Report

-- AI generated e-commerce core KPI report
SELECT 
    DATE_FORMAT(order_date, '%Y-%m') as report_month,
    
    -- sales target
    COUNT(DISTINCT order_id) as total_orders,
    COUNT(DISTINCT customer_id) as active_customers,
    SUM(amount) as total_revenue,
    ROUND(AVG(amount), 2) as avg_order_value,
    
    -- customer behavior
    COUNT(DISTINCT CASE WHEN is_returned THEN order_id END) as returned_orders,
    ROUND(
        COUNT(DISTINCT CASE WHEN is_returned THEN order_id END) * 100.0 / NULLIF(COUNT(DISTINCT order_id), 0), 2
    ) as return_rate_percent,
    
    -- product performance
    COUNT(DISTINCT product_id) as unique_products_sold,
    SUM(quantity) as total_units_sold,
    ROUND(SUM(amount) / NULLIF(SUM(quantity), 0), 2) as avg_price_per_unit,
    
    -- trend analysis
    LAG(SUM(amount), 1) OVER (ORDER BY DATE_FORMAT(order_date, '%Y-%m')) as prev_month_revenue,
    ROUND(
        (SUM(amount) - LAG(SUM(amount), 1) OVER (ORDER BY DATE_FORMAT(order_date, '%Y-%m'))) 
        / NULLIF(LAG(SUM(amount), 1) OVER (ORDER BY DATE_FORMAT(order_date, '%Y-%m')), 0) * 100, 2
    ) as month_on_month_growth
    
FROM orders o
JOIN order_items oi ON o.order_id = oi.order_id
WHERE order_date >= DATE_SUB(NOW(), INTERVAL 6 MONTH)
  AND o.status = 'completed'
GROUP BY report_month
HAVING report_month IS NOT NULL
ORDER BY report_month DESC;

8. Summary and Best Practices

1. Query optimization principles

in principle	illustrate
avoid SELECT *	Select only the necessary fields to reduce network and memory overhead.
Use parameterized queries	Prevent SQL injection and improve execution plan reuse
Use indexes appropriately	Covering index > Composite index > Single column index
Controlling pagination performance	Use cursor pagination instead OFFSET
Early filtration and early polymerization	Reduce intermediate result set size

2. Data Security Specifications

• All user input must be parameterized.
• Implement the principle of least privilege (RBAC).
• Sensitive fields (such as passwords and ID cards) are stored in encrypted form.
• Regular backup and recovery drills
• Enable audit logging

3. AI Usage Recommendations

Scene	Recommended tools/platforms
Natural Language Generating SQL	ChatGPT ,  Tongyi 1000 Questions ,  Google Duet AI
Query optimization suggestions	Percona Monitoring and Management ,  Alibaba Cloud DAS
Data quality analysis	Great Expectations, Deequ, Datadog
Intelligent BI Reports	Power BI + Copilot, Tableau GPT, QuickSight Q

4. Future Trends

• AI-native databases , such as Google Spanner and Snowflake, have integrated AI optimizers.
• Natural Language BI : Users ask questions verbally, and AI automatically generates visual reports.
• Automated security protection : AI detects abnormal query behavior in real time (such as attempts to leak data).
• Predictive maintenance : AI predicts performance bottlenecks and automatically adjusts configurations.

Conclusion

AI is ushering in an era of “autonomous driving” for database operations, moving them from “manual driving” to “autonomous driving.” It’s not just a code generator, but also an intelligent database advisor , helping developers:

• Increase development efficiency by more than 10 times.
• Reduce the incidence of performance problems
• Deepen data insights
• Enhance system security