[docs update]统一数据结构部分的手绘图片样式
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Guide
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title: 布隆过滤器
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category: 计算机基础
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tag:
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- 数据结构
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---
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# 布隆过滤器
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海量数据处理以及缓存穿透这两个场景让我认识了 布隆过滤器 ，我查阅了一些资料来了解它，但是很多现成资料并不满足我的需求，所以就决定自己总结一篇关于布隆过滤器的文章。希望通过这篇文章让更多人了解布隆过滤器，并且会实际去使用它！
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下面我们将分为几个方面来介绍布隆过滤器：
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---
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title: 图
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category: 计算机基础
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tag:
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- 数据结构
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---
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# 图
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> 开头还是求点赞，求转发！原创优质公众号，希望大家能让更多人看到我们的文章。
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>
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> 图片都是我们手绘的，可以说非常用心了！
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图是一种较为复杂的非线性结构。 **为啥说其较为复杂呢？**
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根据前面的内容，我们知道：
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下图所展示的就是图这种数据结构，并且还是一张有向图。
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图在我们日常生活中的例子很多！比如我们在社交软件上好友关系就可以用图来表示。
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下图就是一个带权有向图。
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## 图的存储
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### 邻接矩阵存储
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在无向图中，我们只关心关系的有无，所以当顶点i和顶点j有关系时，`A[i][j]`=1，当顶点i和顶点j没有关系时，`A[i][j]`=0。如下图所示：
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值得注意的是：**无向图的邻接矩阵是一个对称矩阵，因为在无向图中，顶点i和顶点j有关系，则顶点j和顶点i必有关系。**
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邻接矩阵存储的方式优点是简单直接（直接使用一个二维数组即可），并且，在获取两个定点之间的关系的时候也非常高效（直接获取指定位置的数组元素的值即可）。但是，这种存储方式的缺点也比较明显，那就是比较浪费空间，
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邻接链表使用一个链表来存储某个顶点的所有后继相邻顶点。对于图中每个顶点Vi，把所有邻接于Vi的顶点Vj链成一个单链表，这个单链表称为顶点Vi的 **邻接表**。如下图所示：
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大家可以数一数邻接表中所存储的元素的个数以及图中边的条数，你会发现：
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### 广度优先搜索
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广度优先搜索就像水面上的波纹一样一层一层向外扩展，如下图所示：
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**广度优先搜索的具体实现方式用到了之前所学过的线性数据结构——队列** 。具体过程如下图所示：
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**第1步：**
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**第2步：**
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**第3步：**
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**第4步：**
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**第5步：**
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**第6步：**
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### 深度优先搜索
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深度优先搜索就是“一条路走到黑”，从源顶点开始，一直走到没有后继节点，才回溯到上一顶点，然后继续“一条路走到黑”，如下图所示：
