前言

散列表也叫作哈希表（hash table），这种数据结构提供了键（Key）和值（Value）的映射关系。只要给出一个Key，就可以高效查找到它所匹配的Value，时间复杂度接近于O(1)

1. 概念

散列表（Hash table，也叫哈希表），是根据关键码值(Key value)而直接进行访问的数据结构。也就是说，它通过把关键码值映射到表中一个位置来访问记录，以加快查找的速度。这个映射函数叫做散列函数，存放记录的数组叫做散列表。给定表M，存在函数f(key)，对任意给定的关键字值key，代入函数后若能得到包含该关键字的记录在表中的地址，则称表M为哈希（Hash）表，函数f(key)为哈希(Hash) 函数。

2. 存储原理

2.1 散列表存储简介

散列表在本质上也是一个数组。散列表的Key则是以字符串类型为主的，通过hash函数把Key和数组下标进行转换，作用是把任意长度的输入通过散列算法转换成固定类型、固定长度的散列值

//数组下标=取key的hashcode模数组的长度后的余数
index = HashCode (Key) % Array.length
int index=Math.abs("Hello".hashCode())%10; （0-9）

如上代码所示，是一种比较简单的计算方式。

实际应用中，会有很多的hash函数：CRC16、CRC32、siphash 、murmurHash、times 33等，此种Hash计算方式为固定Hash方式，也称为传统Hash。该方式在数组固定时，可以快速检索，但当数组长度变化时，需要重新计算数组下标，所以说传统Hash法虽然比较简单，但不利于扩展，如果要扩展可以采用一致性Hash

2.2 一致性哈希介绍

上面提到，常规的哈希算法可以实现快速检索，但是不利于扩展，当哈希表长度不够的时候，扩容的话，所有内容需要重新哈希。此时成本会比较大，为此，一致性哈希方式被提了出来。

关于一致性哈希更多介绍，因为内容较多，拆到另外一个博客讲解。

3. 操作

3.1 写

写操作就是在散列表中插入新的键值对

通过哈希函数，把Key转化成数组下标
如果数组下标对应的位置没有元素，就把这个Entry填充到数组下标的位置。

3.2 读

读操作就是通过给定的Key，在散列表中查找对应的Value

通过哈希函数，把Key转化成数组下标
找到数组下标所对应的元素，如果key不正确，说明产生了hash冲突，则顺着头节点遍历该单链表，再根据key即可取值

3.3 扩容

散列表是基于数组实现的，当经过多次元素插入，散列表达到一定饱和度时，Key映射位置发生冲突的概率会逐渐提高。这样一来，大量元素拥挤在相同的数组下标位置，形成很长的链表，对后续插入操作和查询操作的性能都有很大影响。

扩容的步骤：

扩容，创建一个新的Entry空数组，长度是原数组的2倍
重新Hash，遍历原Entry数组，把所有的Entry重新Hash到新数组中

关于HashMap的实现，JDK 8和以前的版本有着很大的不同。当多个Entry被Hash到同一个数组下标位置时，为了提升插入和查找的效率，HashMap会把Entry的链表转化为红黑树这种数据结构。JDK1.8前在HashMap扩容时，会反序单链表，这样在高并发时会有死循环的可能。

3.4 代码实现

3.4.1 定义链表节点

package org.wanlong.hash;/*** @author wanlong* @version 1.0* @description: 链表节点* @date 2023/5/24 15:23*/
public class Node {String key;String value;// 指向下一个结点Node next;public Node(String key, String value, Node next) {this.key = key;this.value = value;this.next = next;}
}

3.4.2 定义链表

package org.wanlong.hash;/*** @author wanlong* @version 1.0* @description: 链表实现* @date 2023/5/24 15:23*/
public class ListNode {Node head; //头结点public void addNode(String key, String value) {if (head == null)return;// 创建结点Node node = new Node(key, value, null);// 临时变量Node tmp = head;//循环遍历单链表while (true) {//key相同覆盖值 从head开始if (key.equals(tmp.key)) {tmp.value = value;break;}if (tmp.next == null) {break;}//指向下一个tmp = tmp.next;}//在循环外挂载最后一个结点tmp.next = node;}/*** @param key:* @return java.lang.String* @Description: 从链表获取值* @Author: wanlong* @Date: 2023/5/24 15:27**/public String getVal(String key) {if (head == null)return null;//只有一个结点if (head.next == null) {return head.value;} else {//遍历单链表Node tmp = head;while (tmp != null) {//找到匹配的keyif (key.equals(tmp.key)) {return tmp.value;}//指向下一个tmp = tmp.next;}return null;}}
}

3.4.3 散列表实现

package org.wanlong.hash;/*** @author wanlong* @version 1.0* @description: 测试 哈希map* @date 2023/5/24 15:24*/
public class TestHashMap {//数组初始化 2的3次方ListNode[] map = new ListNode[8];//ListNode的个数int size;/*** @param key:* @param value:* @return void* @Description: 往map放值* @Author: wanlong* @Date: 2023/5/24 15:29**/public void put(String key, String value) {//该扩容了 这里0.75 是负载因子，jdk源码也是这个值 这里需要扩容直接报错if (size >= map.length * 0.75) {System.out.println("map需要扩容");ListNode[] tempMap=map;map=resize();//释放引用，交给jvm回收tempMap=null;}//计算索引 数组下标int index = Math.abs(key.hashCode()) % map.length;//获得该下标处的ListNodeListNode ln = map[index];//该下标处无值if (ln == null) {//创建单链表ListNode lnNew = new ListNode();//创建头结点Node head = new Node(key, value, null);//挂载头结点lnNew.head = head;//把单链放到数组里map[index] = lnNew;size++;} else {//该下标有值，hash碰撞单链表挂结点ln.addNode(key, value);}}/**** @Description: 扩容方法，将原来集合中的数据，放到新的map中* @Author: wanlong* @Date: 2023/5/24 15:54* @return org.wanlong.hash.ListNode[]**/private ListNode[] resize(){//容量扩大两倍ListNode[] newListNodes = new ListNode[map.length * 2];//原来的数据重新哈希，放入新的哈希表中for (int i = 0; i < map.length; i++) {ListNode listNode = map[i];if (listNode!=null){Node head = listNode.head;//遍历单链表，取出每个元素，放到新链表里面while (head!=null){String key = head.key;String value = head.value;//计算索引 数组下标int index = Math.abs(key.hashCode()) % newListNodes.length;//获得该下标处的ListNodeListNode ln = newListNodes[index];//该下标处无值if (ln == null) {//创建单链表ListNode lnNew = new ListNode();//创建头结点Node nodeHead = new Node(key, value, null);//挂载头结点lnNew.head = nodeHead;//把单链放到数组里newListNodes[index] = lnNew;} else {//该下标有值，hash碰撞单链表挂结点ln.addNode(key, value);}//下一个节点head=head.next;}}}return newListNodes;}public String get(String key) {int index = Math.abs(key.hashCode()) % map.length;ListNode ln = map[index];if (ln == null)return null;return ln.getVal(key);}
}

