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MySQL索引优化实战：从基础到高级调优技巧
Orion K Lv6
  2023-09-22 10:15:00  

索引是MySQL性能优化的核心，正确使用索引可以将查询性能提升几个数量级。本文将深入探讨MySQL 5.7和8.4中的索引优化技巧，从基础概念到高级调优策略。

索引基础理论

1. 索引类型详解

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-- 1. 主键索引（聚簇索引）
CREATE TABLE users (
    id INT PRIMARY KEY AUTO_INCREMENT,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- 2. 唯一索引
CREATE UNIQUE INDEX idx_username ON users(username);
CREATE UNIQUE INDEX idx_email ON users(email);

-- 3. 普通索引
CREATE INDEX idx_created_at ON users(created_at);

-- 4. 复合索引
CREATE INDEX idx_username_email ON users(username, email);

-- 5. 前缀索引
CREATE INDEX idx_email_prefix ON users(email(10));

-- 6. 函数索引（MySQL 8.0+）
CREATE INDEX idx_upper_username ON users((UPPER(username)));

-- 7. 降序索引（MySQL 8.0+）
CREATE INDEX idx_created_desc ON users(created_at DESC);

2. 索引存储结构分析

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-- 查看索引信息
SHOW INDEX FROM users;

-- 查看表的存储引擎和索引统计
SHOW TABLE STATUS LIKE 'users'\G

-- 分析索引使用情况
SELECT 
    TABLE_SCHEMA,
    TABLE_NAME,
    INDEX_NAME,
    COLUMN_NAME,
    SEQ_IN_INDEX,
    CARDINALITY,
    INDEX_TYPE
FROM information_schema.STATISTICS 
WHERE TABLE_SCHEMA = 'your_database' 
AND TABLE_NAME = 'users'
ORDER BY TABLE_NAME, INDEX_NAME, SEQ_IN_INDEX;

索引设计策略

1. 复合索引设计原则

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-- 创建测试表
CREATE TABLE orders (
    id INT PRIMARY KEY AUTO_INCREMENT,
    user_id INT NOT NULL,
    status VARCHAR(20) NOT NULL,
    order_date DATE NOT NULL,
    amount DECIMAL(10,2) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- 插入测试数据
INSERT INTO orders (user_id, status, order_date, amount) VALUES
(1, 'pending', '2023-09-01', 100.00),
(2, 'completed', '2023-09-02', 200.00),
(1, 'completed', '2023-09-03', 150.00),
(3, 'pending', '2023-09-04', 300.00),
(2, 'cancelled', '2023-09-05', 250.00);

-- 错误的索引设计
CREATE INDEX idx_bad_1 ON orders(status);
CREATE INDEX idx_bad_2 ON orders(user_id);
CREATE INDEX idx_bad_3 ON orders(order_date);

-- 正确的复合索引设计（遵循最左前缀原则）
-- 根据查询频率和选择性设计
CREATE INDEX idx_user_status_date ON orders(user_id, status, order_date);

-- 测试查询性能
-- 这些查询都能使用上面的复合索引
EXPLAIN SELECT * FROM orders WHERE user_id = 1;
EXPLAIN SELECT * FROM orders WHERE user_id = 1 AND status = 'completed';
EXPLAIN SELECT * FROM orders WHERE user_id = 1 AND status = 'completed' AND order_date = '2023-09-03';

-- 这个查询无法使用索引（跳过了最左列）
EXPLAIN SELECT * FROM orders WHERE status = 'completed' AND order_date = '2023-09-03';

2. 索引选择性分析

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-- 分析列的选择性（不重复值的比例）
SELECT 
    'user_id' as column_name,
    COUNT(DISTINCT user_id) as distinct_values,
    COUNT(*) as total_rows,
    COUNT(DISTINCT user_id) / COUNT(*) as selectivity
FROM orders
UNION ALL
SELECT 
    'status',
    COUNT(DISTINCT status),
    COUNT(*),
    COUNT(DISTINCT status) / COUNT(*)
FROM orders
UNION ALL
SELECT 
    'order_date',
    COUNT(DISTINCT order_date),
    COUNT(*),
    COUNT(DISTINCT order_date) / COUNT(*)
FROM orders;

