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Spring Boot + MySQL: 多线程查询与联表查询性能对比分析

背景

在现代 Web 应用开发中，数据库性能是影响系统响应时间和用户体验的关键因素之一。随着业务需求的不断增长，单表查询和联表查询的效率问题日益凸显。特别是在 Spring Boot 项目中，结合 MySQL 数据库进行复杂查询时，如何优化查询性能已成为开发者必须面对的重要问题。

在本实验中，我们使用了 Spring Boot 框架结合 MySQL 数据库，进行了两种常见查询方式的性能对比：多线程查询 和 联表查询。通过对比这两种查询方式的响应时间，本文旨在探讨在实际业务场景中，选择哪种方式能带来更高的查询效率，尤其是在面对大数据量和复杂查询时的性能表现。

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实验目的

本实验的主要目的是通过对比以下两种查询方式的性能，帮助开发者选择在不同业务场景下的查询方式：

1. 联表查询（使用 SQL 语句中的 LEFT JOIN 等连接操作）
2. 多线程查询（通过 Spring Boot 异步处理，分批查询不同表的数据）

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实验环境

• 开发框架：Spring Boot

• 数据库：MySQL

• 数据库表结构：

  • test_a：主表，包含与其他表（test_b、test_c、test_d、test_e）的关联字段。
  • test_b、test_c、test_d、test_e：附表，分别包含不同的数据字段。

  这些表通过外键(逻辑)关联，test_a 表中的 test_b_id、test_c_id、test_d_id 和 test_e_id 字段指向各自的附表。

• 数据量：约 100,000 条数据，分别在主表和附表中填充数据。

一.建表语句

主表A

CREATE TABLE `test_a` (`id` int NOT NULL AUTO_INCREMENT,`name` varchar(255) NOT NULL,`description` varchar(255) DEFAULT NULL,`test_b_id` int DEFAULT NULL,`test_c_id` int DEFAULT NULL,`test_d_id` int DEFAULT NULL,`test_e_id` int DEFAULT NULL,`created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,`updated_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=6 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;

附表b,c,d,e

CREATE TABLE `test_b` (`id` int NOT NULL AUTO_INCREMENT,`field_b1` varchar(255) DEFAULT NULL,`field_b2` int DEFAULT NULL,`created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=792843462 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;CREATE TABLE `test_c` (`id` int NOT NULL AUTO_INCREMENT,`field_c1` varchar(255) DEFAULT NULL,`field_c2` datetime DEFAULT NULL,`created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=100096 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;CREATE TABLE `test_d` (`id` int NOT NULL AUTO_INCREMENT,`field_d1` text,`field_d2` tinyint(1) DEFAULT NULL,`created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=100300 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;CREATE TABLE `test_e` (`id` int NOT NULL AUTO_INCREMENT,`field_e1` int DEFAULT NULL,`field_e2` varchar(255) DEFAULT NULL,`created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=100444 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;

