如何实现一个缓存服务

　　场景：我们对于需要大量计算的场景，希望将结果缓存起来，然后我们一起来实现一个缓存服务。即对于一个相同的输入，它的输出是不变的（也可以短时间不变）。

实现说明：这里实现采用GuavaCache+装饰器模式。

首先设计一个缓存服务接口。

public interface CacheableService<I, O> {

    /**
     * 计算服务
     * @param i
     * @return
     * @throws Exception 
     */
    O doService(I i) throws Exception;
}

这里定义了一个缓存服务接口，这里的key和Hashmap的key一样，需要覆写equals和hashcode方法。

public class CacheableServiceWrapper<I , O> implements
        CacheableService<I, O>,
        GlobalResource {

    /**
     * 日志
     */
    private final static Logger LOGGER = LoggerFactory
            .getLogger(CacheableServiceWrapper.class);

    /**
     * 缓存大小
     */
    private int MAX_CACHE_SIZE = 20;

    /**
     * 出现异常的时候重试,默认不重试
     */
    private boolean retryOnExp = false;

    /**
     * 重试次数,默认为0,即不重试
     */
    private int retryTimes = 0;

    /**
     * 默认30分钟
     */
    private long expireTimeWhenAccess = 30 * 60;

    /**
     * 缓存
     */
    private LoadingCache<I, Future<O>> cache = null;

    private CacheableService<I, O> cacheableService = null;

    /**
     * Calculate o.
     *
     * @param i the
     * @return the o
     * @throws Exception the exception
     */
    public O doService(final I i) throws Exception {

        Assert.notNull(cacheableService, "请设置好实例");

        int currentTimes = 0;
        while (currentTimes <= retryTimes) {
            try {
                Future<O> oFuture = cache.get(i);
                return oFuture.get();

            } catch (Exception e) {
                if (!retryOnExp) {
                    throw e;
                }
                currentTimes++;
                LoggerUtils.info(LOGGER, "第", currentTimes, "重试,key=", i);
            }
        }
        throw new Exception("任务执行失败");
    }


    /**
     * 提交计算任务
     *
     * @param i
     * @return
     */
    private Future<O> createTask(final I i) {
        Assert.notNull(cacheableService, "请设置好实例");

        LoggerUtils.info(LOGGER, "提交任务,key=", i);
        LoggerUtils.info(LOGGER, "当前cache=", JSON.toJSONString(cache));

        Future<O> resultFuture = THREAD_POOL.submit(new Callable<O>() {

            public O call() throws Exception {
                return cacheableService.doService(i);
            }
        });
        return resultFuture;

    }

    /**
     * 构造函数
     */
    public CacheableServiceWrapper(CacheableService<I, O> cacheableService,
                                            int maxCacheSize, long expireTime) {
        this.cacheableService = cacheableService;
        this.MAX_CACHE_SIZE = maxCacheSize;
        this.expireTimeWhenAccess = expireTime;
        cache = CacheBuilder.newBuilder().maximumSize(MAX_CACHE_SIZE)
                .expireAfterAccess(expireTimeWhenAccess, TimeUnit.SECONDS)
                .build(new CacheLoader<I, Future<O>>() {
                    public Future<O> load(I key) throws ExecutionException {
                        LoggerUtils.warn(LOGGER, "get Element from cacheLoader");
                        return createTask(key);
                    }

                    ;
                });
    }

    /**
     * 构造函数
     */
    public CacheableServiceWrapper(CacheableService<I, O> cacheableService) {
        this.cacheableService = cacheableService;
        cache = CacheBuilder.newBuilder().maximumSize(MAX_CACHE_SIZE)
                .expireAfterAccess(expireTimeWhenAccess, TimeUnit.SECONDS)
                .build(new CacheLoader<I, Future<O>>() {
                    public Future<O> load(I key) throws ExecutionException {
                        LoggerUtils.warn(LOGGER, "get Element from cacheLoader");
                        return createTask(key);
                    }

                    ;
                });
    }

    /**
     * Setter method for property <tt>retryTimes</tt>.
     *
     * @param retryTimes value to be assigned to property retryTimes
     */
    public void setRetryTimes(int retryTimes) {
        this.retryTimes = retryTimes;
    }

