├── .gitignore ├── pics ├── 10.gif ├── 11.gif ├── 7.gif ├── 9.gif ├── flow.png ├── PPjwP.png ├── n2U3N.png ├── pcrypt.gif ├── ppt_img.gif ├── scrypt.gif ├── tGPV0.png ├── urlnuri.jpg ├── 0635cbe8.png ├── BSD_disk.png ├── JVM-Stack.png ├── Semaphore.png ├── image005.jpg ├── image007.jpg ├── image008.jpg ├── image009.jpg ├── 2018040302.jpg ├── Iterator-1.jpg ├── JNIFigure1.gif ├── ProcessState.png ├── _u4E0B_u8F7D.png ├── cookiedata.png ├── master-slave.png ├── url_diagram.png ├── 2017-06-11-ca.png ├── CountdownLatch.png ├── CyclicBarrier.png ├── ssl-offloading.jpg ├── 111521118015898.gif ├── 111521118445538.gif ├── 111521118483039.gif ├── 111521118640738.gif ├── 111521119203347.gif ├── 111521119368714.gif ├── 1492928105791_3.png ├── 1492928416812_4.png ├── 1492929000361_5.png ├── 1492929444818_6.png ├── 1492929553651_7.png ├── 1492930243286_8.png ├── 20150928140509757.png ├── 600px-Sharedmem.jpg ├── How-HTTPS-Works.png ├── java-collections.png ├── java-collections1.png ├── linux-filesystem.png ├── monitor-lock-rule.png ├── mutualssl_small.png ├── thread-join-rule.png ├── thread-start-rule.png ├── HTTP1_x_Connections.png ├── HowLinkedListWorks.png ├── master-slave-proxy.png ├── network-of-networks.gif ├── single-thread-rule.png ├── DP-Decorator-java.io.png ├── JVM-runtime-data-area.jpg ├── class_loader_hierarchy.png ├── inode_with_signatures.jpg ├── volatile-variable-rule.png ├── 7779232-1e8ed39548081a1f.png ├── 7779232-96822582feb08651.png ├── MultiNode-SpringSession.jpg ├── MultiNode-StickySessions.jpg ├── 2018-05-29-15275543393037.jpg ├── HTTP_RequestMessageExample.png ├── HTTP_ResponseMessageExample.png ├── JNI-Java-Native-Interface.jpg ├── 2_14_microkernelArchitecture.jpg ├── MultiNode-SessionReplication.jpg ├── docker-filesystems-busyboxrw.png ├── 76a49594323247f21c9b3a69945445ee.png ├── GUID_Partition_Table_Scheme.svg.png ├── LOun2W9134NxVugmbJPp15d4LalxC4O.png ├── NP4z3i8m38Ntd28NQ4_0KCJ2q044Oez.png ├── SoWkIImgAStDuU8goIp9ILK8IatCoQn.png ├── 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这个交流群不是一个笔者的问题回答群，我更希望大家能够愿意积极回答，我相信提问和回答的过程都可以提高大家对知识的掌握程度。 4 | 5 | 至于交流群和 Issue 有什么区别，主要是两方面：一是交流群实时性高一些，二是交流群会更活跃一些。 6 | 7 | 另外，Issue 主要是用来发布一些项目中的错误和一些改进建议，当然也可以发布一些可以讨论的问题。 8 | 9 | 交流群可以讨论的内容比较广，例如在阅读本项目过程中不理解的地方可以在交流群中寻求别人的帮助、新技术的讨论、招聘信息、学习和工作的感受等等。 10 | 11 | 交流群不讨论政治，不讨论有争议性的话题，不发表仇视言论，不传播谣言，不发布广告（招聘信息之类的可以）。 12 | 13 |

14 | 15 | 16 | -------------------------------------------------------------------------------- /other/leetcode 总结.md: -------------------------------------------------------------------------------- 1 | # LeetCode 面试必备 2 | - 💪 就是干！如果你觉得有帮助请点个star，谢谢！ 3 | 4 | > **欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远** 5 | 6 | ## LeetCode 习题集合 7 | 8 | * [LeetCode 解题集合](https://github.com/apachecn/LeetCode/tree/master/docs/Leetcode_Solutions) 9 | 10 | 11 | ## 模版要求 12 | 13 | > 提交PR基本要求（满足任意一种即可） 14 | 15 | * 1. 不一样的思路 16 | * 2. 优化时间复杂度和空间复杂度，或者解决题目的Follow up 17 | * 3. 有意义的简化代码 18 | * 4. 未提交过的题目 19 | 20 | > **案例模版** 21 | 22 | [模版：007. Reverse Integer 反转整数](https://github.com/apachecn/LeetCode/tree/master/docs/Leetcode_Solutions/007._Reverse_Integer.md) 23 | 24 | 25 | ## 项目贡献者 26 | 27 | > 项目发起人 28 | 29 | * [@Lisanaaa](https://github.com/Lisanaaa) 30 | * [@片刻](https://github.com/jiangzhonglian) 31 | 32 | > 贡献者（欢迎大家来追加） 33 | 34 | * [@Lisanaaa](https://github.com/Lisanaaa) 35 | * [@片刻](https://github.com/jiangzhonglian) 36 | * [@小瑶](https://github.com/chenyyx) 37 | 38 | -------------------------------------------------------------------------------- /SUMMARY.md: -------------------------------------------------------------------------------- 1 | This file used to generate gitbook catalogue. 2 | 3 | # Summary 4 | 5 | * 算法 6 | * [剑指 Offer 题解](/notes/剑指 offer 题解.md) 7 | * [Leetcode 题解](/notes/Leetcode 题解.md) 8 | * [算法](/notes/算法.md) 9 | * 操作系统 10 | * [计算机操作系统](/notes/计算机操作系统.md) 11 | * [Linux](/notes/Linux.md) 12 | * 网络 13 | * [计算机网络](/notes/计算机网络.md) 14 | * [HTTP](/notes/HTTP.md) 15 | * [Socket](/notes/Socket.md) 16 | * 面向对象 17 | * [设计模式](/notes/设计模式.md) 18 | * [面向对象思想](/notes/面向对象思想.md) 19 | * 数据库 20 | * [数据库系统原理](/notes/数据库系统原理.md) 21 | * [SQL](/notes/SQL.md) 22 | * [Leetcode-Database 题解](/notes/Leetcode-Database 题解.md) 23 | * [MySQL](/notes/MySQL.md) 24 | * [Redis](/notes/Redis.md) 25 | * Java 26 | * [Java 基础](/notes/Java 基础.md) 27 | * [Java 虚拟机](/notes/Java 虚拟机.md) 28 | * [Java 并发](/notes/Java 并发.md) 29 | * [Java 容器](/notes/Java 容器.md) 30 | * [Java I/O](/notes/Java IO.md) 31 | * 系统设计 32 | * [系统设计基础](/notes/系统设计基础.md) 33 | * [分布式](/notes/分布式.md) 34 | * [集群](/notes/集群.md) 35 | * [攻击技术](/notes/攻击技术.md) 36 | * [缓存](/notes/缓存.md) 37 | * [消息队列](/notes/消息队列.md) 38 | * 工具 39 | * [Git](/notes/Git.md) 40 | * [Docker](/notes/Docker.md) 41 | * [正则表达式](/notes/正则表达式.md) 42 | * [构建工具](/notes/构建工具.md) 43 | * 编码实践 44 | * [重构](/notes/重构.md) 45 | * [代码可读性](/notes/代码可读性.md) 46 | * [代码风格规范](/notes/代码风格规范.md) 47 | * 参考书目 48 | * [BOOKLIST](/BOOKLIST.md) 49 | 50 | -------------------------------------------------------------------------------- /notes/消息队列.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、消息模型](#一消息模型) 3 | * [点对点](#点对点) 4 | * [发布/订阅](#发布订阅) 5 | * [二、使用场景](#二使用场景) 6 | * [异步处理](#异步处理) 7 | * [流量削锋](#流量削锋) 8 | * [应用解耦](#应用解耦) 9 | * [三、可靠性](#三可靠性) 10 | * [发送端的可靠性](#发送端的可靠性) 11 | * [接收端的可靠性](#接收端的可靠性) 12 | * [参考资料](#参考资料) 13 | 14 | 15 | 16 | # 一、消息模型 17 | 18 | ## 点对点 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。 21 | 22 |

23 | 24 | ## 发布/订阅 25 | 26 | 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。 27 | 28 |

29 | 30 | 发布与订阅模式和观察者模式有以下不同： 31 | 32 | - 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，发布者与订阅者不知道对方的存在，它们之间通过频道进行通信。 33 | - 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，发布者向频道发送一个消息之后，就不需要关心订阅者何时去订阅这个消息，可以立即返回。 34 | 35 |

