From 2cc2d9a969505163c293efa52b48b3c045c15ce3 Mon Sep 17 00:00:00 2001 From: guide Date: Mon, 23 Aug 2021 12:01:00 +0800 Subject: [PATCH 01/67] =?UTF-8?q?[refractor&feat]=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E7=BB=93=E6=9E=84=E5=AE=8C=E5=96=84&=E6=96=87=E4=BB=B6?= =?UTF-8?q?=E5=A4=B9=E9=87=8D=E6=9E=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 56 +++--- .../几道常见的字符串算法题.md | 0 .../几道常见的链表算法题.md | 0 .../algorithms}/剑指offer部分编程题.md | 0 .../data-structure/bloom-filter.md | 45 ++--- .../data-structure/pictures/图/图.png | Bin .../pictures/图/广度优先搜索1.drawio | 0 .../pictures/图/广度优先搜索1.png | Bin .../pictures/图/广度优先搜索2.drawio | 0 .../pictures/图/广度优先搜索2.png | Bin .../pictures/图/广度优先搜索3.drawio | 0 .../pictures/图/广度优先搜索3.png | Bin .../pictures/图/广度优先搜索4.drawio | 0 .../pictures/图/广度优先搜索4.png | Bin .../pictures/图/广度优先搜索5.drawio | 0 .../pictures/图/广度优先搜索5.png | Bin .../pictures/图/广度优先搜索6.drawio | 0 .../pictures/图/广度优先搜索6.png | Bin 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(100%) rename docs/{ => cs-basics}/operating-system/images/修改文件权限.png (100%) rename docs/{ => cs-basics}/operating-system/images/文件inode信息.png (100%) rename docs/{ => cs-basics}/operating-system/images/用户态与内核态.png (100%) rename docs/{ => cs-basics}/operating-system/linux.md (100%) delete mode 100644 docs/dataStructures-algorithms/数据结构.md rename docs/java/{ => tips}/JAD反编译tricks.md (68%) rename docs/java/{ => tips}/手把手教你定位常见Java性能问题.md (54%) diff --git a/README.md b/README.md index 8e214899..786e0ccf 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ ### 基础 -**知识点/面试题:**(必看:+1: ) +**知识点/面试题** : (必看:+1: ) 1. **[Java 基础知识](docs/java/basis/Java基础知识.md)** 2. **[Java 基础知识疑难点/易错点](docs/java/basis/Java基础知识疑难点.md)** @@ -71,11 +71,11 @@ **重要知识点详解：** -2. **线程池**：[Java 线程池学习总结](./docs/java/multi-thread/java线程池学习总结.md)、[拿来即用的线程池最佳实践](./docs/java/multi-thread/拿来即用的线程池最佳实践.md) -4. [ ThreadLocal 关键字解析](docs/java/multi-thread/万字详解ThreadLocal关键字.md) -5. [并发容器总结](docs/java/multi-thread/并发容器总结.md) -6. [JUC 中的 Atomic 原子类总结](docs/java/multi-thread/Atomic原子类总结.md) -7. [AQS 原理以及 AQS 同步组件总结](docs/java/multi-thread/AQS原理以及AQS同步组件总结.md) +1. **线程池**：[Java 