四（3）、springcloud之Ribbon核心组件IRule&自定义RIbbon

刺骨的言语ヽ痛彻心扉 2023-06-11 13:30 39阅读 0赞

### 3.1、Ribbon核心组件 ###

\*\*概述：\*\*IRule根据特定算法中从服务列表中选取一个要访问的服务；

*  **RoundRobinRule**：轮询
 *  **RandomRule**：随机
 *  **AvailabilityFilteringRule**：会先过滤掉由于多次访问故障而处于断路器跳闸状态的服务，还有并发的连接数量超过阈值的服务，然后对剩余的服务列表按照轮询策略进行访问
 *  **WeightedResponseTimeRule**：根据平均响应时间计算所有服务的权重，响应时间越快服务权重越大被选中的概率越高。刚启动时如果统计信息不足，则使用RoundRobinRule策略，等统计信息足够，  
    会切换到WeightedResponseTimeRule；
 *  **RetryRule**：先按照RoundRobinRule的策略获取服务，如果获取服务失败则在指定时间内会进行重试，获取可用的服务
 *  **BestAvailableRule**：会先过滤掉由于多次访问故障而处于断路器跳闸状态的服务，然后选择一个并发量最小的服务；
 *  **ZoneAvoidanceRule**：默认规则,复合判断server所在区域的性能和server的可用性选择服务器

继承关系：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70]

### 3.2、自定义Ribbon ###

**修改消费者端（microservicecloud-consumer-dept-80）**

**1）新建自定义Ribbon配置类**

@Configuration
    public class MySelfRule
    { 
      @Bean
      public IRule myRule()
      { 
       return new RandomRule();//Ribbon默认是轮询，我自定义为随机
      }
    }

\*\*注意：\*\*自定义配置类不能放在@ComponentScan所扫描的当前包下以及子包下，  
否则我们自定义的这个配置类就会被所有的Ribbon客户端所共享，达不到特殊化定制的目的。

**2）修改主启动类**

@SpringBootApplication
    @EnableEurekaClient
    @RibbonClient(name = "MICROSERVICECLOUD-DEPT",configuration = MySelfRule.class	)
    public class DeptConsumer80_App { 
    
    	public static void main(String[] args) { 
    		// TODO Auto-generated method stub
    		 SpringApplication.run(DeptConsumer80_App.class, args);
    	}
    
    }

测试：启动Eureka集群和provider ,再启动消费端80，输入http://localhost/consumer/dept/list

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70 1]

**发现依旧是轮询，思考如何根据个人定制？，每个服务器要求被调用5次。也即以前是每台机器一次，现在是每台机器5次**

查看源码：

/* * * Copyright 2013 Netflix, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * */
    package com.netflix.loadbalancer;
    
    import com.netflix.client.config.IClientConfig;
    
    import java.util.List;
    import java.util.concurrent.ThreadLocalRandom;
    
    /** * A loadbalacing strategy that randomly distributes traffic amongst existing * servers. * * @author stonse * */
    public class RandomRule extends AbstractLoadBalancerRule { 
    
        /** * Randomly choose from all living servers */
        @edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE")
        public Server choose(ILoadBalancer lb, Object key) { 
            if (lb == null) { 
                return null;
            }
            Server server = null;
    
            while (server == null) { 
                if (Thread.interrupted()) { 
                    return null;
                }
                List<Server> upList = lb.getReachableServers();
                List<Server> allList = lb.getAllServers();
    
                int serverCount = allList.size();
                if (serverCount == 0) { 
                    /* * No servers. End regardless of pass, because subsequent passes * only get more restrictive. */
                    return null;
                }
    
                int index = chooseRandomInt(serverCount);
                server = upList.get(index);
    
                if (server == null) { 
                    /* * The only time this should happen is if the server list were * somehow trimmed. This is a transient condition. Retry after * yielding. */
                    Thread.yield();
                    continue;
                }
    
                if (server.isAlive()) { 
                    return (server);
                }
    
                // Shouldn't actually happen.. but must be transient or a bug.
                server = null;
                Thread.yield();
            }
    
            return server;
    
