一级a做爰片凤凰网站,wix和WordPress做小程序,扁平化企业网站,网络架构必须满足哪些特性RedisCaffeine 实现两级缓存
背景
事情的开始是这样的#xff0c;前段时间接了个需求#xff0c;给公司的商城官网提供一个查询预计送达时间的接口。接口很简单#xff0c;根据请求传的城市仓库发货时间查询快递的预计送达时间。因为商城下单就会调用这个接口#xff…RedisCaffeine 实现两级缓存
背景
事情的开始是这样的前段时间接了个需求给公司的商城官网提供一个查询预计送达时间的接口。接口很简单根据请求传的城市仓库发货时间查询快递的预计送达时间。因为商城下单就会调用这个接口所以对接口的性能要求还是挺高的据老员工的说法是特别是大促的时候访问量还是比较大的。
因为数据量不是很大每天会全量推今天和明天的预计送达时间到MySQL总数据量大约7k。每次推完数据后会把数据全量写入到redis中做一个缓存预热然后设置过期时间为1天。
鉴于之前Redis集群出现过压力过大查询缓慢的情况进一步保证接口的高性能和高可用防止redis出现压力大查询慢缓存雪崩缓存穿透等问题我们最终采用了Reids Caffeine两级缓存的策略。
本地缓存优缺点
优点
本地缓存基于本地内存查询速度是很快的。适用于实时性要求不高更新频率不高等场景。我们的数据每天凌晨更新一次总量7k左右查询本地缓存与查询远程缓存相比可以减少网络的I/O降低网络上的一些消耗。我们的redis之前出现过查询缓慢的情况
缺点
Caffeine既然是本地缓存在分布式环境的情况下就要考虑各个节点之间缓存的一致性问题一个节点的本地缓存更新了怎么可以同步到其他的节点。Caffeine不支持持久化的存储。Caffeine使用本地内存需要合理设置大小避免内存溢出。
流程图 代码实现
MySQL表
CREATE TABLE t_estimated_arrival_date (id int(11) UNSIGNED NOT NULL AUTO_INCREMENT COMMENT 主键id,warehouse_id varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL COMMENT 货仓id,warehouse varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL COMMENT 发货仓,city varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL COMMENT 签收城市,delivery_date date NULL DEFAULT NULL COMMENT 发货时间,estimated_arrival_date date NULL DEFAULT NULL COMMENT 预计到货日期,PRIMARY KEY (id) USING BTREE,UNIQUE INDEX uk_warehouse_id_city_delivery_date(warehouse_id, city, delivery_date) USING BTREE
) ENGINE InnoDB COMMENT 预计到货时间表(具体到dayT, T1,近90天到货时间众数) ROW_FORMAT Dynamic;INSERT INTO t_estimated_arrival_date VALUES (9, 6, 湖熟正常仓, 兰州市, 2024-07-08, 2024-07-10);
INSERT INTO t_estimated_arrival_date VALUES (10, 6, 湖熟正常仓, 兰州市, 2024-07-09, 2024-07-11);
INSERT INTO t_estimated_arrival_date VALUES (11, 6, 湖熟正常仓, 兴安盟, 2024-07-08, 2024-07-11);
INSERT INTO t_estimated_arrival_date VALUES (12, 6, 湖熟正常仓, 兴安盟, 2024-07-09, 2024-07-12);
INSERT INTO t_estimated_arrival_date VALUES (13, 6, 湖熟正常仓, 其他, 2024-07-08, 2024-07-19);
INSERT INTO t_estimated_arrival_date VALUES (14, 6, 湖熟正常仓, 其他, 2024-07-09, 2024-07-20);
INSERT INTO t_estimated_arrival_date VALUES (15, 6, 湖熟正常仓, 内江市, 2024-07-08, 2024-07-10);
INSERT INTO t_estimated_arrival_date VALUES (16, 6, 湖熟正常仓, 内江市, 2024-07-09, 2024-07-11);
INSERT INTO t_estimated_arrival_date VALUES (17, 6, 湖熟正常仓, 凉山彝族自治州, 2024-07-08, 2024-07-11);
INSERT INTO t_estimated_arrival_date VALUES (18, 6, 湖熟正常仓, 凉山彝族自治州, 2024-07-09, 2024-07-12);
INSERT INTO t_estimated_arrival_date VALUES (19, 6, 湖熟正常仓, 包头市, 2024-07-08, 2024-07-11);
INSERT INTO t_estimated_arrival_date VALUES (20, 6, 湖熟正常仓, 包头市, 2024-07-09, 2024-07-12);
INSERT INTO t_estimated_arrival_date VALUES (21, 6, 湖熟正常仓, 北京城区, 2024-07-08, 2024-07-10);
INSERT INTO t_estimated_arrival_date VALUES (22, 6, 湖熟正常仓, 北京城区, 2024-07-09, 2024-07-11);pom.xm dependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-starter-aop/artifactId/dependencydependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-starter-data-redis/artifactId/dependency!--redis连接池--dependencygroupIdorg.apache.commons/groupIdartifactIdcommons-pool2/artifactId/dependencydependencygroupIdorg.springframework.boot/groupIdartifactIdspring-boot-starter-cache/artifactId/dependencydependencygroupIdcom.github.ben-manes.caffeine/groupIdartifactIdcaffeine/artifactIdversion2.9.2/version/dependencydependencygroupIdmysql/groupIdartifactIdmysql-connector-java/artifactIdversion8.0.28/version/dependencydependencygroupIdcom.baomidou/groupIdartifactIdmybatis-plus-boot-starter/artifactIdversion3.3.1/version/dependencyapplication.yml
server:port: 9001