线程池学习总结](./docs/java/multi-thread/java线程池学习总结.md)、[拿来即用的线程池最佳实践](./docs/java/multi-thread/拿来即用的线程池最佳实践.md) +2. [ ThreadLocal 关键字解析](docs/java/multi-thread/万字详解ThreadLocal关键字.md) +3. [并发容器总结](docs/java/multi-thread/并发容器总结.md) +4. [JUC 中的 Atomic 原子类总结](docs/java/multi-thread/Atomic原子类总结.md) +5. [AQS 原理以及 AQS 同步组件总结](docs/java/multi-thread/AQS原理以及AQS同步组件总结.md) ### JVM (必看 :+1:) @@ -95,28 +95,38 @@ JVM 这部分内容主要参考 [JVM 虚拟机规范-Java8 ](https://docs.oracle 1. **Java 8** ：[Java 8 新特性总结](docs/java/new-features/Java8新特性总结.md)、[Java8常用新特性总结](docs/java/new-features/java8-common-new-features.md) 、[Java 8 学习资源推荐](docs/java/new-features/Java8教程推荐.md)、[Java8 forEach 指南](docs/java/new-features/Java8foreach指南.md) 2. **Java9~Java15** : [一文带你看遍 JDK9~15 的重要新特性！](./docs/java/new-features/java新特性总结.md) +### 小技巧 + +1. [JAD 反编译](docs/java/tips/JAD反编译tricks.md) +2. [手把手教你定位常见 Java 性能问题](./docs/java/tips/手把手教你定位常见Java性能问题.md) + ## 计算机基础 👉 **[图解计算机基础 PDF 下载](https://mp.weixin.qq.com/s?__biz=Mzg2OTA0Njk0OA==&mid=100021725&idx=1&sn=2db9664ca25363139a81691043e9fd8f&chksm=4ea19a1679d61300d8990f7e43bfc7f476577a81b712cf0f9c6f6552a8b219bc081efddb5c54#rd)** 。 ### 操作系统 -1. [操作系统常见问题总结！](docs/operating-system/basis.md) -2. [后端程序员必备的 Linux 基础知识](docs/operating-system/linux.md) -3. [Shell 编程入门](docs/operating-system/Shell.md) +1. [操作系统常见问题总结！](docs/cs-basics/operating-system/basis.md) +2. [后端程序员必备的 Linux 基础知识总结](docs/cs-basics/operating-system/linux.md) +3. [Shell 编程入门](docs/cs-basics/operating-system/Shell.md) ### 网络 -1. [计算机网络常见面试题](docs/network/计算机网络.md) -2. [计算机网络基础知识总结](docs/network/计算机网络知识总结.md) +1. [计算机网络常见面试题](docs/cs-basics/network/计算机网络.md) +2. [计算机网络基础知识总结](docs/cs-basics/network/计算机网络知识总结.md) ### 数据结构 -- **图解数据结构：** - 1. [线性数据结构 :数组、链表、栈、队列](docs/dataStructures-algorithms/data-structure/线性数据结构.md) - 2. [图](docs/dataStructures-algorithms/data-structure/图.md) - 3. [堆](docs/dataStructures-algorithms/data-structure/堆.md) -- [不了解布隆过滤器？