From 80d6d560746bfb544c2ff9b2a9a03b6841f04ef7 Mon Sep 17 00:00:00 2001 From: CyC2018 Date: Thu, 12 Dec 2019 01:20:12 +0800 Subject: [PATCH] auto commit --- docs/notes/Leetcode 题解 - 二分查找.md | 2 +- docs/notes/消息队列.md | 6 +- docs/notes/缓存.md | 282 ++++++++++++------------- notes/Leetcode 题解 - 二分查找.md | 2 +- notes/pics/image-20191212011250613.png | Bin 0 -> 33909 bytes notes/pics/image-20191212011410374.png | Bin 0 -> 35708 bytes notes/pics/image-20191212011746080.png | Bin 0 -> 55222 bytes notes/pics/image-20191212011747967.png | Bin 0 -> 55222 bytes notes/消息队列.md | 6 +- notes/缓存.md | 282 ++++++++++++------------- 10 files changed, 290 insertions(+), 290 deletions(-) create mode 100644 notes/pics/image-20191212011250613.png create mode 100644 notes/pics/image-20191212011410374.png create mode 100644 notes/pics/image-20191212011746080.png create mode 100644 notes/pics/image-20191212011747967.png diff --git a/docs/notes/Leetcode 题解 - 二分查找.md b/docs/notes/Leetcode 题解 - 二分查找.md index c93b3a4b..f00a100f 100644 --- a/docs/notes/Leetcode 题解 - 二分查找.md +++ b/docs/notes/Leetcode 题解 - 二分查找.md @@ -55,7 +55,7 @@ l + h 可能出现加法溢出,也就是说加法的结果大于整型能够 **变种** -二分查找可以有很多变种,变种实现要注意边界值的判断。例如在一个有重复元素的数组中查找 key 的最左位置的实现如下: +二分查找可以有很多变种,实现变种要注意边界值的判断。例如在一个有重复元素的数组中查找 key 的最左位置的实现如下: ```java public int binarySearch(int[] nums, int key) { diff --git a/docs/notes/消息队列.md b/docs/notes/消息队列.md index 485e25bb..68fe64fb 100644 --- a/docs/notes/消息队列.md +++ b/docs/notes/消息队列.md @@ -19,20 +19,20 @@ 消息生产者向消息队列中发送了一个消息之后,只能被一个消费者消费一次。 -

+

## 发布/订阅 消息生产者向频道发送一个消息之后,多个消费者可以从该频道订阅到这条消息并消费。 -

+

发布与订阅模式和观察者模式有以下不同: - 观察者模式中,观察者和主题都知道对方的存在;而在发布与订阅模式中,生产者与消费者不知道对方的存在,它们之间通过频道进行通信。 - 观察者模式是同步的,当事件触发时,主题会调用观察者的方法,然后等待方法返回;而发布与订阅模式是异步的,生产者向频道发送一个消息之后,就不需要关心消费者何时去订阅这个消息,可以立即返回。 -