        }
    
        protected int chooseRandomInt(int serverCount) { 
            return ThreadLocalRandom.current().nextInt(serverCount);
        }
    
    	@Override
    	public Server choose(Object key) { 
    		return choose(getLoadBalancer(), key);
    	}
    
    	@Override
    	public void initWithNiwsConfig(IClientConfig clientConfig) { 
    		// TODO Auto-generated method stub
    		
    	}
    }

这里RandomRule继承了AbstractLoadBalancerRule，我们可以自己定义自己的IRule,并且参考源码算法：

public class RandomRule_JTP extends AbstractLoadBalancerRule { 
    
    	/** * 问题：依旧轮询策略，但是加上新需求，每个服务器要求被调用5次。也即 以前是每台机器一次，现在是每台机器5次 */
    	/** * total =0 当total==5的时候，我们指针才能往下走 * index=0;表示指正，代表当前要调用的服务器地址， * total需要重新置为0，但是已经达到过一个5次，我们index=1 * * 分析：我们执行5次，此时我们微服务只有8001，,8002,8003，当加到上限值的时候不能继续再加，重新再调用 * */
    	 private int total = 0;    //总共被调用的次数，目前要求每台被调用5次
    	 private int currentIndex = 0;//当前提供服务的机器号
    	  
    
    	 
        public Server choose(ILoadBalancer lb, Object key) { 
            if (lb == null) { 
                return null;
            }
            Server server = null;
    
            while (server == null) { 
                if (Thread.interrupted()) { 
                    return null;
                }
                List<Server> upList = lb.getReachableServers();
                List<Server> allList = lb.getAllServers();
    
                int serverCount = allList.size();
                if (serverCount == 0) { 
                    /* * No servers. End regardless of pass, because subsequent passes * only get more restrictive. */
                    return null;
                }
    
    			/*github源码 * int index = chooseRandomInt(serverCount); * server = upList.get(index); */
                //这里加上自己的逻辑，每个服务调用5次
                if(total < 5) { 
                	
                	server = upList.get(currentIndex);//代表调用当前微服务
                	total++;
                }else { 
                	total = 0;
                	currentIndex++;//当第一个服务器调用完在调用第二个
                	if(currentIndex > upList.size()) { 
                		
                		currentIndex = 0;   // 当设置的5次大于总共的微服务个数时，进行归位
                	}
                	
                }
       
                if (server == null) { 
                    /* * The only time this should happen is if the server list were * somehow trimmed. This is a transient condition. Retry after * yielding. */
                    Thread.yield();
                    continue;
                }
    
                if (server.isAlive()) { 
                    return (server);
                }
    
                // Shouldn't actually happen.. but must be transient or a bug.
                server = null;
                Thread.yield();
            }
    
            return server;
    
        }
    
        protected int chooseRandomInt(int serverCount) { 
            return ThreadLocalRandom.current().nextInt(serverCount);
        }
    
    	@Override
    	public Server choose(Object key) { 
    		
    		
    		return choose(getLoadBalancer(), key);
    	}
    
    	@Override
    	public void initWithNiwsConfig(IClientConfig clientConfig) { 
    		// TODO Auto-generated method stub
    		
    	}
    }

当在这写好了自定义的IRule算法类，这里修改自定义的RIbbon配置类，将自定义的添加到容器中

@Configuration
    public class MySelfRule { 
    
    	// 
    	@Bean
    	public IRule myRule() { 
    		
    		return new RandomRule_JTP();//自定义为随机的 。。。每台机器调用5次
    	}
    	
    	
    	
    }

测试：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70 2]

不断刷新，发现每个服务调用5次，这样就达到了自定义定制。

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70]: /images/20230531/feff1dde6d7b40acb81a8390acc9794c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70 1]: /images/20230531/3dd977235f6146d8becfe158ca148527.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdHBlbg_size_16_color_FFFFFF_t_70 2]: /images/20230531/dda24beb2e9e452a997784cbb450d022.png