diff --git a/docs/database/redis/3-commonly-used-cache-read-and-write-strategies.md b/docs/database/redis/3-commonly-used-cache-read-and-write-strategies.md
index c60fa4c5..9a885ccd 100644
--- a/docs/database/redis/3-commonly-used-cache-read-and-write-strategies.md
+++ b/docs/database/redis/3-commonly-used-cache-read-and-write-strategies.md
@@ -12,105 +12,106 @@ tag:
 
 但是，搞懂3种常见的缓存读写策略对于实际工作中使用缓存以及面试中被问到缓存都是非常有帮助的！
 
-下面我会简单介绍一下自己对于这 3 种缓存读写策略的理解。 
-
-另外，**这3 种缓存读写策略各有优劣，不存在最佳，需要我们根据具体的业务场景选择更适合的。**
-
-*个人能力有限。如果文章有任何需要补充/完善/修改的地方，欢迎在评论区指出，共同进步！——爱你们的 Guide 哥*
+**下面介绍到的三种模式各有优劣，不存在最佳模式，根据具体的业务场景选择适合自己的缓存读写模式。**
 
 ### Cache Aside Pattern（旁路缓存模式）
 
 **Cache Aside Pattern 是我们平时使用比较多的一个缓存读写模式，比较适合读请求比较多的场景。**
 
-Cache Aside Pattern 中服务端需要同时维系 DB 和 cache，并且是以 DB 的结果为准。
+Cache Aside Pattern 中服务端需要同时维系 db 和 cache，并且是以 db 的结果为准。
 
 下面我们来看一下这个策略模式下的缓存读写步骤。
 
 **写** ：
 
-- 先更新 DB
+- 先更新 db
 - 然后直接删除 cache 。
 
 简单画了一张图帮助大家理解写的步骤。
 
-![](https://img-blog.csdnimg.cn/img_convert/5687fe759a1dac9ed9554d27e3a23b6d.png)
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/cache-aside-write.png)
 
 **读** :
 
 - 从 cache 中读取数据，读取到就直接返回
-- cache中读取不到的话，就从 DB 中读取数据返回
+- cache 中读取不到的话，就从 db 中读取数据返回
 - 再把数据放到 cache 中。
 
 简单画了一张图帮助大家理解读的步骤。
 
-![](https://img-blog.csdnimg.cn/img_convert/a8c18b5f5b1aed03234bcbbd8c173a87.png)
-
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/cache-aside-read.png)
 
 你仅仅了解了上面这些内容的话是远远不够的，我们还要搞懂其中的原理。
 
-比如说面试官很可能会追问：“**在写数据的过程中，可以先删除 cache ，后更新 DB 么？**”
+比如说面试官很可能会追问：“**在写数据的过程中，可以先删除 cache ，后更新 db 么？**”
 
-**答案：** 那肯定是不行的！因为这样可能会造成**数据库（DB）和缓存（Cache）数据不一致**的问题。为什么呢？比如说请求1 先写数据A，请求2随后读数据A的话就很有可能产生数据不一致性的问题。这个过程可以简单描述为：
+**答案：** 那肯定是不行的！因为这样可能会造成 **数据库（db）和缓存（Cache）数据不一致**的问题。
 
-> 请求1先把cache中的A数据删除 -> 请求2从DB中读取数据->请求1再把DB中的A数据更新。
+举例：请求 1 先写数据 A，请求 2 随后读数据 A 的话，就很有可能产生数据不一致性的问题。
 
-当你这样回答之后，面试官可能会紧接着就追问：“**在写数据的过程中，先更新DB，后删除cache就没有问题了么？**”
+这个过程可以简单描述为：
 
-**答案：** 理论上来说还是可能会出现数据不一致性的问题，不过概率非常小，因为缓存的写入速度是比数据库的写入速度快很多！
+> 请求 1 先把 cache 中的 A 数据删除 -> 请求 2 从 db 中读取数据->请求 1 再把 db 中的 A 数据更新
 
-比如请求1先读数据 A，请求2随后写数据A，并且数据A不在缓存中的话也有可能产生数据不一致性的问题。这个过程可以简单描述为：
+当你这样回答之后，面试官可能会紧接着就追问：“**在写数据的过程中，先更新 db，后删除 cache 就没有问题了么？**”
 
-> 请求1从DB读数据A->请求2写更新数据 A 到数据库并把删除cache中的A数据->请求1将数据A写入cache。
+**答案：** 理论上来说还是可能会出现数据不一致性的问题，不过概率非常小，因为缓存的写入速度是比数据库的写入速度快很多。
+
+举例：请求 1 先读数据 A，请求 2 随后写数据 A，并且数据 A 在请求 1 请求之前不在缓存中的话，也有可能产生数据不一致性的问题。
+
+这个过程可以简单描述为：
+
+> 请求 1 从 db 读数据 A-> 请求 2 更新 db 中的数据 A（此时缓存中无数据 A ，故不用执行删除缓存操作 ） -> 请求 1 将数据 A 写入 cache
 
 现在我们再来分析一下 **Cache Aside Pattern 的缺陷**。
 
-**缺陷1：首次请求数据一定不在 cache 的问题** 
+**缺陷 1：首次请求数据一定不在 cache 的问题**
 
-解决办法：可以将热点数据可以提前放入cache 中。
+解决办法：可以将热点数据可以提前放入 cache 中。
 
-**缺陷2：写操作比较频繁的话导致cache中的数据会被频繁被删除，这样会影响缓存命中率 。**
+**缺陷 2：写操作比较频繁的话导致 cache 中的数据会被频繁被删除，这样会影响缓存命中率 。**
 
 解决办法：
 
-- 数据库和缓存数据强一致场景 ：更新DB的时候同样更新cache，不过我们需要加一个锁/分布式锁来保证更新cache的时候不存在线程安全问题。
-- 可以短暂地允许数据库和缓存数据不一致的场景 ：更新DB的时候同样更新cache，但是给缓存加一个比较短的过期时间，这样的话就可以保证即使数据不一致的话影响也比较小。
+- 数据库和缓存数据强一致场景 ：更新 db 的时候同样更新 cache，不过我们需要加一个锁/分布式锁来保证更新 cache 的时候不存在线程安全问题。
+- 可以短暂地允许数据库和缓存数据不一致的场景 ：更新 db 的时候同样更新 cache，但是给缓存加一个比较短的过期时间，这样的话就可以保证即使数据不一致的话影响也比较小。
 
 ### Read/Write Through Pattern（读写穿透）
 
-Read/Write Through Pattern 中服务端把 cache 视为主要数据存储，从中读取数据并将数据写入其中。cache 服务负责将此数据读取和写入 DB，从而减轻了应用程序的职责。
+Read/Write Through Pattern 中服务端把 cache 视为主要数据存储，从中读取数据并将数据写入其中。cache 服务负责将此数据读取和写入 db，从而减轻了应用程序的职责。
 
-这种缓存读写策略小伙伴们应该也发现了在平时在开发过程中非常少见。抛去性能方面的影响，大概率是因为我们经常使用的分布式缓存 Redis 并没有提供 cache 将数据写入DB的功能。
+这种缓存读写策略小伙伴们应该也发现了在平时在开发过程中非常少见。抛去性能方面的影响，大概率是因为我们经常使用的分布式缓存 Redis 并没有提供 cache 将数据写入 db 的功能。
 