+

# 二、使用场景 diff --git a/docs/notes/缓存.md b/docs/notes/缓存.md index f52d9c1a..8ff003ad 100644 --- a/docs/notes/缓存.md +++ b/docs/notes/缓存.md @@ -1,11 +1,11 @@ * [一、缓存特征](#一缓存特征) -* [二、LRU](#二lru) -* [三、缓存位置](#三缓存位置) -* [四、CDN](#四cdn) -* [五、缓存问题](#五缓存问题) -* [六、数据分布](#六数据分布) -* [七、一致性哈希](#七一致性哈希) +* [二、缓存位置](#二缓存位置) +* [三、CDN](#三cdn) +* [四、缓存问题](#四缓存问题) +* [五、数据分布](#五数据分布) +* [六、一致性哈希](#六一致性哈希) +* [七、LRU](#七lru) * [参考资料](#参考资料) @@ -32,7 +32,141 @@ - LFU(Least Frequently Used):最不经常使用策略,优先淘汰一段时间内使用次数最少的数据。 -# 二、LRU +# 二、缓存位置 + +## 浏览器 + +当 HTTP 响应允许进行缓存时,浏览器会将 HTML、CSS、JavaScript、图片等静态资源进行缓存。 + +## ISP + +网络服务提供商(ISP)是网络访问的第一跳,通过将数据缓存在 ISP 中能够大大提高用户的访问速度。 + +## 反向代理 + +反向代理位于服务器之前,请求与响应都需要经过反向代理。通过将数据缓存在反向代理,在用户请求反向代理时就可以直接使用缓存进行响应。 + +## 本地缓存 + +使用 Guava Cache 将数据缓存在服务器本地内存中,服务器代码可以直接读取本地内存中的缓存,速度非常快。 + +## 分布式缓存 + +使用 Redis、Memcache 等分布式缓存将数据缓存在分布式缓存系统中。 + +相对于本地缓存来说,分布式缓存单独部署,可以根据需求分配硬件资源。不仅如此,服务器集群都可以访问分布式缓存,而本地缓存需要在服务器集群之间进行同步,实现难度和性能开销上都非常大。 + +## 数据库缓存 + +MySQL 等数据库管理系统具有自己的查询缓存机制来提高查询效率。 + +## Java 内部的缓存 + +Java 为了优化空间,提高字符串、基本数据类型包装类的创建效率,设计了字符串常量池及 Byte、Short、Character、Integer、Long、Boolean 这六种包装类缓冲池。 + +## CPU 多级缓存 + +CPU 为了解决运算速度与主存 IO 速度不匹配的问题,引入了多级缓存结构,同时使用 MESI 等缓存一致性协议来解决多核 CPU 缓存数据一致性的问题。 + +# 三、CDN + +内容分发网络(Content distribution network,CDN)是一种互连的网络系统,它利用更靠近用户的服务器从而更快更可靠地将 HTML、CSS、JavaScript、音乐、图片、视频等静态资源分发给用户。 + +CDN 主要有以下优点: + +- 更快地将数据分发给用户; +- 通过部署多台服务器,从而提高系统整体的带宽性能; +- 多台服务器可以看成是一种冗余机制,从而具有高可用性。 + +

+ +# 四、缓存问题 + +## 缓存穿透 + +指的是对某个一定不存在的数据进行请求,该请求将会穿透缓存到达数据库。 + +解决方案: + +- 对这些不存在的数据缓存一个空数据; +- 对这类请求进行过滤。 + +## 缓存雪崩 + +指的是由于数据没有被加载到缓存中,或者缓存数据在同一时间大面积失效(过期),又或者缓存服务器宕机,导致大量的请求都到达数据库。 + +在有缓存的系统中,系统非常依赖于缓存,缓存分担了很大一部分的数据请求。当发生缓存雪崩时,数据库无法处理这么大的请求,导致数据库崩溃。 + +解决方案: + +- 为了防止缓存在同一时间大面积过期导致的缓存雪崩,可以通过观察用户行为,合理设置缓存过期时间来实现; +- 为了防止缓存服务器宕机出现的缓存雪崩,可以使用分布式缓存,分布式缓存中每一个节点只缓存部分的数据,当某个节点宕机时可以保证其它节点的缓存仍然可用。 +- 也可以进行缓存预热,避免在系统刚启动不久由于还未将大量数据进行缓存而导致缓存雪崩。 + + +## 缓存一致性 + +缓存一致性要求数据更新的同时缓存数据也能够实时更新。 + +解决方案: + +- 在数据更新的同时立即去更新缓存; +- 在读缓存之前先判断缓存是否是最新的,如果不是最新的先进行更新。 + +要保证缓存一致性需要付出很大的代价,缓存数据最好是那些对一致性要求不高的数据,允许缓存数据存在一些脏数据。 + +## 缓存 “无底洞” 现象 + +指的是为了满足业务要求添加了大量缓存节点,但是性能不但没有好转反而下降了的现象。 + +产生原因:缓存系统通常采用 hash 函数将 key 映射到对应的缓存节点,随着缓存节点数目的增加,键值分布到更多的节点上,导致客户端一次批量操作会涉及多次网络操作,这意味着批量操作的耗时会随着节点数目的增加而不断增大。此外,网络连接数变多,对节点的性能也有一定影响。 + +解决方案: + +- 优化批量数据操作命令; +- 减少网络通信次数; +- 降低接入成本,使用长连接 / 连接池,NIO 等。 + +# 五、数据分布 + +## 哈希分布 + +哈希分布就是将数据计算哈希值之后,按照哈希值分配到不同的节点上。例如有 N 个节点,数据的主键为 key,则将该数据分配的节点序号为:hash(key)%N。 + +传统的哈希分布算法存在一个问题:当节点数量变化时,也就是 N 值变化,那么几乎所有的数据都需要重新分布,将导致大量的数据迁移。 + +## 顺序分布 + +将数据划分为多个连续的部分,按数据的 ID 或者时间分布到不同节点上。例如 User 表的 ID 范围为 1 \~ 7000,使用顺序分布可以将其划分成多个子表,对应的主键范围为 1 \~ 1000,1001 \~ 2000,...,6001 \~ 7000。 + +顺序分布相比于哈希分布的主要优点如下: + +- 能保持数据原有的顺序; +- 并且能够准确控制每台服务器存储的数据量,从而使得存储空间的利用率最大。 + +# 六、一致性哈希 + +Distributed Hash Table(DHT) 是一种哈希分布方式,其目的是为了克服传统哈希分布在服务器节点数量变化时大量数据迁移的问题。 + +## 基本原理 + +将哈希空间 [0, 2n-1] 看成一个哈希环,每个服务器节点都配置到哈希环上。每个数据对象通过哈希取模得到哈希值之后,存放到哈希环中顺时针方向第一个大于等于该哈希值的节点上。 + +

