在基于 Java 的 Dubbo 實現中，限流（Rate Limiting）同樣是一個關鍵的需求。Dubbo 是阿里巴巴開源的一款高性能 Java RPC 框架，廣泛應用于分布式服務架構中。實現限流可以幫助服務在高并發場景下保持穩定性和可靠性。以下是幾種常見的限流算法及其在 Dubbo 中的實現方法：
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1. 固定窗口算法 (Fixed Window Algorithm)
固定窗口算法將時間劃分為固定長度的窗口，并在每個窗口內限制請求數。
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicInteger;public class FixedWindowRateLimiter {private final ConcurrentHashMap<Long, AtomicInteger> windows = new ConcurrentHashMap<>();private final int limit;private final long windowSizeInMillis;public FixedWindowRateLimiter(int limit, long windowSizeInMillis) {this.limit = limit;this.windowSizeInMillis = windowSizeInMillis;}public boolean allowRequest() {long currentWindow = System.currentTimeMillis() / windowSizeInMillis;windows.putIfAbsent(currentWindow, new AtomicInteger(0));return windows.get(currentWindow).incrementAndGet() <= limit;}
}
2. 滑動窗口算法 (Sliding Window Algorithm)
滑動窗口算法將固定窗口進一步劃分為更小的時間片，從而更精確地控制流量。
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import java.util.LinkedList;
import java.util.Queue;public class SlidingWindowRateLimiter {private final Queue<Long> requestTimestamps = new LinkedList<>();private final int limit;private final long windowSizeInMillis;public SlidingWindowRateLimiter(int limit, long windowSizeInMillis) {this.limit = limit;this.windowSizeInMillis = windowSizeInMillis;}public synchronized boolean allowRequest() {long now = System.currentTimeMillis();while (!requestTimestamps.isEmpty() && requestTimestamps.peek() <= now - windowSizeInMillis) {requestTimestamps.poll();}if (requestTimestamps.size() < limit) {requestTimestamps.add(now);return true;}return false;}
}
3. 令牌桶算法 (Token Bucket Algorithm)
令牌桶算法允許突發流量，并在平穩流量時重新填充令牌。
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;public class TokenBucketRateLimiter {private final int maxTokens;private final int refillRate;private final AtomicInteger tokens;private final ScheduledExecutorService scheduler;public TokenBucketRateLimiter(int maxTokens, int refillRate) {this.maxTokens = maxTokens;this.refillRate = refillRate;this.tokens = new AtomicInteger(maxTokens);this.scheduler = Executors.newScheduledThreadPool(1);scheduler.scheduleAtFixedRate(this::refill, 1, 1, TimeUnit.SECONDS);}public boolean allowRequest() {if (tokens.get() > 0) {tokens.decrementAndGet();return true;}return false;}private void refill() {if (tokens.get() < maxTokens) {tokens.incrementAndGet();}}
}
4. 漏桶算法 (Leaky Bucket Algorithm)
漏桶算法以恒定速率處理請求，適用于平滑流量，防止流量突發。
import java.util.concurrent.atomic.AtomicInteger;public class LeakyBucketRateLimiter {private final int capacity;private final long leakRateInMillis;private final AtomicInteger waterLevel;private long lastLeakTime;public LeakyBucketRateLimiter(int capacity, long leakRateInMillis) {this.capacity = capacity;this.leakRateInMillis = leakRateInMillis;this.waterLevel = new AtomicInteger(0);this.lastLeakTime = System.currentTimeMillis();}public synchronized boolean allowRequest() {leak();if (waterLevel.get() < capacity) {waterLevel.incrementAndGet();return true;}return false;}private void leak() {long now = System.currentTimeMillis();long elapsedTime = now - lastLeakTime;int leaked = (int) (elapsedTime / leakRateInMillis);if (leaked > 0) {waterLevel.addAndGet(-leaked);if (waterLevel.get() < 0) {waterLevel.set(0);}lastLeakTime = now;}}
}
在 Dubbo 中集成限流器
要在 Dubbo 中集成限流器，可以通過實現自定義的過濾器。以下是一個簡單的示例，展示如何將限流器集成到 Dubbo 過濾器中：
自定義過濾器
import org.apache.dubbo.common.extension.Activate;
import org.apache.dubbo.rpc.*;@Activate(group = {"provider"})
public class RateLimitingFilter implements Filter {private final FixedWindowRateLimiter rateLimiter = new FixedWindowRateLimiter(100, 1000);@Overridepublic Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {if (rateLimiter.allowRequest()) {return invoker.invoke(invocation);} else {throw new RpcException(RpcException.LIMIT_EXCEEDED, "Rate limit exceeded");}}
}
配置 Dubbo 使用自定義過濾器
在 Dubbo 的配置文件中添加自定義過濾器：
<dubbo:provider filter="rateLimitingFilter" />
或者在 Spring 配置文件中添加：
<dubbo:provider><dubbo:parameter key="filter" value="rateLimitingFilter" />
</dubbo:provider>
通過以上方式，可以在 Dubbo 中實現各種限流算法，從而有效控制請求流量，保護服務穩定性。根據具體的業務需求，選擇合適的限流算法，確保系統的性能和可靠性。
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