-- 基于选择性优化索引顺序
-- 高选择性的列放在前面
CREATE INDEX idx_optimized ON orders(order_date, user_id, status);

查询优化技巧

1. EXPLAIN 分析详解

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-- 详细的 EXPLAIN 分析
EXPLAIN FORMAT=JSON 
SELECT o.*, u.username 
FROM orders o 
JOIN users u ON o.user_id = u.id 
WHERE o.status = 'completed' 
AND o.order_date BETWEEN '2023-09-01' AND '2023-09-30'
ORDER BY o.created_at DESC 
LIMIT 10;

-- 分析关键指标
-- 1. type: 连接类型（const > eq_ref > ref > range > index > ALL）
-- 2. key: 使用的索引
-- 3. rows: 扫描的行数
-- 4. Extra: 额外信息

-- 优化前后对比
-- 优化前：全表扫描
SELECT * FROM orders WHERE YEAR(order_date) = 2023;

-- 优化后：使用索引范围扫描
SELECT * FROM orders WHERE order_date >= '2023-01-01' AND order_date < '2024-01-01';

2. 索引覆盖查询优化

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-- 创建覆盖索引
CREATE INDEX idx_covering ON orders(user_id, status, order_date, amount);

-- 覆盖索引查询（不需要回表）
EXPLAIN SELECT user_id, status, order_date, amount 
FROM orders 
WHERE user_id = 1 AND status = 'completed';

-- 非覆盖索引查询（需要回表）
EXPLAIN SELECT * 
FROM orders 
WHERE user_id = 1 AND status = 'completed';

-- 优化技巧：只查询需要的列
-- 不好的做法
SELECT * FROM orders WHERE user_id = 1;

-- 好的做法
SELECT id, status, amount FROM orders WHERE user_id = 1;

高级索引优化

1. 分区表索引优化

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-- 创建分区表
CREATE TABLE orders_partitioned (
    id INT AUTO_INCREMENT,
    user_id INT NOT NULL,
    status VARCHAR(20) NOT NULL,
    order_date DATE NOT NULL,
    amount DECIMAL(10,2) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    PRIMARY KEY (id, order_date)
) PARTITION BY RANGE (YEAR(order_date)) (
    PARTITION p2022 VALUES LESS THAN (2023),
    PARTITION p2023 VALUES LESS THAN (2024),
    PARTITION p2024 VALUES LESS THAN (2025),
    PARTITION p_future VALUES LESS THAN MAXVALUE
);

-- 分区表索引策略
-- 1. 分区键必须包含在主键中
-- 2. 每个分区都有独立的索引
CREATE INDEX idx_user_status ON orders_partitioned(user_id, status);

-- 查询分区信息
SELECT 
    PARTITION_NAME,
    TABLE_ROWS,
    DATA_LENGTH,
    INDEX_LENGTH
FROM information_schema.PARTITIONS 
WHERE TABLE_SCHEMA = 'your_database' 
AND TABLE_NAME = 'orders_partitioned';

2. MySQL 8.4 新特性

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-- 1. 不可见索引（MySQL 8.0+）
CREATE INDEX idx_invisible ON orders(amount) INVISIBLE;

-- 测试索引效果而不删除索引
ALTER INDEX idx_invisible ON orders INVISIBLE;
ALTER INDEX idx_invisible ON orders VISIBLE;

-- 2. 函数索引优化
CREATE INDEX idx_month_year ON orders((YEAR(order_date)), (MONTH(order_date)));

-- 使用函数索引
EXPLAIN SELECT * FROM orders 
WHERE YEAR(order_date) = 2023 AND MONTH(order_date) = 9;

-- 3. 多值索引（MySQL 8.0.17+）
CREATE TABLE products (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    tags JSON,
    INDEX idx_tags ((CAST(tags->'$[*]' AS CHAR(50) ARRAY)))
);