    /**
     * Setter method for property <tt>retryOnExp</tt>.
     *
     * @param retryOnExp value to be assigned to property retryOnExp
     */
    public void setRetryOnExp(boolean retryOnExp) {
        this.retryOnExp = retryOnExp;
    }

}

这个装饰器就是最主要的内容了，实现了对缓存服务的输入和输出的缓存。这里先说明下中间几个重要的属性：

MAX_CACHE_SIZE ：缓存空间的大小

retryOnExp ：当缓存服务发生异常的时候，是否发起重试

retryTimes ：当缓存服务异常需要重试的时候，重新尝试的最大上限。

expireTimeWhenAccess ： 缓存失效时间，当key多久没有访问的时候，淘汰数据

然后是doService采用了Guava的缓存机制，当获取缓存为空的时候，会自动去build缓存，这个操作是原子化的，所以不用自己去采用ConcurrentHashmap的putIfAbsent方法去做啦~~~
这里面实现了最主要的逻辑，就是获取缓存，然后去get数据，然后如果异常，根据配置去重试。

好啦现在咱们去测试啦

public class CacheableCalculateServiceTest {

    private CacheableService<String, String> calculateService;

    @Before
    public void before() {
        CacheableServiceWrapper<String, String> wrapper = new CacheableServiceWrapper<String, String>(
            new CacheableService<String, String>() {

                public String doService(String i) throws Exception {
                    Thread.sleep(999);
                    return i + i;
                }
            });
        wrapper.setRetryOnExp(true);
        wrapper.setRetryTimes(2);
        calculateService = wrapper;
    }

    @Test
    public void test() throws Exception {
        MutiThreadRun.init(5).addTaskAndRun(300, new Callable<String>() {

            public String call() throws Exception {
                return calculateService.doService("1");
            }
        });
    }

这里我们为了模拟大量计算的场景，我们将线程暂停了999ms，然后使用5个线程，执行任务999次，结果如下：

2016-08-24 02:00:18:848 com.zhangwei.learning.calculate.CacheableServiceWrapper get Element from cacheLoader
2016-08-24 02:00:20:119 com.zhangwei.learning.calculate.CacheableServiceWrapper 提交任务,key=1
2016-08-24 02:00:20:122 com.zhangwei.learning.calculate.CacheableServiceWrapper 当前cache={}
2016-08-24 02:00:21:106 com.zhangwei.learning.jedis.JedisPoolMonitorTask poolSize=500 borrowed=0 idle=0
2016-08-24 02:00:21:914 com.zhangwei.learning.run.MutiThreadRun 任务执行完毕,执行时间3080ms,共有300个任务,执行异常0次

可以看到，由于key一样，只执行了一次计算，然后剩下299都是从缓存中获取的。

现在我们修改为5个线程，执行300000次。

2016-08-24 02:03:15:013 com.zhangwei.learning.calculate.CacheableServiceWrapper get Element from cacheLoader
2016-08-24 02:03:16:298 com.zhangwei.learning.calculate.CacheableServiceWrapper 提交任务,key=1
2016-08-24 02:03:16:300 com.zhangwei.learning.calculate.CacheableServiceWrapper 当前cache={}
2016-08-24 02:03:17:289 com.zhangwei.learning.jedis.JedisPoolMonitorTask poolSize=500 borrowed=0 idle=0
2016-08-24 02:03:18:312 com.zhangwei.learning.run.MutiThreadRun 任务执行完毕,执行时间3317ms,共有300000个任务,执行异常0次

发现，执行时间没啥区别啦~~~~缓存的效果真是棒棒的~~

PS：我的个人svn地址：http://code.taobao.org/p/learningIT/wiki/index/   有兴趣的可以看下啦~

后面我们再看基于注解去实现缓存~~~

好啦继续更新，我们使用注解，来实现缓存，首先我们的前提还是跟上面的一样，是对方法做缓存，也就是将方法的输入到输出的映射做缓存。

首先来个注解：

@Target({ ElementType.METHOD})
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface Cache {

    /**
     * 是否打印
     * @return
     */
    public boolean enabled() default true;

    /**
     * Cache type cache type.
     *
     * @return the cache type
     */
    public CacheType cacheType() default CacheType.LOCAL;

