后端下载限速（redis记录实时并发，bucket4j动态限速）

• ✅ 使用 Redis 记录 所有用户的实时并发下载数
• ✅ 使用 Bucket4j 实现 全局下载速率限制（动态）
• ✅ 支持 动态调整限速策略
• ✅ 下载接口安全、稳定、可监控

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🧩 整体架构概览

模块	功能
Redis	存储全局并发数和带宽令牌桶状态
Bucket4j + Redis	分布式限速器（基于令牌桶算法）
Spring Boot Web	提供文件下载接口
AOP / Interceptor（可选）	用于统一处理限流逻辑

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📦 1. Maven 依赖（pom.xml）

<dependencies><!-- Spring Boot --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId></dependency><!-- Redis 连接池 --><dependency><groupId>io.lettuce.core</groupId><artifactId>lettuce-core</artifactId></dependency><!-- Bucket4j 核心与 Redis 集成 --><dependency><groupId>com.github.vladimir-bukhtoyarov</groupId><artifactId>bucket4j-core</artifactId><version>5.3.0</version></dependency><dependency><groupId>com.github.vladimir-bukhtoyarov</groupId><artifactId>bucket4j-redis</artifactId><version>5.3.0</version></dependency></dependencies>

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🛠️ 2. Redis 工具类：记录全局并发数

import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.data.redis.core.script.DefaultRedisScript;
import org.springframework.stereotype.Component;import java.util.Collections;
import java.util.concurrent.TimeUnit;@Component
public class GlobalDownloadCounter {private final StringRedisTemplate redisTemplate;private final DefaultRedisScript<Long> incrScript;private final DefaultRedisScript<Long> decrScript;public static final String KEY_CONCURRENT = "global:download:concurrent";private static final long TTL_SECONDS = 60; // 自动清理僵尸计数public GlobalDownloadCounter(StringRedisTemplate redisTemplate) {this.redisTemplate = redisTemplate;// Lua 脚本：原子增加并发数并设置过期时间String scriptIncr = """local key = KEYS[1]local ttl = tonumber(ARGV[1])local count = redis.call('GET', key)if not count thenredis.call('SET', key, 1)redis.call('EXPIRE', key, ttl)return 1elsecount = tonumber(count) + 1redis.call('SET', key, count)redis.call('EXPIRE', key, ttl)return countend""";incrScript = new DefaultRedisScript<>(scriptIncr, Long.class);// Lua 脚本：原子减少并发数String scriptDecr = """local key = KEYS[1]local count = redis.call('GET', key)if not count or tonumber(count) <= 0 thenreturn 0elsecount = tonumber(count) - 1redis.call('SET', key, count)return countend""";decrScript = new DefaultRedisScript<>(scriptDecr, Long.class);}public long increment() {return redisTemplate.execute(incrScript, Collections.singletonList(KEY_CONCURRENT), TTL_SECONDS).longValue();}public long decrement() {return redisTemplate.execute(decrScript, Collections.singletonList(KEY_CONCURRENT)).longValue();}public long getCurrentCount() {String value = redisTemplate.opsForValue().get(KEY_CONCURRENT);return value == null ? 0 : Long.parseLong(value);}
}

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⚙️ 3. Bucket4j 配置：分布式限速器（带 Redis）

import io.github.bucket4j.Bandwidth;
import io.github.bucket4j.Refill;
import io.github.bucket4j.distributed.proxy.ProxyManager;
import io.github.bucket4j.distributed.proxy.RedisProxyManager;
import io.github.bucket4j.redis.lettuce.cas.LettuceReactiveProxyManager;
import io.lettuce.core.RedisClient;
import io.lettuce.core.api.StatefulRedisConnection;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;import java.time.Duration;@Configuration
public class BandwidthLimiterConfig {@Beanpublic RedisClient redisClient() {return RedisClient.create("redis://localhost:6379");}@Beanpublic StatefulRedisConnection<String, String> redisConnection(RedisClient redisClient) {return redisClient.connect();}@Beanpublic ProxyManager<String> proxyManager(StatefulRedisConnection<String, String> connection) {return LettuceReactiveProxyManager.builder().build(connection.reactive());}@Beanpublic Bandwidth globalBandwidthLimit() {// 默认 10MB/sreturn Bandwidth.classic(10 * 1024 * 1024, Refill.greedy(10 * 1024 * 1024, Duration.ofSeconds(1)));}@Beanpublic Bucket globalBandwidthLimiter(ProxyManager<String> proxyManager, Bandwidth bandwidthLimit) {return proxyManager.builder().build("global:bandwidth:limiter", bandwidthLimit);}
}