一文给你整的明明白白！](docs/dataStructures-algorithms/data-structure/bloom-filter.md) +**图解数据结构：** + +1. [线性数据结构 :数组、链表、栈、队列](docs/cs-basics/data-structure/线性数据结构.md) +2. [图](docs/cs-basics/data-structure/图.md) +3. [堆](docs/cs-basics/data-structure/堆.md) +4. [树](docs/cs-basics/data-structure/树.md) ：重点关注[红黑树](docs/cs-basics/data-structure/红黑树.md)、B-，B+，B*树、LSM树 + +其他常用数据结构 ： + +1. [布隆过滤器](docs/cs-basics/data-structure/bloom-filter.md) ### 算法 @@ -125,11 +135,13 @@ JVM 这部分内容主要参考 [JVM 虚拟机规范-Java8 ](https://docs.oracle - [算法学习书籍+资源推荐](https://www.zhihu.com/question/323359308/answer/1545320858) 。 - [如何刷Leetcode?](https://www.zhihu.com/question/31092580/answer/1534887374) -**常见算法问题总结：** +**常见算法问题总结** ： -- [几道常见的字符串算法题总结 ](docs/dataStructures-algorithms/几道常见的字符串算法题.md) -- [几道常见的链表算法题总结 ](docs/dataStructures-algorithms/几道常见的链表算法题.md) -- [剑指 offer 部分编程题](docs/dataStructures-algorithms/剑指offer部分编程题.md) +- [几道常见的字符串算法题总结 ](docs/cs-basics/algorithms/几道常见的字符串算法题.md) +- [几道常见的链表算法题总结 ](docs/cs-basics/algorithms/几道常见的链表算法题.md) +- [剑指 offer 部分编程题](docs/cs-basics/algorithms/剑指offer部分编程题.md) + +另外，[GeeksforGeeks]( https://www.geeksforgeeks.org/fundamentals-of-algorithms/) 这个网站总结了常见的算法 ，比较全面系统。 ## 数据库 @@ -354,7 +366,6 @@ Dubbo 是一款国产的 RPC 框架，由阿里开源。相关阅读： ## 工具 -1. **Java** ：[JAD 反编译](docs/java/JAD反编译tricks.md)、[手把手教你定位常见 Java 性能问题](./docs/java/手把手教你定位常见Java性能问题.md) 2. **Git** ：[Git 入门](docs/tools/Git.md) 3. **Github** ： [Github小技巧](docs/tools/Github技巧.md) 4. **Docker** : [Docker 基本概念解读](docs/tools/Docker.md) 、[Docker从入门到上手干事](docs/tools/Docker从入门到实战.md) @@ -378,7 +389,8 @@ Dubbo 是一款国产的 RPC 框架，由阿里开源。相关阅读： ### 待办 -- [ ] 数据结构总结重构 +- [ ] 计算机网络知识点完善 +- [ ] 分布式常见理论和算法总结完善 ### 捐赠支持 diff --git a/docs/dataStructures-algorithms/几道常见的字符串算法题.md b/docs/cs-basics/algorithms/几道常见的字符串算法题.md similarity index 100% rename from docs/dataStructures-algorithms/几道常见的字符串算法题.md rename to docs/cs-basics/algorithms/几道常见的字符串算法题.md diff --git a/docs/dataStructures-algorithms/几道常见的链表算法题.md b/docs/cs-basics/algorithms/几道常见的链表算法题.md similarity index 100% rename from docs/dataStructures-algorithms/几道常见的链表算法题.md rename to docs/cs-basics/algorithms/几道常见的链表算法题.md diff --git a/docs/dataStructures-algorithms/剑指offer部分编程题.md b/docs/cs-basics/algorithms/剑指offer部分编程题.md similarity index 100% rename from docs/dataStructures-algorithms/剑指offer部分编程题.md rename to docs/cs-basics/algorithms/剑指offer部分编程题.md diff --git a/docs/dataStructures-algorithms/data-structure/bloom-filter.md