3.4.4 测试类

package org.wanlong.hash;import org.junit.Test;/*** @author wanlong* @version 1.0* @description:* @date 2023/5/24 15:45*/
public class Client {@Testpublic void testMap() {TestHashMap testHashMap = new TestHashMap();testHashMap.put("1", "wo");testHashMap.put("2", "shi");testHashMap.put("3", "dai");testHashMap.put("4", "zi");testHashMap.put("5", "cai");testHashMap.put("6", "cai");testHashMap.put("7", "wo");testHashMap.put("8", "shi");testHashMap.put("9", "shui");for (int i = 1; i <= 9; i++) {String key = "" + i;System.out.println("key:" + key + "的值为:" + testHashMap.get(key));}}
}

3.4.5 运行结果以及分析

map需要扩容
key:1的值为:wo
key:2的值为:shi
key:3的值为:dai
key:4的值为:zi
key:5的值为:cai
key:6的值为:cai
key:7的值为:wo
key:8的值为:shi
key:9的值为:shui

通过运行结果可以看到，我们散列表初始长度设置的是8 ，因为添加了9个元素，在添加的时候，实现了扩容，里面添加的元素正常打印。这个要留意一个细节，其实不是添加到8个元素的时候才开始扩容，而是当元素个数大于容量*负载因子的时候就开始扩容了。

示例代码中用到的负载因子，也是java8 中HashMap的默认负载因子。

4. 时间复杂度

写操作： O(1) + O(m) = O(m) m为单链元素个数
读操作：O(1) + O(m) m为单链元素个数
Hash冲突写单链表：O(m)
Hash扩容：O(n) n是数组元素个数 rehash
Hash冲突读单链表：O(m) m为单链元素个数

5. 优缺点

5.1 优点

读写快

5.2 缺点

哈希表中的元素是没有被排序的
Hash冲突如果频繁发生，散列表会退化为链表
在散列表需要扩容的时候，重新计算散列表中所有元素在新的散列表中的位置

6. 应用

6.1 redis 字典

Redis字典dict又称散列表（hash），是用来存储键值对的一种数据结构。Redis整个数据库是用字典来存储的（K-V结构）。对Redis进行CURD操作其实就是对字典中的数据进行CURD操作。Redis字典实现包括：字典(dict)、Hash表(dictht)、Hash表节点(dictEntry)。
在这里插入图片描述

6.2 布隆过滤器

6.2.1 布隆过滤器简介

布隆过滤器（Bloom Filter）是1970年由布隆提出的。它实际上是一个很长的二进制向量和一系列随机hash映射函数。布隆过滤器可以用于检索一个元素是否在一个集合中。它的优点是空间效率和查询时间都远远超过一般的算法。
在这里插入图片描述

6.2.2 布隆过滤器原理

当一个元素被加入集合时，通过K个Hash函数将这个元素映射成一个数组中的K个点，把它们置为1。检索时，我们只要看看这些点是不是都是1就（大约）知道集合中有没有它了：如果这些点有任何一个0，则被检元素一定不在；如果都是1，则被检元素很可能在。这就是布隆过滤器的基本思想。

简单来说：因为哈希算法，

同样的key，同样的哈希函数，一定会得到同样的摘要值。
同样的key A,用K个哈希算法来计算，一定会得到K个固定的值。
那么如果有一个Key B ,我们通过同样的K 个哈希算法计算得到 K 个值，如果这K 个值和前面的K 个值不完全一样，那么Key B一定和KeyA 不相等。

由此可得，布隆过滤器在检索一个元素是否在一个集合中可以做出如下判断：

如果Key多次哈希的值都能命中，说明这个key 可能在集合中
如果Key多次哈希的值有某一个对不上，说明这个Key一定不存在

6.3 位图

Bitmap 的基本原理就是用一个 bit 来标记某个元素对应的 Value，而Key即是该元素。由于采用一个bit 来存储一个数据，因此可以大大的节省空间。Java 中 int 类型占用 4 个字节，即 4 byte，又 1 byte = 8 bit，所以一个 int 数字的表示大概如下：
在这里插入图片描述
试想以下，如果有一个很大的 int 数组，如 10000000，数组中每一个数值都要占用 4 个字节，则一共需要占用 10000000 * 4 = 40000000 个字节，即 40000000 / 1024.0 / 1024.0 = 38 M。

如果使用 bit 来存放上述 10000000 个元素，只需要 10000000 个 bit 即可， 10000000 / 8.0 / 1024.0/ 1024.0 = 1.19 M 左右，可以看到 bitmap 可以大大的节约内存。使用 bit 来表示数组 [1, 2, 5] 如下所示，可以看到只用 1 字节即可表示：
在这里插入图片描述