+ +一致性哈希在增加或者删除节点时只会影响到哈希环中相邻的节点,例如下图中新增节点 X,只需要将它前一个节点 C 上的数据重新进行分布即可,对于节点 A、B、D 都没有影响。 + +

+ +## 虚拟节点 + +上面描述的一致性哈希存在数据分布不均匀的问题,节点存储的数据量有可能会存在很大的不同。 + +数据不均匀主要是因为节点在哈希环上分布的不均匀,这种情况在节点数量很少的情况下尤其明显。 + +解决方式是通过增加虚拟节点,然后将虚拟节点映射到真实节点上。虚拟节点的数量比真实节点来得多,那么虚拟节点在哈希环上分布的均匀性就会比原来的真实节点好,从而使得数据分布也更加均匀。 + +# 七、LRU 以下是基于 双向链表 + HashMap 的 LRU 算法实现,对算法的解释如下: @@ -165,140 +299,6 @@ public class LRU implements Iterable { } ``` -# 三、缓存位置 - -## 浏览器 - -当 HTTP 响应允许进行缓存时,浏览器会将 HTML、CSS、JavaScript、图片等静态资源进行缓存。 - -## ISP - -网络服务提供商(ISP)是网络访问的第一跳,通过将数据缓存在 ISP 中能够大大提高用户的访问速度。 - -## 反向代理 - -反向代理位于服务器之前,请求与响应都需要经过反向代理。通过将数据缓存在反向代理,在用户请求反向代理时就可以直接使用缓存进行响应。 - -## 本地缓存 - -使用 Guava Cache 将数据缓存在服务器本地内存中,服务器代码可以直接读取本地内存中的缓存,速度非常快。 - -## 分布式缓存 - -使用 Redis、Memcache 等分布式缓存将数据缓存在分布式缓存系统中。 - -相对于本地缓存来说,分布式缓存单独部署,可以根据需求分配硬件资源。不仅如此,服务器集群都可以访问分布式缓存,而本地缓存需要在服务器集群之间进行同步,实现难度和性能开销上都非常大。 - -## 数据库缓存 - -MySQL 等数据库管理系统具有自己的查询缓存机制来提高查询效率。 - -## Java 内部的缓存 - -Java 为了优化空间,提高字符串、基本数据类型包装类的创建效率,设计了字符串常量池及 Byte、Short、Character、Integer、Long、Boolean 这六种包装类缓冲池。 - -## CPU 多级缓存 - -CPU 为了解决运算速度与主存 IO 速度不匹配的问题,引入了多级缓存结构,同时使用 MESI 等缓存一致性协议来解决多核 CPU 缓存数据一致性的问题。 - -# 四、CDN - -内容分发网络(Content distribution network,CDN)是一种互连的网络系统,它利用更靠近用户的服务器从而更快更可靠地将 HTML、CSS、JavaScript、音乐、图片、视频等静态资源分发给用户。 - -CDN 主要有以下优点: - -- 更快地将数据分发给用户; -- 通过部署多台服务器,从而提高系统整体的带宽性能; -- 多台服务器可以看成是一种冗余机制,从而具有高可用性。 - -