spring:application:name: springboot-redisdatasource:name: demourl: jdbc:mysql://localhost:3306/test?userUnicodetruecharacterEncodingutf8allowMultiQueriestrueuseSSLfalsedriver-class-name: com.mysql.cj.jdbc.Driverusername: password: # mybatis相关配置mybatis-plus:mapper-locations: classpath:mapper/*.xmlconfiguration:cache-enabled: trueuse-generated-keys: truedefault-executor-type: REUSEuse-actual-param-name: true# 打印日志# log-impl: org.apache.ibatis.logging.stdout.StdOutImplredis:host: 192.168.117.73port: 6379password: root
# redis:
# lettuce:
# cluster:
# refresh:
# adaptive: true
# period: 10S
# pool:
# max-idle: 50
# min-idle: 8
# max-active: 100
# max-wait: -1
# timeout: 100000
# cluster:
# nodes:
# - 192.168.117.73:6379
logging:level:com.itender.redis.mapper: debug
配置类
RedisConfig
/*** author yuanhewei* date 2024/5/31 16:18* description*/
Configuration
public class RedisConfig {Beanpublic RedisTemplateString, Object redisTemplate(RedisConnectionFactory connectionFactory) {RedisTemplateString, Object redisTemplate new RedisTemplate();redisTemplate.setConnectionFactory(connectionFactory);Jackson2JsonRedisSerializerObject serializer new Jackson2JsonRedisSerializer(Object.class);ObjectMapper mapper new ObjectMapper();mapper.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.ANY);mapper.activateDefaultTyping(LaissezFaireSubTypeValidator.instance, ObjectMapper.DefaultTyping.NON_FINAL, JsonTypeInfo.As.PROPERTY);serializer.setObjectMapper(mapper);// 如果不序列化在key value 使用redis客户端工具 直连redis服务器 查看数据时 前面会有一个 \xac\xed\x00\x05t\x00\x05 字符串// StringRedisSerializer 来序列化和反序列化 String 类型 redis 的 key valueredisTemplate.setKeySerializer(new StringRedisSerializer());redisTemplate.setValueSerializer(serializer);// StringRedisSerializer 来序列化和反序列化 hash 类型 redis 的 key valueredisTemplate.setHashKeySerializer(new StringRedisSerializer());redisTemplate.setHashValueSerializer(serializer);redisTemplate.afterPropertiesSet();return redisTemplate;}
}CaffeineConfig
/*** author yuanhewei* date 2024/7/9 14:16* description*/
Configuration
public class CaffeineConfig {/*** Caffeine 配置类* initialCapacity初始缓存空间大小* maximumSize缓存的最大数量设置这个值避免内存溢出* expireAfterWrite指定缓存的过期时间是最后一次写操作的一个时间* 容量的大小要根据自己的实际应用场景设置** return*/Beanpublic CacheString, Object caffeineCache() {return Caffeine.newBuilder()// 初始大小.initialCapacity(128)//最大数量.maximumSize(1024)//过期时间.expireAfterWrite(60, TimeUnit.SECONDS).build();}Beanpublic CacheManager cacheManager(){CaffeineCacheManager cacheManagernew CaffeineCacheManager();cacheManager.setCaffeine(Caffeine.newBuilder().initialCapacity(128).maximumSize(1024).expireAfterWrite(60, TimeUnit.SECONDS));return cacheManager;}
}Mapper
这里采用了Mybatis Plus
/*** author yuanhewei* date 2024/7/9 18:11* description*/
Mapper
public interface EstimatedArrivalDateMapper extends BaseMapperEstimatedArrivalDateEntity {} Service
/*** author yuanhewei* date 2024/7/9 14:25* description*/
public interface DoubleCacheService {/*** 查询一级送达时间-常规方式** param request* return*/EstimatedArrivalDateEntity getEstimatedArrivalDateCommon(EstimatedArrivalDateEntity request);/*** 查询一级送达时间-注解方式** param request* return*/EstimatedArrivalDateEntity getEstimatedArrivalDate(EstimatedArrivalDateEntity request);
}实现类
/*** author yuanhewei* date 2024/7/9 14:26* description*/
Slf4j
Service