36 | 37 | # 二、使用场景 38 | 39 | ## 异步处理 40 | 41 | 发送者将消息发送给消息队列之后，不需要同步等待消息接收者处理完毕，而是立即返回进行其它操作。消息接收者从消息队列中订阅消息之后异步处理。 42 | 43 | 例如在注册流程中通常需要发送验证邮件来确保注册用户身份的合法性，可以使用消息队列使发送验证邮件的操作异步处理，用户在填写完注册信息之后就可以完成注册，而将发送验证邮件这一消息发送到消息队列中。 44 | 45 | 只有在业务流程允许异步处理的情况下才能这么做，例如上面的注册流程中，如果要求用户对验证邮件进行点击之后才能完成注册的话，就不能再使用消息队列。 46 | 47 | ## 流量削锋 48 | 49 | 在高并发的场景下，如果短时间有大量的请求到达会压垮服务器。 50 | 51 | 可以将请求发送到消息队列中，服务器按照其处理能力从消息队列中订阅消息进行处理。 52 | 53 | ## 应用解耦 54 | 55 | 如果模块之间不直接进行调用，模块之间耦合度就会很低，那么修改一个模块或者新增一个模块对其它模块的影响会很小，从而实现可扩展性。 56 | 57 | 通过使用消息队列，一个模块只需要向消息队列中发送消息，其它模块可以选择性地从消息队列中订阅消息从而完成调用。 58 | 59 | # 三、可靠性 60 | 61 | ## 发送端的可靠性 62 | 63 | 发送端完成操作后一定能将消息成功发送到消息队列中。 64 | 65 | 实现方法： 66 | 67 | - 在本地数据库建一张消息表，将消息数据与业务数据保存在同一数据库实例里，这样就可以利用本地数据库的事务机制。事务提交成功后，将消息表中的消息转移到消息队列中，若转移消息成功则删除消息表中的数据，否则继续重传。 68 | 69 | ## 接收端的可靠性 70 | 71 | 接收端能够从消息队列成功消费一次消息。 72 | 73 | 两种实现方法： 74 | 75 | - 保证接收端处理消息的业务逻辑具有幂等性：只要具有幂等性，那么消费多少次消息，最后处理的结果都是一样的。 76 | - 保证消息具有唯一编号，并使用一张日志表来记录已经消费的消息编号。 77 | 78 | # 参考资料 79 | 80 | - [Observer vs Pub-Sub](http://developers-club.com/posts/270339/) 81 | - [消息队列中点对点与发布订阅区别](https://blog.csdn.net/lizhitao/article/details/47723105) 82 | -------------------------------------------------------------------------------- /notes/系统设计基础.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、性能](#一性能) 3 | * [二、伸缩性](#二伸缩性) 4 | * [三、扩展性](#三扩展性) 5 | * [四、可用性](#四可用性) 6 | * [五、安全性](#五安全性) 7 | * [参考资料](#参考资料) 8 | 9 | 10 | 11 | # 一、性能 12 | 13 | ## 性能指标 14 | 15 | ### 1. 响应时间 16 | 17 | 指某个请求从发出到接收到响应消耗的时间。 18 | 19 | 在对响应时间进行测试时，通常采用重复请求方式，然后计算平均响应时间。 20 | 21 | ### 2. 吞吐量 22 | 23 | 指系统在单位时间内可以处理的请求数量，通常使用每秒的请求数来衡量。 24 | 25 | ### 3. 并发用户数 26 | 27 | 指系统能同时处理的并发用户请求数量。 28 | 29 | 在没有并发存在的系统中，请求被顺序执行，此时响应时间为吞吐量的倒数。例如系统支持的吞吐量为 100 req/s，那么平均响应时间应该为 0.01s。 30 | 31 | 目前的大型系统都支持多线程来处理并发请求，多线程能够提高吞吐量以及缩短响应时间，主要有两个原因： 32 | 33 | - 多 CPU 34 | - IO 等待时间 35 | 36 | 使用 IO 多路复用等方式，系统在等待一个 IO 操作完成的这段时间内不需要被阻塞，可以去处理其它请求。通过将这个等待时间利用起来，使得 CPU 利用率大大提高。 37 | 38 | 并发用户数不是越高越好，因为如果并发用户数太高，系统来不及处理这么多的请求，会使得过多的请求需要等待，那么响应时间就会大大提高。 39 | 40 | ## 性能优化 41 | 42 | ### 1. 集群 43 | 44 | 将多台服务器组成集群，使用负载均衡将请求转发到集群中，避免单一服务器的负载压力过大导致性能降低。 45 | 46 | ### 2. 缓存 47 | 48 | 缓存能够提高性能的原因如下： 49 | 50 | - 缓存数据通常位于内存等介质中，这种介质对于读操作特别快； 51 | - 缓存数据可以位于靠近用户的地理位置上； 52 | - 可以将计算结果进行缓存，从而避免重复计算。 53 | 54 | ### 3. 异步 55 | 56 | 某些流程可以将操作转换为消息，将消息发送到消息队列之后立即返回，之后这个操作会被异步处理。 57 | 58 | # 二、伸缩性 59 | 60 | 指不断向集群中添加服务器来缓解不断上升的用户并发访问压力和不断增长的数据存储需求。 61 | 62 | ## 伸缩性与性能 63 | 64 | 如果系统存在性能问题，那么单个用户的请求总是很慢的； 65 | 66 | 如果系统存在伸缩性问题，那么单个用户的请求可能会很快，但是在并发数很高的情况下系统会很慢。 67 | 68 | ## 实现伸缩性 69 | 70 | 应用服务器只要不具有状态，那么就可以很容易地通过负载均衡器向集群中添加新的服务器。 71 | 72 | 关系型数据库的伸缩性通过 Sharding 来实现，将数据按一定的规则分布到不同的节点上，从而解决单台存储服务器的存储空间限制。 73 | 74 | 对于非关系型数据库，它们天生就是为海量数据而诞生，对伸缩性的支持特别好。 75 | 76 | # 三、扩展性 77 | 78 | 指的是添加新功能时对现有系统的其它应用无影响，这就要求不同应用具备低耦合的特点。 79 | 80 | 实现可扩展主要有两种方式： 81 | 82 | - 使用消息队列进行解耦，应用之间通过消息传递进行通信； 83 | - 使用分布式服务将业务和可复用的服务分离开来，业务使用分布式服务框架调用可复用的服务。新增的产品可以通过调用可复用的服务来实现业务逻辑，对其它产品没有影响。 84 | 85 | # 四、可用性 86 | 87 | ## 冗余 88 | 89 | 保证高可用的主要手段是使用冗余，当某个服务器故障时就请求其它服务器。 90 | 91 | 应用服务器的冗余比较容易实现，只要保证应用服务器不具有状态，那么某个应用服务器故障时，负载均衡器将该应用服务器原先的用户请求转发到另一个应用服务器上，不会对用户有任何影响。 92 | 93 | 存储服务器的冗余需要使用主从复制来实现，当主服务器故障时，需要提升从服务器为主服务器，这个过程称为切换。 94 | 95 | ## 监控 96 | 97 | 对 CPU、内存、磁盘、网络等系统负载信息进行监控，当某个数据达到一定阈值时通知运维人员，从而在系统发生故障之前及时发现问题。 98 | 99 | ## 服务降级 100 | 101 | 服务降级是系统为了应对大量的请求，主动关闭部分功能，从而保证核心功能可用。 102 | 103 | # 五、安全性 104 | 105 | 要求系统在应对各种攻击手段时能够有可靠的应对措施。 106 | 107 | # 参考资料 108 | 109 | - 大型网站技术架构：核心原理与案例分析 110 | -------------------------------------------------------------------------------- /Additional.md: -------------------------------------------------------------------------------- 1 | # 前言 2 | 3 | 本文档提供博客内容的补充资料，记录一些还没写又很重要的内容。欢迎补充！ 4 | 5 | 格式：\[资料名称\]\[标签1\]\[标签2\]... 6 | 7 | 8 | # 系统设计 9 | 10 | - [[system-design-primer][Github]](https://github.com/donnemartin/system-design-primer) 11 | - [[Leetcode / Interview Questions][题集]](https://leetcode.com/discuss/interview-question/?orderBy=most_votes) 12 | - [[系统设计面试题精选][Gitbook]](https://soulmachine.gitbooks.io/system-design/content/cn/) 13 | - [[海量数据面试题]](https://samanthachen.github.io/2016/08/01/%E6%B5%B7%E9%87%8F%E6%95%B0%E6%8D%AE%E9%9D%A2%E8%AF%95%E9%A2%98/) 14 | - [[前端经典面试题: 从输入 URL 到页面加载发生了什么？][具体问题]](https://segmentfault.com/a/1190000006879700) 15 | - [[秒杀系统架构分析与实战][具体问题]](https://my.oschina.net/xianggao/blog/524943) 16 | - [[微信二维码登录原理][具体问题]](https://zhuanlan.zhihu.com/p/22325152?refer=bittiger) 17 | - [[Create a TinyURL System][具体问题]](https://github.com/CyC2018/CS-Notes) 18 | - [[Design a Key-Value Store (Part I)][具体问题]](http://blog.gainlo.co/index.php/2016/06/14/design-a-key-value-store-part-i/) 19 | - [[坦率地讲 服务熔断 & 服务降级][知识点]](http://lexuslee.me/2018/02/01/2018-01-18-Service-fallback/) 20 | - [[理解 HTTP 幂等性][知识点]](https://www.cnblogs.com/weidagang2046/archive/2011/06/04/idempotence.html) 21 | - [[接口限流算法][知识点]](https://blog.csdn.net/ljj821061514/article/details/52512943) 22 | - [[微服务学习资料汇总][知识点]](https://www.infoq.cn/article/2014%2F07%2Fmicroservice-learning-resources) 23 | - [[理解 RESTful 架构][知识点]](http://www.ruanyifeng.com/blog/2011/09/restful.html) 24 | - [[MapReduce 算法][知识点]](https://github.com/xuelangZF/CS_Offer/blob/master/Others/Hadoop_Spark.md) 25 | 26 | # Spring 27 | 28 | - [[Spring 揭秘][书籍]](https://book.douban.com/subject/3897837/) 29 | - [[69 道 Spring 面试题和答案][面试题集锦]](http://ifeve.com/spring-interview-questions-and-answers/) 30 | - [[Spring 面试题][面试题集锦]](https://github.com/Homiss/Java-interview-questions/blob/master/%E6%A1%86%E6%9E%B6/Spring%20%E9%9D%A2%E8%AF%95%E9%A2%98.md) 31 | 32 | # 中间件 33 | 34 | - [[RabbitMQ 实战][书籍]](https://book.douban.com/subject/26649178/) 35 | - [[从 Paxos 到 Zookeeper][书籍]](https://book.douban.com/subject/26292004/)] 36 | - [[Apache Dubbo][文档]](http://dubbo.apache.org/zh-cn/) 37 | - [[nginx 快速入门之基本原理篇][入门]](https://zhuanlan.zhihu.com/p/31196264) 38 | - [[深入理解 Nginx][书籍]](https://book.douban.com/subject/22793675/) 39 | 40 | # 编程语言思想 41 | 42 | - [[函数式编程初探][入门]](http://www.ruanyifeng.com/blog/2012/04/functional_programming.html)] 43 | - [[函数式编程][全面]](https://coolshell.cn/articles/10822.html) 44 | - [[闭包][文档]](https://developer.mozilla.org/zh-CN/docs/Web/JavaScript/Closures) 45 | - [[尾调用优化]](http://www.ruanyifeng.com/blog/2015/04/tail-call.html) 46 | 47 | -------------------------------------------------------------------------------- /notes/Docker.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、解决的问题](#一解决的问题) 3 | * [二、与虚拟机的比较](#二与虚拟机的比较) 4 | * [三、优势](#三优势) 5 | * [四、使用场景](#四使用场景) 6 | * [五、镜像与容器](#五镜像与容器) 7 | * [参考资料](#参考资料) 8 | 9 | 10 | 11 |

12 | 13 | # 一、解决的问题 14 | 15 | 由于不同的机器有不同的操作系统，以及不同的库和组件，在将一个应用部署到多台机器上需要进行大量的环境配置操作。 16 | 17 | Docker 主要解决环境配置问题，它是一种虚拟化技术，对进程进行隔离，被隔离的进程独立于宿主操作系统和其它隔离的进程。使用 Docker 可以不修改应用程序代码，不需要开发人员学习特定环境下的技术，就能够将现有的应用程序部署在其他机器中。 18 | 19 | # 二、与虚拟机的比较 20 | 21 | 虚拟机也是一种虚拟化技术，它与 Docker 最大的区别在于它是通过模拟硬件，并在硬件上安装操作系统来实现。 22 | 23 |

