SpringBoot原生实现分布式MapReduce计算（无第三方中间件版）

一、架构设计调整

核心组件替换方案：

1. 注册中心 → 数据库注册表
2. 任务队列 → 数据库任务表
3. 分布式锁 → 数据库行级锁
4. 节点通信 → HTTP REST接口

二、数据库表结构设计

-- 节点注册表
CREATE TABLE compute_nodes (
    node_id VARCHAR(36) PRIMARY KEY,
    last_heartbeat TIMESTAMP,
    status ENUM('ACTIVE','DOWN')
);

-- 任务分片表
CREATE TABLE task_shards (
    shard_id INT AUTO_INCREMENT PRIMARY KEY,
    data_range VARCHAR(100), -- 例如：1-10000
    status ENUM('PENDING','PROCESSING','COMPLETED'),
    locked_by VARCHAR(36),
    locked_at TIMESTAMP
);

三、核心实现代码

1. 节点自注册实现

@Scheduled(fixedRate = 3000)
public void nodeRegistration() {
    jdbcTemplate.update(
        "INSERT INTO compute_nodes VALUES (?, NOW(), 'ACTIVE') " +
        "ON DUPLICATE KEY UPDATE last_heartbeat = NOW()", 
        nodeId
    );
    
    // 清理过期节点
    jdbcTemplate.update(
        "DELETE FROM compute_nodes WHERE last_heartbeat < ?",
        LocalDateTime.now().minusSeconds(10)
    );
}

2. 任务分片抢占式调度

@Scheduled(fixedDelay = 1000)
public void acquireTasks() {
    List<Long> shardIds = jdbcTemplate.queryForList(
        "SELECT shard_id FROM task_shards " +
        "WHERE status = 'PENDING' " +
        "ORDER BY shard_id LIMIT 5 FOR UPDATE SKIP LOCKED", 
        Long.class
    );
    
    shardIds.forEach(shardId -> {
        int updated = jdbcTemplate.update(
            "UPDATE task_shards SET status = 'PROCESSING', " +
            "locked_by = ?, locked_at = NOW() " +
            "WHERE shard_id = ? AND status = 'PENDING'",
            nodeId, shardId
        );
        if(updated > 0) processShard(shardId);
    });
}

3. Map阶段分布式处理

public void processShard(Long shardId) {
    try {
        DataRange range = getDataRange(shardId);
        List<Record> records = fetchData(range);
        
        Map<String, Double> partialResult = records.parallelStream()
            .collect(Collectors.groupingBy(
                Record::getCategory,
                Collectors.summingDouble(Record::getAmount)
            ));
        
        saveResult(shardId, partialResult);
        markShardCompleted(shardId);
    } catch (Exception e) {
        releaseShard(shardId);
    }
}

4. Reduce阶段聚合实现

public Map<String, Double> reduceAllResults() {
    return jdbcTemplate.query(
        "SELECT category, SUM(amount) AS total " +
        "FROM map_results GROUP BY category",
        (rs, rowNum) -> new AbstractMap.SimpleEntry<>(
            rs.getString("category"),
            rs.getDouble("total")
        )).stream().collect(Collectors.toMap(
            Entry::getKey, Entry::getValue
        ));
}

四、关键优化点

1. 分片锁优化策略

// 使用乐观锁避免长时间占用连接
public boolean tryLockShard(Long shardId) {
    return jdbcTemplate.update(
        "UPDATE task_shards SET version = version + 1 " +
        "WHERE shard_id = ? AND version = ?",
        shardId, currentVersion) > 0;
}

2. 结果缓存优化

@Cacheable(value = "partialResults", key = "#shardId")
public Map<String, Double> getPartialResult(Long shardId) {
    return jdbcTemplate.query(...);
}

// 配置类启用缓存
@Configuration
@EnableCaching
public class CacheConfig {
    @Bean
    public CacheManager cacheManager() {
        return new ConcurrentMapCacheManager();
    }
}

3. 分布式事务处理

@Transactional(propagation = Propagation.REQUIRES_NEW)
public void markShardCompleted(Long shardId) {
    jdbcTemplate.update(
        "UPDATE task_shards SET status = 'COMPLETED' " +
        "WHERE shard_id = ?", shardId);
    
    eventPublisher.publishEvent(
        new ShardCompleteEvent(shardId));
}

五、部署架构对比

六、性能压测数据

测试环境：

• 3台2核4G云主机
• MySQL 8.0 独立实例
• 100万测试数据集

结果对比：

七、生产级改进建议

1. 分片策略优化

// 采用跳跃哈希算法避免热点
public List<Long> assignShards(int totalShards) {
    return IntStream.range(0, totalShards)
        .mapToObj(i -> (nodeHash + i*2654435761L) % totalShards)
        .collect(Collectors.toList());
}

2. 动态分片扩容

@Scheduled(fixedRate = 60000)
public void autoReshard() {
    int currentShards = getCurrentShardCount();
    int required = calculateRequiredShards();
    
    if(required > currentShards) {
        jdbcTemplate.execute("ALTER TABLE task_shards AUTO_INCREMENT = " + required);
    }
}

3. 结果校验机制

public void validateResults() {
    jdbcTemplate.query("SELECT shard_id FROM task_shards WHERE status = 'COMPLETED'", 
        rs -> {
            Long shardId = rs.getLong(1);
            if(!resultCache.contains(shardId)) {
                repairShard(shardId);
            }
        });
}

该方案完全基于SpringBoot原生能力实现，通过关系型数据库+定时任务调度机制，在保持系统简洁性的同时满足基本分布式计算需求。适合中小规模（日处理千万级以下）的离线计算场景，如需更高性能建议仍考虑引入专业分布式计算框架。