public class DoubleCacheServiceImpl implements DoubleCacheService {Resourceprivate CacheString, Object caffeineCache;Resourceprivate RedisTemplateString, Object redisTemplate;Resourceprivate EstimatedArrivalDateMapper estimatedArrivalDateMapper;Overridepublic EstimatedArrivalDateEntity getEstimatedArrivalDateCommon(EstimatedArrivalDateEntity request) {String key request.getDeliveryDate() RedisConstants.COLON request.getWarehouseId() RedisConstants.COLON request.getCity();log.info(Cache key: {}, key);Object value caffeineCache.getIfPresent(key);if (Objects.nonNull(value)) {log.info(get from caffeine);return EstimatedArrivalDateEntity.builder().estimatedArrivalDate(value.toString()).build();}value redisTemplate.opsForValue().get(key);if (Objects.nonNull(value)) {log.info(get from redis);caffeineCache.put(key, value);return EstimatedArrivalDateEntity.builder().estimatedArrivalDate(value.toString()).build();}log.info(get from mysql);DateTime deliveryDate DateUtil.parse(request.getDeliveryDate(), yyyy-MM-dd);EstimatedArrivalDateEntity estimatedArrivalDateEntity estimatedArrivalDateMapper.selectOne(new QueryWrapperEstimatedArrivalDateEntity().eq(delivery_date, deliveryDate).eq(warehouse_id, request.getWarehouseId()).eq(city, request.getCity()));redisTemplate.opsForValue().set(key, estimatedArrivalDateEntity.getEstimatedArrivalDate(), 120, TimeUnit.SECONDS);caffeineCache.put(key, estimatedArrivalDateEntity.getEstimatedArrivalDate());return EstimatedArrivalDateEntity.builder().estimatedArrivalDate(estimatedArrivalDateEntity.getEstimatedArrivalDate()).build();}DoubleCache(cacheName estimatedArrivalDate, key {#request.deliveryDate, #request.warehouseId, #request.city},type DoubleCache.CacheType.FULL)Overridepublic EstimatedArrivalDateEntity getEstimatedArrivalDate(EstimatedArrivalDateEntity request) {DateTime deliveryDate DateUtil.parse(request.getDeliveryDate(), yyyy-MM-dd);EstimatedArrivalDateEntity estimatedArrivalDateEntity estimatedArrivalDateMapper.selectOne(new QueryWrapperEstimatedArrivalDateEntity().eq(delivery_date, deliveryDate).eq(warehouse_id, request.getWarehouseId()).eq(city, request.getCity()));return EstimatedArrivalDateEntity.builder().estimatedArrivalDate(estimatedArrivalDateEntity.getEstimatedArrivalDate()).build();}
}
这里的代码本来是采用了常规的写法没有采用自定义注解的方式注解的方式是参考了后面那位大佬的文章加以修改实现的。因为我的CacheKey可能存在多个属性值的组合。
Annotitions
/*** author yuanhewei* date 2024/7/9 14:51* description*/
Target(ElementType.METHOD)
Retention(RetentionPolicy.RUNTIME)
Documented
public interface DoubleCache {/*** 缓存名称** return*/String cacheName();/*** 缓存的key支持springEL表达式** return*/String[] key();/*** 过期时间单位秒** return*/long expireTime() default 120;/*** 缓存类型** return*/CacheType type() default CacheType.FULL;enum CacheType {/*** 存取*/FULL,/*** 只存*/PUT,/*** 删除*/DELETE}
}
Aspect
/*** author yuanhewei* date 2024/7/9 14:51* description*/
Slf4j
Component
Aspect
public class DoubleCacheAspect {Resourceprivate CacheString, Object caffeineCache;Resourceprivate RedisTemplateString, Object redisTemplate;Pointcut(annotation(com.itender.redis.annotation.DoubleCache))public void doubleCachePointcut() {}Around(doubleCachePointcut())public Object doAround(ProceedingJoinPoint point) throws Throwable {MethodSignature signature (MethodSignature) point.getSignature();Method method signature.getMethod();// 拼接解析springEl表达式的mapString[] paramNames signature.getParameterNames();Object[] args point.getArgs();TreeMapString, Object treeMap new TreeMap();for (int i 0; i paramNames.length; i) {treeMap.put(paramNames[i], args[i]);}DoubleCache annotation method.getAnnotation(DoubleCache.class);String elResult DoubleCacheUtil.arrayParse(Lists.newArrayList(annotation.key()), treeMap);String realKey annotation.cacheName() RedisConstants.COLON