- -# 五、缓存问题 - -## 缓存穿透 - -指的是对某个一定不存在的数据进行请求,该请求将会穿透缓存到达数据库。 - -解决方案: - -- 对这些不存在的数据缓存一个空数据; -- 对这类请求进行过滤。 - -## 缓存雪崩 - -指的是由于数据没有被加载到缓存中,或者缓存数据在同一时间大面积失效(过期),又或者缓存服务器宕机,导致大量的请求都到达数据库。 - -在有缓存的系统中,系统非常依赖于缓存,缓存分担了很大一部分的数据请求。当发生缓存雪崩时,数据库无法处理这么大的请求,导致数据库崩溃。 - -解决方案: - -- 为了防止缓存在同一时间大面积过期导致的缓存雪崩,可以通过观察用户行为,合理设置缓存过期时间来实现; -- 为了防止缓存服务器宕机出现的缓存雪崩,可以使用分布式缓存,分布式缓存中每一个节点只缓存部分的数据,当某个节点宕机时可以保证其它节点的缓存仍然可用。 -- 也可以进行缓存预热,避免在系统刚启动不久由于还未将大量数据进行缓存而导致缓存雪崩。 - - -## 缓存一致性 - -缓存一致性要求数据更新的同时缓存数据也能够实时更新。 - -解决方案: - -- 在数据更新的同时立即去更新缓存; -- 在读缓存之前先判断缓存是否是最新的,如果不是最新的先进行更新。 - -要保证缓存一致性需要付出很大的代价,缓存数据最好是那些对一致性要求不高的数据,允许缓存数据存在一些脏数据。 - -## 缓存 “无底洞” 现象 - -指的是为了满足业务要求添加了大量缓存节点,但是性能不但没有好转反而下降了的现象。 - -产生原因:缓存系统通常采用 hash 函数将 key 映射到对应的缓存节点,随着缓存节点数目的增加,键值分布到更多的节点上,导致客户端一次批量操作会涉及多次网络操作,这意味着批量操作的耗时会随着节点数目的增加而不断增大。此外,网络连接数变多,对节点的性能也有一定影响。 - -解决方案: - -- 优化批量数据操作命令; -- 减少网络通信次数; -- 降低接入成本,使用长连接 / 连接池,NIO 等。 - -# 六、数据分布 - -## 哈希分布 - -哈希分布就是将数据计算哈希值之后,按照哈希值分配到不同的节点上。例如有 N 个节点,数据的主键为 key,则将该数据分配的节点序号为:hash(key)%N。 - -传统的哈希分布算法存在一个问题:当节点数量变化时,也就是 N 值变化,那么几乎所有的数据都需要重新分布,将导致大量的数据迁移。 - -## 顺序分布 - -将数据划分为多个连续的部分,按数据的 ID 或者时间分布到不同节点上。例如 User 表的 ID 范围为 1 \~ 7000,使用顺序分布可以将其划分成多个子表,对应的主键范围为 1 \~ 1000,1001 \~ 2000,...,6001 \~ 7000。 - -顺序分布相比于哈希分布的主要优点如下: - -- 能保持数据原有的顺序; -- 并且能够准确控制每台服务器存储的数据量,从而使得存储空间的利用率最大。 - -# 七、一致性哈希 - -Distributed Hash Table(DHT) 是一种哈希分布方式,其目的是为了克服传统哈希分布在服务器节点数量变化时大量数据迁移的问题。 - -## 基本原理 - -将哈希空间 [0, 2n-1] 看成一个哈希环,每个服务器节点都配置到哈希环上。每个数据对象通过哈希取模得到哈希值之后,存放到哈希环中顺时针方向第一个大于等于该哈希值的节点上。 - -

- -一致性哈希在增加或者删除节点时只会影响到哈希环中相邻的节点,例如下图中新增节点 X,只需要将它前一个节点 C 上的数据重新进行分布即可,对于节点 A、B、D 都没有影响。 - -