7.HashMap源码解析

7.1 源码注释翻译

package java.util;import java.io.IOException;
import java.io.InvalidObjectException;
import java.io.Serializable;
import java.lang.reflect.ParameterizedType;
import java.lang.reflect.Type;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.Consumer;
import java.util.function.Function;
import sun.misc.SharedSecrets;/*** Hash table based implementation of the <tt>Map</tt> interface.  This* implementation provides all of the optional map operations, and permits* <tt>null</tt> values and the <tt>null</tt> key.  (The <tt>HashMap</tt>* class is roughly equivalent to <tt>Hashtable</tt>, except that it is* unsynchronized and permits nulls.)  This class makes no guarantees as to* the order of the map; in particular, it does not guarantee that the order* will remain constant over time.** 基于哈希表的Map接口实现，hashmap提供了所有map相关操作。允许null 作为key 和 value* (hashmap和hashtable 很相似，唯一的差别是hashmap 不是线程安全的，允许空元素)* hashmap 不保证元素的顺序，特别要说明的是，hashmap也不保证顺序一直不变** <p>This implementation provides constant-time performance for the basic* operations (<tt>get</tt> and <tt>put</tt>), assuming the hash function* disperses the elements properly among the buckets.  Iteration over* collection views requires time proportional to the "capacity" of the* <tt>HashMap</tt> instance (the number of buckets) plus its size (the number* of key-value mappings).  Thus, it's very important not to set the initial* capacity too high (or the load factor too low) if iteration performance is* important.** 如果哈希函数能把元素正确均匀的分配在一个个桶里，基本操作（get和put）性能是很好的。* 对集合视图的迭代需要与HashMap实例的“容量”（bucket的数量）加上其大小（键值映射的数量）成比例的时间* 因此，如果关注迭代性能，不要把初始化容量设置的太高（或者设置负载因子太低）*** <p>An instance of <tt>HashMap</tt> has two parameters that affect its* performance: <i>initial capacity</i> and <i>load factor</i>.  The* <i>capacity</i> is the number of buckets in the hash table, and the initial* capacity is simply the capacity at the time the hash table is created.  The* <i>load factor</i> is a measure of how full the hash table is allowed to* get before its capacity is automatically increased.  When the number of* entries in the hash table exceeds the product of the load factor and the* current capacity, the hash table is <i>rehashed</i> (that is, internal data* structures are rebuilt) so that the hash table has approximately twice the* number of buckets.** 一个hashmap对象有两个因素影响性能，初始化容量capacity和负载因子load factor。容量是散列表中桶的数量，* capacity是散列表中桶的数量，初始化capacity是在散列表创建的时候的容量。* 载因子load factor是一种度量方式，表中元素达到容量的多少的时候，需要自动扩容* 当表中的元素达到 负载因子* 容量 ，散列表重新哈希（内部数据结构重建）。容量会*2** <p>As a general rule, the default load factor (.75) offers a good* tradeoff between time and space costs.  Higher values decrease the* space overhead but increase the lookup cost (reflected in most of* the operations of the <tt>HashMap</tt> class, including* <tt>get</tt> and <tt>put</tt>).  The expected number of entries in* the map and its load factor should be taken into account when* setting its initial capacity, so as to minimize the number of* rehash operations.  If the initial capacity is greater than the* maximum number of entries divided by the load factor, no rehash* operations will ever occur.** 作为一个通用规则，默认的负载因子（0.75）在时间和内存空间开销上面表现良好，所以一般不建议调整这个参数* 更高的负载因子减少了存储空间消耗但是增加了hashmap查询时间(包括get和put操作)* 当设置初始容量的时候，map存储的元素预期个数和它的负载因子应该考虑在内，以便能减少重新扩容，重新哈希的次数* 如果初始化容量* 负载因子 大于 预期的最大元素个数，那么map不需要扩容，重新哈希，此时性能会比较高** <p>If many mappings are to be stored in a <tt>HashMap</tt>* instance, creating it with a sufficiently large capacity will allow* the mappings to be stored more efficiently than letting it perform* automatic rehashing as needed to grow the table.  Note that using* many keys with the same {@code hashCode()} is a sure way to slow* down performance of any hash table. To ameliorate impact, when keys* are {@link Comparable}, this class may use comparison order among* keys to help break ties.** 如果确定知道map要存储大量的元素，创建一个大容量的map来存储元素* 比让hashmap随着元素个数增多，多次重新哈希效率要高得多* 要注意如果很多存储元素的key的hashcode一样的话，一定会拖慢散列表的性能，因为这会退化为链表* 为了改善这种情况，当键为{@link-Comparable}时，此类可以使用键之间的比较顺序来帮助打破联系。*** <p><strong>Note that this implementation is not synchronized.</strong>* If multiple threads access a hash map concurrently, and at least one of* the threads modifies the map structurally, it <i>must</i> be* synchronized externally.  (A structural modification is any operation* that adds or deletes one or more mappings; merely changing the value* associated with a key that an instance already contains is not a* structural modification.)  This is typically accomplished by* synchronizing on some object that naturally encapsulates the map.** 注意hashmap 不是线程安全的。如果多个现在并发的访问一个map对象，某个线程修改结构，需要调用端在外面加锁。*** If no such object exists, the map should be "wrapped" using the* {@link Collections#synchronizedMap Collections.synchronizedMap}* method.  This is best done at creation time, to prevent accidental* unsynchronized access to the map:<pre>*   Map m = Collections.synchronizedMap(new HashMap(...));</pre>**   可以在创建的时候使用这种封装的方式加锁 Map m = Collections.synchronizedMap(new HashMap(...));** <p>The iterators returned by all of this class's "collection view methods"* are <i>fail-fast</i>: if the map is structurally modified at any time after* the iterator is created, in any way except through the iterator's own* <tt>remove</tt> method, the iterator will throw a* {@link ConcurrentModificationException}.  Thus, in the face of concurrent* modification, the iterator fails quickly and cleanly, rather than risking* arbitrary, non-deterministic behavior at an undetermined time in the* future.** 这个类的所有“集合视图方法”返回的迭代器是fail-fast：在迭代器创建后，除了迭代器自己的remove方法外，这个map发生任何结构性变化* 迭代器会抛出异常ConcurrentModificationException* 因此，面对并发修改的时候，迭代器快速干净失败，而不是把风险留给将来，以免发生预期之外的影响*** <p>Note that the fail-fast behavior of an iterator cannot be guaranteed* as it is, generally speaking, impossible to make any hard guarantees in the* presence of unsynchronized concurrent modification.  Fail-fast iterators* throw <tt>ConcurrentModificationException</tt> on a best-effort basis.* Therefore, it would be wrong to write a program that depended on this* exception for its correctness: <i>the fail-fast behavior of iterators* should be used only to detect bugs.</i>*** 注意，迭代器的快速失败行为不能确保一定生效。通常来说，在一个不加同步锁的并发修改中，做出任何坚决保证是不可能的。* Fail-fast iterators 尽最大努力会抛出ConcurrentModificationException异常* 因此，写一个 依赖这个异常 确保程序正确性 是不正确的做法，迭代器的快速失败行为应该只是用来检测bug** <p>This class is a member of the* <a href="{@docRoot}/../technotes/guides/collections/index.html">* Java Collections Framework</a>.** @param <K> the type of keys maintained by this map* @param <V> the type of mapped values** @author  Doug Lea* @author  Josh Bloch* @author  Arthur van Hoff* @author  Neal Gafter* @see     Object#hashCode()* @see     Collection* @see     Map* @see     TreeMap* @see     Hashtable* @since   1.2*/