elResult;// 强制更新if (annotation.type() DoubleCache.CacheType.PUT) {Object object point.proceed();redisTemplate.opsForValue().set(realKey, object, annotation.expireTime(), TimeUnit.SECONDS);caffeineCache.put(realKey, object);return object;}// 删除else if (annotation.type() DoubleCache.CacheType.DELETE) {redisTemplate.delete(realKey);caffeineCache.invalidate(realKey);return point.proceed();}// 读写查询CaffeineObject caffeineCacheObj caffeineCache.getIfPresent(realKey);if (Objects.nonNull(caffeineCacheObj)) {log.info(get data from caffeine);return caffeineCacheObj;}// 查询RedisObject redisCache redisTemplate.opsForValue().get(realKey);if (Objects.nonNull(redisCache)) {log.info(get data from redis);caffeineCache.put(realKey, redisCache);return redisCache;}log.info(get data from database);Object object point.proceed();if (Objects.nonNull(object)) {// 写入Redislog.info(get data from database write to cache: {}, object);redisTemplate.opsForValue().set(realKey, object, annotation.expireTime(), TimeUnit.SECONDS);// 写入CaffeinecaffeineCache.put(realKey, object);}return object;}
}
因为注解上的配置要支持Spring的EL表达式。
public static String parse(String elString, SortedMapString, Object map) {elString String.format(#{%s}, elString);// 创建表达式解析器ExpressionParser parser new SpelExpressionParser();// 通过evaluationContext.setVariable可以在上下文中设定变量。EvaluationContext context new StandardEvaluationContext();map.forEach(context::setVariable);// 解析表达式Expression expression parser.parseExpression(elString, new TemplateParserContext());// 使用Expression.getValue()获取表达式的值这里传入了Evaluation上下文return expression.getValue(context, String.class);}public static String arrayParse(ListString elStrings, SortedMapString, Object map) {ListString result Lists.newArrayList();elStrings.forEach(elString - {elString String.format(#{%s}, elString);// 创建表达式解析器ExpressionParser parser new SpelExpressionParser();// 通过evaluationContext.setVariable可以在上下文中设定变量。EvaluationContext context new StandardEvaluationContext();map.forEach(context::setVariable);// 解析表达式Expression expression parser.parseExpression(elString, new TemplateParserContext());// 使用Expression.getValue()获取表达式的值这里传入了Evaluation上下文result.add(expression.getValue(context, String.class));});return String.join(RedisConstants.COLON, result);}Controller
/*** author yuanhewei* date 2024/7/9 14:14* description*/
RestController
RequestMapping(/doubleCache)
public class DoubleCacheController {Resourceprivate DoubleCacheService doubleCacheService;PostMapping(/common)public EstimatedArrivalDateEntity getEstimatedArrivalDateCommon(RequestBody EstimatedArrivalDateEntity estimatedArrivalDate) {return doubleCacheService.getEstimatedArrivalDateCommon(estimatedArrivalDate);}PostMapping(/annotation)public EstimatedArrivalDateEntity getEstimatedArrivalDate(RequestBody EstimatedArrivalDateEntity estimatedArrivalDate) {return doubleCacheService.getEstimatedArrivalDate(estimatedArrivalDate);}
}代码中演示了Redis Caffeine实现两级缓存的方式一种是传统常规的方式另一种是基于注解的方式实现的。具体实现可以根据自己项目中的实际场景。
最后的测试结果也是两种方式都可以实现查询先走一级缓存一级缓存不存在查询二级缓存然后写入一级缓存二级缓存不存在查询MySQL然后写入二级缓存再写入一级缓存的目的。测试结果就不贴出来了
总结
本文介绍RedisCaffeine实现两级缓存的方式。一种是常规的方式一种的基于注解的方式。具体的实现可根据自己项目中的业务场景。
至于为什么要用RedisCaffeine的方式文章也提到了目前我们Redis集群压力还算挺大的而且接口对RT的要求也是比较高的。有一点好的就是我们的数据是每天全量推一边总量也不大实时性要求也不强。所以就很适合本地缓存的方式。
使用本地缓存也要注意设置容量的大小和过期时间否则容易出现内存溢出。
其实现实中很多的场景直接使用Redis就可以搞定的没必要硬要使用Caffeine。这里也只是简单的介绍了最简单基础的实现方式。对于其他一些复杂的场景还要根据自己具体的业务进行设计。我自己也是边学边用。如果有问题或者其他好的实现方式欢迎各位大佬评论一起进步
参考
https://blog.csdn.net/weixin_45334346/article/details/136310010