-- 4. 直方图统计（MySQL 8.0+）
ANALYZE TABLE orders UPDATE HISTOGRAM ON user_id, status WITH 100 BUCKETS;

-- 查看直方图信息
SELECT * FROM information_schema.COLUMN_STATISTICS 
WHERE SCHEMA_NAME = 'your_database' AND TABLE_NAME = 'orders';

索引监控和维护

1. 索引使用情况监控

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-- 查看索引使用统计
SELECT 
    OBJECT_SCHEMA,
    OBJECT_NAME,
    INDEX_NAME,
    COUNT_FETCH,
    COUNT_INSERT,
    COUNT_UPDATE,
    COUNT_DELETE
FROM performance_schema.table_io_waits_summary_by_index_usage
WHERE OBJECT_SCHEMA = 'your_database'
ORDER BY COUNT_FETCH DESC;

-- 查找未使用的索引
SELECT 
    t.TABLE_SCHEMA,
    t.TABLE_NAME,
    t.INDEX_NAME
FROM information_schema.STATISTICS t
LEFT JOIN performance_schema.table_io_waits_summary_by_index_usage p 
    ON t.TABLE_SCHEMA = p.OBJECT_SCHEMA 
    AND t.TABLE_NAME = p.OBJECT_NAME 
    AND t.INDEX_NAME = p.INDEX_NAME
WHERE t.TABLE_SCHEMA = 'your_database'
    AND p.INDEX_NAME IS NULL
    AND t.INDEX_NAME != 'PRIMARY'
GROUP BY t.TABLE_SCHEMA, t.TABLE_NAME, t.INDEX_NAME;

-- 查看索引碎片情况
SELECT 
    TABLE_SCHEMA,
    TABLE_NAME,
    DATA_LENGTH,
    INDEX_LENGTH,
    DATA_FREE,
    (DATA_FREE / (DATA_LENGTH + INDEX_LENGTH)) * 100 as fragmentation_percent
FROM information_schema.TABLES 
WHERE TABLE_SCHEMA = 'your_database'
    AND DATA_FREE > 0
ORDER BY fragmentation_percent DESC;

2. 索引维护脚本

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#!/bin/bash
# MySQL 索引维护脚本

MYSQL_USER="your_user"
MYSQL_PASS="your_pass"
MYSQL_HOST="localhost"
MYSQL_DB="your_database"

# 分析表和索引
analyze_tables() {
    echo "开始分析表和索引..."
    mysql -u${MYSQL_USER} -p${MYSQL_PASS} -h${MYSQL_HOST} ${MYSQL_DB} -e "
    SELECT CONCAT('ANALYZE TABLE ', TABLE_SCHEMA, '.', TABLE_NAME, ';') as analyze_stmt
    FROM information_schema.TABLES 
    WHERE TABLE_SCHEMA = '${MYSQL_DB}' 
    AND TABLE_TYPE = 'BASE TABLE';" | grep ANALYZE | mysql -u${MYSQL_USER} -p${MYSQL_PASS} -h${MYSQL_HOST} ${MYSQL_DB}
}

# 优化表
optimize_tables() {
    echo "开始优化表..."
    mysql -u${MYSQL_USER} -p${MYSQL_PASS} -h${MYSQL_HOST} ${MYSQL_DB} -e "
    SELECT CONCAT('OPTIMIZE TABLE ', TABLE_SCHEMA, '.', TABLE_NAME, ';') as optimize_stmt
    FROM information_schema.TABLES 
    WHERE TABLE_SCHEMA = '${MYSQL_DB}' 
    AND TABLE_TYPE = 'BASE TABLE'
    AND DATA_FREE > 1024*1024;" | grep OPTIMIZE | mysql -u${MYSQL_USER} -p${MYSQL_PASS} -h${MYSQL_HOST} ${MYSQL_DB}
}

