java查缺补漏之第十三天（一天又一天，疫情还不结束，兄弟们，心态要崩了啊）

系统管理员 2023-07-10 12:55 16阅读 0赞

一天又一天，疫情还不结束，兄弟们，心态要崩了啊

1.B-树？

参考程序员小灰这个公众号，写得还真是不错

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二叉查找树的结构：

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第1次磁盘IO：

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第2次磁盘IO：

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第3次磁盘IO：

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第4次磁盘IO：

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下面来具体介绍一下B-树（Balance Tree），一个m阶的B树具有如下几个特征：

1.根结点至少有两个子女。

2.每个中间节点都包含k-1个元素和k个孩子，其中 m/2 <= k <= m

3.每一个叶子节点都包含k-1个元素，其中 m/2 <= k <= m

4.所有的叶子结点都位于同一层。

5.每个节点中的元素从小到大排列，节点当中k-1个元素正好是k个孩子包含的元素的值域分划。

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第1次磁盘IO：

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在内存中定位（和9比较）：

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第2次磁盘IO：

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在内存中定位（和2，6比较）：

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第3次磁盘IO：

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在内存中定位（和3，5比较）：

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自顶向下查找4的节点位置，发现4应当插入到节点元素3，5之间。

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节点3，5已经是两元素节点，无法再增加。父亲节点 2， 6 也是两元素节点，也无法再增加。根节点9是单元素节点，可以升级为两元素节点。于是**拆分**节点3，5与节点2，6，让根节点9升级为两元素节点4，9。节点6独立为根节点的第二个孩子。

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自顶向下查找元素11的节点位置。

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删除11后，节点12只有一个孩子，不符合B树规范。因此找出12,13,15三个节点的中位数13，取代节点12，而节点12自身下移成为第一个孩子。（这个过程称为**左旋**）

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B+树在B-树的基础上增加了链表，并且叶子节点包含所有信息。

**2.trie树和AC自动机？**

参考：[https://blog.csdn.net/bestsort/article/details/82947639][https_blog.csdn.net_bestsort_article_details_82947639]

参考：[https://blog.csdn.net/niushuai666/article/details/7002823][https_blog.csdn.net_niushuai666_article_details_7002823]

用于多个字符串的匹配，关键在于如何构建失败指针。

**3.TCP拥塞控制？**

其实这个地方问的就是，滑动窗口，慢启动，拥塞避免，快重传，快恢复。

**4.springmvc的原理？**

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流程说明：（1）客户端（浏览器）发送请求，直接请求到DispatcherServlet。（2）DispatcherServlet根据请求信息调用HandlerMapping，解析请求对应的Handler。（3）解析到对应的Handler后，开始由HandlerAdapter适配器处理。（4）HandlerAdapter会根据Handler来调用真正的处理器开处理请求，并处理相应的业务逻辑。（5）处理器处理完业务后，会返回一个ModelAndView对象，Model是返回的数据对象，View是个逻辑上的View。（6）ViewResolver会根据逻辑View查找实际的View。（7）DispaterServlet把返回的Model传给View。（8）通过View返回给请求者（浏览器）

**5.如何在海量数据中找到前几名的数据？**

方案一：堆。一般说在很多数据中取前多少个数据，我们都会想到堆，这里我们使用堆来解决问题。首先取K个数建立一个小根堆（堆顶是堆中最小的元素），建堆的时间复杂度是O(KlgK)，这个时间复杂度虽然是上界，但不是渐进紧确的，渐进紧确的上界为O(K)，具体推导见《算法导论.第三版》P88。接着依次从海量数据(N个数)中拿出一个数据与堆顶元素进行比较，若小于堆顶元素则继续从海量数据中取下一个数，若取出的数比堆顶元素大，则替换堆顶元素，并维护堆的性质，维护堆的性质所需要的时间复杂度是O(lgK)。直到将(N-m)个数遍历完，堆中剩余的数就是N个数的最大前K个数。 故总时间复杂度为O(K+(N-m)lgK) => O(N·lgK) 【N为海量数据，K为要取的最大前K个数。】

方案二：分块。面对大量数据时，分块也是比较容易想到的解决方法。假设将海量数据分成1000块，然后在每块中找到最大的前K个数。然后再从1000·K个数中直到最大的前K个数。

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[https_blog.csdn.net_bestsort_article_details_82947639]: https://blog.csdn.net/bestsort/article/details/82947639
[https_blog.csdn.net_niushuai666_article_details_7002823]: https://blog.csdn.net/niushuai666/article/details/7002823
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