- -## 虚拟节点 - -上面描述的一致性哈希存在数据分布不均匀的问题,节点存储的数据量有可能会存在很大的不同。 - -数据不均匀主要是因为节点在哈希环上分布的不均匀,这种情况在节点数量很少的情况下尤其明显。 - -解决方式是通过增加虚拟节点,然后将虚拟节点映射到真实节点上。虚拟节点的数量比真实节点来得多,那么虚拟节点在哈希环上分布的均匀性就会比原来的真实节点好,从而使得数据分布也更加均匀。 - # 参考资料 - 大规模分布式存储系统 diff --git a/notes/Leetcode 题解 - 二分查找.md b/notes/Leetcode 题解 - 二分查找.md index c93b3a4b..f00a100f 100644 --- a/notes/Leetcode 题解 - 二分查找.md +++ b/notes/Leetcode 题解 - 二分查找.md @@ -55,7 +55,7 @@ l + h 可能出现加法溢出,也就是说加法的结果大于整型能够 **变种** -二分查找可以有很多变种,变种实现要注意边界值的判断。例如在一个有重复元素的数组中查找 key 的最左位置的实现如下: +二分查找可以有很多变种,实现变种要注意边界值的判断。例如在一个有重复元素的数组中查找 key 的最左位置的实现如下: ```java public int binarySearch(int[] nums, int key) { diff --git a/notes/pics/image-20191212011250613.png b/notes/pics/image-20191212011250613.png new file mode 100644 index 0000000000000000000000000000000000000000..1b11dced3aea9892387e25f195f3252db33ac603 GIT binary patch literal 33909 zcmeGEXH-+`7e0!{0w{=VrKljVr7B&dC{+bPx|GnHH0dC{1yBS8M5G%KkS+uWMTm3| z0qIgAz1L7e4L!+y!~Xs6IQRSga>h9sIL2bRveta(`^-6?Sr!p5)D@{I87UzU2({94 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+

## 发布/订阅 消息生产者向频道发送一个消息之后,多个消费者可以从该频道订阅到这条消息并消费。 -

+

发布与订阅模式和观察者模式有以下不同: - 观察者模式中,观察者和主题都知道对方的存在;而在发布与订阅模式中,生产者与消费者不知道对方的存在,它们之间通过频道进行通信。 - 观察者模式是同步的,当事件触发时,主题会调用观察者的方法,然后等待方法返回;而发布与订阅模式是异步的,生产者向频道发送一个消息之后,就不需要关心消费者何时去订阅这个消息,可以立即返回。 -

+

# 二、使用场景 diff --git a/notes/缓存.md b/notes/缓存.md index f52d9c1a..8ff003ad 100644 --- a/notes/缓存.md +++ b/notes/缓存.md @@ -1,11 +1,11 @@ * [一、缓存特征](#一缓存特征) -* [二、LRU](#二lru) -* [三、缓存位置](#三缓存位置) -* [四、CDN](#四cdn) -* [五、缓存问题](#五缓存问题) -* [六、数据分布](#六数据分布) -* [七、一致性哈希](#七一致性哈希) +* [二、缓存位置](#二缓存位置) +* [三、CDN](#三cdn) +* [四、缓存问题](#四缓存问题) +* [五、数据分布](#五数据分布) +* [六、一致性哈希](#六一致性哈希) +* [七、LRU](#七lru) * [参考资料](#参考资料) @@ -32,7 +32,141 @@ - LFU(Least Frequently Used):最不经常使用策略,优先淘汰一段时间内使用次数最少的数据。 -# 二、LRU +# 二、缓存位置 + +## 浏览器 + +当 HTTP 响应允许进行缓存时,浏览器会将 HTML、CSS、JavaScript、图片等静态资源进行缓存。 + +## ISP + +网络服务提供商(ISP)是网络访问的第一跳,通过将数据缓存在 ISP 中能够大大提高用户的访问速度。 + +## 反向代理 + +反向代理位于服务器之前,请求与响应都需要经过反向代理。通过将数据缓存在反向代理,在用户请求反向代理时就可以直接使用缓存进行响应。 + +## 本地缓存 + +使用 Guava Cache 将数据缓存在服务器本地内存中,服务器代码可以直接读取本地内存中的缓存,速度非常快。 + +## 分布式缓存 + +使用 Redis、Memcache 等分布式缓存将数据缓存在分布式缓存系统中。 + +相对于本地缓存来说,分布式缓存单独部署,可以根据需求分配硬件资源。不仅如此,服务器集群都可以访问分布式缓存,而本地缓存需要在服务器集群之间进行同步,实现难度和性能开销上都非常大。 + +## 数据库缓存 + +MySQL 等数据库管理系统具有自己的查询缓存机制来提高查询效率。 + +## Java 内部的缓存 + +Java 为了优化空间,提高字符串、基本数据类型包装类的创建效率,设计了字符串常量池及 Byte、Short、Character、Integer、Long、Boolean 这六种包装类缓冲池。 + +## CPU 多级缓存 + +CPU 为了解决运算速度与主存 IO 速度不匹配的问题,引入了多级缓存结构,同时使用 MESI 等缓存一致性协议来解决多核 CPU 缓存数据一致性的问题。 + +# 三、CDN + +内容分发网络(Content distribution network,CDN)是一种互连的网络系统,它利用更靠近用户的服务器从而更快更可靠地将 HTML、CSS、JavaScript、音乐、图片、视频等静态资源分发给用户。 + +CDN 主要有以下优点: + +- 更快地将数据分发给用户; +- 通过部署多台服务器,从而提高系统整体的带宽性能; +- 多台服务器可以看成是一种冗余机制,从而具有高可用性。 + +