# 检查重复索引
check_duplicate_indexes() {
    echo "检查重复索引..."
    mysql -u${MYSQL_USER} -p${MYSQL_PASS} -h${MYSQL_HOST} ${MYSQL_DB} -e "
    SELECT 
        TABLE_NAME,
        GROUP_CONCAT(INDEX_NAME) as duplicate_indexes,
        GROUP_CONCAT(COLUMN_NAME ORDER BY SEQ_IN_INDEX) as columns
    FROM information_schema.STATISTICS 
    WHERE TABLE_SCHEMA = '${MYSQL_DB}'
    GROUP BY TABLE_NAME, GROUP_CONCAT(COLUMN_NAME ORDER BY SEQ_IN_INDEX)
    HAVING COUNT(*) > 1;"
}

# 主函数
main() {
    echo "=== MySQL 索引维护开始 $(date) ==="
    analyze_tables
    optimize_tables
    check_duplicate_indexes
    echo "=== MySQL 索引维护完成 $(date) ==="
}

main

实战案例分析

1. 慢查询优化案例

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-- 原始慢查询
SELECT 
    u.username,
    COUNT(o.id) as order_count,
    SUM(o.amount) as total_amount
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at >= '2023-01-01'
    AND o.status = 'completed'
GROUP BY u.id, u.username
HAVING total_amount > 1000
ORDER BY total_amount DESC
LIMIT 20;

-- 分析查询执行计划
EXPLAIN FORMAT=JSON [上述查询];

-- 优化步骤1：创建合适的索引
CREATE INDEX idx_users_created ON users(created_at);
CREATE INDEX idx_orders_status_user ON orders(status, user_id, amount);

-- 优化步骤2：重写查询
SELECT 
    u.username,
    o.order_count,
    o.total_amount
FROM users u
JOIN (
    SELECT 
        user_id,
        COUNT(*) as order_count,
        SUM(amount) as total_amount
    FROM orders 
    WHERE status = 'completed'
    GROUP BY user_id
    HAVING total_amount > 1000
) o ON u.id = o.user_id
WHERE u.created_at >= '2023-01-01'
ORDER BY o.total_amount DESC
LIMIT 20;

2. 大表索引优化

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-- 大表索引创建策略
-- 1. 在线添加索引（MySQL 5.7+）
ALTER TABLE large_table ADD INDEX idx_new_column(column_name), ALGORITHM=INPLACE, LOCK=NONE;

-- 2. 分批创建索引（避免锁表）
-- 使用 pt-online-schema-change 工具
-- pt-online-schema-change --alter "ADD INDEX idx_new(column_name)" D=database,t=large_table --execute

-- 3. 监控索引创建进度
SELECT 
    PROCESSLIST_ID,
    PROCESSLIST_STATE,
    PROCESSLIST_INFO,
    PROCESSLIST_TIME
FROM performance_schema.threads 
WHERE PROCESSLIST_INFO LIKE '%ADD INDEX%';

性能测试和基准

1. 索引性能测试

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-- 创建测试数据
DELIMITER //
CREATE PROCEDURE generate_test_data(IN num_rows INT)
BEGIN
    DECLARE i INT DEFAULT 1;
    DECLARE batch_size INT DEFAULT 1000;
    
    WHILE i <= num_rows DO
        INSERT INTO orders (user_id, status, order_date, amount)
        SELECT 
            FLOOR(1 + RAND() * 1000) as user_id,
            ELT(FLOOR(1 + RAND() * 4), 'pending', 'completed', 'cancelled', 'shipped') as status,
            DATE_ADD('2023-01-01', INTERVAL FLOOR(RAND() * 365) DAY) as order_date,
            ROUND(RAND() * 1000, 2) as amount
        FROM 
            (SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5) t1,
            (SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5) t2,
            (SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5) t3,
            (SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION SELECT 5) t4
        LIMIT batch_size;
        
        SET i = i + batch_size;
    END WHILE;
END //
DELIMITER ;

-- 生成测试数据
CALL generate_test_data(100000);