public class HashMap<K,V> extends AbstractMap<K,V>implements Map<K,V>, Cloneable, Serializable {private static final long serialVersionUID = 362498820763181265L;/** Implementation notes.** This map usually acts as a binned (bucketed) hash table, but* when bins get too large, they are transformed into bins of* TreeNodes, each structured similarly to those in* java.util.TreeMap. Most methods try to use normal bins, but* relay to TreeNode methods when applicable (simply by checking* instanceof a node).  Bins of TreeNodes may be traversed and* used like any others, but additionally support faster lookup* when overpopulated. However, since the vast majority of bins in* normal use are not overpopulated, checking for existence of* tree bins may be delayed in the course of table methods.** 这个map通常是链表，但是当元素太多的时候，节点转换为树节点，（每个树节点和java.util.TreeMap里面的类似）* 大多数方法使用普通的散列表，合适的时候调整到树节点方法（只需检查节点的实例）* 树节点使用和其他一样，但是当元素很多的时候会提供更高的查询性能。* 但是，通常情况下，大多数map存不了很多元素，此时使用树因为涉及到树结构调整，可能会比链表性能还差** Tree bins (i.e., bins whose elements are all TreeNodes) are* ordered primarily by hashCode, but in the case of ties, if two* elements are of the same "class C implements Comparable<C>",* type then their compareTo method is used for ordering. (We* conservatively check generic types via reflection to validate* this -- see method comparableClassFor).  The added complexity* of tree bins is worthwhile in providing worst-case O(log n)* operations when keys either have distinct hashes or are* orderable, Thus, performance degrades gracefully under* accidental or malicious usages in which hashCode() methods* return values that are poorly distributed, as well as those in* which many keys share a hashCode, so long as they are also* Comparable. (If neither of these apply, we may waste about a* factor of two in time and space compared to taking no* precautions. But the only known cases stem from poor user* programming practices that are already so slow that this makes* little difference.)** 树的节点主要是根据元素的hashcode排序，但是在关联的过程中，如果两个元素是同样的（类实现了Comparable接口），* 会用接口定义的compareTo方法做排序（我们适当的通过反射校验类型来验证这一点  详见comparableClassFor方法）* 当key既没有hash分散到不同的位置，也不是有序的时候，操作树节点的时间复杂度是O(log n),是值得的* 因此，当元素的hashcode()返回的值不是均衡分布的，或者很多的key用同一个hashcode, 只要它们也是可比较的，* 这种意外或者恶意使用会导致元素操作性能适度下降。* (如果两者都不适用，比起不采取预防措施，我们可能会花费两边的时间在时间和空间上面* 但唯一已知的案例源于糟糕的用户编程实践，这些实践已经非常缓慢，几乎没有什么区别)* todo** Because TreeNodes are about twice the size of regular nodes, we* use them only when bins contain enough nodes to warrant use* (see TREEIFY_THRESHOLD). And when they become too small (due to* removal or resizing) they are converted back to plain bins.  In* usages with well-distributed user hashCodes, tree bins are* rarely used.  Ideally, under random hashCodes, the frequency of* nodes in bins follows a Poisson distribution* (http://en.wikipedia.org/wiki/Poisson_distribution) with a* parameter of about 0.5 on average for the default resizing* threshold of 0.75, although with a large variance because of* resizing granularity. Ignoring variance, the expected* occurrences of list size k are (exp(-0.5) * pow(0.5, k) /* factorial(k)). The first values are:** 0:    0.60653066* 1:    0.30326533* 2:    0.07581633* 3:    0.01263606* 4:    0.00157952* 5:    0.00015795* 6:    0.00001316* 7:    0.00000094* 8:    0.00000006* more: less than 1 in ten million** 因为树节点是常规节点两倍大小，我们只有当包含足够多的节点的时候再使用它。详见TREEIFY_THRESHOLD* 当删除元素或者重置大小的时候树节点很少，树节点会退化为普通链表节点* 在哈希算法分布的很好的情况下，树节点这种方式是很少使用的* 理想情况下，在随机的hashcode下，节点的频率遵循泊松分布（http://en.wikipedia.org/wiki/Poisson_distribution）* 对于默认调整大小阈值0.75，平均约为0.5 ，尽管由于调整粒度的原因，差异很大* 忽略误差，期待的数组大小占用k 是  exp(-0.5) * pow(0.5, k) /factorial(k)** 0:    0.60653066* 1:    0.30326533* 2:    0.07581633* 3:    0.01263606* 4:    0.00157952* 5:    0.00015795* 6:    0.00001316* 7:    0.00000094* 8:    0.00000006* 更大的: 小于千万分之一*** The root of a tree bin is normally its first node.  However,* sometimes (currently only upon Iterator.remove), the root might* be elsewhere, but can be recovered following parent links* (method TreeNode.root()).** 树的根节点通常是第一个节点，然而，有时候（当前仅在Iterator.remove上），根节点可能在其他地方* 但是，可以沿着父链接恢复（TreeNode.root()）** All applicable internal methods accept a hash code as an* argument (as normally supplied from a public method), allowing* them to call each other without recomputing user hashCodes.* Most internal methods also accept a "tab" argument, that is* normally the current table, but may be a new or old one when* resizing or converting.** 所有适用的内部方法都接受哈希代码作为参数（通常由公共方法提供）* 允许他们调用不重新计算哈希码* 大多数内部方法也接受一个tab参数，通常是当前的table表，但是当重置大小或者转换的时候，* 也可能 是之前的或者新的表**** When bin lists are treeified, split, or untreeified, we keep* them in the same relative access/traversal order (i.e., field* Node.next) to better preserve locality, and to slightly* simplify handling of splits and traversals that invoke* iterator.remove. When using comparators on insertion, to keep a* total ordering (or as close as is required here) across* rebalancings, we compare classes and identityHashCodes as* tie-breakers.** 当bin列表被树化、拆分或未搜索时，* 我们将它们保持在相同的相对访问/遍历顺序（即字段Node.next）中，* 以更好地保留局部性，并稍微简化调用迭代器.remove的拆分和遍历的处理。* 当在插入的时候使用比较器的时候，通过重新平衡保持全局有序，* 我们将类和identityHashCodes作为平局决胜器进行比较。***** The use and transitions among plain vs tree modes is* complicated by the existence of subclass LinkedHashMap. See* below for hook methods defined to be invoked upon insertion,* removal and access that allow LinkedHashMap internals to* otherwise remain independent of these mechanics. (This also* requires that a map instance be passed to some utility methods* that may create new nodes.)** 普通模式与树模式之间的使用和转换* 因子类LinkedHashMap的存在而变得复杂。* 请参阅以下内容，了解定义为在插入、移除和访问时调用的钩子方法，* 这些方法允许LinkedHashMap内部保持独立于这些机制。* (这也要求将map对象传递给一些可能创建新节点的公共程序方法。)** The concurrent-programming-like SSA-based coding style helps* avoid aliasing errors amid all of the twisty pointer operations.** 基于SSA的并发编程风格有助于避免在所有扭曲的指针操作中出现混叠错误。**//*** The default initial capacity - MUST be a power of two.** 默认初始化容量，必须是2 的幂* 下面左移四位相当于 1 * 2 的 四次方*/static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16/*** The maximum capacity, used if a higher value is implicitly specified* by either of the constructors with arguments.* MUST be a power of two <= 1<<30.** 最大容量，如果一个更大的值被通过构造器参数指定，必须是一个小于 2的30次方的 2的幂**/static final int MAXIMUM_CAPACITY = 1 << 30;/*** The load factor used when none specified in constructor.* 默认的负载因子*/static final float DEFAULT_LOAD_FACTOR = 0.75f;/*** The bin count threshold for using a tree rather than list for a* bin.  Bins are converted to trees when adding an element to a* bin with at least this many nodes. The value must be greater* than 2 and should be at least 8 to mesh with assumptions in* tree removal about conversion back to plain bins upon* shrinkage.** 使用树而不是列表作为垃圾箱的垃圾箱计数阈值。* 当向至少有这么多节点的bin添加元素时，bin会转换为树。* 该值必须大于2，并且应至少为8，以符合移除树木时关于在shrinka转换回普通垃圾箱的假设***/static final int TREEIFY_THRESHOLD = 8;/*** The bin count threshold for untreeifying a (split) bin during a* resize operation. Should be less than TREEIFY_THRESHOLD, and at* most 6 to mesh with shrinkage detection under removal.