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**和广度优先搜索类似，深度优先搜索的具体实现用到了另一种线性数据结构——栈** 。具体过程如下图所示：
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**第1步：**
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**第2步：**
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**第3步：**
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**第4步：**
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**第5步：**
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**第6步：**
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---
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title: 线性数据结构
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category: 计算机基础
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tag:
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- 数据结构
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---
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# 线性数据结构 :数组、链表、栈、队列
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> 开头还是求点赞，求转发！原创优质公众号，希望大家能让更多人看到我们的文章。
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>
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> 图片都是我们手绘的，可以说非常用心了！
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## 1. 数组
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**数组（Array）** 是一种很常见的数据结构。它由相同类型的元素（element）组成，并且是使用一块连续的内存来存储。
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删除：O（n）//最坏的情况发生在删除数组的开头发生并需要移动第一元素后面所有的元素时
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```
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## 2. 链表
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**单链表** 单向链表只有一个方向，结点只有一个后继指针 next 指向后面的节点。因此，链表这种数据结构通常在物理内存上是不连续的。我们习惯性地把第一个结点叫作头结点，链表通常有一个不保存任何值的 head 节点(头结点)，通过头结点我们可以遍历整个链表。尾结点通常指向 null。
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#### 2.2.2. 循环链表
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**循环链表** 其实是一种特殊的单链表，和单链表不同的是循环链表的尾结点不是指向 null，而是指向链表的头结点。
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#### 2.2.3. 双向链表
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**双向链表** 包含两个指针，一个 prev 指向前一个节点，一个 next 指向后一个节点。
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#### 2.2.4. 双向循环链表
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**双向循环链表** 最后一个节点的 next 指向 head，而 head 的 prev 指向最后一个节点，构成一个环。
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### 2.3. 应用场景
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### 3.1. 栈简介
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**栈** (stack)只允许在有序的线性数据集合的一端（称为栈顶 top）进行加入数据（push）和移除数据（pop）。因而按照 **后进先出（LIFO, Last In First Out）** 的原理运作。**在栈中，push 和 pop 的操作都发生在栈顶。**
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**栈 (Stack)** 只允许在有序的线性数据集合的一端（称为栈顶 top）进行加入数据（push）和移除数据（pop）。因而按照 **后进先出（LIFO, Last In First Out）** 的原理运作。**在栈中，push 和 pop 的操作都发生在栈顶。**
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栈常用一维数组或链表来实现，用数组实现的栈叫作 **顺序栈** ，用链表实现的栈叫作 **链式栈** 。
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### 4.1. 队列简介
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**队列** 是 **先进先出( FIFO，First In, First Out)** 的线性表。在具体应用中通常用链表或者数组来实现，用数组实现的队列叫作 **顺序队列** ，用链表实现的队列叫作 **链式队列** 。**队列只允许在后端（rear）进行插入操作也就是 入队 enqueue，在前端（front）进行删除操作也就是出队 dequeue**
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**队列（Queue）** 是 **先进先出( FIFO，First In, First Out)** 的线性表。在具体应用中通常用链表或者数组来实现，用数组实现的队列叫作 **顺序队列** ，用链表实现的队列叫作 **链式队列** 。**队列只允许在后端（rear）进行插入操作也就是 入队 enqueue，在前端（front）进行删除操作也就是出队 dequeue**