 **写（Write Through）：**
 
-- 先查 cache，cache 中不存在，直接更新 DB。
-- cache 中存在，则先更新 cache，然后 cache 服务自己更新 DB（**同步更新 cache 和 DB**）。
+- 先查 cache，cache 中不存在，直接更新 db。
+- cache 中存在，则先更新 cache，然后 cache 服务自己更新 db（**同步更新 cache 和 db**）。
 
 简单画了一张图帮助大家理解写的步骤。
 
-![](https://img-blog.csdnimg.cn/20210201100340808.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM0MzM3Mjcy,size_16,color_FFFFFF,t_70)
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/write-through.png)
 
-**读(Read Through)：** 
+**读(Read Through)：**
 
 - 从 cache 中读取数据，读取到就直接返回 。
-- 读取不到的话，先从 DB 加载，写入到 cache 后返回响应。
+- 读取不到的话，先从 db 加载，写入到 cache 后返回响应。
 
 简单画了一张图帮助大家理解读的步骤。
 
-![](https://img-blog.csdnimg.cn/img_convert/9ada757c78614934aca11306f334638d.png)
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/read-through.png)
 
-Read-Through Pattern 实际只是在 Cache-Aside Pattern 之上进行了封装。在 Cache-Aside Pattern 下，发生读请求的时候，如果 cache 中不存在对应的数据，是由客户端自己负责把数据写入 cache，而 Read-Through Pattern 则是 cache 服务自己来写入缓存的，这对客户端是透明的。
+Read-Through Pattern 实际只是在 Cache-Aside Pattern 之上进行了封装。在 Cache-Aside Pattern 下，发生读请求的时候，如果 cache 中不存在对应的数据，是由客户端自己负责把数据写入 cache，而 Read Through Pattern 则是 cache 服务自己来写入缓存的，这对客户端是透明的。
 
 和 Cache Aside Pattern 一样， Read-Through Pattern 也有首次请求数据一定不再 cache 的问题，对于热点数据可以提前放入缓存中。
 
 ### Write Behind Pattern（异步缓存写入）
 
-Write Behind Pattern 和 Read/Write Through Pattern 很相似，两者都是由 cache 服务来负责 cache 和 DB 的读写。
+Write Behind Pattern 和 Read/Write Through Pattern 很相似，两者都是由 cache 服务来负责 cache 和 db 的读写。
 
-但是，两个又有很大的不同：**Read/Write Through 是同步更新 cache 和 DB，而 Write Behind Caching 则是只更新缓存，不直接更新 DB，而是改为异步批量的方式来更新 DB。**
+但是，两个又有很大的不同：**Read/Write Through 是同步更新 cache 和 db，而 Write Behind 则是只更新缓存，不直接更新 db，而是改为异步批量的方式来更新 db。**
 
-很明显，这种方式对数据一致性带来了更大的挑战，比如cache数据可能还没异步更新DB的话，cache服务可能就就挂掉了。
+很明显，这种方式对数据一致性带来了更大的挑战，比如 cache 数据可能还没异步更新 db 的话，cache 服务可能就就挂掉了。
 
-这种策略在我们平时开发过程中也非常非常少见，但是不代表它的应用场景少，比如消息队列中消息的异步写入磁盘、MySQL 的 InnoDB Buffer Pool 机制都用到了这种策略。
+这种策略在我们平时开发过程中也非常非常少见，但是不代表它的应用场景少，比如消息队列中消息的异步写入磁盘、MySQL 的 Innodb Buffer Pool 机制都用到了这种策略。
 