** 在调整大小操作期间，用于取消搜索（拆分）垃圾箱的垃圾箱计数阈值。* 应小于TREEIFY_THRESHOLD，且最多为6，以便在移除时进行收缩检测。*/static final int UNTREEIFY_THRESHOLD = 6;/*** The smallest table capacity for which bins may be treeified.* (Otherwise the table is resized if too many nodes in a bin.)* Should be at least 4 * TREEIFY_THRESHOLD to avoid conflicts* between resizing and treeification thresholds.**  可以将存储箱树化的最小表容量。*  （否则，如果一个bin中的节点太多，则会调整表的大小。）*  应至少为4*TREEIFY_THRESHOLD，以避免调整大小阈值和树化阈值之间的冲突。**/static final int MIN_TREEIFY_CAPACITY = 64;/*** Basic hash bin node, used for most entries.  (See below for* TreeNode subclass, and in LinkedHashMap for its Entry subclass.)** 大多数entry用到的，基本的哈希 node**/static class Node<K,V> implements Map.Entry<K,V> {final int hash;final K key;V value;Node<K,V> next;Node(int hash, K key, V value, Node<K,V> next) {this.hash = hash;this.key = key;this.value = value;this.next = next;}public final K getKey()        { return key; }public final V getValue()      { return value; }public final String toString() { return key + "=" + value; }public final int hashCode() {return Objects.hashCode(key) ^ Objects.hashCode(value);}public final V setValue(V newValue) {V oldValue = value;value = newValue;return oldValue;}public final boolean equals(Object o) {if (o == this)return true;if (o instanceof Map.Entry) {Map.Entry<?,?> e = (Map.Entry<?,?>)o;if (Objects.equals(key, e.getKey()) &&Objects.equals(value, e.getValue()))return true;}return false;}}/* ---------------- Static utilities -------------- *//*** Computes key.hashCode() and spreads (XORs) higher bits of hash* to lower.  Because the table uses power-of-two masking, sets of* hashes that vary only in bits above the current mask will* always collide. (Among known examples are sets of Float keys* holding consecutive whole numbers in small tables.)  So we* apply a transform that spreads the impact of higher bits* downward. There is a tradeoff between speed, utility, and* quality of bit-spreading. Because many common sets of hashes* are already reasonably distributed (so don't benefit from* spreading), and because we use trees to handle large sets of* collisions in bins, we just XOR some shifted bits in the* cheapest possible way to reduce systematic lossage, as well as* to incorporate impact of the highest bits that would otherwise* never be used in index calculations because of table bounds.** 计算key的哈希code并和hash码的高位做 （异或） 运算。* 因为这样相当于让哈希码的高位也参与运算了，（因为使用2的指数次方标记，* 大多数只在bit上有差异的哈希码会造成频繁的哈希碰撞）* 所以应该将哈希吗高位的影响也能影响到低位的运算* 因为通常很多哈希码是合理的均匀分布的（所以从高位到低位的运算中并不会更好）* 而且因为我们用了树结构来解决管理大量的碰撞。我们仅仅需要用更高效快速的方式* 异或运算一些转换的高位字节来减少一些系统性损失，并且 合并 由于哈希表边界而在索引计算中永远不会使用 的最高位的影响** 意思就是正常因为哈希表的长度问题，可能正常的哈希码的高位不会参与运算，但是通过将哈希码的高位下移，* 可以让高位也参与运算，这样相当于哈希的更均匀了**/static final int hash(Object key) {int h;return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);}/*** Returns x's Class if it is of the form "class C implements* Comparable<C>", else null.** 如果某个类实现了接口Comparable 会返回这个类信息，否则返回null*/static Class<?> comparableClassFor(Object x) {if (x instanceof Comparable) {Class<?> c; Type[] ts, as; Type t; ParameterizedType p;if ((c = x.getClass()) == String.class) // bypass checksreturn c;if ((ts = c.getGenericInterfaces()) != null) {for (int i = 0; i < ts.length; ++i) {if (((t = ts[i]) instanceof ParameterizedType) &&((p = (ParameterizedType)t).getRawType() ==Comparable.class) &&(as = p.getActualTypeArguments()) != null &&as.length == 1 && as[0] == c) // type arg is creturn c;}}}return null;}/*** Returns k.compareTo(x) if x matches kc (k's screened comparable* class), else 0.** 如果x 的类和 kc 一样的，使用k的compareTo方法和x 比较，否则返回0*/@SuppressWarnings({"rawtypes","unchecked"}) // for cast to Comparablestatic int compareComparables(Class<?> kc, Object k, Object x) {return (x == null || x.getClass() != kc ? 0 :((Comparable)k).compareTo(x));}/*** Returns a power of two size for the given target capacity.* 返回 一个2的指数对于给定的容量 ，意思是不管给的构造器容量是多少，哈希map都会最终变成2的指数次方管理容量*/static final int tableSizeFor(int cap) {int n = cap - 1;n |= n >>> 1;n |= n >>> 2;n |= n >>> 4;n |= n >>> 8;n |= n >>> 16;return (n < 0) ? 1 : (n >= MAXIMUM_CAPACITY) ? MAXIMUM_CAPACITY : n + 1;}/* ---------------- Fields -------------- *//*** The table, initialized on first use, and resized as* necessary. When allocated, length is always a power of two.* (We also tolerate length zero in some operations to allow* bootstrapping mechanics that are currently not needed.)** 这个表在第一次初始化的时候使用，并且可以在需要的时候重置大小。* 分配的时候，长度一定是2的次方* （我们也一些操作中，允许长度为0 ，以允许目前不需要的自举机制。）* 这个没太懂，啥是自举机制？*/transient Node<K,V>[] table;/*** Holds cached entrySet(). Note that AbstractMap fields are used* for keySet() and values().** 持有缓存的entryset,注意AbstractMap字段用于keySet（）和values（）。**/transient Set<Map.Entry<K,V>> entrySet;/*** The number of key-value mappings contained in this map.* 这个map中包含的键值对*/transient int size;/*** The number of times this HashMap has been structurally modified* Structural modifications are those that change the number of mappings in* the HashMap or otherwise modify its internal structure (e.g.,* rehash).  This field is used to make iterators on Collection-views of* the HashMap fail-fast.  (See ConcurrentModificationException).** 这个map结构性修改的次数*/transient int modCount;/*** The next size value at which to resize (capacity * load factor).** 下一次重置大小的值* @serial*/// (The javadoc description is true upon serialization.// Additionally, if the table array has not been allocated, this// field holds the initial array capacity, or zero signifying// DEFAULT_INITIAL_CAPACITY.)int threshold;/*** The load factor for the hash table.* 负载因子* @serial*/final float loadFactor;/* ---------------- Public operations -------------- *//*** Constructs an empty <tt>HashMap</tt> with the specified initial* capacity and load factor.** 用指定的容量和负载因子构造一个空的哈希map** @param  initialCapacity the initial capacity* @param  loadFactor      the load factor* @throws IllegalArgumentException if the initial capacity is negative*         or the load factor is nonpositive*/public HashMap(int initialCapacity, float loadFactor) {if (initialCapacity < 0)throw new IllegalArgumentException("Illegal initial capacity: " +initialCapacity);if (initialCapacity > MAXIMUM_CAPACITY)initialCapacity = MAXIMUM_CAPACITY;if (loadFactor <= 0 || Float.isNaN(loadFactor))throw new IllegalArgumentException("Illegal load factor: " +loadFactor);this.loadFactor = loadFactor;this.threshold = tableSizeFor(initialCapacity);}/*** Constructs an empty <tt>HashMap</tt> with the specified initial* capacity and the default load factor (0.75).**  用指定的容量大小和默认的负载因子创建空的hashmap** @param  initialCapacity the initial capacity.* @throws IllegalArgumentException if the initial capacity is negative.*/public HashMap(int initialCapacity) {this(initialCapacity, DEFAULT_LOAD_FACTOR);}/*** Constructs an empty <tt>HashMap</tt> with the default initial capacity* (16) and the default load factor (0.75).