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队列的操作方式和堆栈类似，唯一的区别在于队列只允许新数据在后端进行添加。
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插入删除：O（1）//后端插入前端删除元素
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```
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### 4.2. 队列分类
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> 为了避免当只有一个元素的时候，队头和队尾重合使处理变得麻烦，所以引入两个指针，front 指针指向对头元素，rear 指针指向队列最后一个元素的下一个位置，这样当 front 等于 rear 时，此队列不是还剩一个元素，而是空队列。——From 《大话数据结构》
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#### 4.2.2. 循环队列
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还是用上面的图，我们将 rear 指针指向数组下标为 0 的位置就不会有越界问题了。当我们再向队列中添加元素的时候， rear 向后移动。
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顺序队列中，我们说 `front==rear` 的时候队列为空，循环队列中则不一样，也可能为满，如上图所示。解决办法有两种：
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1. 可以设置一个标志变量 `flag`,当 `front==rear` 并且 `flag=0` 的时候队列为空，当`front==rear` 并且 `flag=1` 的时候队列为满。
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2. 队列为空的时候就是 `front==rear` ，队列满的时候，我们保证数组还有一个空闲的位置，rear 就指向这个空闲位置，如下图所示，那么现在判断队列是否为满的条件就是： `(rear+1) % QueueSize= front` 。
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### 4.3. 常见应用场景
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当我们需要按照一定顺序来处理数据的时候可以考虑使用队列这个数据结构。
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@ -1,11 +1,10 @@
|
|||
---
|
||||
title: 树
|
||||
category: 计算机基础
|
||||
tag:
|
||||
- 数据结构
|
||||
---
|
||||
|
||||
# 树
|
||||
|
||||
树就是一种类似现实生活中的树的数据结构（倒置的树）。任何一颗非空树只有一个根节点。
|
||||
|
||||
一棵树具有以下特点：
|
||||
|
|
@ -41,13 +40,15 @@ tag:
|
|||
|
||||
**二叉树** 的第 i 层至多拥有 `2^(i-1)` 个节点，深度为 k 的二叉树至多总共有 `2^(k+1)-1` 个节点（满二叉树的情况），至少有 2^(k) 个节点（关于节点的深度的定义国内争议比较多，我个人比较认可维基百科对[节点深度的定义](https://zh.wikipedia.org/wiki/%E6%A0%91_(%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84)#/%E6%9C%AF%E8%AF%AD)）。
|
||||
|
||||
|
||||
|
||||
|
||||
### 满二叉树
|
||||
|
||||
一个二叉树，如果每一个层的结点数都达到最大值，则这个二叉树就是 **满二叉树**。也就是说，如果一个二叉树的层数为 K，且结点总数是(2^k) -1 ，则它就是 **满二叉树**。如下图所示：
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### 完全二叉树
|
||||
|
||||
|
|
@ -55,7 +56,7 @@ tag:
|
|||
|
||||
大家可以想象为一棵树从根结点开始扩展，扩展完左子节点才能开始扩展右子节点，每扩展完一层，才能继续扩展下一层。如下图所示：
|
||||
|
||||
|
||||
|
||||
|
||||
完全二叉树有一个很好的性质：**父结点和子节点的序号有着对应关系。**
|
||||
|
||||
|
|
@ -72,7 +73,7 @@ tag:
|
|||
|
||||
在给大家展示平衡二叉树之前，先给大家看一棵树：
|
||||
|
||||
|
||||
|
||||
|
||||
**你管这玩意儿叫树？？？**
|
||||
|
||||
|
|
@ -86,7 +87,7 @@ tag:
|
|||
|
||||
但是，如果二叉树退化为一个链表了，那么那么树所具有的优秀性质就难以表现出来，效率也会大打折，为了避免这样的情况，我们希望每个做 “家长”（父结点） 的，都 **一碗水端平**，分给左儿子和分给右儿子的尽可能一样多，相差最多不超过一层，如下图所示：
|
||||
|
||||
|
||||
|
||||
|
||||
## 二叉树的存储
|
||||
|
||||
|
|
@ -106,7 +107,7 @@ tag:
|
|||
|
||||
那就直接引用对象呗（别问我对象哪里找）
|
||||
|
||||
|
||||
|
||||
|
||||
### 顺序存储
|
||||
|
||||
|
|
@ -114,11 +115,11 @@ tag:
|
|||
|
||||
一棵完全二叉树的数组顺序存储如下图所示：
|
||||
|
||||
|
||||
|
||||
|
||||
大家可以试着填写一下存储如下二叉树的数组，比较一下和完全二叉树的顺序存储有何区别：
|
||||
|
||||
|
||||
|
||||
|
||||
可以看到，如果我们要存储的二叉树不是完全二叉树，在数组中就会出现空隙，导致内存利用率降低
|
||||
|
||||
|
|
@ -126,7 +127,7 @@ tag:
|
|||
|
||||
### 先序遍历
|
||||
|
||||
|
||||
|
||||
|
||||
二叉树的先序遍历，就是先输出根结点，再遍历左子树，最后遍历右子树，遍历左子树和右子树的时候，同样遵循先序遍历的规则，也就是说，我们可以递归实现先序遍历。
|
||||
|
||||
|
|
@ -145,11 +146,11 @@ public void preOrder(TreeNode root){
|
|||
|
||||
### 中序遍历
|
||||
|
||||
|
||||
|
||||
|
||||