-Write Behind Pattern 下 DB 的写性能非常高，非常适合一些数据经常变化又对数据一致性要求没那么高的场景，比如浏览量、点赞量。
+Write Behind Pattern 下 db 的写性能非常高，非常适合一些数据经常变化又对数据一致性要求没那么高的场景，比如浏览量、点赞量。
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@@ -0,0 +1 @@
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diff --git a/docs/database/redis/images/redis-event-handler.drawio b/docs/database/redis/images/redis-event-handler.drawio
new file mode 100644
index 00000000..0bd93481
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index 00000000..6a12bdc5
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diff --git a/docs/database/redis/images/缓存读写策略/cache-aside-read.drawio b/docs/database/redis/images/缓存读写策略/cache-aside-read.drawio
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TOoxgBwL02t6pPg7ZK8MU+2GgWInYzR95ue8dICqCUF77Q6ON86xVDOWKycHO99g4GfwRfmfohyU2sEgumdoyPRAgPXbGlNXK0nk31Zx8hWyCzDxcesnDpwv0MfptRJzVUhtGM2QpMNetdErfMSBhl9WH5m/OuW6NGeArapZthVY8Tl5Q29KhFeTteRsEba3dDAwcrN6O9aenju2lRIdeZYpWdZW4eKChXCH1qFHsxq3k9HoGQMBnJwOFCN6ctYEX3yK+wv3qTsZbGfp4j6wL2FUnAJqVoXraeiEo5AJaSy0pv4ilqfRb48mIbvJ21Nuseycs+myuUHMmiq/CM+3E9bvAh72XVSvl1viex2PDbSN3THUnjWMCCFCWCf7Yegu7dfFtM6Ipo0NUjXb08xqMBpmyaDL/dUOgxbP4UITnT2mHnWIiMTdBSnDSM8AstqK3zDoc8CLVXOVSos3ehCsKvJOyCuiRATHxDj+u7bmt3K4xpmC+nWhVfLQRqIU/pIquMMZ1NToBRy4NmkV9jP2TY4N2+XOBfc3Go11A2w6i7sXXbUzKFtqnLrIHGrG4sl1S3qlkpbAB4/Gk2wdr3R/HeHiWQGGZZdmbsrXRl5k4oILkwn6cJ70E7pqnFfRm1clKy25fSei11YQzFXQsQnOWB/+t1xFAh4m8r84r6E72YydLM4O32Mp6fFtWjB5UZvETrRt+svhgWF9aULUi54kO2DvliM6axG3F2K+g6nNGgr+069DBxrllU7rUdlylHSo+dpMxOEaNmIHNpLcfLm0MFuvbKqg4JquPZgS8/0IkWhBkNb+6iN0/F1an1JtzQ+yN8fXOzC0MM2dvzCIrOufQ1zCxBjcIpzbQZEztN9exDfxQzebkBJTz+pJ6nuNty1yFFgwu01JtOLp5ajGOVLXpRlWPvvXygYMw7+f6zW7Wmjd+fq68byr3b5b1lmHwVIcWIqeENb+/GVtHl9BVMxXwzEOVguxjMXFNvxdMTCkpzMd365O5HCWwTQ7B8tQWCyd2GJmeDEjn7oiATaO5WqUguLHJLsrCaQikLP5LwfpzWgFRbHXBFP+ER7qES1HQJ8O15UUyoRLdz6BpADaUxziS+8x7b220lVaVkX01qQ1FXY8IN3nPZXWSaN81NmUxhupTqE8/1WINhkb3ISd9FthfhaN2PWVXQMbLQB3WNutR96MssaygyP6MBIK+wCCfbkloAUAp6RlQPPvTGxJhfdHMFmS+/q2MvW2YMhICsqdK6+rbqSCHL9IL1pF4D1uddFk9siEkiMixgwMunQwJyIDXXoH0ZWIYSFjJR1imCU/MJJcEd9vkZI4P7TXT3Brx0q4gUG9Fu79IreRvTJw7sBWO5bXB0znWese5q3S5Sc4ZZx9ge7L5U7DAHfH5kh1/sykK8VRy0TkTw3DyI/DGdV4zpVJrwBXNA1nGDiMhOn0IONsvz+fNNmWRVkkRRiDpbHInrBW6DOUbBygqN+ToedAHMm69lZx7AKeQJnNEa/PLETQplvmRCmSf7VjZaLCOdfiEk3VKEjDg2xtMpKhOykKk17lIMAYuadQGWECfQQjxaZnTlZHmU+N3izDG9e3l1O41yXajo21XalqbjzqQyaXnYuHjVK3yRr34yQqyZKC2I/IeuXarMTwWUCtpeXEyXiyq4TJNWNRZ9U5fyrXByqXEIWGF6t80mgNLPLh88H+un7njX3cbnPW0n4Eb4HvZuyjkNxTCTTji40sqj86FPXrYstzD1gtVFIXKyl4l2KphsqSPAkbg3N64Ypzs3sF/0rKt5lLd0ynsJ/RTt4LYzN+iNLLeRgLah24vm1MLbUNwuSJO2h8AbQOpIuoIPWN9jWjfPt8Pv0fnZWNdwSMjwmAVIDW3nGeu1ls1DT5UHCVEYHGMnE8tsCTlgKx+bhCq6rDPGcbNJhC4OSqvYlD4C2j09dPK2hRB3sbW6KvULOMn9Mx/69kZkyg+QGdH9DjeJ3g6ZFpuYcHcSN0pEE6UAkscoUZAzyKqFAb5lMFzOVx3HCNKSdp8S2areXOkJnAAcHpdZnASHRXaT0773Ky4HRk/PqidvfjW+hlhnLISVrFGjq9P1GCBDyhJT+S5/eTlBrahfbVffUzoX5kg1sn4v5uf5FXdwCQK63VmtZrhWJEatVvJ+mZbytNOrGtcbG/NNySPsEj0B6DF1u5xcoXXpdMxVuqUXx1SjE5SY2+lLcmwhLynbuZfSTZX7N6HZwCtQlU/ARCNKUCT7Kr7LZ7A9yKFWpV9J2LyYj/1nGWZNumRQqK4VyE47gEJDqlhVOJEgWITXDqSwJmmYQURleOMAp01dYLP5RFeC5tw7XMamzoiFpWaRC5pMXYqlay6/V/LXKQ9twMOv2rFuWHASk4JvduJ/NIgQ4hlCbnaZz+hjE9cuhljCLPlzlDwlx5lBVPc+GtvlbmnOMcbr/WI7MvUKLPIiVlglrMt/C9tNnOJEV9ZnJvVGf5J6c8TuFTMcrj6GSeHSIKpBrODbfUDNjDZvhuOG5PnU2A6jPmVftu9xNHymtC+etq66zhpOJl1GOEiRbvdrjf2Na4wl7w4WCt6w1DBp+NJ7WFI7bfNVo7+NPd5alubYYmLNL7CyPkAwTIxjYPwqiKqkmPNyiSL/eSMvs2zUj7IbQpIQZB25xqAeJrOlfKsa9W1DuE4IMhUskifuQtu4Ze3UftsxB+RgXmK5JyibvTgZLZ4999MHWIYGeIL4c3XPjL2or+AqMVp8j1T8+7YnkgMlPdFl7pshO2hAUnID48wczY4enKhQmACstUclzx60kunZkmq0QjFzpvbj0viHsIVQHwm4n5FNj15PLMdy3kk6NjiKjIZJzHDhZPG5jNTFr4zi2UR8QlWk98Peh/GB6WN15TOwCWborl7BMDCqfhAEkBgxzORzLnb/MbrO6ntQ1Dq0thswAKtr52DPaNBrtlzSG9RauR2KwaQ4lS6TWYoZoSZwhq/FjBMT/aTQVBpA2zwi71BrKbw9Dv1tJMnSOUAm2ROqP/v+IkdbAWDeT+SrNJl8wKZQ9itlvKbAqcEuXYGstki2jUQK3C8bG4UPPRrCh9vcYDVDoj5TdDiNVTMMWr+ZEN7e+09ynDGlyKoaCqChI7eLV3JwLCczgEHWskouyH5Zk87BISE1PU30puGI3rx+TIIhwjfttHrtmE5Q9+UsAEQUOTAKFkxW8kK4kbXZM70LAkyVnYC425p0PTodK71fv2GBKGj/duNXNk/Vcuc+fIhbN0ln82EgIEol1fzZ4NsDju7HRGZpRHovdrt2Gal2sxWhMsoQiVBO89PxkSm0aTOneAcdhCKwJbPmXQ1goMeIGiq5OUUiNppm7cwoFopMpNs4jfZyOxDEcR2iXVFGGEaKFad2KpBcZxaUH9EcC2L+46yogrhgpJEgliJ1aNKNRIrOcvABuk0CBFIGLgvAli0C3V6cj25l5UiEqfmI8WSBgIOEzQ/XI317jLVFROdHeG+moYTpLHWn/UTVfCDyNd8FXQKmlGbTqGSeUjiDH5VpxxwXBSEhg+tNtTfn+LRGb3MNn8ubHNl5bBoxln/FT9AxRWqlPVnGyTxH8fzy9NnYus3noyH3I3CMZK4lj+OSODzSPaQWvEGWjD/3Ly3++C9UCASQqRkt2Y7DDg3LEkSHtDd/MktCnFcMecIK8yQgxE2uj/3WqBdx+1Fp8v54DR7mNz8MA9J+Nq83LUGaH48G+6rUCyLDjARyjSAMEreLlOLIx3OBIKdcIYRZ66mU3z1qa7Z9VVFyUayVnhge0ACfPr3PBx/uSoxTUi4RwGCEx89J3Lom2xQ9VQxSqpWZGj7Lc07Ge7r/vis4z1mbSzgYiD79xfL6qO82Saf2VSGRho+6373xvu6Dw4cLmNriPMJ5mAD69Dbx20Ng7yrPKZmUawm6ZOqu4zQYZPS7dL878mzynKoSluNqpoRQgSA8p7q/Shj4dw/g/34WwfnHVIC/ezr+r8gZ+OXpOPqLDBPsKyHk9eSCEPS3N/5fNBfkV9ke1o9B/iK5/v/jjv1McIB/pn38bs8w8D8xywP635/lgUD/QKIU/Cs5Rv+j5PgfWJX/a7M8bKD+c5ZHiu6QaXYsDZY76dqM31DHew9GMt+gqWKs9WShWXEk47Eae3mMWNCvZaCavaWdbwKVqC1QS8oYPIfl54f/tYr87MmWYl9BqbB5ha/P7ccfEIaB9nX7idcQdte65+3B7MSyMAGRVsFuKbscmArD4qlZkrJJ7IZ/IBzB2IeaEUxS0DutCnwLr8tNSmyRYjV1oi9GJ0ilo6/I8e7pi3NB8hlSIJSY51lcIiAjGArDeHCb68qOk8xp6CqSRhQhtCcjJJjHt4JHUYjGt06+kDthhqWkvC96v/sg8Bd7jIJRBgKxHIuQhQVMiHWfxdYJMISp1kKAQYqu3H3wZ8JrQ2GReV5eDGAiLZ8LN/VCVnlyjKfjwq6iHT/onbDFHYeeuBRBxYJhvtYge/p4JpeaFEOTJLuKPX0pOmEqNX1lvpfn1D05itfAAinZPF/tAmkE2UhuOxmUlZ78nNwJudDQWdEyNNfD04UnJI1/cYYCmN7yc9K91gqWHr6evD5PyFB8bxOkP5t39JRY55Ff8a8n6G8vTCq6IHJ1oD3iINuSkkjePgIYAE8glVMxbubq05g+0HtFjdgC+2YPnTYb2uR1j/V9OukT0GhgKzmipXKElqI7UnOi+p4qageFEVIIhOG0E8M1aq3WVsYD+VI+qfoVKQyBXEUkIIQTxzpJpoNWOkS6imjq/CJay15BO+EQpKkDg+LJl+UvMlRvVpN9WoCZqiCMkYDKUmdsU2QcUqJmIQq1CE6yryeSmbGR7/c2zPT8PvE6cH6Fxdt3Zy2kXFxCIqf7xuaRfC8XA/O220YeZL/mCwbyJIzzTG4zu4sgJa9wUaQbP5Q0ydUfd0H2lcIinog/a8G4Qrz1cVp3l7NeJSfCqLmguYVMqEGLKrgCDRrEz3hCK4VnNAJtPhMdyhdl67sn9SziQDyj2z/Tmwfyufn44AgjcGRYcIF6LcmzOp1N3vfTbEQGEMRnuFOehUGEgZRUNQDBdx5XRIHpa4wZmMwq6hy9nZHPyvYQsYfvr61qhNdHFUgiN66vtshIE3I5JSpbTAlXDBnhgYI6k5EiuB9Ab44LypYRPpXXseYKzLjYQMLF6phospnKWGQgHq5ERTgERqn38S2rLLXT03NgZqoiwevo8aKomjrcWUSzUbm5mT+kdiaqzVAThlHAAvlugeTD8spsV9UVp6UzLX4E0rcgRJZsDtp14p/M+NiI7mk1B9vvt8Nj+IBGmqUXkoB/5lZ3o815Hfe+3N1lDfoOe9TEXziOtS+RktQWU1Xf3xHifGY4oPFxEUSo3xLpnIyycSHmoGvMPH5abGFDJTDXEh5jOcpNHClDJN4AkhTUs/s9S1BMcdyGIovk5Jr4KVEPipMEZ3K1ftwjVb0sY2FW41V3wYIRdNbq99pjFuChH8rRprIubkmwOCtuRhhf3tI6jfXr8FYVW1Ewr06xbvWTxNTVmK3E6IdXA+12k4JqLmNkJBgsO0mtxm+Pj9p+BqtPnPotk3y7DozG0VJov0vIqgYagBwZXMNxYTyCFSv9CW32HpE1LEy7Z79ux+iVjDC7Bs7sO6jXQiqttW2cy/lgxM5gBVXyfAnzkMRcpqqp+sQfbCDs48vwjSQ/Z+V1aBTdtyBOkvDH6CXuBmNbfUg2MQcfi6i5oJnq5px8n+zkPUjuMXh0LuIZZeUNV02+uvBAkZ4UkvTgLpJ6cWVf5kYpwK7i7Jr1vbCi5AM2eHqQTuOdecrbT3UDIuyisbjt3UlKm+lEJwjr/GIYnHRkqJFU7zFTkwUP5jV2zdcpwKc9CZ164vexk3/OtG5nYTJVcmfxY1hXl9PVRzcWm0J8Sb9CMhnDT9+Wm2f0BIgpBylXnsPrtQFRUmJs6nTPWa3u5ypY2SoLF8yaVegEpQgWfyM2w30lLpF4xSizd1YAZLyBpvT9VvKdj5WCiqRx+EVpFBafH6h4MiPeCwDMznlh9/wQW6UJzF5zJ+FHCOg66BDID19NpHBw70/nUIS0xvRKLPL7yfowjO5dBlOq1oxNlGqKflohDCGcJvyDv1hIai1iiAQXgxhoSettbid1yw3yJT0OpHXwqlAOdS55JbsJ1t6/xLLd4ecYQDhAUWrafDSeWx9xbY/Wc9NYVoTt9gyDS4pVhjnzSA+sPX5OP42jB2vHZjSi4qHnoHf1IgqhBP8s+LIqNPyJlS2EQMy+fHdsIalSE5ImpFYKJfZkGCPhRYSU17mYPhlJJ1+a0L4bAyySAWnNohAMPksGjBpxo1PO0gpAkUDUGipubvSWMaFx2ucUqQZRKxQMigBBynY7NwAtx5llH1fdUHf5Luesjyq6K9qXuBcV7O26Dy1rN30hMkMIgizN3otlA6eh6rhyzlAst4FrMOkJcvZAnlltCJLjnTUwMz3+0dMJekTvOcTKN5RTHvznSboBAE5rFAdfJsUMWD7SnyM4Gyk/iHBctC5wUrlFpkYJFEXOT/hUgoHVloTXiG3Jk0+6TGoPGGZfnFz/2ui3wMHwYaTTbqDvLBOywa8I9/IsSlQI0eOU9wyd2yPsGyQJVzy/nBcKXxgrouBmDM8pwITYkKTuaaDrYAyI8JOQC34FbAVMf1B8fgIbmrRPmIF8UN56/PmPP5YDs71iNBY7qfSHZ7JnhsCt+X4SCp2HH1oFSSQPpTk9UH/xgrDnD0FB9w+7BZ9XrHzCo/bzOBQzuW8WbNpbPWH81bBEO6b0g9VopiuoGUdRwbC0OrCdC+TS981JSJ3imdKACkeoyqyL1QqmMB2EQ6acNpslme2wy5I6LBPLBbCJPk6h5lxqeX4j7lHIYu9LO2qntQQxtg6lVucnkAmTFFu5F2SvpT6