24 | 25 |

26 | 27 | ## 启动速度 28 | 29 | 启动虚拟机需要启动虚拟机的操作系统，再启动应用，这个过程非常慢； 30 | 31 | 而启动 Docker 相当于启动宿主操作系统上的一个进程。 32 | 33 | ## 占用资源 34 | 35 | 虚拟机是一个完整的操作系统，需要占用大量的磁盘、内存和 CPU，一台机器只能开启几十个的虚拟机。 36 | 37 | 而 Docker 只是一个进程，只需要将应用以及相关的组件打包，在运行时占用很少的资源，一台机器可以开启成千上万个 Docker。 38 | 39 | # 三、优势 40 | 41 | 除了启动速度快以及占用资源少之外，Docker 具有以下优势： 42 | 43 | ## 更容易迁移 44 | 45 | 提供一致性的运行环境，可以在不同的机器上进行迁移，而不用担心环境变化导致无法运行。 46 | 47 | ## 更容易维护 48 | 49 | 使用分层技术和镜像，使得应用可以更容易复用重复部分。复用程度越高，维护工作也越容易。 50 | 51 | ## 更容易扩展 52 | 53 | 可以使用基础镜像进一步扩展得到新的镜像，并且官方和开源社区提供了大量的镜像，通过扩展这些镜像可以非常容易得到我们想要的镜像。 54 | 55 | # 四、使用场景 56 | 57 | ## 持续集成 58 | 59 | 持续集成指的是频繁地将代码集成到主干上，这样能够更快地发现错误。 60 | 61 | Docker 具有轻量级以及隔离性的特点，在将代码集成到一个 Docker 中不会对其它 Docker 产生影响。 62 | 63 | ## 提供可伸缩的云服务 64 | 65 | 根据应用的负载情况，可以很容易地增加或者减少 Docker。 66 | 67 | ## 搭建微服务架构 68 | 69 | Docker 轻量级的特点使得它很适合用于部署、维护、组合微服务。 70 | 71 | # 五、镜像与容器 72 | 73 | 镜像是一种静态的结构，可以看成面向对象里面的类，而容器是镜像的一个实例。 74 | 75 | 镜像包含着容器运行时所需要的代码以及其它组件，它是一种分层结构，每一层都是只读的（read-only layers）。构建镜像时，会一层一层构建，前一层是后一层的基础。镜像的这种分层存储结构很适合镜像的复用以及定制。 76 | 77 | 构建容器时，通过在镜像的基础上添加一个可写层（writable layer），用来保存着容器运行过程中的修改。 78 | 79 |

80 | 81 | # 参考资料 82 | 83 | - [DOCKER 101: INTRODUCTION TO DOCKER WEBINAR RECAP](https://blog.docker.com/2017/08/docker-101-introduction-docker-webinar-recap/) 84 | - [Docker 入门教程](http://www.ruanyifeng.com/blog/2018/02/docker-tutorial.html) 85 | - [Docker container vs Virtual machine](http://www.bogotobogo.com/DevOps/Docker/Docker_Container_vs_Virtual_Machine.php) 86 | - [How to Create Docker Container using Dockerfile](https://linoxide.com/linux-how-to/dockerfile-create-docker-container/) 87 | - [理解 Docker（2）：Docker 镜像](http://www.cnblogs.com/sammyliu/p/5877964.html) 88 | - [为什么要使用 Docker？](https://yeasy.gitbooks.io/docker_practice/introduction/why.html) 89 | - [What is Docker](https://www.docker.com/what-docker) 90 | - [持续集成是什么？](http://www.ruanyifeng.com/blog/2015/09/continuous-integration.html) 91 | 92 | -------------------------------------------------------------------------------- /other/算法与数据结构.md: -------------------------------------------------------------------------------- 1 | # Algorithm 2 | leetcode/lintcode上的算法题 3 | 4 | **关于问题的答案和解体的思路，可以移步 : https://github.com/zhaozhengcoder/Algorithm** 5 | 6 | ### About 7 | 8 | 这个仓库最初的想法是把lintcode/lintocde上面的算法题目整理一下，因为很多题目太多了显得太乱了，就不继续在GitHub上面写了，以前写的一部分移到我的博客上面了。 9 | GitHub上面打算整理一些比较典型 或者是 自己思考过的觉得很好的问题。 10 | 11 | 12 | 在博客上面开了两个专栏 13 | 14 | 1. 数据结构/算法导论 : 15 | https://www.jianshu.com/nb/12397278 16 | 17 | 2. OJ练习题 : 18 | https://www.jianshu.com/nb/9973135 19 | 20 | 推荐两篇自己对 递归搜索和动态规划 的理解的blog ： 21 | 22 | 1. https://www.jianshu.com/p/5eb4da919efe 23 | 24 | 2. https://www.jianshu.com/p/6b3a2304f63f 25 | 26 | 27 | 28 | ### 题目的索引 29 | GITHUB上面打算整理一些比较典型 或者是 自己思考过的觉得很好的问题。 30 | 31 | 1.从数据结构的角度索引 : 32 | 33 | a. 数组 34 | 35 | 两数之和 36 | 37 | 连续最大子数组 38 | 39 | 乘积最大子数组 40 | 41 | 买卖股票的最佳时机1，2，3 42 | 43 | 买卖股票的最佳时机1：寻找数组里面的最大上升子序列 44 | 买卖股票的最佳时机2：寻找数组里面所有的上升子序列 45 | 买卖股票的最佳时机3：寻找数组里面两个不重合的上升子序列，并且使他们的和最大 to-do 46 | 47 | 区间合并（将有交集的区间合并） 48 | 49 | 寻找缺失的数 50 | 51 | 1. 一个顺序的数组[1,2,3,5,6],缺少了一个数字，如何找到它？ 52 | 53 | 2. 一个arr的数组，只有一个数字出现了一次，其他都出现了两次，如何找到它？ 54 | 55 | 数组的近似划分（将一个数组分成两个，但是差最小） 56 | 57 | 数组里面第k大的数 58 | 59 | 跳跃游戏1，2 60 | 61 | 跳跃游戏1： 62 | 给出一个非负整数数组，你最初定位在数组的第一个位置, 63 | 数组中的每个元素代表你在那个位置可以跳跃的最大长度, 64 | 返回 是否能到达数组的最后一个位置 65 | 66 | 跳跃游戏2： 67 | 给出一个非负整数数组，你最初定位在数组的第一个位置, 68 | 数组中的每个元素代表你在那个位置可以跳跃的最大长度,　　　 69 | 返回 使用最少的跳跃次数到达数组的最后一个位置 70 | 71 | a+. 二维矩阵 72 | 73 | 顺时针打印二维矩阵 74 | 75 | 给出一个二维矩阵，找到一个路径（从某个左上角到某个角右下）使这条路径的值最大 76 | 77 | b. 链表 78 | 79 | c. 字符串 80 | 81 | 最长公共子序列（并不是连续的） 82 | 83 | 最长回文子串 84 | 85 | d. 二叉树 86 | 87 | 返回一个平衡二叉树的第k大的节点 88 | 89 | 二叉树的最低公共祖先 90 | 91 | 非递归遍历二叉树 92 | 93 | e. 图 94 | 95 | 最短路径 96 | 97 | 深度/广度优先遍历 98 | 99 | 2. 从算法的角度建立索引 ： 100 | 101 | a. 递归搜索问题 102 | 103 | N后问题 104 | 105 | 全排列 106 | 107 | 组合问题1，2 108 | 109 | b. 动态规划 110 | 111 | 背包问题1，2 112 | 113 | 数组的近似划分（将一个数组分成两个，但是差最小） 114 | 115 | 跳跃游戏1，2 116 | 117 | 给出一个二维矩阵，找到一个路径（从某个左上角到某个角右下）使这条路径的值最大 118 | 119 | 120 | 3. 常用 121 | 122 | a. 排列/组合 123 | 124 | b. 深度优先遍历 125 | 126 | c. 最短路径 127 | 128 | 4. 智力题（算法本身很简单，就是想不到的那种） 129 | 130 | 最多有多少个点在同一条直线上 131 | 132 | 133 | ### Others 134 | 135 | 1. 类似于系统设计的题目 136 | 137 | 带最小值的栈/队列 138 | 139 | url长链接转短链接 140 | 141 | 2. 解决特定问题 142 | 143 | 并查集 144 | 145 | 布隆过滤器 146 | 147 | 148 | 149 | 如果你对机器学习的算法感兴趣，欢迎共同讨论： 150 | 151 | https://github.com/zhaozhengcoder/Machine-Learning 152 | -------------------------------------------------------------------------------- /BOOKLIST.md: -------------------------------------------------------------------------------- 1 | # 数据结构与算法 2 | 3 | - [算法](https://book.douban.com/subject/19952400/) 4 | - [数据结构与算法分析](https://book.douban.com/subject/3351237/) 5 | - [编程珠玑](https://book.douban.com/subject/3227098/) 6 | - [剑指 Offer](https://book.douban.com/subject/25910559/) 7 | 8 | # 操作系统 9 | 10 | - [现代操作系统](https://book.douban.com/subject/3852290/) 11 | - [深入理解计算机系统](https://book.douban.com/subject/26912767/) 12 | - [鸟哥的 Linux 私房菜](https://book.douban.com/subject/4889838/) 13 | - [Unix 环境高级编程](https://book.douban.com/subject/25900403/) 14 | - [Unix/Linux 编程实践教程](https://book.douban.com/subject/1219329/) 15 | - [Operating Systems: Three Easy Pieces (OSTEP)](http://pages.cs.wisc.edu/~remzi/OSTEP/) 16 | 17 | # 计算机网络 18 | 19 | - [计算机网络](https://book.douban.com/subject/2970300/) 20 | - [计算机网络 自顶向下方法](https://book.douban.com/subject/1391207/) 21 | - [图解 HTTP](https://book.douban.com/subject/25863515/) 22 | - [TCP/IP 详解 卷 1：协议](https://book.douban.com/subject/1088054/) 23 | - [UNIX 网络编程](https://book.douban.com/subject/1500149/) 24 | - [Linux 多线程服务端编程](https://book.douban.com/subject/20471211/) 25 | 26 | # 面向对象 27 | 28 | - [Head First 设计模式](https://book.douban.com/subject/2243615/) 29 | - [设计模式 可复用面向对象软件的基础](https://book.douban.com/subject/1052241/) 30 | - [敏捷软件开发 原则、模式与实践](https://book.douban.com/subject/1140457/) 31 | 32 | # 数据库 33 | 34 | - [数据库系统概念](https://book.douban.com/subject/10548379/) 35 | - [MySQL 必知必会](https://book.douban.com/subject/3354490/) 36 | - [高性能 MySQL](https://book.douban.com/subject/23008813/) 37 | - [Redis 设计与实现](https://book.douban.com/subject/25900156/) 38 | - [Redis 实战](https://book.douban.com/subject/26612779/) 39 | 40 | # Java 41 | 42 | - [Java 编程思想](https://book.douban.com/subject/2130190/) 43 | - [Effective java 中文版](https://book.douban.com/subject/3360807/) 44 | - [深入Java虚拟机(原书第2版)](https://book.douban.com/subject/1138768/) 45 | - [深入理解 Java 虚拟机](https://book.douban.com/subject/24722612/) 46 | - [Java 并发编程实战](https://book.douban.com/subject/10484692/) 47 | - [精通 Spring 4.x](https://book.douban.com/subject/26952826/) 48 | - [Spring 揭秘](https://book.douban.com/subject/3897837/) 49 | 50 | # C++ 51 | 52 | - [C++ Primer](https://book.douban.com/subject/25708312/) 53 | - [Effective C++](https://book.douban.com/subject/1842426/) 54 | - [C++ 编程规范](https://book.douban.com/subject/1480481/) 55 | - [STL 源码剖析](https://book.douban.com/subject/1110934/) 56 | - [深度探索 C++ 对象模型](https://book.douban.com/subject/1091086/) 57 | 58 | # 系统设计 59 | 60 | - [大规模分布式存储系统](https://book.douban.com/subject/25723658/) 61 | - [从 Paxos 到 Zookeeper](https://book.douban.com/subject/26292004/) 62 | - [大型网站系统与 Java 中间件开发实践](https://book.douban.com/subject/25867042/) 63 | - [淘宝技术这十年](https://book.douban.com/subject/24335672/) 64 | - [深入理解 Nginx](https://book.douban.com/subject/22793675/) 65 | 66 | # 开发工具 67 | 68 | - [Pro Git](https://git-scm.com/book/zh/v2) 69 | - [正则表达式必知必会](https://book.douban.com/subject/2269648/) 70 | 71 | # 编码实践 72 | 73 | - [重构](https://book.douban.com/subject/4262627/) 74 | - [代码大全](https://book.douban.com/subject/1477390/) 75 | - [人月神话](https://book.douban.com/subject/1102259/) 76 | - [程序员的职业素养](https://book.douban.com/subject/11614538/) 77 | - [编写可读代码的艺术](https://book.douban.com/subject/10797189/) 78 | 79 | # 其它 80 | 81 | - [JavaScript 语言精粹](https://book.douban.com/subject/3590768/) 82 | - [利用 Python 进行数据分析](https://book.douban.com/subject/25779298/) 83 | - [概率论与数理统计](https://book.douban.com/subject/2201479/) 84 | -------------------------------------------------------------------------------- /notes/构建工具.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、构建工具的作用](#一构建工具的作用) 3 | * [二、Java 主流构建工具](#二java-主流构建工具) 4 | * [三、Maven](#三maven) 5 | * [参考资料](#参考资料) 6 | 7 | 8 | 9 | # 一、构建工具的作用 10 | 11 | 构建工具是用于构建项目的自动化工具，主要包含以下工作： 12 | 13 | ## 依赖管理 14 | 15 | 不再需要手动导入 Jar 依赖包，并且可以自动处理依赖关系，也就是说某个依赖如果依赖于其它依赖，构建工具可以帮助我们自动处理这种依赖管理。 16 | 17 | ## 运行单元测试 18 | 19 | 不再需要在项目代码中添加测试代码，从而避免了污染项目代码。 20 | 21 | ## 将源代码转化为可执行文件 22 | 23 | 包含预处理、编译、汇编、链接等步骤。 24 | 25 | ## 将可执行文件进行打包 26 | 27 | 不再需要使用 IDE 将应用程序打包成 Jar 包。 28 | 29 | ## 发布到生产服务器上 30 | 31 | 不再需要通过 FTP 将 Jar 包上传到服务器上。 32 | 33 | # 二、Java 主流构建工具 34 | 35 | 主要包括 Ant、Maven 和 Gradle。 36 | 37 |