}

该注解是注解在方法上的

package com.zhangwei.learning.utils.cache;

import com.alibaba.fastjson.JSON;
import com.google.common.collect.Lists;
import com.zhangwei.learning.model.ToString;
import com.zhangwei.learning.utils.log.LoggerUtils;
import org.slf4j.LoggerFactory;

import java.lang.reflect.Method;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;

/**
 * 用于缓存的key,如果接口需要缓存,那么复杂对象参数都需要实现这个接口
 * Created by Administrator on 2016/8/22.
 */
public class CacheKey extends ToString {

    /**
     * The A class.
     */
    private String classPath;

    /**
     * The Method.
     */
    private Method method;

    /**
     * The Input params.
     */
    private List<Object> inputParams;

    /**
     * Instantiates a new Cache key.
     *
     * @param clazz  the clazz
     * @param method the method
     * @param inputs the inputs
     */
    public CacheKey(Class clazz, Method method, Object[] inputs) {
        this.classPath = clazz.getName();
        this.method = method;
        List<Object> list = Lists.newArrayList();
        if(inputs==null || inputs.length==0){
            inputParams = list;
        }
        for(Object o : inputs){
            list.add(o);
        }
        inputParams = list;
    }

    /**
     * Equals boolean.
     *
     * @param obj the obj
     * @return the boolean
     */
    @Override
    public boolean equals(Object obj) {
        if (obj == null || !(obj instanceof CacheKey)) {
            return false;
        }
        CacheKey key = (CacheKey) obj;
        if (classPath.equals(key.getClassPath()) && method.equals(key.getMethod())) {
            if (key.getInputParams().size() != getInputParams().size()) {
                return false;
            }
            for (int i = 0; i < inputParams.size(); i++) {
                Object param = getInputParams().get(i);
                //如果有自定义的convertor,那么使用自定义的convertor
                ObjEqualsConvertor convertor = CacheInterceptor.getConvertors().get(param.getClass().getName());
                if(convertor !=null){
                    if(!convertor.extraEquals(param,key.getInputParams().get(i))){
                        return false;
                    }
                    continue;
                }
                if (!getInputParams().get(i).equals(key.getInputParams().get(i))) {
                    return false;
                }
            }
            return true;
        }
        return false;
    }

    /**
     * Hash code int.
     *
     * @return the int
     */
    @Override
    public int hashCode() {
        return classPath.hashCode()+method.hashCode()+inputParams.hashCode();
    }

    /**
     * Gets class path.
     *
     * @return the class path
     */
    public String getClassPath() {
        return classPath;
    }

    /**
     * Sets class path.
     *
     * @param classPath the class path
     */
    public void setClassPath(String classPath) {
        this.classPath = classPath;
    }

    /**
     * Gets method.
     *
     * @return the method
     */
    public Method getMethod() {
        return method;
    }

    /**
     * Sets method.
     *
     * @param method the method
     */
    public void setMethod(Method method) {
        this.method = method;
    }

    /**
     * Gets input params.
     *
     * @return the input params
     */
    public List<Object> getInputParams() {
        return inputParams;
    }

    /**
     * Sets input params.
     *
     * @param inputParams the input params
     */
    public void setInputParams(List<Object> inputParams) {
        this.inputParams = inputParams;
    }
}

我们要做缓存，肯定要有个key，这里就是我们定义的key，最主要的是我们使用了一个专门的类，主要包含调用的类、方法、以及入参。这里有下面几个需要注意的点：

1、需要修改equals方法，这点跟hashmap自定义key一样。

2、比较类的时候直接用class全名。如果用class的equals方法，有可能class地址不一致导致判断有问题。这里method的equals方法已经是覆写了，所以没问题。

3、hashcode使用三个参数合起来的hashcode，这样尽量让key散列到不同的捅，如果用classpath的，那么如果这个类调用量很大，其他的类调用很少，那么桶分布就很不均匀了。

4、入参都需要注意下equals方法，但是对于有些类我们没有办法修改它的equals方法，这个时候我们有个转换map，可以自定义对某个类的equal比较器，然后可以在不对类的修改的情况下，达到比较的效果。

