dubbo-源码阅读之Filter默认实现

SPI配置的默认实现

cache=com.alibaba.dubbo.cache.filter.CacheFilter
validation=com.alibaba.dubbo.validation.filter.ValidationFilter
echo=com.alibaba.dubbo.rpc.filter.EchoFilter
generic=com.alibaba.dubbo.rpc.filter.GenericFilter
genericimpl=com.alibaba.dubbo.rpc.filter.GenericImplFilter
token=com.alibaba.dubbo.rpc.filter.TokenFilter
accesslog=com.alibaba.dubbo.rpc.filter.AccessLogFilter
activelimit=com.alibaba.dubbo.rpc.filter.ActiveLimitFilter
classloader=com.alibaba.dubbo.rpc.filter.ClassLoaderFilter
context=com.alibaba.dubbo.rpc.filter.ContextFilter
consumercontext=com.alibaba.dubbo.rpc.filter.ConsumerContextFilter
exception=com.alibaba.dubbo.rpc.filter.ExceptionFilter
executelimit=com.alibaba.dubbo.rpc.filter.ExecuteLimitFilter
deprecated=com.alibaba.dubbo.rpc.filter.DeprecatedFilter
compatible=com.alibaba.dubbo.rpc.filter.CompatibleFilter
timeout=com.alibaba.dubbo.rpc.filter.TimeoutFilter
trace=com.alibaba.dubbo.rpc.protocol.dubbo.filter.TraceFilter
future=com.alibaba.dubbo.rpc.protocol.dubbo.filter.FutureFilter
monitor=com.alibaba.dubbo.monitor.support.MonitorFilter

Consumer

ConsumerContextFilter

记录一些基础信息到当前线程的PRCContext

@Activate(
            group = {"consumer"},
            order = -10000
    )
    public class ConsumerContextFilter implements Filter {
        public ConsumerContextFilter() {
        }

        public Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {
            /***
             * 从线程缓存获取本次RpcContext.getContext()
             * 设置一些本次请求的基础信息到RpcContext
             */
            RpcContext.getContext().setInvoker(invoker)
                    .setInvocation(invocation)
                    .setLocalAddress(NetUtils.getLocalHost(), 0)
                    .setRemoteAddress(invoker.getUrl().getHost(), invoker.getUrl().getPort());
            if (invocation instanceof RpcInvocation) {
                ((RpcInvocation)invocation).setInvoker(invoker);
            }

            Result var3;
            try {
                /**
                 * 客户端相关参数是根据 invocation传递给消费者的 可以打断点看 也可以自定义一些数据 比如traceId
                 */
                var3 = invoker.invoke(invocation);
            } finally {
                RpcContext.getContext().clearAttachments();
            }

            return var3;
        }
    }

ActiveLimitFilter

例子

同时只支持1的并发量

<dubbo:method actives="1" ... />

源码

ActiveLimitFilter主要用于 限制同一个客户端对于一个服务端方法的并发调用量（客户端限流）。

/**
 * 控制调用服务的并发量 限流
 * 同时支持多少请求
 */
@Activate(
        group = {"consumer"},
        value = {"actives"}
)
public class ActiveLimitFilter implements Filter {
    public ActiveLimitFilter() {
    }

    public Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {
        URL url = invoker.getUrl();
        String methodName = invocation.getMethodName();
        //获得 <dubbo:reference actives="1"> actives的数量
        int max = invoker.getUrl().getMethodParameter(methodName, "actives", 0);
        //获取当前service当前方法的请求数量
        RpcStatus count = RpcStatus.getStatus(invoker.getUrl(), invocation.getMethodName());
        long timeout;
        //配置并发控制大于0才写
        if (max > 0) {
            //获得当前方法的等待时间
            timeout = (long)invoker.getUrl().getMethodParameter(invocation.getMethodName(), "timeout", 0);
            long start = System.currentTimeMillis();
            long remain = timeout;
            //判断是否大于并发数 如果大于则等待
            int active = count.getActive();
            if (active >= max) {
                synchronized(count) {
                    /**
                     *1.while循环是有必要的
                     *  当收到其他线程notify 获得执行权
                     *  但是这个时候其他线程提前进入(active >= max)  判断为false获得执行权 count+1 
                     *  这个时候 还需要while判断是否还有空闲请求 否则继续wait
                     *
                     */
                    while((active = count.getActive()) >= max) {
                        try {
                            //超时时间为 配置的超时时间
                            count.wait(remain);
                        } catch (InterruptedException var32) {
                            ;
                        }

                        long elapsed = System.currentTimeMillis() - start;
                        remain = timeout - elapsed;
                         //当其他线程通知等待线程执行 判断是否超时 如果超时了则不执行了
                        if (remain <= 0L) {
                            throw new RpcException("Waiting concurrent invoke timeout in client-side for service:  " + invoker.getInterface().getName() + ", method: " + invocation.getMethodName() + ", elapsed: " + elapsed + ", timeout: " + timeout + ". concurrent invokes: " + active + ". max concurrent invoke limit: " + max);
                        }
                    }
                }
            }
        }

        boolean var28 = false;

        Result var10;
        try {
            var28 = true;
            timeout = System.currentTimeMillis();
            //获得执行权的 count+1
            RpcStatus.beginCount(url, methodName);

            try {
                //执行
                Result result = invoker.invoke(invocation);
                //执行完毕关闭
                RpcStatus.endCount(url, methodName, System.currentTimeMillis() - timeout, true);
                var10 = result;
                var28 = false;
            } catch (RuntimeException var31) {
                RpcStatus.endCount(url, methodName, System.currentTimeMillis() - timeout, false);
                throw var31;
            }
        } finally {
            if (var28) {
                if (max > 0) {
                    //通知等待的线程执行
                    synchronized(count) {
                        count.notify();
                    }
                }

            }
        }

        if (max > 0) {
            synchronized(count) {
                //通知等待的线程执行
                count.notify();
            }
        }

        return var10;
    }
}

FutureFilter

用于处理事件回调