38 | 39 | Gradle 和 Maven 的区别是，它使用 Groovy 这种特定领域语言（DSL）来管理构建脚本，而不再使用 XML 这种标记性语言。因为项目如果庞大的话，XML 很容易就变得臃肿。 40 | 41 | 例如要在项目中引入 Junit，Maven 的代码如下： 42 | 43 | ```xml 44 | 45 | 47 | 4.0.0 48 | 49 | jizg.study.maven.hello 50 | hello-first 51 | 0.0.1-SNAPSHOT 52 | 53 | 54 | 55 | junit 56 | junit 57 | 4.10 58 | test 59 | 60 | 61 | 62 | ``` 63 | 64 | 而 Gradle 只需要几行代码： 65 | 66 | ```java 67 | dependencies { 68 | testCompile "junit:junit:4.10" 69 | } 70 | ``` 71 | 72 | # 三、Maven 73 | 74 | ## 概述 75 | 76 | 提供了项目对象模型（POM）文件来管理项目的构建。 77 | 78 | ## 仓库 79 | 80 | 仓库的搜索顺序为：本地仓库、中央仓库、远程仓库。 81 | 82 | - 本地仓库用来存储项目的依赖库； 83 | - 中央仓库是下载依赖库的默认位置； 84 | - 远程仓库，因为并非所有的库存储在中央仓库，或者中央仓库访问速度很慢，远程仓库是中央仓库的补充。 85 | 86 | ## POM 87 | 88 | POM 代表项目对象模型，它是一个 XML 文件，保存在项目根目录的 pom.xml 文件中。 89 | 90 | ```xml 91 | 92 | junit 93 | junit 94 | 4.12 95 | test 96 | 97 | ``` 98 | 99 | [groupId, artifactId, version, packaging, classifier] 称为一个项目的坐标，其中 groupId、artifactId、version 必须定义，packaging 可选（默认为 Jar），classifier 不能直接定义的，需要结合插件使用。 100 | 101 | 102 | - groupId：项目组 Id，必须全球唯一； 103 | - artifactId：项目 Id，即项目名； 104 | - version：项目版本； 105 | - packaging：项目打包方式。 106 | 107 | ## 依赖原则 108 | 109 | ### 1. 依赖路径最短优先原则 110 | 111 | ```html 112 | A -> B -> C -> X(1.0) 113 | A -> D -> X(2.0) 114 | ``` 115 | 由于 X(2.0) 路径最短，所以使用 X(2.0)。 116 | 117 | ### 2. 声明顺序优先原则 118 | 119 | ```html 120 | A -> B -> X(1.0) 121 | A -> C -> X(2.0) 122 | ``` 123 | 124 | 在 POM 中最先声明的优先，上面的两个依赖如果先声明 B，那么最后使用 X(1.0)。 125 | 126 | ### 3. 覆写优先原则 127 | 128 | 子 POM 内声明的依赖优先于父 POM 中声明的依赖。 129 | 130 | ## 解决依赖冲突 131 | 132 | 找到 Maven 加载的 Jar 包版本，使用 `mvn dependency:tree` 查看依赖树，根据依赖原则来调整依赖在 POM 文件的声明顺序。 133 | 134 | # 参考资料 135 | 136 | - [POM Reference](http://maven.apache.org/pom.html#Dependency_Version_Requirement_Specification) 137 | - [What is a build tool?](https://stackoverflow.com/questions/7249871/what-is-a-build-tool) 138 | - [Java Build Tools Comparisons: Ant vs Maven vs Gradle](https://programmingmitra.blogspot.com/2016/05/java-build-tools-comparisons-ant-vs.html) 139 | - [maven 2 gradle](http://sagioto.github.io/maven2gradle/) 140 | - [新一代构建工具 gradle](https://www.imooc.com/learn/833) 141 | 142 | -------------------------------------------------------------------------------- /notes/Git.md: -------------------------------------------------------------------------------- 1 | 2 | * [集中式与分布式](#集中式与分布式) 3 | * [中心服务器](#中心服务器) 4 | * [工作流](#工作流) 5 | * [分支实现](#分支实现) 6 | * [冲突](#冲突) 7 | * [Fast forward](#fast-forward) 8 | * [分支管理策略](#分支管理策略) 9 | * [储藏（Stashing）](#储藏stashing) 10 | * [SSH 传输设置](#ssh-传输设置) 11 | * [.gitignore 文件](#gitignore-文件) 12 | * [Git 命令一览](#git-命令一览) 13 | * [参考资料](#参考资料) 14 | 15 | 16 | 17 | # 集中式与分布式 18 | 19 | Git 属于分布式版本控制系统，而 SVN 属于集中式。 20 | 21 | 集中式版本控制只有中心服务器拥有一份代码，而分布式版本控制每个人的电脑上就有一份完整的代码。 22 | 23 | 集中式版本控制有安全性问题，当中心服务器挂了所有人都没办法工作了。 24 | 25 | 集中式版本控制需要连网才能工作，如果网速过慢，那么提交一个文件的会慢的无法让人忍受。而分布式版本控制不需要连网就能工作。 26 | 27 | 分布式版本控制新建分支、合并分支操作速度非常快，而集中式版本控制新建一个分支相当于复制一份完整代码。 28 | 29 | # 中心服务器 30 | 31 | 中心服务器用来交换每个用户的修改，没有中心服务器也能工作，但是中心服务器能够 24 小时保持开机状态，这样就能更方便的交换修改。 32 | 33 | Github 就是一个中心服务器。 34 | 35 | # 工作流 36 | 37 |

38 | 39 | 新建一个仓库之后，当前目录就成为了工作区，工作区下有一个隐藏目录 .git，它属于 Git 的版本库。 40 | 41 | Git 版本库有一个称为 stage 的暂存区，还有自动创建的 master 分支以及指向分支的 HEAD 指针。 42 | 43 |

44 | 45 | - git add files 把文件的修改添加到暂存区 46 | - git commit 把暂存区的修改提交到当前分支，提交之后暂存区就被清空了 47 | - git reset -- files 使用当前分支上的修改覆盖暂存区，用来撤销最后一次 git add files 48 | - git checkout -- files 使用暂存区的修改覆盖工作目录，用来撤销本地修改 49 | 50 |

51 | 52 | 可以跳过暂存区域直接从分支中取出修改，或者直接提交修改到分支中。 53 | 54 | - git commit -a 直接把所有文件的修改添加到暂存区然后执行提交 55 | - git checkout HEAD -- files 取出最后一次修改，可以用来进行回滚操作 56 | 57 | # 分支实现 58 | 59 | 使用指针将每个提交连接成一条时间线，HEAD 指针指向当前分支指针。 60 | 61 |

62 | 63 | 新建分支是新建一个指针指向时间线的最后一个节点，并让 HEAD 指针指向新分支表示新分支成为当前分支。 64 | 65 |

66 | 67 | 每次提交只会让当前分支指针向前移动，而其它分支指针不会移动。 68 | 69 |

70 | 71 | 合并分支也只需要改变指针即可。 72 | 73 |

74 | 75 | # 冲突 76 | 77 | 当两个分支都对同一个文件的同一行进行了修改，在分支合并时就会产生冲突。 78 | 79 |

80 | 81 | Git 会使用 <<<<<<< ，======= ，>>>>>>> 标记出不同分支的内容，只需要把不同分支中冲突部分修改成一样就能解决冲突。 82 | 83 | ``` 84 | <<<<<<< HEAD 85 | Creating a new branch is quick & simple. 86 | ======= 87 | Creating a new branch is quick AND simple. 88 | >>>>>>> feature1 89 | ``` 90 | 91 | # Fast forward 92 | 93 | "快进式合并"（fast-farward merge），会直接将 master 分支指向合并的分支，这种模式下进行分支合并会丢失分支信息，也就不能在分支历史上看出分支信息。 94 | 95 | 可以在合并时加上 --no-ff 参数来禁用 Fast forward 模式，并且加上 -m 参数让合并时产生一个新的 commit。 96 | 97 | ``` 98 | $ git merge --no-ff -m "merge with no-ff" dev 99 | ``` 100 | 101 |