jQYUr3iOg5g18w/uNMJBx64BNfaR3r0cpoDzZBa5t5FZ9VIVz3gjcNZuyO5hms1VLJBuiB55rHBNy/1reJDMTpQQuFx8ugdg+Z+7xrbwehppq0QDNu2vEvjcRplO80mblS8VSYxF1uezamw/lQ1S7QFdqQC7mp8mMvoR7QS2FN6FodmXwREDUnCmQTuctxEx5E0ZzVlWq4JqByBaZnOT90YV7NWOevA5J+SS12VjBob3eCVOVvsoh4Br5mRyz1lNbkcXr9dqeeHBU4glww1cimOKhN2unch0490kdB/2u40LJwF0jIU8KDQ11/dWZ2NDkrYGGoFJ7afIuCdUMpoHTmM845E7FDN3+MYWYnv0Lqs3goYRBgdzsmMyDj1HItfxEtbK9cQbUP610YAHAp+du/nwY3xbay7R0Dc84YyQr6uq96sW9MMbCN8sAB+i6XjevmfV9Dsk32emqAuRrMjIJu48SbOGx95hOuQE8gpHIjo3zVHWYh+ZkXlUP8AWT7t578LwVQ7YDmqIdKbXnxkDe+JFwGxmLEkoCPimXJyr11REzXScHJUW5wk2RcjdIfQELVW1yH4dpvPY4dxYwFMigaOjIFmmr5OMxP05TEmnOzWHyJGlwtkhRtlUyKjnMafG67Fys2LknhNrC9TFRT2RscCDjMxUutfs8Hgnk1aMFS+N9U6k4egoZ7VKJX8HixpOpqLZ5u1uY7S78Vx5RzYEtu8gfc8V0SIE+/iFdpNZ4N2c/k/7GX5FJZ7XH6ZI1nkij4Vr+6XaTfrwftNAZ6RJn23c5UqvxaNUBDhWYvKOTaIy27lKL3UyNyVrpVqNUBjXS9IUFRumEfCRCTZbUCPXiUg2Gxdq12FvqXjqlR098N5yTE7rtLdOTk2fnK93r8A3qniWRT1ZxtC6fMcqe1UyVUilYpTYpnLjMualheWaw0VlWwSjiiQYGnTFaVfNm4dQJrlA8W2ZffeBXWdVXxUkIxz0TujYHMbFwBCH8BgZDlnHZQlP28gpqnxBpihFuR/ZTrEzGADrHDAMEKrmcRNNNJ9j28+I6mWRq4lMWZNDctl0YUI9NxUh9h+ep7k6dANWQhCMUUSJHZt/xCImEpKfFDLIB4t3xAUYg18bAlM8s885YCYWqDIfasZIJ3lC8LKqwcSzidxaCayEucRPtaKTNN5OUbufOCsn10Zx4rCUPlHFHTGjPbIwZr1cJrkg0FIg2H0yJGbfMOHhwnTbuiFXcyJUknl3wksBBWNPH3+sZBC98OlnXTNoaMnFTSfzYGv/REaMlqkAIpawDi9Z7d2FDxIVGyuTtZuyQDncjYOdiszSPK/oqfdgldj8bxgt8WMhOuU884yY84bbUIpbB0nHwUWXeSEG8SDRCOHYWD0uAvKUA6o+1VR1ye+IrI7IV/lYlNbELps4iEb7pX/DhV3H8uMR2FRk86zBbMLawkqZOIeCEGwlNtfSFkL1VObZIeket4/uTu82qMruOnDp4fiLsBMBEzUhCDvqCJTVaZ2mHxb4iNFslmoPuowAcJuQDtMqutRQV7iynrNRRZuD4Kl+eMAPwPJVzeYCVVyYhaN5OPuNjj3MxEw1vTUNk6GgUCArxIE057Y028vdi3jhcnohWkQ/L3Ag4KAVBVG5TWRaKcUA75XlXJbzIXGCyhzK5t04+WSskojq1KbOn5bAGg06la7cXWTDKkOyMFLj1gKCvggAaIk09fudKBiEoaHpTznmjjktWjYlL0/H5+DF3U53s7Z8097JGKngOBuj0cS5TT9tXwXltx0yI8iwuBVg+1DW8yPx1RWjzARh7oN9e/tLPoV9CMiXE3iafl3pqRpKMiofdeJyK7dlw9R5mllXw1MFgTsMxSyN5TMhPJJ7lxGYvvMv/Sk3aecrmOajCYpLraqGUcXi+PbuyFFMDkVvwBbdKi3JORcoMG9jG0uaTnRe2+zY0XOpHQroQlglUPKkIUjlJwrZWsI9ktxfrUvaXhutwbK4FX1XijUri9xqTx7y8d7dq9fRT2kxhThMKxqniMFan3mZOPT5uY3grPMqOVa02IpeZ1xNCghOOS2nL53N8WJdVQrnM8xaOK2kqMsUCGCR1jIL0FPs6SBpv3qSTgE/CGmSxsgu1ZjUittuaB4hYKsFswJwh5V39cUfmvaCUpV69lNM07GZ/bLicV8GYGTsb1aoiV7tZzG0xLIQODh6iEnKP13cVGTCllg08olGhgTdHvTtWRaeYpX7iFP8VH/UnvyFPnVQ/aynikaqyYWs1r3nMKD13eFsMRISgGGZOc5gUwT2JHnKsFLdHH0ENZUoxUj2hJpPhswJ6MbHuGMHNkVJZ0jCqGwJYeE5NJMEz4iE1lP4mhbdLrOAzZSBRjTw5yBOffI416G7WRYg5hqjAC7bxkARu//ITGmQIxZbnejrnQfo9lPIj1EgO3oick+lmIW6pJeTlnzp4cuVikiNUKgaIfORgtWTO2jvOKKjCrapURhODwifBF1AzT62YZ4ag4pUExMfcqrKPo9gh6Q9nYTWirGmolVtHCtsVtYlp/ritq3CcTHKAEgHsva6zCVy1W90GPgZMKzobDQVS1aSA4uLg4vESRRawS01XcAOJhyvqk/j03xylrXrfsabz4Rz6Y2zuVWpgMDBecPtnh8YYn9u+f0VliIpq77kxuXV7OjldZdYW7yb7eFAfUBMmyuqulCx498nPIz3N+EpmOcfaN+cOl02CUTx9tX1tVjMOxoEpMk0Zqx+NnkqeTNgvrpN3t+G0mx0VJmYyNVO7XjOYoEeGP2Qd+1x4dey7hDihUFwvnen0cbE2f/EgQ6Rii5PVTEJCjYL9NZ4aK6/4OYsfn1Ge0pc83Z5OQdFvfMcZiC0ynduESXv5gQjH+cvqJgBS1LGwSDpEOKGH/eqYoNLKu+elDHvuh2HfsY6q3Tyn6OrQddkxBDK6/Q6zvqBBovB7/256wNT8TgZr8eVQ+We5J+4+ie+P7zACVnH4maJd9AXD0MIE1hNHYCVls12p7f3qHNnQuXLt9WQqtD6b8eDbKA0zd98oAV6KeGRRsRpVrnU/cldADEQHt29uDbFM2qpunTOP5xj2lagnIk9CzzNZAo3A9IzQMmAjCuu8PUnxjU65dSkFHeWzVxVrcsvKFYoVlVtFhTaTWU/q8jriMhCP5Mg043xPFU2EH2VWwOgE9V5lYAK13oLAObK5+3Td8dQ1FJk3jqBuu7fYoCIPCx8rnHXDuxLx8TfjW+9oCHPIV/YQ6BVeo5BhRAotR6Oz/AlfZcKNfHC4NYZ4r33Ob59KyIu1F6WBsUxq7pL8bAtZsT95e9UgniJEJWmd+rh8pV1+9MRAhKLeUOBN8nl141jhAYMCjKHG3FTySpYVny1hVlBW4BlcotazEtOmHbXV5LZrHV+RBEYcs23c8lgzLy2SUxVSon9ioKdoaixyX7ALMt6TecKQIkEFkwXUEyN4ndJYZJIUmhQ4FiGNvpGHJMvbUmk5uDIzXiL7T4aRDg4hUUvY5VFJVjoiXt/879G2V0Tybbng80zYrGGCLSMHQD4CWOq8ZVXMALfqjm52Gvw5rS6tQdMpxuQQM50sKpbHlDJxRp5bwaL5C5j3UOCDE9Y3VihMHFMXYuHw4FpF5PCFvNyHed+iuTjNd9iWgrExGE9aGi1cBjFmEnRMjE48rxmFGDkKRNOih8bimAY5kL07TGeJzJNdZXKy0wgcRYkC7SrnurUUI01IiOR45J3HUUHr1K00cDmJhvOWCF7YB23dbGU4FprAAYMbuGn3e+nc0NeK5DljQTJVrkZBIm6fhXWbBGV3U6nlBgZWrX/OW5zc9dyr95+zh9WvLBgtqZz47zb