Caffeine缓存使用

Bertha 。 2023-10-09 12:24 10阅读 0赞

package com.study.caffeine_cache;
    
    import com.alibaba.fastjson.JSON;
    import com.github.benmanes.caffeine.cache.*;
    import com.study.domin.Airport;
    import com.study.mapper.AirPortMapper;
    import org.checkerframework.checker.nullness.qual.NonNull;
    import org.springframework.beans.factory.annotation.Autowired;
    import org.springframework.util.StopWatch;
    import org.springframework.util.StringUtils;
    import org.springframework.web.bind.annotation.GetMapping;
    import org.springframework.web.bind.annotation.RequestMapping;
    import org.springframework.web.bind.annotation.RestController;
    
    import javax.annotation.Nullable;
    import java.util.ArrayList;
    import java.util.HashMap;
    import java.util.List;
    import java.util.Map;
    import java.util.concurrent.CompletableFuture;
    import java.util.concurrent.ExecutionException;
    import java.util.concurrent.TimeUnit;
    
    /** * @author shengda.ji * @date 2019/9/23 11:56 AM */
    @RestController
    @RequestMapping("/")
    public class Controller { 
    
        @Autowired
        private AirPortMapper mapper;
    
        StopWatch stopWatch = new StopWatch("任务耗时");
    
        @GetMapping(value = "/get_all_airport")
        public List<Airport> testCustomInterface() { 
    
            stopWatch.start("获取所有机场信息");
    
            List<Airport> result = mapper.getAll();
    
            stopWatch.stop();
    
            System.err.println(stopWatch.prettyPrint());
    
            return result;
        }
    
    
        Cache<String, String> cache = Caffeine.newBuilder()
                .expireAfterWrite(1, TimeUnit.MINUTES)
                .expireAfterAccess(1, TimeUnit.MINUTES)
                .maximumSize(10000)
                .build();
    
        @GetMapping(value = "/get_all_airport_cache")
        public List<Airport> testCustomInterface_Cache() { 
    
            stopWatch.start("获取所有机场信息_cache");
    
            String key = "all_airport";
    
            // 根据key查询一个缓存，如果没有返回NULL
            String graph = cache.getIfPresent(key);
    
            if (StringUtils.isEmpty(graph)) { 
    
                List<Airport> result = mapper.getAll();
    
                cache.put(key, JSON.toJSONString(result));
    
                System.out.println("————————————————————————————————————————————————没有缓存————————————————————————————————————————————————");
    
                stopWatch.stop();
    
                System.err.println(stopWatch.prettyPrint());
    
                return result;
    
            } else { 
                System.out.println("——————————————————————————————————————————————————有缓存————————————————————————————————————————————————");
    
                stopWatch.stop();
    
                System.err.println(stopWatch.prettyPrint());
    
                return JSON.parseArray(graph, Airport.class);
            }
        }
    
        LoadingCache<String, List<Airport>> loadingCache = Caffeine.newBuilder()
                .maximumSize(10000)
                .expireAfterWrite(10, TimeUnit.SECONDS)
                .refreshAfterWrite(1, TimeUnit.SECONDS)
                .build(new CacheLoader<String, List<Airport>>() { 
                    @Nullable
                    @Override
                    public List<Airport> load(@NonNull String i) { 
                        System.out.println("缓存里没有，load数据");
                        return mapper.getAll();
                    }
    
                    @Override
                    public List<Airport> reload(@NonNull String key, @NonNull List<Airport> oldValue) { 
                        System.out.println("缓存里没有，reload数据" + oldValue.size());
                        return mapper.getAll();
                    }
                });
    
        @GetMapping(value = "/get_all_airport_loading_cache")
        public List<Airport> testCustomInterfaceLoadingCache() { 
    
            stopWatch.start("获取所有机场信息_loading_cache");
    
            String key = "all_airport";
    
            List<Airport> result = loadingCache.get(key);
    
            stopWatch.stop();
    
            System.err.println(stopWatch.prettyPrint());
    
            return result;
        }
    
    
        /** * 和cache一样，只不过返回的CompletableFuture */
        AsyncLoadingCache<String, List<Airport>> asyncCache = Caffeine.newBuilder()
                .expireAfterWrite(1, TimeUnit.MINUTES)
                .expireAfterAccess(1, TimeUnit.MINUTES)
                .refreshAfterWrite(1, TimeUnit.MINUTES)
                .maximumSize(10000)
                .buildAsync(new CacheLoader<String, List<Airport>>() { 
                    @Nullable
                    @Override
                    public List<Airport> load(@NonNull String i) throws Exception { 
                        System.out.println("缓存里没有，load数据");
                        return mapper.getAll();
                    }
    
                    @Override
                    public List<Airport> reload(@NonNull String key, @NonNull List<Airport> oldValue) { 
                        System.out.println("缓存里没有，reload数据");
                        return mapper.getAll();
                    }
    
                    @Override
                    public Map<String, List<Airport>> loadAll(Iterable<? extends String> keys) { 
                        Map<String, List<Airport>> map = new HashMap<>();
                        for (String i : keys) { 
                            map.put(i + "1", new ArrayList<>());
                        }
                        return map;
                    }
                });
    
        @GetMapping(value = "/get_all_airport_async_cache")
        public List<Airport> testCustomInterfaceAsyncCache() throws ExecutionException, InterruptedException { 
    
            stopWatch.start("获取所有机场信息_async_cache");
    
            String key = "all_airport";
    
            CompletableFuture<List<Airport>> completableFuture = asyncCache.get(key);
    
            List<Airport> result = completableFuture.get();
    
            stopWatch.stop();
    
            System.err.println(stopWatch.prettyPrint());
    
            return result;
        }
    
    }

看下结果：  
![结果][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdmFfeWVz_size_16_color_FFFFFF_t_70]  
我的数据量是2000+条  
分别说：

*  174ms：无缓存，第一次从数据库取数据。
 *  57ms：使用cache。但是没添加缓存，准确来说也是查询数据库，但是因为mysql本身的优化，返回上次查询结果，所以较第一次少很多。
 *  2ms：明显使用到缓存 cache。
 *  30ms：和57ms同理，loading\_cache内无数据，查询数据库，30ms。
 *  9ms和0ms：使用到loading\_cache缓存。
 *  24ms：和57ms同理，async\_cache内无数据，查询数据库，24ms。
 *  0ms：使用到async\_cache缓存。

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdmFfeWVz_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/2019092517535499.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2phdmFfeWVz,size_16,color_FFFFFF,t_70