** 创建map 容量16 负载因子 0.75**/public HashMap() {this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted}/*** Constructs a new <tt>HashMap</tt> with the same mappings as the* specified <tt>Map</tt>.  The <tt>HashMap</tt> is created with* default load factor (0.75) and an initial capacity sufficient to* hold the mappings in the specified <tt>Map</tt>.** 创建一个新的map，和指定的map包含相同的元素 。* 新的map负载因子是0.75 初始化容量和入参map的元素数量有关** @param   m the map whose mappings are to be placed in this map* @throws  NullPointerException if the specified map is null*/public HashMap(Map<? extends K, ? extends V> m) {this.loadFactor = DEFAULT_LOAD_FACTOR;putMapEntries(m, false);}/*** Implements Map.putAll and Map constructor** 实现putall 方法** @param m the map* @param evict false when initially constructing this map, else* true (relayed to method afterNodeInsertion).*/final void putMapEntries(Map<? extends K, ? extends V> m, boolean evict) {int s = m.size();if (s > 0) {if (table == null) { // pre-sizefloat ft = ((float)s / loadFactor) + 1.0F;int t = ((ft < (float)MAXIMUM_CAPACITY) ?(int)ft : MAXIMUM_CAPACITY);if (t > threshold)threshold = tableSizeFor(t);}else if (s > threshold)resize();for (Map.Entry<? extends K, ? extends V> e : m.entrySet()) {K key = e.getKey();V value = e.getValue();putVal(hash(key), key, value, false, evict);}}}/*** Returns the number of key-value mappings in this map.** 返回map中的键值对** @return the number of key-value mappings in this map*/public int size() {return size;}/*** Returns <tt>true</tt> if this map contains no key-value mappings.** @return <tt>true</tt> if this map contains no key-value mappings*/public boolean isEmpty() {return size == 0;}/*** Returns the value to which the specified key is mapped,* or {@code null} if this map contains no mapping for the key.** 返回key对应的value ,如果key不存在，返回null** <p>More formally, if this map contains a mapping* from a key* {@code k} to a value {@code v} such that {@code (key==null ? k==null :* key.equals(k))}, then this method returns {@code v}; otherwise* it returns {@code null}.  (There can be at most one such mapping.)** <p>A return value of {@code null} does not <i>necessarily</i>* indicate that the map contains no mapping for the key; it's also* possible that the map explicitly maps the key to {@code null}.* The {@link #containsKey containsKey} operation may be used to* distinguish these two cases.** 返回为null 不代表一定是这个map不包含这个key* 也可能是这个map明确这个key 是null* 方法containsKey 可以区分这两种情况** @see #put(Object, Object)*/public V get(Object key) {Node<K,V> e;return (e = getNode(hash(key), key)) == null ? null : e.value;}/*** Implements Map.get and related methods* 实现Map的get方法* @param hash hash for key* @param key the key* @return the node, or null if none*/final Node<K,V> getNode(int hash, Object key) {Node<K,V>[] tab; Node<K,V> first, e; int n; K k;if ((tab = table) != null && (n = tab.length) > 0 &&(first = tab[(n - 1) & hash]) != null) {if (first.hash == hash && // always check first node((k = first.key) == key || (key != null && key.equals(k))))return first;if ((e = first.next) != null) {if (first instanceof TreeNode)return ((TreeNode<K,V>)first).getTreeNode(hash, key);do {if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k))))return e;} while ((e = e.next) != null);}}return null;}/*** Returns <tt>true</tt> if this map contains a mapping for the* specified key.* 如果map 包含指定key的键值对 返回true** @param   key   The key whose presence in this map is to be tested* @return <tt>true</tt> if this map contains a mapping for the specified* key.*/public boolean containsKey(Object key) {return getNode(hash(key), key) != null;}/*** Associates the specified value with the specified key in this map.* If the map previously contained a mapping for the key, the old* value is replaced.** 替换指定key的指定value** @param key key with which the specified value is to be associated* @param value value to be associated with the specified key* @return the previous value associated with <tt>key</tt>, or*         <tt>null</tt> if there was no mapping for <tt>key</tt>.*         (A <tt>null</tt> return can also indicate that the map*         previously associated <tt>null</tt> with <tt>key</tt>.)*/public V put(K key, V value) {return putVal(hash(key), key, value, false, true);}/*** Implements Map.put and related methods** @param hash hash for key* @param key the key* @param value the value to put* @param onlyIfAbsent if true, don't change existing value* @param evict if false, the table is in creation mode.* @return previous value, or null if none*/final V putVal(int hash, K key, V value, boolean onlyIfAbsent,boolean evict) {Node<K,V>[] tab; Node<K,V> p; int n, i;if ((tab = table) == null || (n = tab.length) == 0)n = (tab = resize()).length;if ((p = tab[i = (n - 1) & hash]) == null)tab[i] = newNode(hash, key, value, null);else {Node<K,V> e; K k;if (p.hash == hash &&((k = p.key) == key || (key != null && key.equals(k))))e = p;else if (p instanceof TreeNode)e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);else {for (int binCount = 0; ; ++binCount) {if ((e = p.next) == null) {p.next = newNode(hash, key, value, null);if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1sttreeifyBin(tab, hash);break;}if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k))))break;p = e;}}if (e != null) { // existing mapping for keyV oldValue = e.value;if (!onlyIfAbsent || oldValue == null)e.value = value;afterNodeAccess(e);return oldValue;}}++modCount;if (++size > threshold)resize();afterNodeInsertion(evict);return null;}/*** Initializes or doubles table size.  If null, allocates in* accord with initial capacity target held in field threshold.* Otherwise, because we are using power-of-two expansion, the* elements from each bin must either stay at same index, or move* with a power of two offset in the new table.** 初始化或者扩容一杯表的大小，** 否则，因为我们使用2的指数次方扩容，每个桶中的元素一定是要么在原来的下标桶下面* 要么在新表中的一个2的指数资方下标下面** @return the table*/final Node<K,V>[] resize() {Node<K,V>[] oldTab = table;int oldCap = (oldTab == null) ? 0 : oldTab.length;int oldThr = threshold;int newCap, newThr = 0;if (oldCap > 0) {if (oldCap >= MAXIMUM_CAPACITY) {threshold = Integer.MAX_VALUE;return oldTab;}else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&oldCap >= DEFAULT_INITIAL_CAPACITY)newThr = oldThr << 1; // double threshold}else if (oldThr > 0) // initial capacity was placed in thresholdnewCap = oldThr;else {               // zero initial threshold signifies using defaultsnewCap = DEFAULT_INITIAL_CAPACITY;newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);}if (newThr == 0) {float ft = (float)newCap * loadFactor;newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?