102 | 103 | # 分支管理策略 104 | 105 | master 分支应该是非常稳定的，只用来发布新版本； 106 | 107 | 日常开发在开发分支 dev 上进行。 108 | 109 |

110 | 111 | # 储藏（Stashing） 112 | 113 | 在一个分支上操作之后，如果还没有将修改提交到分支上，此时进行切换分支，那么另一个分支上也能看到新的修改。这是因为所有分支都共用一个工作区的缘故。 114 | 115 | 可以使用 git stash 将当前分支的修改储藏起来，此时当前工作区的所有修改都会被存到栈上，也就是说当前工作区是干净的，没有任何未提交的修改。此时就可以安全的切换到其它分支上了。 116 | 117 | ``` 118 | $ git stash 119 | Saved working directory and index state \ "WIP on master: 049d078 added the index file" 120 | HEAD is now at 049d078 added the index file (To restore them type "git stash apply") 121 | ``` 122 | 123 | 该功能可以用于 bug 分支的实现。如果当前正在 dev 分支上进行开发，但是此时 master 上有个 bug 需要修复，但是 dev 分支上的开发还未完成，不想立即提交。在新建 bug 分支并切换到 bug 分支之前就需要使用 git stash 将 dev 分支的未提交修改储藏起来。 124 | 125 | # SSH 传输设置 126 | 127 | Git 仓库和 Github 中心仓库之间的传输是通过 SSH 加密。 128 | 129 | 如果工作区下没有 .ssh 目录，或者该目录下没有 id_rsa 和 id_rsa.pub 这两个文件，可以通过以下命令来创建 SSH Key： 130 | 131 | ``` 132 | $ ssh-keygen -t rsa -C "youremail@example.com" 133 | ``` 134 | 135 | 然后把公钥 id_rsa.pub 的内容复制到 Github "Account settings" 的 SSH Keys 中。 136 | 137 | # .gitignore 文件 138 | 139 | 忽略以下文件： 140 | 141 | - 操作系统自动生成的文件，比如缩略图； 142 | - 编译生成的中间文件，比如 Java 编译产生的 .class 文件； 143 | - 自己的敏感信息，比如存放口令的配置文件。 144 | 145 | 不需要全部自己编写，可以到 [https://github.com/github/gitignore](https://github.com/github/gitignore) 中进行查询。 146 | 147 | # Git 命令一览 148 | 149 |

150 | 151 | 比较详细的地址：http://www.cheat-sheets.org/saved-copy/git-cheat-sheet.pdf 152 | 153 | # 参考资料 154 | 155 | - [Git - 简明指南](http://rogerdudler.github.io/git-guide/index.zh.html) 156 | - [图解 Git](http://marklodato.github.io/visual-git-guide/index-zh-cn.html) 157 | - [廖雪峰 : Git 教程](https://www.liaoxuefeng.com/wiki/0013739516305929606dd18361248578c67b8067c8c017b000) 158 | - [Learn Git Branching](https://learngitbranching.js.org/) 159 | -------------------------------------------------------------------------------- /notes/攻击技术.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、跨站脚本攻击](#一跨站脚本攻击) 3 | * [二、跨站请求伪造](#二跨站请求伪造) 4 | * [三、SQL 注入攻击](#三sql-注入攻击) 5 | * [四、拒绝服务攻击](#四拒绝服务攻击) 6 | * [参考资料](#参考资料) 7 | 8 | 9 | 10 | # 一、跨站脚本攻击 11 | 12 | ## 概念 13 | 14 | 跨站脚本攻击（Cross-Site Scripting, XSS），可以将代码注入到用户浏览的网页上，这种代码包括 HTML 和 JavaScript。 15 | 16 | ## 攻击原理 17 | 18 | 例如有一个论坛网站，攻击者可以在上面发布以下内容： 19 | 20 | ```html 21 | 22 | ``` 23 | 24 | 之后该内容可能会被渲染成以下形式： 25 | 26 | ```html 27 |

28 | ``` 29 | 30 | 另一个用户浏览了含有这个内容的页面将会跳转到 domain.com 并携带了当前作用域的 Cookie。如果这个论坛网站通过 Cookie 管理用户登录状态，那么攻击者就可以通过这个 Cookie 登录被攻击者的账号了。 31 | 32 | ## 危害 33 | 34 | - 窃取用户的 Cookie 35 | - 伪造虚假的输入表单骗取个人信息 36 | - 显示伪造的文章或者图片 37 | 38 | ## 防范手段 39 | 40 | ### 1. 设置 Cookie 为 HttpOnly 41 | 42 | 设置了 HttpOnly 的 Cookie 可以防止 JavaScript 脚本调用，就无法通过 document.cookie 获取用户 Cookie 信息。 43 | 44 | ### 2. 过滤特殊字符 45 | 46 | 例如将 `<` 转义为 `<`，将 `>` 转义为 `>`，从而避免 HTML 和 Jascript 代码的运行。 47 | 48 | 富文本编辑器允许用户输入 HTML 代码，就不能简单地将 `<` 等字符进行过滤了，极大地提高了 XSS 攻击的可能性。 49 | 50 | 富文本编辑器通常采用 XSS filter 来防范 XSS 攻击，通过定义一些标签白名单或者黑名单，从而不允许有攻击性的 HTML 代码的输入。 51 | 52 | 以下例子中，form 和 script 等标签都被转义，而 h 和 p 等标签将会保留。 53 | 54 | ```html 55 |

XSS Demo

56 | 57 |

123

58 | 59 |
60 | 61 |
62 | 63 | 
hello
64 | 65 | 68 | ``` 69 | 70 | ```html 71 |

XSS Demo

72 | 73 |

123

74 | 75 | <form> 76 | <input type="text" name="q" value="test"> 77 | </form> 78 | 79 | 
hello
80 | 81 | <script type="text/javascript"> 82 | alert(/xss/); 83 | </script> 84 | ``` 85 | 86 | > [XSS 过滤在线测试](http://jsxss.com/zh/try.html) 87 | 88 | # 二、跨站请求伪造 89 | 90 | ## 概念 91 | 92 | 跨站请求伪造（Cross-site request forgery，CSRF），是攻击者通过一些技术手段欺骗用户的浏览器去访问一个自己曾经认证过的网站并执行一些操作（如发邮件，发消息，甚至财产操作如转账和购买商品）。由于浏览器曾经认证过，所以被访问的网站会认为是真正的用户操作而去执行。 93 | 94 | XSS 利用的是用户对指定网站的信任，CSRF 利用的是网站对用户浏览器的信任。 95 | 96 | ## 攻击原理 97 | 98 | 假如一家银行用以执行转账操作的 URL 地址如下： 99 | 100 | ``` 101 | http://www.examplebank.com/withdraw?account=AccoutName&amount=1000&for=PayeeName。 102 | ``` 103 | 104 | 那么，一个恶意攻击者可以在另一个网站上放置如下代码： 105 | 106 | ``` 107 | 。 108 | ``` 109 | 110 | 如果有账户名为 Alice 的用户访问了恶意站点，而她之前刚访问过银行不久，登录信息尚未过期，那么她就会损失 1000 美元。 111 | 112 | 这种恶意的网址可以有很多种形式，藏身于网页中的许多地方。此外，攻击者也不需要控制放置恶意网址的网站。例如他可以将这种地址藏在论坛，博客等任何用户生成内容的网站中。这意味着如果服务器端没有合适的防御措施的话，用户即使访问熟悉的可信网站也有受攻击的危险。 113 | 114 | 通过例子能够看出，攻击者并不能通过 CSRF 攻击来直接获取用户的账户控制权，也不能直接窃取用户的任何信息。他们能做到的，是欺骗用户浏览器，让其以用户的名义执行操作。 115 | 116 | ## 防范手段 117 | 118 | ### 1. 检查 Referer 首部字段 119 | 120 | Referer 首部字段位于 HTTP 报文中，用于标识请求来源的地址。检查这个首部字段并要求请求来源的地址在同一个域名下，可以极大的防止 CSRF 攻击。 121 | 122 | 这种办法简单易行，工作量低，仅需要在关键访问处增加一步校验。但这种办法也有其局限性，因其完全依赖浏览器发送正确的 Referer 字段。虽然 HTTP 协议对此字段的内容有明确的规定，但并无法保证来访的浏览器的具体实现，亦无法保证浏览器没有安全漏洞影响到此字段。并且也存在攻击者攻击某些浏览器，篡改其 Referer 字段的可能。 123 | 124 | ### 2. 添加校验 Token 125 | 126 | 在访问敏感数据请求时，要求用户浏览器提供不保存在 Cookie 中，并且攻击者无法伪造的数据作为校验。例如服务器生成随机数并附加在表单中，并要求客户端传回这个随机数。 127 | 128 | ### 3. 输入验证码 129 | 130 | 因为 CSRF 攻击是在用户无意识的情况下发生的，所以要求用户输入验证码可以让用户知道自己正在做的操作。 131 | 132 | # 三、SQL 注入攻击 133 | 134 | ## 概念 135 | 136 | 服务器上的数据库运行非法的 SQL 语句，主要通过拼接来完成。 137 | 138 | ## 攻击原理 139 | 140 | 例如一个网站登录验证的 SQL 查询代码为： 141 | 142 | ```sql 143 | strSQL = "SELECT * FROM users WHERE (name = '" + userName + "') and (pw = '"+ passWord +"');" 144 | ``` 145 | 146 | 如果填入以下内容： 147 | 148 | ```sql 149 | userName = "1' OR '1'='1"; 150 | passWord = "1' OR '1'='1"; 151 | ``` 152 | 153 | 那么 SQL 查询字符串为： 154 | 155 | ```sql 156 | strSQL = "SELECT * FROM users WHERE (name = '1' OR '1'='1') and (pw = '1' OR '1'='1');" 157 | ``` 158 | 159 | 此时无需验证通过就能执行以下查询： 160 | 161 | ```sql 162 | strSQL = "SELECT * FROM users;" 163 | ``` 164 | 165 | ## 防范手段 166 | 167 | ### 1. 使用参数化查询 168 | 169 | Java 中的 PreparedStatement 是预先编译的 SQL 语句，可以传入适当参数并且多次执行。由于没有拼接的过程，因此可以防止 SQL 注入的发生。 170 | 171 | ```java 172 | PreparedStatement stmt = connection.prepareStatement("SELECT * FROM users WHERE userid=? AND password=?"); 173 | stmt.setString(1, userid); 174 | stmt.setString(2, password); 175 | ResultSet rs = stmt.executeQuery(); 176 | ``` 177 | 178 | ### 2. 单引号转换 179 | 180 | 将传入的参数中的单引号转换为连续两个单引号，PHP 中的 Magic quote 可以完成这个功能。 181 | 182 | # 四、拒绝服务攻击 183 | 184 | 拒绝服务攻击（denial-of-service attack，DoS），亦称洪水攻击，其目的在于使目标电脑的网络或系统资源耗尽，使服务暂时中断或停止，导致其正常用户无法访问。 185 | 186 | 分布式拒绝服务攻击（distributed denial-of-service attack，DDoS），指攻击者使用两个或以上被攻陷的电脑作为“僵尸”向特定的目标发动“拒绝服务”式攻击。 187 | 188 | # 参考资料 189 | 190 | - [维基百科：跨站脚本](https://zh.wikipedia.org/wiki/%E8%B7%A8%E7%B6%B2%E7%AB%99%E6%8C%87%E4%BB%A4%E7%A2%BC) 191 | - [维基百科：SQL 注入攻击](https://zh.wikipedia.org/wiki/SQL%E8%B3%87%E6%96%99%E9%9A%B1%E7%A2%BC%E6%94%BB%E6%93%8A) 192 | - [维基百科：跨站点请求伪造](https://zh.wikipedia.org/wiki/%E8%B7%A8%E7%AB%99%E8%AF%B7%E6%B1%82%E4%BC%AA%E9%80%A0) 193 | - [维基百科：拒绝服务攻击](https://zh.wikipedia.org/wiki/%E9%98%BB%E6%96%B7%E6%9C%8D%E5%8B%99%E6%94%BB%E6%93%8A) 194 | -------------------------------------------------------------------------------- /notes/集群.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、负载均衡](#一负载均衡) 3 | * [负载均衡算法](#负载均衡算法) 4 | * [转发实现](#转发实现) 5 | * [二、集群下的 Session 管理](#二集群下的-session-管理) 6 | * [Sticky Session](#sticky-session) 7 | * [Session Replication](#session-replication) 8 | * [Session Server](#session-server) 9 | 10 | 11 | 12 | # 一、负载均衡 13 | 14 | 集群中的应用服务器（节点）通常被设计成无状态，用户可以请求任何一个应用服务器。 15 | 16 | 负载均衡器会根据集群中每个节点的负载情况，将用户请求转发到合适的节点上。 17 | 18 | 负载均衡器可以用来实现高可用以及伸缩性： 19 | 20 | - 高可用：当某个节点故障时，负载均衡器会将用户请求转发到另外的节点上，从而保证所有服务持续可用； 21 | - 伸缩性：根据系统整体负载情况，可以很容易地添加移除节点。 22 | 23 | 负载均衡运行过程包含两个部分： 24 | 25 | 1. 根据负载均衡算法得到转发的节点； 26 | 2. 进行转发。 27 | 28 | ## 负载均衡算法 29 | 30 | ### 1. 轮询（Round Robin） 31 | 32 | 轮询算法把每个请求轮流发送到每个服务器上。 33 | 34 | 下图中，一共有 6 个客户端产生了 6 个请求，这 6 个请求按 (1, 2, 3, 4, 5, 6) 的顺序发送。(1, 3, 5) 的请求会被发送到服务器 1，(2, 4, 6) 的请求会被发送到服务器 2。 35 | 36 |