+VhtPtEcR8YNgU3ocvtIHCFI0HYSZajHP868Egn8ihwD6G+e2/1QOwS/PXv/D3rCHf5FD8NdfRnJ/JJ6sgv97kwn+8a37mUzw/qute/9nfmHELzbuPzuV4E9/+Z1Sv0olAH8pzsh/kDi//n+cSmBSv/vCiAzeP67p8xfSLXFZth+AcBx/z8VR6L0X9rryd8K9GcGxzfEj0eftXFDllgsRaal9SUb5wlNuDgSmNXnkSZilwr96wL8pCX7h58y4tT4RAADorUpYmNrcZvt121AIyl5Zlh7IS38IWb1BE4Ic2LV9Ehv6tOkLvnbY0FmBoKzzboAIJEPgGtYgcogEDZggJIIkZ/rMCUMm+J55slhJn2DOKIk3eizveQ+WpJphBbME83wRwSujXcZwCItJosqWL8kgCXytuQmGe4IhUKAtndbBSUIUdfVVgE8SJTCR3BMy3949XPWj2oWQlxz4m2Xe9P540OVsvpw3syLF88ViZNWjzwsT7TAGWeRWoz8GtEFOBrhq+nMCiYTVhaGo1ikfuje3hCgH+1mnqAGloLb8MIQUpR52gRCldA/jCswmyM5LAq6CXHlePGMF5qMWwtI7pMK1u0ftwvN+I+uLoUkItDMzPqWwZ7XEqFEy8BK9mrYbzrQLgYTuE70pHDsJcsISPMxgJiQmFE55TgN4IL498pu4PcwfS+u3bgOXpoFr4XgEaRnx2jRE1gViWR2DMnq0cPSMaS5ySy3xkRE9AccjbgQFcHEXzX0FJUFPr+L8pAKe2DkdbYr4KfVIitKgsmQ0ymHhw9OaObfe94xYXXUVGw5Zgsi5xDqV7AVm40oLNMlXsgO9Cz9qn/XFxfnDHWXUDeNzAONlHDq5sJSLtrNt/IEnKmcC9y8t3EKnKfzARO4ltR4WzYByL2NsoRGNSppS5mLORSusvLs8+gLdTil4/HNMESis944QTk7CibLrtJeG6D4wUui593NVFgl1aYt+7LTAQrOEU2rHlAdvzRV60PBpsvebQyNCtWiRPmOssrkqj9VG/8IDlYJ7aSYOToqvYwN8h2QKsIUXFWBu0gfkO1m3EZjgKLXFC4HqUM4ITxAxeGLp5NpzQ6w9ybRnoM1gppK+elopTxtUzdvx4lWGe92YbfYAURBltGr5BCFSy7eT0w5VXVovUITk8+swhw5yPb9Qv8WVVq30zyjaZ7rQgeWQOK/bYcxFvjrBGlfNrEUk3MeCw1nolldEHQxMKjZzzp3Yhu1nbWLzvm9HMRR5qgqrHLazIvkCW2LCIiRACIxx3OieDOAuhKIVEytLC2+bKLnn3CcAqOAFP8epe6QKp1JxWLo951OofrTxgMK4q2TFdCCx7a1vJX5b6JoDYfZimD2iNtpdnhd8j/MJWAUI8Fnk3vC06R5yPr3AXFZOGA10zs0lyniCztju5kzDPN/8oE2um3O0gRx4QJ3D+JKogw3IHNCSN0fMcqyc+S1uBEXx3lDj/SFRW4IcBna3d+RiSXYgybBnvtho82DyTLHW2+jlkHxaV4g9IvSEOguDguhjDgjgeVXg1aGoo004+3guGEOPiJ+Hr6EHlYmzbY/ucyMYzvA6uXK+kafmR/B4MhQWZ6VaN1q8Lm6aEwl5PwsT6SQLTkSb5QMrjP0Kw5ATsAig6D0kcSkmA5rcXY9dciIv5NZvLXB3i6lKHYecPi5qiez7dHzmx33+vg84hVilrkCF/pGygAc2LogjAiMJCtIhKOT4eVcYuVBmXPI5eHxw+kYkxDJo6QEK27ilSxH5hAUnkIanEGuUikBrbbKpqFbPmjfuCJaE9vNOMcQsySyZB8fFI1tND/45kq3BYzmwDR4uJDnBLmq02aJck5AnuGSdG33+at/f81wahAGCmiJD04KlAKsWxCqc1iOsJxOANYC701MdjtuOsPmIawJWaedrptvCoiz6/Xkc061Rh0xlO9jY2Zi8BQw8XTHo0MLJCpuyyNsAJSbgJnJoDJ0kkiphA5C6xUz/pCgz0z4a571Hwbsb88txJCkiwG4sUXXBxl2uW8AEsok9YQNglXLd1R6r0EvFLl/rSZIoDj5ssSIVWRkxBGmmLv5AWMb5XFn/emGwZUiWg9QNWhzh92eEhFLx9AEzKUg+T1oB4hKeIA9C/ZFYws6fcBI/f47zJaNYp9AC4zv5Q5jsx37aGW7g4+ISBmmNj9IEm1ZLhEi16V4hrKsRrZ2yjm9pDSs3JWESSZ9zzSvoE/HzHH6v+22pkGYaHNKi16gor/TrnXQD7Ib0RsX2QMGL3+nS77IZ4A221btSEiZPRoSO4BoUR6iPsz9NJSlJksgztDidoeeQwWYOGyYOqcMpFXFuhG5AtaPhD5WFpnDj4GsRdPHsZyILG9g3U0ZtFoYsJddQuEexiOHDJuEF5gFDDDr4NndflnRyGVKLfc58+d2+WocoCcQUVFk7LFjeSwrfI3q3cHkgFoLqcx6EgrFX5al6wnhuTcKE5LBMwZW32chmb/JHBlyhWG1sp+6JgTIKJgozMrno/sKTZ2u4zQJgOheNWVJEd8QJ2ltn+ezGCLakgUDI57UFuHOtxn5eDaBLbQOLclNcijBpnijV57jeGJIxtNCgyh8TP75w4CvgdyvNyJCN3EgUAW4T9uBlgxU4Zjba7Aw9y4jONNNbmZWlJjzfKeN/Oml9wl41ObfHIDuUt67yvVehwZgIto6XvyZEUVWsVKvsQRVpHt12V813jnhwx3tNdUww935H70ADLcJ77O7oyiNemuJnDc/OOI/bWnKzR492/xAV5aPzfVmi1wJYqyXQNXuee9QopA0j2rVgSACnz/kn4oYLZQU5Z9S9ybTCLkUS8KrgU/HpOqCVCxOQDSUsUsWCi88+N9+VUOX52qnboi6GYLCTcSPY826CvHlZB1miqCzSTZoWqQhjBwGfM6IRxMx37OYGEbkdDQ3xGrvTRDut1araTezgu4MVA3spwSC+vZmiE3sIOeWpxAQUsAms+nSAF8LWz4wHCfCreAGILNcGTpwWE/k3Au3y/nmJh0YHHzoKcOaWskyNtdXpUK+rkfeEwgwvwbJBnhL62nrfuWHFvHj3tSQSQR+r3GjWA3bPqanAL2b0AjvT90ENnQiFqom5CMRwrInkQDw8l50izzWjqKxS9WqCkRYPNt3ZrUQkurV2EnvJkI4aXwghZ3PwtmnBmVkFY1BHVPbEHMqicSpEsLgOwQlNDaccofyv9q5r2XHciH6N30kx6pE5iaSYwxtzELNIieTXG9Cd8e56Z+yt8tqucrnqvuAKJVINoPscnEbDmWeLB3glurcWp/i+zjaqr8v1yAra1gNEsfTuG2EaNnAtr/r/s37yrARhCTkXcs32HZc7vJm9VUI1DRe5qAOlO++EFOKvTzouAjiuyBLnXFg5QKfaBR9G3/Oc+qvIHssNlipwsjCDyLuhA8bwRikLIUybmuHahI7isjCbddp3h1XGgVup7Vv8czxniK8ID2KKJApheWxTyB78S9XUpWksDbvg3ONVjlHuOsGDBn5dDR8p6h629MC+NsrLRXXJLBYFPF+NTyxaFz5UDJgjO+/Rs7i9t6+eX38Qw8VykYGYpCmB90lP9fSmGHo7NmWqfPv0651O13B0LZt9KrPWQr/v5H1IJ3Yuq0X4Xu60d3B4XBfqEmwrT0SG2JTlDRNjxnXbtk7CF0YNULjSiQ2+qAn9kX43Cc+2r2pYMOhNOhS8jt13s7dxV/q8DXOZ2DvBKZWFP9S+bGn7nqJVi10BLimlLD2oiD1mkWUmIu2Eqxk0TnYvbwgnCNJVAZjJhUdcWRTdNgBHerwVG5+ZlMIOiJ5oi+ARHiH0NfWbNYcv84lYfPG70xz8pEkxQDXehZ2L1zhznrLKQcDk66d+EjWLF+xDzHkfkDqBuK51y++PXC7Rd7HakRdE974cDk2B4T7oICYV9qbMyZdtAj9dJ34t5q1hBJauvKH2KgaX7mBgEZigSmlDlofhsZvDRMJMoVBMn4ElnIf3mE49TepF0EhxzldYaQvu0M9dx/d5MfZtuF5enbycGd9ktxYSkDTiK0fUnm2AsCpz8uLRdLf+1j/xyS8F/pV685QTlgE4zQ5Cp87Ma7icRLNHo49eJKWXMGplJEtnzjAhMaPPVPaSLLdYxgxsQak71r5eJRR6VMIMp1BeK/3spCitfeCxuQM7qWWPH29BhaWHOFpUzmGTZg+gj1NgnHQ8ajgHa7PfK7apwRvShuU9OjgReaz0By4VUkepvOf8eT6Z2+rS2g1Rb743GWKJ22P/IfOM2t2Cx0KEjXLYux5imBslqzFuR8Qqs09zrZkiZIcuD7sl2tXRJWJfs7jJ7KfaCB1Syif9NGTbs0x4ot9f48mp10B7TtDOBUBC5hJqjwCnmqA62s1c5AyK6JHRSkyrhTnMJAuDHfDCSHykNsIZYWhslWBb894RFe0grmQa8hJVvHDoC5SJPvWaIRfCOGRf1FqCRlJCxuca0YwuEX80fbCQAHmGEaMogWV9CvU4wUcT8cx4+c7Xr+eavY8JSd5czOILMdAb81yBG4WCsDFX6aOXTFPyNyLbvLAx25VvguvdClv1TYUH65B5xEXkeiszGs2H/QjmQL232lOwmXYXguhy7ha+qyLNucbd7iTk4ss0Jokpl9XZWqJfPwbMro6RxdJ8+bNH+5CjhM+7PmA2vt7JkwnuYDpdXOR6d3UEfUbak+1tHeF1HSXL84FyiUZxytDhTJZf993GSb2Vj6IRqVUZMhcBYNM8n5Rpv036PF9H5yct/loA1Dktc9BHQ/AVSYjKh7jAVUWEJsXPHw5pmwswjUDqt+qdHO8IesRwUGLPfRDqvC0PoWTEjmNyWm4Dj/3ifSH5ORwwHQRDymPcLXp42a4IsZuoG44aOrsdUligR3LDzb56cs8mMYI1oPL+JLAR+FHMdlMHAiyKYLFrG11M10MNKB3LCVbftROr9PdEgnnytCowy6e7AJa/XJDbDqh+s6aR5PF9lWpm71whJDb8DokJSFaLjFXvF/QqRE0xL76y9obUluOBUnCXtSZzhna8G+nRvVLpqpRI+OY0q1S33et+Z8bGo5dOiNXPCVT91uFbSl/pCJbqGm4BTdSp+pbimaIMQN1TGEvQHZZc9bCjhokwLcy7RMTkQgHorDd5KVmFccC4edryoXUF5qd5hzA50zTj+6SuRn5aGxsBf96HeiqQQ9YidgnMfznTMSDJgijx3CZYUZQWfpSFWrmhBYXA3TfkfGEaMW0lkV/SYp7cpOdsdQpLhMh61GVp5jk6iWvTyFnlAncXpxismlNB9ts14kkbvQ7hmxUqNo67DM1zdmRYkXupQXw6pmvXGINnQbWQ1cbotvO2IlParrupv/Z4ZYdLjnenxSp2FYiClFqJGQ343uWXC79hCfeazYpRlYIDfQFbvKpwiWqrzFubBognnH2sed2ogXkwmsY1XSlH5FdagBxhBQ/PAoDuRam9mWneYHuo2nhrn8l23kbtOhWMqHrvEO69pnaFJDP6UjjlPI9PNsiLceJgRQAmbUYeNWTenpe06I2DysmZfaSIDkJisaY5Vz1zk3/Nt5V1gJWDcxtZiX/VKqyOoQmGAJZiwNtg3qXq1ESWwdwEa0Zod//0a7W0Tq6Eg6auvh60VZhUY1aKQ2XdCXh4dTDKHfoywcayYEnQrdx3Ntl2BizfV9hOMI9xpMxHNhkA+6yA3e+nb5l0sGeM0hjOSKzImO8S0pqOIBJ4DaJ2tb5oRIvlo+/SujJhuItkWCWkwn2GY0zWwiCWaJFL2vZs3FJts+gv4TmtrqE+OpSIaOU1wB1a9brR5qwwFfdQcFTqy4fHcDyRK0Zo7xajsOhk6Is+vRnmT96VnkRppHeaqzirvC3sfOiQgLN59bazw4ZVUMr0/a+KlPhPFKGfipTY7w/N/ljVQf9dIiX1A5HyS5LEP5cg4H9h/pcPOv/xEfuuTaI/OOhMUv9BdfL3dxAUeVV8/+HjstZjNQ5JJ/zy378z3C99biNU0z4j2BbrenwzZ7Kt42/H9+uZ8EG/ETp/cFfAc9yWrPgHM+77bXfJUhXrP9Mbfz8OS9Ela/P67Xv86UYm/xtGBqZcjvDXjQh+GZhw35r8/u3Lv1rHt9afODiXPzg4P1kk/+rgwHD8t4sDP5/96vpFTPgr</diagram></mxfile>
\ No newline at end of file
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diff --git a/docs/database/redis/images/缓存读写策略/read-through.drawio b/docs/database/redis/images/缓存读写策略/read-through.drawio
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--- a/docs/database/redis/images/缓存读写策略/read-through.drawio
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</diagram></mxfile>
\ No newline at end of file
diff --git a/docs/database/redis/images/缓存读写策略/read-through.png b/docs/database/redis/images/缓存读写策略/read-through.png
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diff --git a/docs/database/redis/images/缓存读写策略/write-through.drawio b/docs/database/redis/images/缓存读写策略/write-through.drawio
deleted file mode 100644
index 7626c8d1..00000000
--- a/docs/database/redis/images/缓存读写策略/write-through.drawio
+++ /dev/null
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</diagram></mxfile>
\ No newline at end of file
diff --git a/docs/database/redis/images/缓存读写策略/write-through.png b/docs/database/redis/images/缓存读写策略/write-through.png
deleted file mode 100644
index f63c2e81..00000000
Binary files a/docs/database/redis/images/缓存读写策略/write-through.png and /dev/null differ
diff --git a/docs/database/redis/redis-data-structures-01.md b/docs/database/redis/redis-data-structures-01.md
index c98754f5..dbfaecdc 100644
--- a/docs/database/redis/redis-data-structures-01.md
+++ b/docs/database/redis/redis-data-structures-01.md
@@ -172,7 +172,7 @@ Redis 中的 List 其实就是链表数据结构的实现。我在 [线性数据
 