+ +# 四、缓存问题 + +## 缓存穿透 + +指的是对某个一定不存在的数据进行请求,该请求将会穿透缓存到达数据库。 + +解决方案: + +- 对这些不存在的数据缓存一个空数据; +- 对这类请求进行过滤。 + +## 缓存雪崩 + +指的是由于数据没有被加载到缓存中,或者缓存数据在同一时间大面积失效(过期),又或者缓存服务器宕机,导致大量的请求都到达数据库。 + +在有缓存的系统中,系统非常依赖于缓存,缓存分担了很大一部分的数据请求。当发生缓存雪崩时,数据库无法处理这么大的请求,导致数据库崩溃。 + +解决方案: + +- 为了防止缓存在同一时间大面积过期导致的缓存雪崩,可以通过观察用户行为,合理设置缓存过期时间来实现; +- 为了防止缓存服务器宕机出现的缓存雪崩,可以使用分布式缓存,分布式缓存中每一个节点只缓存部分的数据,当某个节点宕机时可以保证其它节点的缓存仍然可用。 +- 也可以进行缓存预热,避免在系统刚启动不久由于还未将大量数据进行缓存而导致缓存雪崩。 + + +## 缓存一致性 + +缓存一致性要求数据更新的同时缓存数据也能够实时更新。 + +解决方案: + +- 在数据更新的同时立即去更新缓存; +- 在读缓存之前先判断缓存是否是最新的,如果不是最新的先进行更新。 + +要保证缓存一致性需要付出很大的代价,缓存数据最好是那些对一致性要求不高的数据,允许缓存数据存在一些脏数据。 + +## 缓存 “无底洞” 现象 + +指的是为了满足业务要求添加了大量缓存节点,但是性能不但没有好转反而下降了的现象。 + +产生原因:缓存系统通常采用 hash 函数将 key 映射到对应的缓存节点,随着缓存节点数目的增加,键值分布到更多的节点上,导致客户端一次批量操作会涉及多次网络操作,这意味着批量操作的耗时会随着节点数目的增加而不断增大。此外,网络连接数变多,对节点的性能也有一定影响。 + +解决方案: + +- 优化批量数据操作命令; +- 减少网络通信次数; +- 降低接入成本,使用长连接 / 连接池,NIO 等。 + +# 五、数据分布 + +## 哈希分布 + +哈希分布就是将数据计算哈希值之后,按照哈希值分配到不同的节点上。例如有 N 个节点,数据的主键为 key,则将该数据分配的节点序号为:hash(key)%N。 + +传统的哈希分布算法存在一个问题:当节点数量变化时,也就是 N 值变化,那么几乎所有的数据都需要重新分布,将导致大量的数据迁移。 + +## 顺序分布 + +将数据划分为多个连续的部分,按数据的 ID 或者时间分布到不同节点上。例如 User 表的 ID 范围为 1 \~ 7000,使用顺序分布可以将其划分成多个子表,对应的主键范围为 1 \~ 1000,1001 \~ 2000,...,6001 \~ 7000。 + +顺序分布相比于哈希分布的主要优点如下: + +- 能保持数据原有的顺序; +- 并且能够准确控制每台服务器存储的数据量,从而使得存储空间的利用率最大。 + +# 六、一致性哈希 + +Distributed Hash Table(DHT) 是一种哈希分布方式,其目的是为了克服传统哈希分布在服务器节点数量变化时大量数据迁移的问题。 + +## 基本原理 + +将哈希空间 [0, 2n-1] 看成一个哈希环,每个服务器节点都配置到哈希环上。每个数据对象通过哈希取模得到哈希值之后,存放到哈希环中顺时针方向第一个大于等于该哈希值的节点上。 + +