37 | 38 | 该算法比较适合每个服务器的性能差不多的场景，如果有性能存在差异的情况下，那么性能较差的服务器可能无法承担过大的负载（下图的 Server 2）。 39 | 40 |

41 | 42 | ### 2. 加权轮询（Weighted Round Robbin） 43 | 44 | 加权轮询是在轮询的基础上，根据服务器的性能差异，为服务器赋予一定的权值，性能高的服务器分配更高的权值。 45 | 46 | 例如下图中，服务器 1 被赋予的权值为 5，服务器 2 被赋予的权值为 1，那么 (1, 2, 3, 4, 5) 请求会被发送到服务器 1，(6) 请求会被发送到服务器 2。 47 | 48 |

49 | 50 | ### 3. 最少连接（least Connections） 51 | 52 | 由于每个请求的连接时间不一样，使用轮询或者加权轮询算法的话，可能会让一台服务器当前连接数过大，而另一台服务器的连接过小，造成负载不均衡。 53 | 54 | 例如下图中，(1, 3, 5) 请求会被发送到服务器 1，但是 (1, 3) 很快就断开连接，此时只有 (5) 请求连接服务器 1；(2, 4, 6) 请求被发送到服务器 2，只有 (2) 的连接断开，此时 (6, 4) 请求连接服务器 2。该系统继续运行时，服务器 2 会承担过大的负载。 55 | 56 |

57 | 58 | 最少连接算法就是将请求发送给当前最少连接数的服务器上。 59 | 60 | 例如下图中，服务器 1 当前连接数最小，那么新到来的请求 6 就会被发送到服务器 1 上。 61 | 62 |

63 | 64 | ### 4. 加权最少连接（Weighted Least Connection） 65 | 66 | 在最少连接的基础上，根据服务器的性能为每台服务器分配权重，再根据权重计算出每台服务器能处理的连接数。 67 | 68 |

69 | 70 | ### 5. 随机算法（Random） 71 | 72 | 把请求随机发送到服务器上。 73 | 74 | 和轮询算法类似，该算法比较适合服务器性能差不多的场景。 75 | 76 |

77 | 78 | ### 6. 源地址哈希法 (IP Hash) 79 | 80 | 源地址哈希通过对客户端 IP 计算哈希值之后，再对服务器数量取模得到目标服务器的序号。 81 | 82 | 可以保证同一 IP 的客户端的请求会转发到同一台服务器上，用来实现会话粘滞（Sticky Session） 83 | 84 |

85 | 86 | ## 转发实现 87 | 88 | ### 1. HTTP 重定向 89 | 90 | HTTP 重定向负载均衡服务器使用某种负载均衡算法计算得到服务器的 IP 地址之后，将该地址写入 HTTP 重定向报文中，状态码为 302。客户端收到重定向报文之后，需要重新向服务器发起请求。 91 | 92 | 缺点： 93 | 94 | - 需要两次请求，因此访问延迟比较高； 95 | - HTTP 负载均衡器处理能力有限，会限制集群的规模。 96 | 97 | 该负载均衡转发的缺点比较明显，实际场景中很少使用它。 98 | 99 |

100 | 101 | ### 2. DNS 域名解析 102 | 103 | 在 DNS 解析域名的同时使用负载均衡算法计算服务器 IP 地址。 104 | 105 | 优点： 106 | 107 | - DNS 能够根据地理位置进行域名解析，返回离用户最近的服务器 IP 地址。 108 | 109 | 缺点： 110 | 111 | - 由于 DNS 具有多级结构，每一级的域名记录都可能被缓存，当下线一台服务器需要修改 DNS 记录时，需要过很长一段时间才能生效。 112 | 113 | 大型网站基本使用了 DNS 做为第一级负载均衡手段，然后在内部使用其它方式做第二级负载均衡。也就是说，域名解析的结果为内部的负载均衡服务器 IP 地址。 114 | 115 |

116 | 117 | ### 3. 反向代理服务器 118 | 119 | 反向代理服务器位于源服务器前面，用户的请求需要先经过反向代理服务器才能到达源服务器。反向代理可以用来进行缓存、日志记录等，同时也可以用来做为负载均衡服务器。 120 | 121 | 在这种负载均衡转发方式下，客户端不直接请求源服务器，因此源服务器不需要外部 IP 地址，而反向代理需要配置内部和外部两套 IP 地址。 122 | 123 | 优点： 124 | 125 | - 与其它功能集成在一起，部署简单。 126 | 127 | 缺点： 128 | 129 | - 所有请求和响应都需要经过反向代理服务器，它可能会成为性能瓶颈。 130 | 131 | ### 4. 网络层 132 | 133 | 在操作系统内核进程获取网络数据包，根据负载均衡算法计算源服务器的 IP 地址，并修改请求数据包的目的 IP 地址，最后进行转发。 134 | 135 | 源服务器返回的响应也需要经过负载均衡服务器，通常是让负载均衡服务器同时作为集群的网关服务器来实现。 136 | 137 | 优点： 138 | 139 | - 在内核进程中进行处理，性能比较高。 140 | 141 | 缺点： 142 | 143 | - 和反向代理一样，所有的请求和响应都经过负载均衡服务器，会成为性能瓶颈。 144 | 145 | ### 5. 链路层 146 | 147 | 在链路层根据负载均衡算法计算源服务器的 MAC 地址，并修改请求数据包的目的 MAC 地址，并进行转发。 148 | 149 | 通过配置源服务器的虚拟 IP 地址和负载均衡服务器的 IP 地址一致，从而不需要修改 IP 地址就可以进行转发。也正因为 IP 地址一样，所以源服务器的响应不需要转发回负载均衡服务器，可以直接转发给客户端，避免了负载均衡服务器的成为瓶颈。 150 | 151 | 这是一种三角传输模式，被称为直接路由。对于提供下载和视频服务的网站来说，直接路由避免了大量的网络传输数据经过负载均衡服务器。 152 | 153 | 这是目前大型网站使用最广负载均衡转发方式，在 Linux 平台可以使用的负载均衡服务器为 LVS（Linux Virtual Server）。 154 | 155 | 参考： 156 | 157 | - [Comparing Load Balancing Algorithms](http://www.jscape.com/blog/load-balancing-algorithms) 158 | - [Redirection and Load Balancing](http://slideplayer.com/slide/6599069/#) 159 | 160 | # 二、集群下的 Session 管理 161 | 162 | 一个用户的 Session 信息如果存储在一个服务器上，那么当负载均衡器把用户的下一个请求转发到另一个服务器，由于服务器没有用户的 Session 信息，那么该用户就需要重新进行登录等操作。 163 | 164 | ## Sticky Session 165 | 166 | 需要配置负载均衡器，使得一个用户的所有请求都路由到同一个服务器，这样就可以把用户的 Session 存放在该服务器中。 167 | 168 | 缺点： 169 | 170 | - 当服务器宕机时，将丢失该服务器上的所有 Session。 171 | 172 |

173 | 174 | ## Session Replication 175 | 176 | 在服务器之间进行 Session 同步操作，每个服务器都有所有用户的 Session 信息，因此用户可以向任何一个服务器进行请求。 177 | 178 | 缺点： 179 | 180 | - 占用过多内存； 181 | - 同步过程占用网络带宽以及服务器处理器时间。 182 | 183 |