b/docs/cs-basics/data-structure/bloom-filter.md similarity index 74% rename from docs/dataStructures-algorithms/data-structure/bloom-filter.md rename to docs/cs-basics/data-structure/bloom-filter.md index bfb7efe7..9b5afd6d 100644 --- a/docs/dataStructures-algorithms/data-structure/bloom-filter.md +++ b/docs/cs-basics/data-structure/bloom-filter.md @@ -6,18 +6,18 @@ 2. 布隆过滤器的原理介绍。 3. 布隆过滤器使用场景。 4. 通过 Java 编程手动实现布隆过滤器。 -5. 利用Google开源的Guava中自带的布隆过滤器。 +5. 利用 Google 开源的 Guava 中自带的布隆过滤器。 6. Redis 中的布隆过滤器。 ### 1.什么是布隆过滤器？ 首先，我们需要了解布隆过滤器的概念。 -布隆过滤器（Bloom Filter）是一个叫做 Bloom 的老哥于1970年提出的。我们可以把它看作由二进制向量（或者说位数组）和一系列随机映射函数（哈希函数）两部分组成的数据结构。相比于我们平时常用的的 List、Map 、Set 等数据结构，它占用空间更少并且效率更高，但是缺点是其返回的结果是概率性的，而不是非常准确的。理论情况下添加到集合中的元素越多，误报的可能性就越大。并且，存放在布隆过滤器的数据不容易删除。 +布隆过滤器（Bloom Filter）是一个叫做 Bloom 的老哥于 1970 年提出的。我们可以把它看作由二进制向量（或者说位数组）和一系列随机映射函数（哈希函数）两部分组成的数据结构。相比于我们平时常用的的 List、Map 、Set 等数据结构，它占用空间更少并且效率更高，但是缺点是其返回的结果是概率性的，而不是非常准确的。理论情况下添加到集合中的元素越多，误报的可能性就越大。并且，存放在布隆过滤器的数据不容易删除。 ![布隆过滤器示意图](https://my-blog-to-use.oss-cn-beijing.aliyuncs.com/2019-11/布隆过滤器-bit数组.png) -位数组中的每个元素都只占用 1 bit ，并且每个元素只能是 0 或者 1。这样申请一个 100w 个元素的位数组只占用 1000000Bit / 8 = 125000 Byte = 125000/1024 kb ≈ 122kb 的空间。 +位数组中的每个元素都只占用 1 bit ，并且每个元素只能是 0 或者 1。这样申请一个 100w 个元素的位数组只占用 1000000Bit / 8 = 125000 Byte = 125000/1024 kb ≈ 122kb 的空间。 总结：**一个名叫 Bloom 的人提出了一种来检索元素是否在给定大集合中的数据结构，这种数据结构是高效且性能很好的，但缺点是具有一定的错误识别率和删除难度。并且，理论情况下，添加到集合中的元素越多，误报的可能性就越大。** @@ -35,11 +35,9 @@ 举个简单的例子： - - ![布隆过滤器hash计算](https://my-blog-to-use.oss-cn-beijing.aliyuncs.com/2019-11/布隆过滤器-hash运算.png) -如图所示，当字符串存储要加入到布隆过滤器中时，该字符串首先由多个哈希函数生成不同的哈希值，然后将对应的位数组的下标设置为 1（当位数组初始化时，所有位置均为0）。当第二次存储相同字符串时，因为先前的对应位置已设置为 1，所以很容易知道此值已经存在（去重非常方便）。 +如图所示，当字符串存储要加入到布隆过滤器中时，该字符串首先由多个哈希函数生成不同的哈希值，然后将对应的位数组的下标设置为 1（当位数组初始化时，所有位置均为 0）。当第二次存储相同字符串时，因为先前的对应位置已设置为 1，所以很容易知道此值已经存在（去重非常方便）。 如果我们需要判断某个字符串是否在布隆过滤器中时，只需要对给定字符串再次进行相同的哈希计算，得到值之后判断位数组中的每个元素是否都为 1，如果值都为 1，那么说明这个值在布隆过滤器中，如果存在一个值不为 1，说明该元素不在布隆过滤器中。 @@ -49,7 +47,7 @@ ### 3.布隆过滤器使用场景 -1. 判断给定数据是否存在：比如判断一个数字是否存在于包含大量数字的数字集中（数字集很大，5亿以上！）、 防止缓存穿透（判断请求的数据是否有效避免直接绕过缓存请求数据库）等等、邮箱的垃圾邮件过滤、黑名单功能等等。 +1. 判断给定数据是否存在：比如判断一个数字是否存在于包含大量数字的数字集中（数字集很大，5 亿以上！）、 防止缓存穿透（判断请求的数据是否有效避免直接绕过缓存请求数据库）等等、邮箱的垃圾邮件过滤、黑名单功能等等。 