(int)ft : Integer.MAX_VALUE);}threshold = newThr;@SuppressWarnings({"rawtypes","unchecked"})Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];table = newTab;if (oldTab != null) {for (int j = 0; j < oldCap; ++j) {Node<K,V> e;if ((e = oldTab[j]) != null) {oldTab[j] = null;if (e.next == null)newTab[e.hash & (newCap - 1)] = e;else if (e instanceof TreeNode)((TreeNode<K,V>)e).split(this, newTab, j, oldCap);else { // preserve orderNode<K,V> loHead = null, loTail = null;Node<K,V> hiHead = null, hiTail = null;Node<K,V> next;do {next = e.next;if ((e.hash & oldCap) == 0) {if (loTail == null)loHead = e;elseloTail.next = e;loTail = e;}else {if (hiTail == null)hiHead = e;elsehiTail.next = e;hiTail = e;}} while ((e = next) != null);if (loTail != null) {loTail.next = null;newTab[j] = loHead;}if (hiTail != null) {hiTail.next = null;newTab[j + oldCap] = hiHead;}}}}}return newTab;}/*** Replaces all linked nodes in bin at index for given hash unless* table is too small, in which case resizes instead.** 根绝给定的哈希码计算下标，替换对应下标的所有节点转换为树* 除非表太小，此时需要扩容**/final void treeifyBin(Node<K,V>[] tab, int hash) {int n, index; Node<K,V> e;if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY)resize();else if ((e = tab[index = (n - 1) & hash]) != null) {TreeNode<K,V> hd = null, tl = null;do {TreeNode<K,V> p = replacementTreeNode(e, null);if (tl == null)hd = p;else {p.prev = tl;tl.next = p;}tl = p;} while ((e = e.next) != null);if ((tab[index] = hd) != null)hd.treeify(tab);}}/*** Copies all of the mappings from the specified map to this map.* These mappings will replace any mappings that this map had for* any of the keys currently in the specified map.** 复制指定的map里面的键值对到新的map里面* 如果两个map有重复的key的话，会覆盖** @param m mappings to be stored in this map* @throws NullPointerException if the specified map is null*/public void putAll(Map<? extends K, ? extends V> m) {putMapEntries(m, true);}/*** Removes the mapping for the specified key from this map if present.** 如果存在的话，会删除指定的key** @param  key key whose mapping is to be removed from the map* @return the previous value associated with <tt>key</tt>, or*         <tt>null</tt> if there was no mapping for <tt>key</tt>.*         (A <tt>null</tt> return can also indicate that the map*         previously associated <tt>null</tt> with <tt>key</tt>.)*/public V remove(Object key) {Node<K,V> e;return (e = removeNode(hash(key), key, null, false, true)) == null ?null : e.value;}/*** Implements Map.remove and related methods** 实现了map 的remove 方法和相关方法* @param hash hash for key* @param key the key* @param value the value to match if matchValue, else ignored* @param matchValue if true only remove if value is equal* @param movable if false do not move other nodes while removing* @return the node, or null if none*/final Node<K,V> removeNode(int hash, Object key, Object value,boolean matchValue, boolean movable) {Node<K,V>[] tab; Node<K,V> p; int n, index;if ((tab = table) != null && (n = tab.length) > 0 &&(p = tab[index = (n - 1) & hash]) != null) {Node<K,V> node = null, e; K k; V v;if (p.hash == hash &&((k = p.key) == key || (key != null && key.equals(k))))node = p;else if ((e = p.next) != null) {if (p instanceof TreeNode)node = ((TreeNode<K,V>)p).getTreeNode(hash, key);else {do {if (e.hash == hash &&((k = e.key) == key ||(key != null && key.equals(k)))) {node = e;break;}p = e;} while ((e = e.next) != null);}}if (node != null && (!matchValue || (v = node.value) == value ||(value != null && value.equals(v)))) {if (node instanceof TreeNode)((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);else if (node == p)tab[index] = node.next;elsep.next = node.next;++modCount;--size;afterNodeRemoval(node);return node;}}return null;}/*** Removes all of the mappings from this map.* The map will be empty after this call returns.** 删除所有元素*/public void clear() {Node<K,V>[] tab;modCount++;if ((tab = table) != null && size > 0) {size = 0;for (int i = 0; i < tab.length; ++i)tab[i] = null;}}/*** Returns <tt>true</tt> if this map maps one or more keys to the* specified value.* 如果有一个或者多个键值对的值能对应，返回true** @param value value whose presence in this map is to be tested* @return <tt>true</tt> if this map maps one or more keys to the*         specified value*/public boolean containsValue(Object value) {Node<K,V>[] tab; V v;if ((tab = table) != null && size > 0) {for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next) {if ((v = e.value) == value ||(value != null && value.equals(v)))return true;}}}return false;}/*** Returns a {@link Set} view of the keys contained in this map.* The set is backed by the map, so changes to the map are* reflected in the set, and vice-versa.  If the map is modified* while an iteration over the set is in progress (except through* the iterator's own <tt>remove</tt> operation), the results of* the iteration are undefined.  The set supports element removal,* which removes the corresponding mapping from the map, via the* <tt>Iterator.remove</tt>, <tt>Set.remove</tt>,* <tt>removeAll</tt>, <tt>retainAll</tt>, and <tt>clear</tt>* operations.  It does not support the <tt>add</tt> or <tt>addAll</tt>* operations.** 返回map中包含的key的Set集合* 这个set底层是map,为此map的修改会体现到set上面。反之亦然* 当有一个创建的迭代器在运行中的时候，如果map被修改了，迭代的结果是出乎意料的。* 通过Iterator.remove, Set.remove, removeAll, retainAll, and clear* 操作，这个set集合支持元素删除* 不支持 add 和 addall操作***** @return a set view of the keys contained in this map*/public Set<K> keySet() {Set<K> ks = keySet;if (ks == null) {ks = new KeySet();keySet = ks;}return ks;}final class KeySet extends AbstractSet<K> {public final int size()                 { return size; }public final void clear()               { HashMap.this.clear(); }public final Iterator<K> iterator()     { return new KeyIterator(); }public final boolean contains(Object o) { return containsKey(o); }public final boolean remove(Object key) {return removeNode(hash(key), key, null, false, true) != null;}public final Spliterator<K> spliterator() {return new KeySpliterator<>(HashMap.this, 0, -1, 0, 0);}public final void forEach(Consumer<? super K> action) {Node<K,V>[] tab;if (action == null)throw new NullPointerException();if (size > 0 && (tab = table) != null) {int mc = modCount;for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next)action.accept(e.key);}if (modCount != mc)throw new ConcurrentModificationException();}}}/*** Returns a {@link Collection} view of the values contained in this map.* The collection is backed by the map, so changes to the map are* reflected in the collection, and vice-versa.  If the map is* modified while an iteration over the collection is in progress* (except through the iterator's own <tt>remove</tt> operation),* the results of the iteration are undefined.  The collection* supports element removal, which removes the corresponding* mapping from the map, via the <tt>Iterator.remove</tt>,* <tt>Collection.remove</tt>, <tt>removeAll</tt>,* <tt>retainAll</tt> and <tt>clear</tt> operations.  It does not* support the <tt>add</tt> or <tt>addAll</tt> operations.*** 返回map中包含的Value的Collection集合* 这个集合底层是map,为此map的修改会体现到集合上面。