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📡 4. 下载接口实现

import io.github.bucket4j.Bucket;
import org.springframework.web.bind.annotation.*;import javax.servlet.http.HttpServletResponse;
import java.io.*;
import java.nio.file.Files;
import java.nio.file.Paths;@RestController
@RequestMapping("/api/download")
public class DownloadController {private static final int MAX_CONCURRENT_DOWNLOADS = 100;private final GlobalDownloadCounter downloadCounter;private final Bucket bandwidthLimiter;public DownloadController(GlobalDownloadCounter downloadCounter, Bucket bandwidthLimiter) {this.downloadCounter = downloadCounter;this.bandwidthLimiter = bandwidthLimiter;}@GetMapping("/{fileId}")public void downloadFile(@PathVariable String fileId, HttpServletResponse response) throws IOException {long currentCount = downloadCounter.getCurrentCount();if (currentCount >= MAX_CONCURRENT_DOWNLOADS) {response.setStatus(HttpServletResponse.SC_TOO_MANY_REQUESTS);response.getWriter().write("Too many downloads. Please try again later.");return;}downloadCounter.increment();try {// 设置响应头response.setContentType("application/octet-stream");response.setHeader("Content-Disposition", "attachment; filename=" + fileId + ".bin");ServletOutputStream out = response.getOutputStream();// 文件路径（示例）String filePath = "/path/to/files/" + fileId + ".bin";if (!Files.exists(Paths.get(filePath))) {response.setStatus(HttpServletResponse.SC_NOT_FOUND);response.getWriter().write("File not found.");return;}byte[] buffer = new byte[8192]; // 每次读取 8KBRandomAccessFile file = new RandomAccessFile(filePath, "r");int bytesRead;while ((bytesRead = file.read(buffer)) != -1) {if (bytesRead > 0) {boolean consumed = bandwidthLimiter.tryConsume(bytesRead);if (!consumed) {Thread.sleep(100); // 等待令牌生成continue;}out.write(buffer, 0, bytesRead);out.flush();}}file.close();out.close();} catch (InterruptedException e) {Thread.currentThread().interrupt(); // 恢复中断状态} finally {downloadCounter.decrement();}}
}

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🔁 5. 动态调整下载速率接口（可选）

@RestController
@RequestMapping("/api/limit")
public class RateLimitController {private final Bucket bandwidthLimiter;private final Bandwidth globalBandwidthLimit;public RateLimitController(Bucket bandwidthLimiter, Bandwidth globalBandwidthLimit) {this.bandwidthLimiter = bandwidthLimiter;this.globalBandwidthLimit = globalBandwidthLimit;}@PostMapping("/set-bandwidth")public String setBandwidth(@RequestParam int mbPerSecond) {Bandwidth newLimit = Bandwidth.classic(mbPerSecond * 1024 * 1024,Refill.greedy(mbPerSecond * 1024 * 1024, Duration.ofSeconds(1)));bandwidthLimiter.replaceConfiguration(newLimit);return "Global bandwidth limit updated to " + mbPerSecond + " MB/s";}
}

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📊 6. 监控接口（可选）

@GetMapping("/monitor/concurrent")
public ResponseEntity<Long> getConcurrentDownloads() {return ResponseEntity.ok(downloadCounter.getCurrentCount());
}

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🧪 7. 测试建议

你可以使用 curl 或 Postman 发起多并发请求测试：

for i in {1..200}; docurl -X GET "http://localhost:8080/api/download/file1" --output "file$i.bin" &
done

观察是否触发限流、并发控制是否生效。

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✅ 总结

组件	作用
Redis	分布式存储并发数和限流令牌桶
Lua 脚本	原子操作并发计数器
Bucket4j + Redis	全局下载速率限制
Spring Boot Controller	处理下载逻辑
try-finally	保证资源释放
动态接口 /set-bandwidth	支持运行时修改限速

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📌 扩展建议（可选）

• 将限流逻辑封装到 AOP 切面 中
• 添加 Prometheus 指标暴露并发数、限流次数等
• 使用 Nginx 或 Gateway 做额外的限流保护
• 加入用户身份识别，支持 用户级限速
• 使用 Kafka 异步记录日志或审计下载行为

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