2. 去重：比如爬给定网址的时候对已经爬取过的 URL 去重。 ### 4.通过 Java 编程手动实现布隆过滤器 @@ -206,7 +204,7 @@ true 实际使用如下： -我们创建了一个最多存放 最多 1500个整数的布隆过滤器，并且我们可以容忍误判的概率为百分之（0.01） +我们创建了一个最多存放 最多 1500 个整数的布隆过滤器，并且我们可以容忍误判的概率为百分之（0.01） ```java // 创建布隆过滤器对象 @@ -224,7 +222,7 @@ System.out.println(filter.mightContain(1)); System.out.println(filter.mightContain(2)); ``` -在我们的示例中，当`mightContain()` 方法返回 *true* 时，我们可以99％确定该元素在过滤器中，当过滤器返回 *false* 时，我们可以100％确定该元素不存在于过滤器中。 +在我们的示例中，当`mightContain()` 方法返回 _true_ 时，我们可以 99％确定该元素在过滤器中，当过滤器返回 _false_ 时，我们可以 100％确定该元素不存在于过滤器中。 **Guava 提供的布隆过滤器的实现还是很不错的（想要详细了解的可以看一下它的源码实现），但是它有一个重大的缺陷就是只能单机使用（另外，容量扩展也不容易），而现在互联网一般都是分布式的场景。为了解决这个问题，我们就需要用到 Redis 中的布隆过滤器了。** @@ -234,17 +232,17 @@ System.out.println(filter.mightContain(2)); Redis v4.0 之后有了 Module（模块/插件） 功能，Redis Modules 让 Redis 可以使用外部模块扩展其功能 。布隆过滤器就是其中的 Module。详情可以查看 Redis 官方对 Redis Modules 的介绍 ：https://redis.io/modules -另外，官网推荐了一个 RedisBloom 作为 Redis 布隆过滤器的 Module，地址：https://github.com/RedisBloom/RedisBloom。其他还有： +另外，官网推荐了一个 RedisBloom 作为 Redis 布隆过滤器的 Module，地址：https://github.com/RedisBloom/RedisBloom。其他还有： - redis-lua-scaling-bloom-filter（lua 脚本实现）：https://github.com/erikdubbelboer/redis-lua-scaling-bloom-filter -- pyreBloom（Python中的快速Redis 布隆过滤器） ：https://github.com/seomoz/pyreBloom +- pyreBloom（Python 中的快速 Redis 布隆过滤器） ：https://github.com/seomoz/pyreBloom - ...... RedisBloom 提供了多种语言的客户端支持，包括：Python、Java、JavaScript 和 PHP。 -#### 6.2 使用Docker安装 +#### 6.2 使用 Docker 安装 -如果我们需要体验 Redis 中的布隆过滤器非常简单，通过 Docker 就可以了！我们直接在 Google 搜索 **docker redis bloomfilter** 然后在排除广告的第一条搜素结果就找到了我们想要的答案（这是我平常解决问题的一种方式，分享一下），具体地址：https://hub.docker.com/r/redislabs/rebloom/ （介绍的很详细 ）。 +如果我们需要体验 Redis 中的布隆过滤器非常简单，通过 Docker 就可以了！我们直接在 Google 搜索 **docker redis bloomfilter** 然后在排除广告的第一条搜素结果就找到了我们想要的答案（这是我平常解决问题的一种方式，分享一下），具体地址：https://hub.docker.com/r/redislabs/rebloom/ （介绍的很详细 ）。 **具体操作如下：** @@ -252,15 +250,15 @@ RedisBloom 提供了多种语言的客户端支持，包括：Python、Java、Ja ➜ ~ docker run -p 6379:6379 --name redis-redisbloom redislabs/rebloom:latest ➜ ~ docker exec -it redis-redisbloom bash root@21396d02c252:/data# redis-cli -127.0.0.1:6379> +127.0.0.1:6379> ``` -#### 6.3常用命令一览 +#### 6.3 常用命令一览 -> 注意： key : 布隆过滤器的名称，item : 添加的元素。 +> 注意： key : 布隆过滤器的名称，item : 添加的元素。 -1. **`BF.ADD `**：将元素添加到布隆过滤器中，如果该过滤器尚不存在，则创建该过滤器。格式：`BF.ADD {key} {item}`。 -2. **`BF.MADD `** : 将一个或多个元素添加到“布隆过滤器”中，并创建一个尚不存在的过滤器。该命令的操作方式`BF.ADD`与之相同，只不过它允许多个输入并返回多个值。格式：`BF.MADD {key} {item} [item ...]` 。 +1. **`BF.ADD`**：将元素添加到布隆过滤器中，如果该过滤器尚不存在，则创建该过滤器。格式：`BF.ADD {key} {item}`。 +2. **`BF.MADD`** : 将一个或多个元素添加到“布隆过滤器”中，并创建一个尚不存在的过滤器。该命令的操作方式`BF.ADD`与之相同，只不过它允许多个输入并返回多个值。格式：`BF.MADD {key} {item} [item ...]` 。 3. **`BF.EXISTS` ** : 确定元素是否在布隆过滤器中存在。格式：`BF.EXISTS {key} {item}`。 4. **`BF.MEXISTS`** ： 确定一个或者多个元素是否在布隆过滤器中存在格式：`BF.MEXISTS {key} {item} [item ...]`。 @@ -268,19 +266,19 @@ root@21396d02c252:/data# redis-cli 这个命令的格式如下： -`BF.RESERVE {key} {error_rate} {capacity} [EXPANSION expansion] `。 +`BF.RESERVE {key} {error_rate} {capacity} [EXPANSION expansion]`。 下面简单介绍一下每个参数的具体含义： 1. key：布隆过滤器的名称 -2. error_rate :误报的期望概率。这应该是介于0到1之间的十进制值。例如，对于期望的误报率0.1％（1000中为1），error_rate应该设置为0.001。该数字越接近零，则每个项目的内存消耗越大，并且每个操作的CPU使用率越高。 -3. capacity: 过滤器的容量。当实际存储的元素个数超过这个值之后，性能将开始下降。实际的降级将取决于超出限制的程度。随着过滤器元素数量呈指数增长，性能将线性下降。 +2. error_rate :误报的期望概率。这应该是介于 0 到 1 之间的十进制值。例如，对于期望的误报率 0.1％（1000 中为 1），error_rate 应该设置为 0.001。该数字越接近零，则每个项目的内存消耗越大，并且每个操作的 CPU 使用率越高。 +3. capacity: 过滤器的容量。当实际存储的元素个数超过这个值之后，性能将开始下降。实际的降级将取决于超出限制的程度。随着过滤器元素数量呈指数增长，性能将线性下降。 可选参数： -- expansion：如果创建了一个新的子过滤器，则其大小将是当前过滤器的大小乘以`expansion`。默认扩展值为2。这意味着每个后续子过滤器将是前一个子过滤器的两倍。 +- expansion：如果创建了一个新的子过滤器，则其大小将是当前过滤器的大小乘以`expansion`。默认扩展值为 2。这意味着每个后续子过滤器将是前一个子过滤器的两倍。 -#### 6.4实际使用 +#### 6.4 实际使用 ```shell 127.0.0.1:6379> BF.ADD myFilter java @@ -294,4 +292,3 @@ root@21396d02c252:/data# redis-cli 127.0.0.1:6379> BF.EXISTS myFilter github (integer) 0 ``` - diff --git a/docs/dataStructures-algorithms/data-structure/pictures/图/图.png b/docs/cs-basics/data-structure/pictures/图/图.png similarity index 100% rename from docs/dataStructures-algorithms/data-structure/pictures/图/图.png rename to docs/cs-basics/data-structure/pictures/图/图.png diff --git a/docs/dataStructures-algorithms/data-structure/pictures/图/广度优先搜索1.drawio b/docs/cs-basics/data-structure/pictures/图/广度优先搜索1.drawio similarity index 100% rename from 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