-- 性能测试脚本
-- 测试无索引查询
SET @start_time = NOW(6);
SELECT COUNT(*) FROM orders WHERE user_id = 500 AND status = 'completed';
SET @end_time = NOW(6);
SELECT TIMESTAMPDIFF(MICROSECOND, @start_time, @end_time) as execution_time_microseconds;

-- 创建索引后测试
CREATE INDEX idx_user_status ON orders(user_id, status);

SET @start_time = NOW(6);
SELECT COUNT(*) FROM orders WHERE user_id = 500 AND status = 'completed';
SET @end_time = NOW(6);
SELECT TIMESTAMPDIFF(MICROSECOND, @start_time, @end_time) as execution_time_microseconds;

2. 基准测试工具

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#!/bin/bash
# 使用 sysbench 进行索引性能测试

# 准备测试数据
sysbench oltp_read_write \
    --mysql-host=localhost \
    --mysql-port=3306 \
    --mysql-user=test_user \
    --mysql-password=test_pass \
    --mysql-db=test_db \
    --tables=1 \
    --table-size=100000 \
    prepare

# 测试读性能
sysbench oltp_read_only \
    --mysql-host=localhost \
    --mysql-port=3306 \
    --mysql-user=test_user \
    --mysql-password=test_pass \
    --mysql-db=test_db \
    --tables=1 \
    --table-size=100000 \
    --threads=16 \
    --time=60 \
    --report-interval=10 \
    run

# 清理测试数据
sysbench oltp_read_write \
    --mysql-host=localhost \
    --mysql-port=3306 \
    --mysql-user=test_user \
    --mysql-password=test_pass \
    --mysql-db=test_db \
    --tables=1 \
    cleanup

最佳实践总结

1. 索引设计原则

  1. 选择性原则: 高选择性的列适合建索引
  2. 最左前缀原则: 复合索引遵循最左前缀匹配
  3. 覆盖索引原则: 尽量使用覆盖索引避免回表
  4. 适度原则: 不要过度创建索引，影响写性能

2. 索引维护策略

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-- 定期维护索引的SQL脚本
-- 1. 分析表统计信息
ANALYZE TABLE your_table;

-- 2. 优化表碎片
OPTIMIZE TABLE your_table;

-- 3. 检查索引使用情况
SELECT 
    OBJECT_NAME,
    INDEX_NAME,
    COUNT_FETCH,
    COUNT_INSERT,
    COUNT_UPDATE,
    COUNT_DELETE
FROM performance_schema.table_io_waits_summary_by_index_usage
WHERE OBJECT_SCHEMA = 'your_database'
ORDER BY COUNT_FETCH DESC;

-- 4. 删除未使用的索引
-- DROP INDEX unused_index_name ON table_name;

3. 监控告警

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# Python 索引监控脚本
import pymysql
import json
from datetime import datetime

class IndexMonitor:
    def __init__(self, db_config):
        self.db_config = db_config
    
    def get_connection(self):
        return pymysql.connect(**self.db_config)
    
    def check_index_usage(self):
        """检查索引使用情况"""
        conn = self.get_connection()
        try:
            with conn.cursor(pymysql.cursors.DictCursor) as cursor:
                sql = """
                SELECT 
                    OBJECT_NAME as table_name,
                    INDEX_NAME as index_name,
                    COUNT_FETCH as fetch_count,
                    COUNT_INSERT as insert_count,
                    COUNT_UPDATE as update_count,
                    COUNT_DELETE as delete_count
                FROM performance_schema.table_io_waits_summary_by_index_usage
                WHERE OBJECT_SCHEMA = %s
                    AND COUNT_FETCH = 0
                    AND INDEX_NAME != 'PRIMARY'
                ORDER BY OBJECT_NAME, INDEX_NAME
                """
                cursor.execute(sql, (self.db_config['database'],))
                return cursor.fetchall()
        finally:
            conn.close()
    
