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Spring Cache监控配置与使用规范的建议

作者：仍在奔跑的男人

这篇文章主要介绍了Spring Cache监控配置与使用规范的建议，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教

建议

spring boot 2.x 监控设置

spring boot从1.x升级到2.x之后，原先在spring-boot-actuator里默认的一些metrics不在自动生效，而是将这些metrics的功能从spring-boot-actuator迁移到了micrometer.io项目里，作为一个独立的微服务监控项目维护。

为此，为了使spring cache的相关metrics生效，需要进行如下配置：

1.在management.endpoints.web里添加caches与metrics两个接口暴露

默认只暴露health、info。

management:
  endpoints:
    web:
      exposure:
        include: info, health, metrics, caches

2.在spring.cache里指定cache-names的配置

spring:
  cache:
    cache-names: books, rooms

如果不指定cache-names，spring cache metrics是不会生效的，因为spring是在加载的过程中来确认需要对哪些cache来监控，像Cacheable之类需要动态加入的cache，spring在加载过程中无法感知到。

3.对cache provider启动调用记录功能

以caffeine为例，在spec中加入recordStats，如下：

spring:
  cache:
    cache-names: books, rooms
    caffeine:
      spec: recordStats

4.相关url

5.举例

curl localhost:8080/actuator/metrics/ 访问所有支持的metrics

{
    "names":[
        "jvm.memory.max",
        "jvm.threads.states",
        "process.files.max",
        "jvm.gc.memory.promoted",
        "cache.puts",
        "cache.size",
        "cache.evictions",
        "system.load.average.1m",
        "jvm.memory.used",
        "jvm.gc.max.data.size",
        "jvm.gc.pause",
        "jvm.memory.committed",
        "system.cpu.count",
        "logback.events",
        "http.server.requests",
        "tomcat.global.sent",
        "jvm.buffer.memory.used",
        "cache.eviction.weight",
        "tomcat.sessions.created",
        "jvm.threads.daemon",
        "system.cpu.usage",
        "jvm.gc.memory.allocated",
        "tomcat.global.request.max",
        "tomcat.global.request",
        "cache.gets",
        "tomcat.sessions.expired",
        "jvm.threads.live",
        "jvm.threads.peak",
        "tomcat.global.received",
        "process.uptime",
        "tomcat.sessions.rejected",
        "process.cpu.usage",
        "tomcat.threads.config.max",
        "jvm.classes.loaded",
        "jvm.classes.unloaded",
        "tomcat.global.error",
        "tomcat.sessions.active.current",
        "tomcat.sessions.alive.max",
        "jvm.gc.live.data.size",
        "tomcat.threads.current",
        "process.files.open",
        "jvm.buffer.count",
        "jvm.buffer.total.capacity",
        "tomcat.sessions.active.max",
        "tomcat.threads.busy",
        "process.start.time"
    ]
}

curl localhost:8080/actuator/metrics/cache.gets 访问缓存get情况

{
    "name":"cache.gets",
    "description":"The number of times cache lookup methods have returned a cached value.",
    "baseUnit":null,
    "measurements":[
        {
            "statistic":"COUNT",
            "value":0
        }
    ],
    "availableTags":[
        {
            "tag":"result",
            "values":[
                "hit",
                "miss"
            ]
        },
        {
            "tag":"cache",
            "values":[
                "rooms",
                "books"
            ]
        },
        {
            "tag":"name",
            "values":[
                "rooms",
                "books"
            ]
        },
        {
            "tag":"cacheManager",
            "values":[
                "cacheManager"
            ]
        }
    ]
}

curl “localhost:8080/actuator/metrics/cache.gets?tag=result:hit&tag=cache:books” 获取名为books的cache的命中次数

{
    "name":"cache.gets",
    "description":"The number of times cache lookup methods have returned a cached value.",
    "baseUnit":null,
    "measurements":[
        {
            "statistic":"COUNT",
            "value":0
        }
    ],
    "availableTags":[
        {
            "tag":"name",
            "values":[
                "books"
            ]
        },
        {
            "tag":"cacheManager",
            "values":[
                "cacheManager"
            ]
        }
    ]
}

定制化配置

默认coffeine.spec的配置是对全体配置的，如果要分开配置可以自定义实现CacheManager，参见common-spring-cache-configurer. 使用时，直接引入该jar包即可。

        <dependency>
            <groupId>com.iqiyi.intl.common</groupId>
            <artifactId>common-spring-cache-configurer</artifactId>
            <version>1.4.0-SNAPSHOT</version>
        </dependency>

使用配置如下（替换掉原先的spring.cache的配置）：

cache.items:
  - name: books
    spec: recordStats,softValues, maximumSize=1,expireAfterWrite=100s
  - name: rooms
    spec: expireAfterWrite=50s, maximumSize=10000

CacheManager自定义实现如下:

@Configuration
@ConditionalOnClass({ Caffeine.class, CaffeineCacheManager.class })
@EnableConfigurationProperties(CacheProperties.class)
public class AutoCustomizedCaffeineCacheConfigurer {
    @Autowired
    private CacheProperties cacheProperties;
    @Bean
    @Primary
    public CacheManager caffeineCacheManager() {
        SimpleCacheManager cacheManager = new SimpleCacheManager();
        List<CaffeineCache> caches = cacheProperties.getItems().stream()
                .map(item -> new CaffeineCache(item.getName(), Caffeine.from(item.getSpec()).build()))
                .collect(Collectors.toList());
        cacheManager.setCaches(caches);
        return cacheManager;
    }
}

缓存策略最佳配置

可以这么理解：

首先通过size或者weight来确定缓存总共占用的最大空间，softValues是用于兜底策略，防止万一size/weight设置的不正确，导致的OOM。对于ttl/tti，则是针对业务场景，来保证数据的时效性，用于程序运行的正确性。

举例如下：

cache.items:
  - name: books
    spec: softValues, maximumSize=1,expireAfterWrite=100s

说明：

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。

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