上面实现了注解和缓存的key，下面来拦截器啦

/**
 * Alipay.com Inc.
 * Copyright (c) 2004-2016 All Rights Reserved.
 */
package com.zhangwei.learning.utils.cache;

import com.google.common.cache.CacheBuilder;
import com.google.common.cache.RemovalListener;
import com.google.common.cache.RemovalNotification;
import com.google.common.collect.Maps;
import com.zhangwei.learning.resource.GlobalResource;
import com.zhangwei.learning.utils.log.LoggerUtils;
import org.aopalliance.intercept.Invocation;
import org.aopalliance.intercept.MethodInterceptor;
import org.aopalliance.intercept.MethodInvocation;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.InitializingBean;

import java.util.Map;
import java.util.concurrent.*;

/**
 * 可以对接口做缓存的拦截器
 *
 * @author Administrator
 * @version $Id: CacheInterceptor.java, v 0.1 2016年8月22日 上午2:50:32 Administrator Exp $
 */
public class CacheInterceptor implements MethodInterceptor, InitializingBean, GlobalResource {

    /**
     * The constant logger.
     */
    private static final Logger logger = LoggerFactory
            .getLogger(CacheInterceptor.class);

    /**
     * 本地缓存大小.
     */
    private long maxCacheSize = 300;

    /**
     * The constant expireTimeWhenAccess.
     */
    private long expireTimeWhenAccess = 20;

    /**
     * The Local Cache.
     */
    private com.google.common.cache.Cache<CacheKey, FutureTask<Object>> cache = null;

    /**
     * The equal Convertors.
     */
    private static Map<String, ObjEquality> convertors = Maps.newHashMap();

    /**
     * @see org.aopalliance.intercept.MethodInterceptor#invoke(org.aopalliance.intercept.MethodInvocation)
     */
    @Override
    public Object invoke(MethodInvocation invocation) throws Throwable {
        Cache cacheAnnotation = invocation.getMethod().getAnnotation(Cache.class);
        if (cacheAnnotation == null || !cacheAnnotation.enabled()) {
            return invocation.proceed();
        }
        //需要cache
        CacheKey cacheKey = new CacheKey(invocation.getMethod().getDeclaringClass(), invocation.getMethod(), invocation.getArguments());
        CacheType cacheType = cacheAnnotation.cacheType();
        if (cacheType == CacheType.LOCAL) {
            Object result = getLocalCacheResult(cacheKey, invocation);
            return result;
        }
        throw new RuntimeException("not supported cacheType");
    }

    /**
     * Get local cache result object.
     *
     * @param key the key
     * @return the object
     */

    private Object getLocalCacheResult(CacheKey key, final Invocation i) throws ExecutionException, InterruptedException {
        FutureTask<Object> f = new FutureTask<Object>(new Callable<Object>() {
            @Override
            public Object call() throws Exception {
                try {
                    return i.proceed();
                } catch (Throwable throwable) {
                    throw new ExecutionException(throwable);
                }
            }
        });
        FutureTask<Object> result = cache.asMap().putIfAbsent(key, f);
        if (result == null) {
            f.run();
            result = f;
            LoggerUtils.debug(logger,"提交任务,key=",key);
        }else {
            LoggerUtils.debug(logger, "从缓存获取,key=", key);
        }
        return result.get();
    }

    /**
     * Sets expire time when access.
     *
     * @param expireTimeWhenAccess the expire time when access
     */
    public void setExpireTimeWhenAccess(long expireTimeWhenAccess) {
        this.expireTimeWhenAccess = expireTimeWhenAccess;
    }

    @Override
    public void afterPropertiesSet() throws Exception {
        cache = CacheBuilder
                .newBuilder()
                .maximumSize(maxCacheSize)
                .expireAfterAccess(
                        expireTimeWhenAccess,
                        TimeUnit.SECONDS).removalListener(new RemovalListener<CacheKey, Future<Object>>() {
                    @Override
                    public void onRemoval(RemovalNotification<CacheKey, Future<Object>> notification) {
                        LoggerUtils.info(logger, "移除key=", notification.getKey(), ",value=", notification.getValue(), ",cause=", notification.getCause());
                    }
                })
                .build();
    }

    /**
     * Sets convertors.
     *
     * @param convertors the convertors
     */
    public void setConvertors(Map<String, ObjEquality> convertors) {
        this.convertors = convertors;
    }