二.填充数据

@SpringBootTest
class DemoTestQuerySpringbootApplicationTests {@Autowiredprivate TestAMapper testAMapper;@Autowiredprivate TestBMapper testBMapper;@Autowiredprivate TestCMapper testCMapper;@Autowiredprivate TestDMapper testDMapper;@Autowiredprivate TestEMapper testEMapper;@Testvoid contextLoads() {// 随机数生成器Random random = new Random();for (int i = 1; i <= 100000; i++) {// 插入 test_b 数据int testBId = insertTestB(random);// 插入 test_c 数据int testCId = insertTestC(random);// 插入 test_d 数据int testDId = insertTestD(random);// 插入 test_e 数据int testEId = insertTestE(random);// 插入 test_a 数据insertTestA(testBId, testCId, testDId, testEId, random);}}private int insertTestB(Random random) {TestB testB = new TestB();testB.setFieldB1("B Field " + random.nextInt(1000));testB.setFieldB2(random.nextInt(1000));testBMapper.insert(testB);  // 插入数据return testB.getId();  }private int insertTestC(Random random) {TestC testC = new TestC();testC.setFieldC1("C Field " + random.nextInt(1000));testC.setFieldC2(new java.sql.Timestamp(System.currentTimeMillis()));testCMapper.insert(testC);  // 插入数据return testC.getId();  }private int insertTestD(Random random) {TestD testD = new TestD();testD.setFieldD1("D Field " + random.nextInt(1000));testD.setFieldD2(random.nextBoolean());testDMapper.insert(testD);  // 插入数据return testD.getId();  }private int insertTestE(Random random) {TestE testE = new TestE();testE.setFieldE1(random.nextInt(1000));testE.setFieldE2("E Field " + random.nextInt(1000));testEMapper.insert(testE);  // 插入数据return testE.getId();  }private void insertTestA(int testBId, int testCId, int testDId, int testEId, Random random) {TestA testA = new TestA();testA.setName("Test A Name " + random.nextInt(1000));testA.setDescription("Test A Description " + random.nextInt(1000));testA.setTestBId(testBId);testA.setTestCId(testCId);testA.setTestDId(testDId);testA.setTestEId(testEId);testAMapper.insert(testA);  // 插入数据}}

三.配置线程池

3.1配置

/*** 实现AsyncConfigurer接口* 并重写了 getAsyncExecutor方法，* 这个方法返回 myExecutor()，* Spring 默认会将 myExecutor 作为 @Async 方法的线程池。*/
@Configuration
@EnableAsync
public class ThreadPoolConfig implements AsyncConfigurer {/*** 项目共用线程池*/public static final String TEST_QUERY = "testQuery";@Overridepublic Executor getAsyncExecutor() {return myExecutor();}@Bean(TEST_QUERY)@Primarypublic ThreadPoolTaskExecutor myExecutor() {//spring的线程池ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();//线程池优雅停机的关键executor.setWaitForTasksToCompleteOnShutdown(true);executor.setCorePoolSize(10);executor.setMaxPoolSize(10);executor.setQueueCapacity(200);executor.setThreadNamePrefix("my-executor-");//拒绝策略->满了调用线程执行，认为重要任务executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());//自己就是一个线程工程executor.setThreadFactory(new MyThreadFactory(executor));executor.initialize();return executor;}}

3.2异常处理

public class MyUncaughtExceptionHandler implements Thread.UncaughtExceptionHandler {private static final Logger log = LoggerFactory.getLogger(MyUncaughtExceptionHandler.class);@Overridepublic void uncaughtException(Thread t, Throwable e) {log.error("Exception in thread",e);}
}

3.3线程工厂

@AllArgsConstructor
public class MyThreadFactory implements ThreadFactory {private static final MyUncaughtExceptionHandler MyUncaughtExceptionHandler = new MyUncaughtExceptionHandler();private ThreadFactory original;@Overridepublic Thread newThread(Runnable r) {//执行Spring线程自己的创建逻辑Thread thread = original.newThread(r);//我们自己额外的逻辑thread.setUncaughtExceptionHandler(MyUncaughtExceptionHandler);return thread;}
}

四.Service查询方法

4.1left join连接查询

    @Overridepublic IPage<TestAll> getTestAllPage_1(int current, int size) {// 创建 Page 对象，current 为当前页，size 为每页大小Page<TestAll> page = new Page<>(current, size);return testAMapper.selectAllWithPage(page);}