184 | 185 | ## Session Server 186 | 187 | 使用一个单独的服务器存储 Session 数据，可以使用传统的 MySQL，也使用 Redis 或者 Memcached 这种内存型数据库。 188 | 189 | 优点： 190 | 191 | - 为了使得大型网站具有伸缩性，集群中的应用服务器通常需要保持无状态，那么应用服务器不能存储用户的会话信息。Session Server 将用户的会话信息单独进行存储，从而保证了应用服务器的无状态。 192 | 193 | 缺点： 194 | 195 | - 需要去实现存取 Session 的代码。 196 | 197 |

198 | 199 | 参考： 200 | 201 | - [Session Management using Spring Session with JDBC DataStore](https://sivalabs.in/2018/02/session-management-using-spring-session-jdbc-datastore/) 202 | 203 | -------------------------------------------------------------------------------- /notes/正则表达式.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、概述](#一概述) 3 | * [二、匹配单个字符](#二匹配单个字符) 4 | * [三、匹配一组字符](#三匹配一组字符) 5 | * [四、使用元字符](#四使用元字符) 6 | * [五、重复匹配](#五重复匹配) 7 | * [六、位置匹配](#六位置匹配) 8 | * [七、使用子表达式](#七使用子表达式) 9 | * [八、回溯引用](#八回溯引用) 10 | * [九、前后查找](#九前后查找) 11 | * [十、嵌入条件](#十嵌入条件) 12 | * [参考资料](#参考资料) 13 | 14 | 15 | 16 | # 一、概述 17 | 18 | 正则表达式用于文本内容的查找和替换。 19 | 20 | 正则表达式内置于其它语言或者软件产品中，它本身不是一种语言或者软件。 21 | 22 | [正则表达式在线工具](https://regexr.com/) 23 | 24 | # 二、匹配单个字符 25 | 26 | **.** 可以用来匹配任何的单个字符，但是在绝大多数实现里面，不能匹配换行符； 27 | 28 | **.** 是元字符，表示它有特殊的含义，而不是字符本身的含义。如果需要匹配 . ，那么要用 \ 进行转义，即在 . 前面加上 \ 。 29 | 30 | 正则表达式一般是区分大小写的，但是也有些实现是不区分。 31 | 32 | **正则表达式** 33 | 34 | ``` 35 | nam. 36 | ``` 37 | 38 | **匹配结果** 39 | 40 | My **name** is Zheng. 41 | 42 | # 三、匹配一组字符 43 | 44 | **[ ]** 定义一个字符集合； 45 | 46 | 0-9、a-z 定义了一个字符区间，区间使用 ASCII 码来确定，字符区间在 [ ] 中使用。 47 | 48 | **-** 只有在 [ ] 之间才是元字符，在 [ ] 之外就是一个普通字符； 49 | 50 | **^** 在 [ ] 中是取非操作。 51 | 52 | **应用** 53 | 54 | 匹配以 abc 为开头，并且最后一个字母不为数字的字符串： 55 | 56 | **正则表达式** 57 | 58 | ``` 59 | abc[^0-9] 60 | ``` 61 | 62 | **匹配结果** 63 | 64 | 1. **abcd** 65 | 2. abc1 66 | 3. abc2 67 | 68 | # 四、使用元字符 69 | 70 | ## 匹配空白字符 71 | 72 | | 元字符 | 说明 | 73 | | :---: | :---: | 74 | | [\b] | 回退（删除）一个字符 | 75 | | \f | 换页符 | 76 | | \n | 换行符 | 77 | | \r | 回车符 | 78 | | \t | 制表符 | 79 | | \v | 垂直制表符 | 80 | 81 | \r\n 是 Windows 中的文本行结束标签，在 Unix/Linux 则是 \n。 82 | 83 | \r\n\r\n 可以匹配 Windows 下的空白行，因为它将匹配两个连续的行尾标签，而这正是两条记录之间的空白行； 84 | 85 | ## 匹配特定的字符类别 86 | 87 | ### 1. 数字元字符 88 | 89 | | 元字符 | 说明 | 90 | | :---: | :---: | 91 | | \d | 数字字符，等价于 [0-9] | 92 | | \D | 非数字字符，等价于 [^0-9] | 93 | 94 | ### 2. 字母数字元字符 95 | 96 | | 元字符 | 说明 | 97 | | :---: | :---: | 98 | | \w | 大小写字母，下划线和数字，等价于 [a-zA-Z0-9\_] | 99 | | \W | 对 \w 取非 | 100 | 101 | ### 3. 空白字符元字符 102 | 103 | | 元字符 | 说明 | 104 | | :---: | :---: | 105 | | \s | 任何一个空白字符，等价于 [\f\n\r\t\v] | 106 | | \S | 对 \s 取非 | 107 | 108 | \x 匹配十六进制字符，\0 匹配八进制，例如 \x0A 对应 ASCII 字符 10，等价于 \n。 109 | 110 | # 五、重复匹配 111 | 112 | - **\+** 匹配 1 个或者多个字符 113 | - **\** * 匹配 0 个或者多个 114 | - **?** 匹配 0 个或者 1 个 115 | 116 | **应用** 117 | 118 | 匹配邮箱地址。 119 | 120 | **正则表达式** 121 | 122 | ``` 123 | [\w.]+@\w+\.\w+ 124 | ``` 125 | 126 | [\w.] 匹配的是字母数字或者 . ，在其后面加上 + ，表示匹配多次。在字符集合 [ ] 里，. 不是元字符； 127 | 128 | **匹配结果** 129 | 130 | **abc.def@qq.com** 131 | 132 | - **{n}** 匹配 n 个字符 133 | - **{m, n}** 匹配 m\~n 个字符 134 | - **{m,}** 至少匹配 m 个字符 135 | 136 | \* 和 + 都是贪婪型元字符，会匹配最多的内容。在后面加 ? 可以转换为懒惰型元字符，例如 \*?、+? 和 {m, n}? 。 137 | 138 | **正则表达式** 139 | 140 | ``` 141 | a.+c 142 | ``` 143 | 144 | 由于 + 是贪婪型的，因此 .+ 会匹配更可能多的内容，所以会把整个 abcabcabc 文本都匹配，而不是只匹配前面的 abc 文本。用懒惰型可以实现匹配前面的。 145 | 146 | **匹配结果** 147 | 148 | **abcabcabc** 149 | 150 | # 六、位置匹配 151 | 152 | ## 单词边界 153 | 154 | **\b** 可以匹配一个单词的边界，边界是指位于 \w 和 \W 之间的位置；**\B** 匹配一个不是单词边界的位置。 155 | 156 | \b 只匹配位置，不匹配字符，因此 \babc\b 匹配出来的结果为 3 个字符。 157 | 158 | ## 字符串边界 159 | 160 | **^** 匹配整个字符串的开头，**$** 匹配结尾。 161 | 162 | ^ 元字符在字符集合中用作求非，在字符集合外用作匹配字符串的开头。 163 | 164 | 分行匹配模式（multiline）下，换行被当做字符串的边界。 165 | 166 | **应用** 167 | 168 | 匹配代码中以 // 开始的注释行 169 | 170 | **正则表达式** 171 | 172 | ``` 173 | ^\s*\/\/.*$ 174 | ``` 175 | 176 |