    /**
     * Gets convertors.
     *
     * @return the convertors
     */
    public static Map<String, ObjEquality> getConvertors() {
        return convertors;
    }

    /**
     * Sets max cache size.
     *
     * @param maxCacheSize the max cache size
     */
    public void setMaxCacheSize(long maxCacheSize) {
        this.maxCacheSize = maxCacheSize;
    }
}

这里我们实现了缓存的拦截器，缓存采用Guava cache，这里我们在使用上主要是使用了guava的缓存自动淘汰、原子化的功能。我们可以看到，缓存的是CacheKey--->FutureTask<Object>的映射，这里我们采用了FutureTask的异步执行的功能。并且将Guava 作为ConcurrentHashMap来使用。

好了我们来配置下。

<bean id="cacheInteceptor" class="com.zhangwei.learning.utils.cache.CacheInterceptor">
        <property name="maxCacheSize" value="300"/>
        <property name="expireTimeWhenAccess" value="300"/>
        <property name="convertors">
            <map>
                <entry key="java.lang.String" value-ref="stringConvertor" />
            </map>
        </property>
    </bean>
    <bean class="org.springframework.aop.framework.autoproxy.BeanNameAutoProxyCreator">
        <property name="order" value="90"></property>        
        <property name="interceptorNames">
            <list>
                <value>digestInteceptor</value>
                <value>cacheInteceptor</value>
            </list>
        </property>
        <property name="beanNames">
            <value>*</value>
        </property>
    </bean>

上面的那个map就是配置的自定义equals比较器

上测试类

@Component
@Digest
public class TestBean {

    @Cache(cacheType = CacheType.LOCAL, enabled = true)
    public String test(String one, String two) throws Exception {
        Thread.sleep(999);
        //        throw new Exception("lalal");
        return one + two;
    }
}
public class CacheTest {

    private final static Logger LOGGER = LoggerFactory.getLogger(CacheTest.class);

    @org.junit.Test
    public void Test() {
        final TestBean client = GlobalResourceUtils.getBean(TestBean.class);
        LoggerUtils.info(LOGGER, "获取到的client=", JSON.toJSONString(client));
        MutiThreadRun.init(5).addTaskAndRun(10, new Callable<Object>() {
            @Override
            public Object call() throws Exception {
                return client.test("aaa","bbb");
            }
        });
    }
}
public class StringConvertor extends ObjEquality {
    @Override
    public boolean extraEquals(Object a, Object b) {
        return false;
    }
}

这里我们就是讲一个方法多线程执行了10次，该方法中间会将线程暂停1s，所以可以看每次方法的执行时间就知道是否走缓存了。我们这里自定义了一个equal比较器，总是返回false，所以这里我们理论上每次都不会走缓存的，因为比较的时候key不同。

2016-08-28 23:38:09:527 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1043ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:09:530 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1035ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:09:530 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1034ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:09:531 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1036ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:09:534 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1033ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:527 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1000ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:530 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1000ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:531 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1000ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:531 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1000ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:534 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1000ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:38:10:534 com.zhangwei.learning.run.MutiThreadRun 任务执行完毕,执行时间2051ms,共有10个任务,执行异常0次

可以看到 每次执行时间都超过了1s，因为没走缓存，每次线程都暂停了1s。

然后我们把那个String比较器删掉。理论上这次调用的就是String的equals方法，就能走上缓存了。

2016-08-28 23:52:27:418 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,986ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:418 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1020ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:418 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,987ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:418 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1026ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:419 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,0ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:420 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:420 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,2ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:418 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1037ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:420 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,0ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:420 com.zhangwei.learning.utils.digest.DigestInterceptor (TestBean,test,1ms,No Exception,aaa^bbb,aaabbb)
2016-08-28 23:52:27:421 com.zhangwei.learning.run.MutiThreadRun 任务执行完毕,执行时间1043ms,共有10个任务,执行异常0次

可以看到，除了5个结果执行时间超过1s，其他的都很快，为啥呢？因为方法是多线程执行的，5个线程，最开始执行，5个线程中一个线程会执行方法，并且把结果放到缓存里面。然后5个线程一起等待方法执行完成然后把结果返回，然后后面的所有的都只需要从缓存获取就好了，这似不似很赞~~~~