对应的xml 的sql语句

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="org.fth.demotestqueryspringboot.com.test.mapper.TestAMapper"><!-- 基本的 ResultMap 映射 --><resultMap id="BaseResultMap" type="org.fth.demotestqueryspringboot.com.test.entity.vo.TestAll"><id column="test_a_id" jdbcType="INTEGER" property="testAId" /><result column="name" jdbcType="VARCHAR" property="name" /><result column="description" jdbcType="VARCHAR" property="description" /><result column="test_b_id" jdbcType="INTEGER" property="testBId" /><result column="test_c_id" jdbcType="INTEGER" property="testCId" /><result column="test_d_id" jdbcType="INTEGER" property="testDId" /><result column="test_e_id" jdbcType="INTEGER" property="testEId" /><result column="created_at" jdbcType="TIMESTAMP" property="createdAt" /><result column="updated_at" jdbcType="TIMESTAMP" property="updatedAt" /><!-- TestB --><result column="field_b1" jdbcType="VARCHAR" property="fieldB1" /><result column="field_b2" jdbcType="INTEGER" property="fieldB2" /><result column="test_b_created_at" jdbcType="TIMESTAMP" property="testBCreatedAt" /><!-- TestC --><result column="field_c1" jdbcType="VARCHAR" property="fieldC1" /><result column="field_c2" jdbcType="TIMESTAMP" property="fieldC2" /><result column="test_c_created_at" jdbcType="TIMESTAMP" property="testCCreatedAt" /><!-- TestD --><result column="field_d1" jdbcType="VARCHAR" property="fieldD1" /><result column="field_d2" jdbcType="BOOLEAN" property="fieldD2" /><result column="test_d_created_at" jdbcType="TIMESTAMP" property="testDCreatedAt" /><!-- TestE --><result column="field_e1" jdbcType="INTEGER" property="fieldE1" /><result column="field_e2" jdbcType="VARCHAR" property="fieldE2" /><result column="test_e_created_at" jdbcType="TIMESTAMP" property="testECreatedAt" /></resultMap><!-- 分页查询 TestA 和其他表的数据 --><select id="selectAllWithPage" resultMap="BaseResultMap">SELECTa.id AS test_a_id,a.name,a.description,a.test_b_id,a.test_c_id,a.test_d_id,a.test_e_id,a.created_at,a.updated_at,-- TestBb.field_b1,b.field_b2,b.created_at AS test_b_created_at,-- TestCc.field_c1,c.field_c2,c.created_at AS test_c_created_at,-- TestDd.field_d1,d.field_d2,d.created_at AS test_d_created_at,-- TestEe.field_e1,e.field_e2,e.created_at AS test_e_created_atFROM test_a aLEFT JOIN test_b b ON a.test_b_id = b.idLEFT JOIN test_c c ON a.test_c_id = c.idLEFT JOIN test_d d ON a.test_d_id = d.idLEFT JOIN test_e e ON a.test_e_id = e.id</select></mapper>

