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 {

    @Autowired
    private TestAMapper testAMapper;
    @Autowired
    private TestBMapper testBMapper;
    @Autowired
    private TestCMapper testCMapper;
    @Autowired
    private TestDMapper testDMapper;
    @Autowired
    private TestEMapper testEMapper;

    @Test
    void 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";


    @Override
    public Executor getAsyncExecutor() {
        return myExecutor();
    }

    @Bean(TEST_QUERY)
    @Primary
    public 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);

    @Override
    public 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;

    @Override
    public 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

    @Override
    public 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">
    SELECT
    a.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,
    -- TestB
    b.field_b1,
    b.field_b2,
    b.created_at AS test_b_created_at,
    -- TestC
    c.field_c1,
    c.field_c2,
    c.created_at AS test_c_created_at,
    -- TestD
    d.field_d1,
    d.field_d2,
    d.created_at AS test_d_created_at,
    -- TestE
    e.field_e1,
    e.field_e2,
    e.created_at AS test_e_created_at
    FROM test_a a
    LEFT JOIN test_b b ON a.test_b_id = b.id
    LEFT JOIN test_c c ON a.test_c_id = c.id
    LEFT JOIN test_d d ON a.test_d_id = d.id
    LEFT JOIN test_e e ON a.test_e_id = e.id

  </select>

</mapper>

4.2多线程查询

 @Override
    public 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;
    }


    @Async
    public CompletableFuture<List<TestB>> selectTestBids(Set<Integer> bids) {
        return CompletableFuture.supplyAsync(() -> testBMapper.selectBatchIds(bids));
    }

    @Async
    public CompletableFuture<List<TestC>> selectTestCids(Set<Integer> cids) {
        return CompletableFuture.supplyAsync(() -> testCMapper.selectBatchIds(cids));
    }

    @Async
    public CompletableFuture<List<TestD>> selectTestDids(Set<Integer> dids) {
        return CompletableFuture.supplyAsync(() -> testDMapper.selectBatchIds(dids));
    }

    @Async
    public 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