    def check_table_fragmentation(self):
        """检查表碎片情况"""
        conn = self.get_connection()
        try:
            with conn.cursor(pymysql.cursors.DictCursor) as cursor:
                sql = """
                SELECT 
                    TABLE_NAME,
                    DATA_LENGTH,
                    INDEX_LENGTH,
                    DATA_FREE,
                    ROUND((DATA_FREE / (DATA_LENGTH + INDEX_LENGTH)) * 100, 2) as fragmentation_percent
                FROM information_schema.TABLES 
                WHERE TABLE_SCHEMA = %s
                    AND DATA_FREE > 0
                    AND (DATA_LENGTH + INDEX_LENGTH) > 0
                ORDER BY fragmentation_percent DESC
                """
                cursor.execute(sql, (self.db_config['database'],))
                return cursor.fetchall()
        finally:
            conn.close()
    
    def generate_report(self):
        """生成监控报告"""
        unused_indexes = self.check_index_usage()
        fragmented_tables = self.check_table_fragmentation()
        
        report = {
            'timestamp': datetime.now().isoformat(),
            'unused_indexes': unused_indexes,
            'fragmented_tables': fragmented_tables,
            'recommendations': []
        }
        
        # 生成建议
        if unused_indexes:
            report['recommendations'].append("发现未使用的索引，建议考虑删除")
        
        if fragmented_tables:
            high_frag_tables = [t for t in fragmented_tables if t['fragmentation_percent'] > 10]
            if high_frag_tables:
                report['recommendations'].append("发现高碎片表，建议执行 OPTIMIZE TABLE")
        
        return report

# 使用示例
if __name__ == "__main__":
    db_config = {
        'host': 'localhost',
        'user': 'monitor_user',
        'password': 'monitor_pass',
        'database': 'your_database',
        'charset': 'utf8mb4'
    }
    
    monitor = IndexMonitor(db_config)
    report = monitor.generate_report()
    
    print(json.dumps(report, indent=2, ensure_ascii=False))

通过以上索引优化技巧和监控策略，可以显著提升MySQL数据库的查询性能，确保数据库在高并发场景下的稳定运行。记住，索引优化是一个持续的过程，需要根据业务变化和数据增长不断调整优化策略。

  1. 索引基础理论
    1. 1. 索引类型详解
    2. 2. 索引存储结构分析
  2. 索引设计策略
    1. 1. 复合索引设计原则
    2. 2. 索引选择性分析
  3. 查询优化技巧
    1. 1. EXPLAIN 分析详解
    2. 2. 索引覆盖查询优化
  4. 高级索引优化
    1. 1. 分区表索引优化
    2. 2. MySQL 8.4 新特性
  5. 索引监控和维护
    1. 1. 索引使用情况监控
    2. 2. 索引维护脚本
  6. 实战案例分析
    1. 1. 慢查询优化案例
    2. 2. 大表索引优化
  7. 性能测试和基准
    1. 1. 索引性能测试
    2. 2. 基准测试工具
  8. 最佳实践总结
    1. 1. 索引设计原则
    2. 2. 索引维护策略
    3. 3. 监控告警
© 2025    Orion K
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  1. 索引基础理论
    1. 1. 索引类型详解
    2. 2. 索引存储结构分析
  2. 索引设计策略
    1. 1. 复合索引设计原则
    2. 2. 索引选择性分析
  3. 查询优化技巧
    1. 1. EXPLAIN 分析详解
    2. 2. 索引覆盖查询优化
  4. 高级索引优化
    1. 1. 分区表索引优化
    2. 2. MySQL 8.4 新特性
  5. 索引监控和维护
    1. 1. 索引使用情况监控
    2. 2. 索引维护脚本
  6. 实战案例分析
    1. 1. 慢查询优化案例
    2. 2. 大表索引优化
  7. 性能测试和基准
    1. 1. 索引性能测试
    2. 2. 基准测试工具
  8. 最佳实践总结
    1. 1. 索引设计原则
    2. 2. 索引维护策略
    3. 3. 监控告警