+ +一致性哈希在增加或者删除节点时只会影响到哈希环中相邻的节点,例如下图中新增节点 X,只需要将它前一个节点 C 上的数据重新进行分布即可,对于节点 A、B、D 都没有影响。 + +

+ +## 虚拟节点 + +上面描述的一致性哈希存在数据分布不均匀的问题,节点存储的数据量有可能会存在很大的不同。 + +数据不均匀主要是因为节点在哈希环上分布的不均匀,这种情况在节点数量很少的情况下尤其明显。 + +解决方式是通过增加虚拟节点,然后将虚拟节点映射到真实节点上。虚拟节点的数量比真实节点来得多,那么虚拟节点在哈希环上分布的均匀性就会比原来的真实节点好,从而使得数据分布也更加均匀。 + +# 七、LRU 以下是基于 双向链表 + HashMap 的 LRU 算法实现,对算法的解释如下: @@ -165,140 +299,6 @@ public class LRU implements Iterable { } ``` -# 三、缓存位置 - -## 浏览器 - -当 HTTP 响应允许进行缓存时,浏览器会将 HTML、CSS、JavaScript、图片等静态资源进行缓存。 - -## ISP - -网络服务提供商(ISP)是网络访问的第一跳,通过将数据缓存在 ISP 中能够大大提高用户的访问速度。 - -## 反向代理 - -反向代理位于服务器之前,请求与响应都需要经过反向代理。通过将数据缓存在反向代理,在用户请求反向代理时就可以直接使用缓存进行响应。 - -## 本地缓存 - -使用 Guava Cache 将数据缓存在服务器本地内存中,服务器代码可以直接读取本地内存中的缓存,速度非常快。 - -## 分布式缓存 - -使用 Redis、Memcache 等分布式缓存将数据缓存在分布式缓存系统中。 - -相对于本地缓存来说,分布式缓存单独部署,可以根据需求分配硬件资源。不仅如此,服务器集群都可以访问分布式缓存,而本地缓存需要在服务器集群之间进行同步,实现难度和性能开销上都非常大。 - -## 数据库缓存 - -MySQL 等数据库管理系统具有自己的查询缓存机制来提高查询效率。 - -## Java 内部的缓存 - -Java 为了优化空间,提高字符串、基本数据类型包装类的创建效率,设计了字符串常量池及 Byte、Short、Character、Integer、Long、Boolean 这六种包装类缓冲池。 - -## CPU 多级缓存 - -CPU 为了解决运算速度与主存 IO 速度不匹配的问题,引入了多级缓存结构,同时使用 MESI 等缓存一致性协议来解决多核 CPU 缓存数据一致性的问题。 - -# 四、CDN - -内容分发网络(Content distribution network,CDN)是一种互连的网络系统,它利用更靠近用户的服务器从而更快更可靠地将 HTML、CSS、JavaScript、音乐、图片、视频等静态资源分发给用户。 - -CDN 主要有以下优点: - -- 更快地将数据分发给用户; -- 通过部署多台服务器,从而提高系统整体的带宽性能; -- 多台服务器可以看成是一种冗余机制,从而具有高可用性。 - -