二叉树的中序遍历，就是先递归中序遍历左子树，再输出根结点的值，再递归中序遍历右子树，大家可以想象成一巴掌把树压扁，父结点被拍到了左子节点和右子节点的中间，如下图所示：
|
||||
|
||||
|
||||
|
||||
|
||||
代码如下：
|
||||
|
||||
|
|
@ -166,7 +167,7 @@ public void inOrder(TreeNode root){
|
|||
|
||||
### 后序遍历
|
||||
|
||||
|
||||
|
||||
|
||||
二叉树的后序遍历，就是先递归后序遍历左子树，再递归后序遍历右子树，最后输出根结点的值
|
||||
|
||||
|
|
|
|||
|
|
@ -1 +0,0 @@
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|
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<mxfile host="Electron" modified="2022-08-21T08:44:29.230Z" agent="5.0 (Macintosh; Intel Mac OS X 10_16_0) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/13.4.5 Chrome/83.0.4103.122 Electron/9.1.0 Safari/537.36" etag="IXUWKcjrU-7bWdaFvlTH" version="13.4.5" type="device"><diagram id="KJUpccsywgqX94dHTPwA" name="Page-1">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</diagram></mxfile>
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@ -1 +0,0 @@
|
|||
<mxfile host="Electron" modified="2022-08-21T08:46:03.477Z" agent="5.0 (Macintosh; Intel Mac OS X 10_16_0) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/13.4.5 Chrome/83.0.4103.122 Electron/9.1.0 Safari/537.36" etag="2J7jDIJtH6gUTma6yj0l" version="13.4.5" type="device"><diagram id="mPrK13Eo7WhzfbSKrwnZ" name="Page-1">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</diagram></mxfile>
|
||||
|
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@ -45,7 +45,7 @@ Java 虚拟机（JVM）是运行 Java 字节码的虚拟机。JVM 有针对不
|
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|
||||
#### JDK 和 JRE
|
||||
|
||||
JDK 是 Java Development Kit 缩写，它是功能齐全的 Java SDK。它拥有 JRE 所拥有的一切，还有编译器（javac）和工具（如 javadoc 和 jdb）。它能够创建和编译程序。
|
||||
JDK 是 Java Development Kit 缩写，它是功能齐全的 Java SDK。它拥有 JRE 所拥有的一切（包含了 JRE），还有编译器（javac）和工具（如 javadoc 和 jdb）。它能够创建和编译 Java 程序。
|
||||
|
||||
JRE 是 Java 运行时环境。它是运行已编译 Java 程序所需的所有内容的集合，包括 Java 虚拟机（JVM），Java 类库，java 命令和其他的一些基础构件。但是，它不能用于创建新程序。
|
||||
|
||||
|
|
@ -55,14 +55,20 @@ JRE 是 Java 运行时环境。它是运行已编译 Java 程序所需的所有
|
|||
|
||||
在 Java 中，JVM 可以理解的代码就叫做字节码（即扩展名为 `.class` 的文件），它不面向任何特定的处理器，只面向虚拟机。Java 语言通过字节码的方式，在一定程度上解决了传统解释型语言执行效率低的问题，同时又保留了解释型语言可移植的特点。所以， Java 程序运行时相对来说还是高效的（不过，和 C++，Rust，Go 等语言还是有一定差距的），而且，由于字节码并不针对一种特定的机器，因此，Java 程序无须重新编译便可在多种不同操作系统的计算机上运行。
|
||||
|
||||
**Java 程序从源代码到运行的过程如下图所示：**
|
||||
**Java 程序从源代码到运行的过程如下图所示** ：
|
||||
|
||||
|
||||
|
||||
我们需要格外注意的是 `.class->机器码` 这一步。在这一步 JVM 类加载器首先加载字节码文件，然后通过解释器逐行解释执行，这种方式的执行速度会相对比较慢。而且，有些方法和代码块是经常需要被调用的(也就是所谓的热点代码)，所以后面引进了 JIT（just-in-time compilation） 编译器，而 JIT 属于运行时编译。当 JIT 编译器完成第一次编译后，其会将字节码对应的机器码保存下来，下次可以直接使用。而我们知道，机器码的运行效率肯定是高于 Java 解释器的。这也解释了我们为什么经常会说 **Java 是编译与解释共存的语言** 。
|
||||
|
||||
|
||||
|
||||
> HotSpot 采用了惰性评估(Lazy Evaluation)的做法，根据二八定律，消耗大部分系统资源的只有那一小部分的代码（热点代码），而这也就是 JIT 所需要编译的部分。JVM 会根据代码每次被执行的情况收集信息并相应地做出一些优化，因此执行的次数越多，它的速度就越快。JDK 9 引入了一种新的编译模式 AOT(Ahead of Time Compilation)，它是直接将字节码编译成机器码，这样就避免了 JIT 预热等各方面的开销。JDK 支持分层编译和 AOT 协作使用。
|
||||
|
||||
JDK、JRE、JVM、JIT 这四者的关系如下图所示。
|
||||
|
||||
|
||||
|
||||
### 为什么不全部使用 AOT 呢？
|
||||
|
||||
AOT 可以提前编译节省启动时间，那为什么不全部使用这种编译方式呢？
|
||||
|
|
@ -155,6 +161,8 @@ AOT 可以提前编译节省启动时间，那为什么不全部使用这种编
|
|||
|
||||
Java 中的注释有三种：
|
||||
|
||||
|
||||
|
||||
1. **单行注释** ：通常用于解释方法内某单行代码的作用。
|
||||
|
||||
2. **多行注释** ：通常用于解释一段代码的作用。
|
||||
|
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|
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Loading…
Reference in New Issue