 我专门画了一个图方便大家理解 `RPUSH` , `LPOP` , `lpush` , `RPOP` 命令：
 
-![redis list](./images/redis-all/redis-list.png)
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-list.png)
 
 **通过 `LRANGE` 查看对应下标范围的列表元素** ：
 
diff --git a/docs/database/redis/redis-questions-01.md b/docs/database/redis/redis-questions-01.md
index dc312379..ae812971 100644
--- a/docs/database/redis/redis-questions-01.md
+++ b/docs/database/redis/redis-questions-01.md
@@ -24,7 +24,7 @@ Redis 提供了多种数据类型来支持不同的业务场景。Redis 还支
 
 你可以自己本机安装 Redis 或者通过 Redis 官网提供的[在线 Redis 环境](https://try.redis.io/)来实际体验 Redis。
 
-![try-redis](./images/redis-all/try-redis.png)
+![try-redis](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/try.redis.io.png)
 
 ### 分布式缓存常见的技术选型方案有哪些？
 
@@ -61,7 +61,7 @@ Memcached 是分布式缓存最开始兴起的那会，比较常用的。后来
 
 作为暖男一号，我给大家画了一个草图。
 
-![正常缓存处理流程](./images/redis-all/cache-process.png)
+![正常缓存处理流程](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/normal-cache-process.png)
 
 简单来说就是:
 
@@ -76,7 +76,7 @@ _简单，来说使用缓存主要是为了提升用户体验以及应对更多
 
 下面我们主要从“高性能”和“高并发”这两点来看待这个问题。
 
-![](./images/redis-all/使用缓存之后.png)
+![使用缓存之后](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/after-using-the-cache.png)
 
 **高性能** ：
 
@@ -247,15 +247,13 @@ Redis 通过**IO 多路复用程序** 来监听来自客户端的大量连接（
 - 文件事件分派器（将 socket 关联到相应的事件处理器）
 - 事件处理器（连接应答处理器、命令请求处理器、命令回复处理器）
 
-![](./images/redis-all/redis事件处理器.png)
-
-<p style="text-align:right; font-size:14px; color:gray">《Redis设计与实现：12章》</p>
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-event-handler.png)
 
 ### Redis6.0 之前为什么不使用多线程？
 
 虽然说 Redis 是单线程模型，但是，实际上，**Redis 在 4.0 之后的版本中就已经加入了对多线程的支持。**
 
-![redis4.0 more thread](./images/redis-all/redis4.0-more-thread.png)
+![redis4.0 more thread](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis4.0-more-thread.png)
 
 不过，Redis 4.0 增加的多线程主要是针对一些大键值对的删除操作的命令，使用这些命令就会使用主处理之外的其他线程来“异步处理”。
 
@@ -323,7 +321,7 @@ OK
 
 Redis 通过一个叫做过期字典（可以看作是 hash 表）来保存数据过期的时间。过期字典的键指向 Redis 数据库中的某个 key(键)，过期字典的值是一个 long long 类型的整数，这个整数保存了 key 所指向的数据库键的过期时间（毫秒精度的 UNIX 时间戳）。
 
-![redis过期字典](./images/redis-all/redis过期时间.png)
+![redis过期字典](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-expired-dictionary.png)
 
 过期字典是存储在 redisDb 这个结构里的：
 
@@ -435,7 +433,7 @@ Redis 4.0 开始支持 RDB 和 AOF 的混合持久化（默认关闭，可以通
 
 官方文档地址：https://redis.io/topics/persistence
 
-![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/booksimage-20210807145107290.png)
+![](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis4.0-persitence.png)
 
 ## 参考
 
diff --git a/docs/database/redis/redis-questions-02.md b/docs/database/redis/redis-questions-02.md
index 7d85611a..d58555a1 100644
--- a/docs/database/redis/redis-questions-02.md
+++ b/docs/database/redis/redis-questions-02.md
@@ -67,7 +67,7 @@ ERR EXEC without MULTI
 
 Redis 官网相关介绍 [https://redis.io/topics/transactions](https://redis.io/topics/transactions) 如下：
 
-![redis事务](./images/redis-all/redis事务.png)
+![Redis 事务](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-transactions.png)
 
 ### Redis 支持原子性吗？
 
@@ -82,7 +82,7 @@ Redis 事务在运行错误的情况下，除了执行过程中出现错误的
 
 Redis 官网也解释了自己为啥不支持回滚。简单来说就是 Redis 开发者们觉得没必要支持回滚，这样更简单便捷并且性能更好。Redis 开发者觉得即使命令执行错误也应该在开发过程中就被发现而不是生产过程中。
 
-![redis roll back](./images/redis-all/redis-rollBack.png)
+![Redis 为什么不支持回滚](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-rollback.png)
 
 你可以将 Redis 中的事务就理解为 ：**Redis 事务提供了一种将多个命令请求打包的功能。然后，再按顺序执行打包的所有命令，并且不会被中途打断。**
 
@@ -185,7 +185,7 @@ Biggest string found '"ballcat:oauth:refresh_auth:f6cdb384-9a9d-4f2f-af01-dc3f28
 
 如下图所示，用户的请求最终都要跑到数据库中查询一遍。
 
-![缓存穿透情况](https://img-blog.csdnimg.cn/6358650a9bf742838441d636430c90b9.png)
+![缓存穿透情况](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-cache-penetration.png)
 
 #### 有哪些解决办法？
 
@@ -228,7 +228,7 @@ public Object getObjectInclNullById(Integer id) {
 
 加入布隆过滤器之后的缓存处理流程图如下。
 
-![](./images/redis-all/加入布隆过滤器后的缓存处理流程.png)
+![加入布隆过滤器之后的缓存处理流程图](https://guide-blog-images.oss-cn-shenzhen.aliyuncs.com/github/javaguide/database/redis/redis-cache-penetration-bloom-filter.png)
 
 但是，需要注意的是布隆过滤器可能会存在误判的情况。总结来说就是： **布隆过滤器说某个元素存在，小概率会误判。布隆过滤器说某个元素不在，那么这个元素一定不在。**