- -# 五、缓存问题 - -## 缓存穿透 - -指的是对某个一定不存在的数据进行请求,该请求将会穿透缓存到达数据库。 - -解决方案: - -- 对这些不存在的数据缓存一个空数据; -- 对这类请求进行过滤。 - -## 缓存雪崩 - -指的是由于数据没有被加载到缓存中,或者缓存数据在同一时间大面积失效(过期),又或者缓存服务器宕机,导致大量的请求都到达数据库。 - -在有缓存的系统中,系统非常依赖于缓存,缓存分担了很大一部分的数据请求。当发生缓存雪崩时,数据库无法处理这么大的请求,导致数据库崩溃。 - -解决方案: - -- 为了防止缓存在同一时间大面积过期导致的缓存雪崩,可以通过观察用户行为,合理设置缓存过期时间来实现; -- 为了防止缓存服务器宕机出现的缓存雪崩,可以使用分布式缓存,分布式缓存中每一个节点只缓存部分的数据,当某个节点宕机时可以保证其它节点的缓存仍然可用。 -- 也可以进行缓存预热,避免在系统刚启动不久由于还未将大量数据进行缓存而导致缓存雪崩。 - - -## 缓存一致性 - -缓存一致性要求数据更新的同时缓存数据也能够实时更新。 - -解决方案: - -- 在数据更新的同时立即去更新缓存; -- 在读缓存之前先判断缓存是否是最新的,如果不是最新的先进行更新。 - -要保证缓存一致性需要付出很大的代价,缓存数据最好是那些对一致性要求不高的数据,允许缓存数据存在一些脏数据。 - -## 缓存 “无底洞” 现象 - -指的是为了满足业务要求添加了大量缓存节点,但是性能不但没有好转反而下降了的现象。 - -产生原因:缓存系统通常采用 hash 函数将 key 映射到对应的缓存节点,随着缓存节点数目的增加,键值分布到更多的节点上,导致客户端一次批量操作会涉及多次网络操作,这意味着批量操作的耗时会随着节点数目的增加而不断增大。此外,网络连接数变多,对节点的性能也有一定影响。 - -解决方案: - -- 优化批量数据操作命令; -- 减少网络通信次数; -- 降低接入成本,使用长连接 / 连接池,NIO 等。 - -# 六、数据分布 - -## 哈希分布 - -哈希分布就是将数据计算哈希值之后,按照哈希值分配到不同的节点上。例如有 N 个节点,数据的主键为 key,则将该数据分配的节点序号为:hash(key)%N。 - -传统的哈希分布算法存在一个问题:当节点数量变化时,也就是 N 值变化,那么几乎所有的数据都需要重新分布,将导致大量的数据迁移。 - -## 顺序分布 - -将数据划分为多个连续的部分,按数据的 ID 或者时间分布到不同节点上。例如 User 表的 ID 范围为 1 \~ 7000,使用顺序分布可以将其划分成多个子表,对应的主键范围为 1 \~ 1000,1001 \~ 2000,...,6001 \~ 7000。 - -顺序分布相比于哈希分布的主要优点如下: - -- 能保持数据原有的顺序; -- 并且能够准确控制每台服务器存储的数据量,从而使得存储空间的利用率最大。 - -# 七、一致性哈希 - -Distributed Hash Table(DHT) 是一种哈希分布方式,其目的是为了克服传统哈希分布在服务器节点数量变化时大量数据迁移的问题。 - -## 基本原理 - -将哈希空间 [0, 2n-1] 看成一个哈希环,每个服务器节点都配置到哈希环上。每个数据对象通过哈希取模得到哈希值之后,存放到哈希环中顺时针方向第一个大于等于该哈希值的节点上。 - -

- -一致性哈希在增加或者删除节点时只会影响到哈希环中相邻的节点,例如下图中新增节点 X,只需要将它前一个节点 C 上的数据重新进行分布即可,对于节点 A、B、D 都没有影响。 - -

- -## 虚拟节点 - -上面描述的一致性哈希存在数据分布不均匀的问题,节点存储的数据量有可能会存在很大的不同。 - -数据不均匀主要是因为节点在哈希环上分布的不均匀,这种情况在节点数量很少的情况下尤其明显。 - -解决方式是通过增加虚拟节点,然后将虚拟节点映射到真实节点上。虚拟节点的数量比真实节点来得多,那么虚拟节点在哈希环上分布的均匀性就会比原来的真实节点好,从而使得数据分布也更加均匀。 - # 参考资料 - 大规模分布式存储系统