4.2多线程查询

 @Overridepublic IPage<TestAll> getTestAllPage_2(int current, int size) {IPage<TestA> testAPage = testAMapper.selectPage(new Page<>(current, size), null);List<TestA> testAS = testAPage.getRecords();CompletableFuture<List<TestB>> futureBs = selectTestBids(testAS.stream().map(TestA::getTestBId).collect(Collectors.toSet()));CompletableFuture<List<TestC>> futureCs = selectTestCids(testAS.stream().map(TestA::getTestCId).collect(Collectors.toSet()));CompletableFuture<List<TestD>> futureDs = selectTestDids(testAS.stream().map(TestA::getTestDId).collect(Collectors.toSet()));CompletableFuture<List<TestE>> futureEs = selectTestEids(testAS.stream().map(TestA::getTestEId).collect(Collectors.toSet()));// 等待所有异步任务完成并收集结果CompletableFuture<Void> allFutures = CompletableFuture.allOf(futureBs, futureCs, futureDs, futureEs);try {// 等待所有异步任务完成allFutures.get();} catch (InterruptedException | ExecutionException e) {e.printStackTrace();throw new RuntimeException("Failed to fetch data", e);}// 获取异步查询的结果List<TestB> bs = futureBs.join();List<TestC> cs = futureCs.join();List<TestD> ds = futureDs.join();List<TestE> es = futureEs.join();// 将结果映射到Map以便快速查找Map<Integer, TestB> bMap = bs.stream().collect(Collectors.toMap(TestB::getId, b -> b));Map<Integer, TestC> cMap = cs.stream().collect(Collectors.toMap(TestC::getId, c -> c));Map<Integer, TestD> dMap = ds.stream().collect(Collectors.toMap(TestD::getId, d -> d));Map<Integer, TestE> eMap = es.stream().collect(Collectors.toMap(TestE::getId, e -> e));List<TestAll> testAllList = testAS.stream().map(testA -> {TestAll testAll = new TestAll();testAll.setTestAId(testA.getId());testAll.setName(testA.getName());testAll.setDescription(testA.getDescription());testAll.setCreatedAt(testA.getCreatedAt());// 根据 testBId 填充 TestB 的字段if (testA.getTestBId() != null) {TestB testB = bMap.get(testA.getTestBId());if (testB != null) {testAll.setFieldB1(testB.getFieldB1());testAll.setFieldB2(testB.getFieldB2());testAll.setTestBCreatedAt(testB.getCreatedAt());}}// 根据 testCId 填充 TestC 的字段if (testA.getTestCId() != null) {TestC testC = cMap.get(testA.getTestCId());if (testC != null) {testAll.setFieldC1(testC.getFieldC1());testAll.setFieldC2(testC.getFieldC2());testAll.setTestCCreatedAt(testC.getCreatedAt());}}// 根据 testDId 填充 TestD 的字段if (testA.getTestDId() != null) {TestD testD = dMap.get(testA.getTestDId());if (testD != null) {testAll.setFieldD1(testD.getFieldD1());testAll.setFieldD2(testD.getFieldD2());testAll.setTestDCreatedAt(testD.getCreatedAt());}}// 根据 testEId 填充 TestE 的字段if (testA.getTestEId() != null) {TestE testE = eMap.get(testA.getTestEId());if (testE != null) {testAll.setFieldE1(testE.getFieldE1());testAll.setFieldE2(testE.getFieldE2());testAll.setTestECreatedAt(testE.getCreatedAt());}}return testAll;}).collect(Collectors.toList());// 创建并返回新的分页对象IPage<TestAll> page = new Page<>(testAPage.getCurrent(), testAPage.getSize(), testAPage.getTotal());page.setRecords(testAllList);return page;}@Asyncpublic CompletableFuture<List<TestB>> selectTestBids(Set<Integer> bids) {return CompletableFuture.supplyAsync(() -> testBMapper.selectBatchIds(bids));}@Asyncpublic CompletableFuture<List<TestC>> selectTestCids(Set<Integer> cids) {return CompletableFuture.supplyAsync(() -> testCMapper.selectBatchIds(cids));}@Asyncpublic CompletableFuture<List<TestD>> selectTestDids(Set<Integer> dids) {return CompletableFuture.supplyAsync(() -> testDMapper.selectBatchIds(dids));}@Asyncpublic CompletableFuture<List<TestE>> selectTestEids(Set<Integer> eids) {return CompletableFuture.supplyAsync(() -> testEMapper.selectBatchIds(eids));}

五.结果测试

5.1连接查询

查询结果表格

current	size	响应时间
1	20	16ms
50	20	23ms
100	20	22ms
500	20	52ms
200	200	213ms
500	200	517ms

5.2多线程查询

查询结果表格

current	size	响应时间
1	20	18ms
50	20	17ms
100	20	17ms
500	20	21ms
200	200	56ms
500	200	80ms

总结与建议

• 选择联表查询：当数据量较小，或者查询逻辑较为简单时，使用联表查询可以更简单直接，查询性能也较为优秀。
• 选择多线程查询：当面对大数据量或者复杂查询时，采用多线程查询将带来更显著的性能提升。通过异步并行查询，可以有效缩短响应时间，提升系统的整体性能。

在实际开发中，可以根据具体的业务需求和数据库的规模，合理选择查询方式，从而提高数据库查询效率，优化系统性能