177 | 178 | **匹配结果** 179 | 180 | 1. public void fun() { 181 | 2.      **// 注释 1** 182 | 3.      int a = 1; 183 | 4.      int b = 2; 184 | 5.      **// 注释 2** 185 | 6.      int c = a + b; 186 | 7. } 187 | 188 | # 七、使用子表达式 189 | 190 | 使用 **( )** 定义一个子表达式。子表达式的内容可以当成一个独立元素，即可以将它看成一个字符，并且使用 * 等元字符。 191 | 192 | 子表达式可以嵌套，但是嵌套层次过深会变得很难理解。 193 | 194 | **正则表达式** 195 | 196 | ``` 197 | (ab){2,} 198 | ``` 199 | 200 | **匹配结果** 201 | 202 | **ababab** 203 | 204 | **|** 是或元字符，它把左边和右边所有的部分都看成单独的两个部分，两个部分只要有一个匹配就行。 205 | 206 | **正则表达式** 207 | 208 | ``` 209 | (19|20)\d{2} 210 | ``` 211 | 212 | **匹配结果** 213 | 214 | 1. **1900** 215 | 2. **2010** 216 | 3. 1020 217 | 218 | **应用** 219 | 220 | 匹配 IP 地址。 221 | 222 | IP 地址中每部分都是 0-255 的数字，用正则表达式匹配时以下情况是合法的： 223 | 224 | - 一位数字 225 | - 不以 0 开头的两位数字 226 | - 1 开头的三位数 227 | - 2 开头，第 2 位是 0-4 的三位数 228 | - 25 开头，第 3 位是 0-5 的三位数 229 | 230 | **正则表达式** 231 | 232 | ``` 233 | ((25[0-5]|(2[0-4]\d)|(1\d{2})|([1-9]\d)|(\d))\.){3}(25[0-5]|(2[0-4]\d)|(1\d{2})|([1-9]\d)|(\d)) 234 | ``` 235 | 236 | **匹配结果** 237 | 238 | 1. **192.168.0.1** 239 | 2. 00.00.00.00 240 | 3. 555.555.555.555 241 | 242 | # 八、回溯引用 243 | 244 | 回溯引用使用 **\n** 来引用某个子表达式，其中 n 代表的是子表达式的序号，从 1 开始。它和子表达式匹配的内容一致，比如子表达式匹配到 abc，那么回溯引用部分也需要匹配 abc 。 245 | 246 | **应用** 247 | 248 | 匹配 HTML 中合法的标题元素。 249 | 250 | **正则表达式** 251 | 252 | \1 将回溯引用子表达式 (h[1-6]) 匹配的内容，也就是说必须和子表达式匹配的内容一致。 253 | 254 | ``` 255 | <(h[1-6])>\w*?<\/\1> 256 | ``` 257 | 258 | **匹配结果** 259 | 260 | 1. **<h1>x</h1>** 261 | 2. **<h2>x</h2>** 262 | 3. <h3>x</h1> 263 | 264 | ## 替换 265 | 266 | 需要用到两个正则表达式。 267 | 268 | **应用** 269 | 270 | 修改电话号码格式。 271 | 272 | **文本** 273 | 274 | 313-555-1234 275 | 276 | **查找正则表达式** 277 | 278 | ``` 279 | (\d{3})(-)(\d{3})(-)(\d{4}) 280 | ``` 281 | 282 | **替换正则表达式** 283 | 284 | 在第一个子表达式查找的结果加上 () ，然后加一个空格，在第三个和第五个字表达式查找的结果中间加上 - 进行分隔。 285 | 286 | ``` 287 | ($1) $3-$5 288 | ``` 289 | 290 | **结果** 291 | 292 | (313) 555-1234 293 | 294 | ## 大小写转换 295 | 296 | | 元字符 | 说明 | 297 | | :---: | :---: | 298 | | \l | 把下个字符转换为小写 | 299 | | \u| 把下个字符转换为大写 | 300 | | \L | 把\L 和\E 之间的字符全部转换为小写 | 301 | | \U | 把\U 和\E 之间的字符全部转换为大写 | 302 | | \E | 结束\L 或者\U | 303 | 304 | **应用** 305 | 306 | 把文本的第二个和第三个字符转换为大写。 307 | 308 | **文本** 309 | 310 | abcd 311 | 312 | **查找** 313 | 314 | ``` 315 | (\w)(\w{2})(\w) 316 | ``` 317 | 318 | **替换** 319 | 320 | ``` 321 | $1\U$2\E$3 322 | ``` 323 | 324 | **结果** 325 | 326 | aBCd 327 | 328 | # 九、前后查找 329 | 330 | 前后查找规定了匹配的内容首尾应该匹配的内容，但是又不包含首尾匹配的内容。向前查找用 **?=** 来定义，它规定了尾部匹配的内容，这个匹配的内容在 ?= 之后定义。所谓向前查找，就是规定了一个匹配的内容，然后以这个内容为尾部向前面查找需要匹配的内容。向后匹配用 ?<= 定义（注: javaScript 不支持向后匹配, java 对其支持也不完善）。 331 | 332 | **应用** 333 | 334 | 查找出邮件地址 @ 字符前面的部分。 335 | 336 | **正则表达式** 337 | 338 | ``` 339 | \w+(?=@) 340 | ``` 341 | 342 | **结果** 343 | 344 | **abc** @qq.com 345 | 346 | 对向前和向后查找取非，只要把 = 替换成 ! 即可，比如 (?=) 替换成 (?!) 。取非操作使得匹配那些首尾不符合要求的内容。 347 | 348 | # 十、嵌入条件 349 | 350 | ## 回溯引用条件 351 | 352 | 条件判断为某个子表达式是否匹配，如果匹配则需要继续匹配条件表达式后面的内容。 353 | 354 | **正则表达式** 355 | 356 | 子表达式 (\\() 匹配一个左括号，其后的 ? 表示匹配 0 个或者 1 个。 ?(1) 为条件，当子表达式 1 匹配时条件成立，需要执行 \) 匹配，也就是匹配右括号。 357 | 358 | ``` 359 | (\()?abc(?(1)\)) 360 | ``` 361 | 362 | **结果** 363 | 364 | 1. **(abc)** 365 | 2. **abc** 366 | 3. (abc 367 | 368 | ## 前后查找条件 369 | 370 | 条件为定义的首尾是否匹配，如果匹配，则继续执行后面的匹配。注意，首尾不包含在匹配的内容中。 371 | 372 | **正则表达式** 373 | 374 | ?(?=-) 为前向查找条件，只有在以 - 为前向查找的结尾能匹配 \d{5} ，才继续匹配 -\d{4} 。 375 | 376 | ``` 377 | \d{5}(?(?=-)-\d{4}) 378 | ``` 379 | 380 | **结果** 381 | 382 | 1. **11111** 383 | 2. 22222- 384 | 3. **33333-4444** 385 | 386 | # 参考资料 387 | 388 | - BenForta. 正则表达式必知必会 [M]. 人民邮电出版社, 2007. 389 | -------------------------------------------------------------------------------- /notes/缓存.md: -------------------------------------------------------------------------------- 1 | 2 | * [一、缓存特征](#一缓存特征) 3 | * [二、LRU](#二lru) 4 | * [三、缓存位置](#三缓存位置) 5 | * [四、CDN](#四cdn) 6 | * [五、缓存问题](#五缓存问题) 7 | * [六、数据分布](#六数据分布) 8 | * [七、一致性哈希](#七一致性哈希) 9 | * [参考资料](#参考资料) 10 | 11 | 12 | 13 | # 一、缓存特征 14 | 15 | ## 命中率 16 | 17 | 当某个请求能够通过访问缓存而得到响应时，称为缓存命中。 18 | 19 | 缓存命中率越高，缓存的利用率也就越高。 20 | 21 | ## 最大空间 22 | 23 | 缓存通常位于内存中，内存的空间通常比磁盘空间小的多，因此缓存的最大空间不可能非常大。 24 | 25 | 当缓存存放的数据量超过最大空间时，就需要淘汰部分数据来存放新到达的数据。 26 | 27 | ## 淘汰策略 28 | 29 | - FIFO（First In First Out）：先进先出策略，在实时性的场景下，需要经常访问最新的数据，那么就可以使用 FIFO，使得最先进入的数据（最晚的数据）被淘汰。 30 | 31 | - LRU（Least Recently Used）：最近最久未使用策略，优先淘汰最久未使用的数据，也就是上次被访问时间距离现在最久的数据。该策略可以保证内存中的数据都是热点数据，也就是经常被访问的数据，从而保证缓存命中率。 32 | 33 | # 二、LRU 34 | 35 | 以下是基于 双向链表 + HashMap 的 LRU 算法实现，对算法的解释如下： 36 | 37 | - 访问某个节点时，将其从原来的位置删除，并重新插入到链表头部。这样就能保证链表尾部存储的就是最近最久未使用的节点，当节点数量大于缓存最大空间时就淘汰链表尾部的节点。 38 | - 为了使删除操作时间复杂度为 O(1)，就不能采用遍历的方式找到某个节点。HashMap 存储着 Key 到节点的映射，通过 Key 就能以 O(1) 的时间得到节点，然后再以 O(1) 的时间将其从双向队列中删除。 39 | 40 | ```java 41 | public class LRU implements Iterable { 42 | 43 | private Node head; 44 | private Node tail; 45 | private HashMap map; 46 | private int maxSize; 47 | 48 | private class Node { 49 | 50 | Node pre; 51 | Node next; 52 | K k; 53 | V v; 54 | 55 | public Node(K k, V v) { 56 | this.k = k; 57 | this.v = v; 58 | } 59 | } 60 | 61 | 62 | public LRU(int maxSize) { 63 | 64 | this.maxSize = maxSize; 65 | this.map = new HashMap<>(maxSize * 4 / 3); 66 | 67 | head = new Node(null, null); 68 | tail = new Node(null, null); 69 | 70 | head.next = tail; 71 | tail.pre = head; 72 | } 73 | 74 | 75 | public V get(K key) { 76 | 77 | if (!map.containsKey(key)) { 78 | return null; 79 | } 80 | 81 | Node node = map.get(key); 82 | unlink(node); 83 | appendHead(node); 84 | 85 | return node.v; 86 | } 87 | 88 | 89 | public void put(K key, V value) { 90 | 91 | if (map.containsKey(key)) { 92 | Node node = map.get(key); 93 | unlink(node); 94 | } 95 | 96 | Node node = new Node(key, value); 97 | map.put(key, node); 98 | appendHead(node); 99 | 100 | if (map.size() > maxSize) { 101 | Node toRemove = removeTail(); 102 | map.remove(toRemove.k); 103 | } 104 | } 105 | 106 | 107 | private void unlink(Node node) { 108 | 109 | Node pre = node.pre; 110 | Node next = node.next; 111 | 112 | pre.next = next; 113 | next.pre = pre; 114 | 115 | node.pre = null; 116 | node.next = null; 117 | } 118 | 119 | 120 | private void appendHead(Node node) { 121 | Node next = head.next; 122 | node.next = next; 123 | next.pre = node; 124 | node.pre = head; 125 | head.next = node; 126 | } 127 | 128 | 129 | private Node removeTail() { 130 | 131 | Node node = tail.pre; 132 | 133 | Node pre = node.pre; 134 | tail.pre = pre; 135 | pre.next = tail; 136 | 137 | node.pre = null; 138 | node.next = null; 139 | 140 | return node; 141 | } 142 | 143 | 144 | @Override 145 | public Iterator iterator() { 146 | 147 | return new Iterator() { 148 | private Node cur = head.next; 149 | 150 | @Override 151 | public boolean hasNext() { 152 | return cur != tail; 153 | } 154 | 155 | @Override 156 | public K next() { 157 | Node node = cur; 158 | cur = cur.next; 159 | return node.k; 160 | } 161 | }; 162 | } 163 | } 164 | ``` 165 | 166 | # 三、缓存位置 167 | 168 | ## 浏览器 169 | 170 | 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。 171 | 172 | ## ISP 173 | 174 | 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。 175 | 176 | ## 反向代理 177 | 178 | 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。 179 | 180 | ## 本地缓存 181 | 182 | 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。 183 | 184 | ## 分布式缓存 185 | 186 | 使用 Redis、Memcache 等分布式缓存将数据缓存在分布式缓存系统中。 187 | 188 | 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。 189 | 190 | ## 数据库缓存 191 | 192 | 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。 193 | 194 | # 四、CDN 195 | 196 | 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。 197 | 198 | CDN 主要有以下优点： 199 | 200 | - 更快地将数据分发给用户； 201 | - 通过部署多台服务器，从而提高系统整体的带宽性能； 202 | - 多台服务器可以看成是一种冗余机制，从而具有高可用性。 203 | 204 |

205 | 206 | # 五、缓存问题 207 | 208 | ## 缓存穿透 209 | 210 | 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。 211 | 212 | 解决方案： 213 | 214 | - 对这些不存在的数据缓存一个空数据； 215 | - 对这类请求进行过滤。 216 | 217 | ## 缓存雪崩 218 | 219 | 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。 220 | 221 | 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。 222 | 223 | 解决方案： 224 | 225 | - 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； 226 | - 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。 227 | - 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。 228 | 229 | ## 缓存一致性 230 | 231 | 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。 232 | 233 | 解决方案： 234 | 235 | - 在数据更新的同时立即去更新缓存； 236 | - 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。 237 | 238 | 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。 239 | 240 | # 六、数据分布 241 | 242 | ## 哈希分布 243 | 244 | 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。 245 | 246 | 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。 247 | 248 | ## 顺序分布 249 | 250 | 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。 251 | 252 | 顺序分布相比于哈希分布的主要优点如下： 253 | 254 | - 能保持数据原有的顺序； 255 | - 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。 256 | 257 | # 七、一致性哈希 258 | 259 | 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。 260 | 261 | ## 基本原理 262 | 263 | 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。 264 | 265 |

266 | 267 | 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。 268 | 269 |

270 | 271 | ## 虚拟节点 272 | 273 | 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。 274 | 275 | 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。 276 | 277 | 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。 278 | 279 | # 参考资料 280 | 281 | - 大规模分布式存储系统 282 | - [缓存那些事](https://tech.meituan.com/cache_about.html) 283 | - [一致性哈希算法](https://my.oschina.net/jayhu/blog/732849) 284 | - [内容分发网络](https://zh.wikipedia.org/wiki/%E5%85%A7%E5%AE%B9%E5%82%B3%E9%81%9E%E7%B6%B2%E8%B7%AF) 285 | - [How Aspiration CDN helps to improve your website loading speed?](https://www.aspirationhosting.com/aspiration-cdn/) 286 | --------------------------------------------------------------------------------