反之亦然* 当有一个创建的迭代器在运行中的时候，如果map被修改了，迭代的结果是出乎意料的。* 通过 Iterator.remove, Collection.remove, removeAll, retainAll and clear* 操作，这个集合支持元素删除* 不支持 add 和 addall操作*** @return a view of the values contained in this map*/public Collection<V> values() {Collection<V> vs = values;if (vs == null) {vs = new Values();values = vs;}return vs;}final class Values extends AbstractCollection<V> {public final int size()                 { return size; }public final void clear()               { HashMap.this.clear(); }public final Iterator<V> iterator()     { return new ValueIterator(); }public final boolean contains(Object o) { return containsValue(o); }public final Spliterator<V> spliterator() {return new ValueSpliterator<>(HashMap.this, 0, -1, 0, 0);}public final void forEach(Consumer<? super V> action) {Node<K,V>[] tab;if (action == null)throw new NullPointerException();if (size > 0 && (tab = table) != null) {int mc = modCount;for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next)action.accept(e.value);}if (modCount != mc)throw new ConcurrentModificationException();}}}/*** Returns a {@link Set} view of the mappings contained in this map.* The set is backed by the map, so changes to the map are* reflected in the set, and vice-versa.  If the map is modified* while an iteration over the set is in progress (except through* the iterator's own <tt>remove</tt> operation, or through the* <tt>setValue</tt> operation on a map entry returned by the* iterator) the results of the iteration are undefined.  The set* supports element removal, which removes the corresponding* mapping from the map, via the <tt>Iterator.remove</tt>,* <tt>Set.remove</tt>, <tt>removeAll</tt>, <tt>retainAll</tt> and* <tt>clear</tt> operations.  It does not support the* <tt>add</tt> or <tt>addAll</tt> operations.** 返回map中包含的键值对的Set集合* 这个集合底层是map,为此map的修改会体现到集合上面。反之亦然* 当有一个创建的迭代器在运行中的时候，如果map被修改了，迭代的结果是出乎意料的。* 通过Iterator.remove, Set.remove, removeAll, retainAll, and clear* 操作，这个集合支持元素删除* 不支持 add 和 addall操作** @return a set view of the mappings contained in this map*/public Set<Map.Entry<K,V>> entrySet() {Set<Map.Entry<K,V>> es;return (es = entrySet) == null ? (entrySet = new EntrySet()) : es;}final class EntrySet extends AbstractSet<Map.Entry<K,V>> {public final int size()                 { return size; }public final void clear()               { HashMap.this.clear(); }public final Iterator<Map.Entry<K,V>> iterator() {return new EntryIterator();}public final boolean contains(Object o) {if (!(o instanceof Map.Entry))return false;Map.Entry<?,?> e = (Map.Entry<?,?>) o;Object key = e.getKey();Node<K,V> candidate = getNode(hash(key), key);return candidate != null && candidate.equals(e);}public final boolean remove(Object o) {if (o instanceof Map.Entry) {Map.Entry<?,?> e = (Map.Entry<?,?>) o;Object key = e.getKey();Object value = e.getValue();return removeNode(hash(key), key, value, true, true) != null;}return false;}public final Spliterator<Map.Entry<K,V>> spliterator() {return new EntrySpliterator<>(HashMap.this, 0, -1, 0, 0);}public final void forEach(Consumer<? super Map.Entry<K,V>> action) {Node<K,V>[] tab;if (action == null)throw new NullPointerException();if (size > 0 && (tab = table) != null) {int mc = modCount;for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next)action.accept(e);}if (modCount != mc)throw new ConcurrentModificationException();}}}// Overrides of JDK8 Map extension methods//重写jdk8map 的扩展方法@Overridepublic V getOrDefault(Object key, V defaultValue) {Node<K,V> e;return (e = getNode(hash(key), key)) == null ? defaultValue : e.value;}@Overridepublic V putIfAbsent(K key, V value) {return putVal(hash(key), key, value, true, true);}@Overridepublic boolean remove(Object key, Object value) {return removeNode(hash(key), key, value, true, true) != null;}@Overridepublic boolean replace(K key, V oldValue, V newValue) {Node<K,V> e; V v;if ((e = getNode(hash(key), key)) != null &&((v = e.value) == oldValue || (v != null && v.equals(oldValue)))) {e.value = newValue;afterNodeAccess(e);return true;}return false;}@Overridepublic V replace(K key, V value) {Node<K,V> e;if ((e = getNode(hash(key), key)) != null) {V oldValue = e.value;e.value = value;afterNodeAccess(e);return oldValue;}return null;}@Overridepublic V computeIfAbsent(K key,Function<? super K, ? extends V> mappingFunction) {if (mappingFunction == null)throw new NullPointerException();int hash = hash(key);Node<K,V>[] tab; Node<K,V> first; int n, i;int binCount = 0;TreeNode<K,V> t = null;Node<K,V> old = null;if (size > threshold || (tab = table) == null ||(n = tab.length) == 0)n = (tab = resize()).length;if ((first = tab[i = (n - 1) & hash]) != null) {if (first instanceof TreeNode)old = (t = (TreeNode<K,V>)first).getTreeNode(hash, key);else {Node<K,V> e = first; K k;do {if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k)))) {old = e;break;}++binCount;} while ((e = e.next) != null);}V oldValue;if (old != null && (oldValue = old.value) != null) {afterNodeAccess(old);return oldValue;}}V v = mappingFunction.apply(key);if (v == null) {return null;} else if (old != null) {old.value = v;afterNodeAccess(old);return v;}else if (t != null)t.putTreeVal(this, tab, hash, key, v);else {tab[i] = newNode(hash, key, v, first);if (binCount >= TREEIFY_THRESHOLD - 1)treeifyBin(tab, hash);}++modCount;++size;afterNodeInsertion(true);return v;}public V computeIfPresent(K key,BiFunction<? super K, ? super V, ? extends V> remappingFunction) {if (remappingFunction == null)throw new NullPointerException();Node<K,V> e; V oldValue;int hash = hash(key);if ((e = getNode(hash, key)) != null &&(oldValue = e.value) != null) {V v = remappingFunction.apply(key, oldValue);if (v != null) {e.value = v;afterNodeAccess(e);return v;}elseremoveNode(hash, key, null, false, true);}return null;}@Overridepublic V compute(K key,BiFunction<? super K, ? super V, ? extends V> remappingFunction) {if (remappingFunction == null)throw new NullPointerException();int hash = hash(key);Node<K,V>[] tab; Node<K,V> first; int n, i;int binCount = 0;TreeNode<K,V> t = null;Node<K,V> old = null;if (size > threshold || (tab = table) == null ||(n = tab.length) == 0)n = (tab = resize()).length;if ((first = tab[i = (n - 1) & hash]) != null) {if (first instanceof TreeNode)old = (t = (TreeNode<K,V>)first).getTreeNode(hash, key);else {Node<K,V> e = first; K k;do {if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k)))) {old = e;break;}++binCount;} while ((e = e.next) != null);}}V oldValue = (old == null) ? null : old.value;V v = remappingFunction.apply(key, oldValue);if (old != null) {if (v != null) {old.value = v;afterNodeAccess(old);}elseremoveNode(hash, key, null, false, true);}else if (v != null) {if (t != null)t.putTreeVal(this, tab, hash, key, v);else {tab[i] = newNode(hash, key, v, first);if (binCount >= TREEIFY_THRESHOLD - 1)treeifyBin(tab, hash);}++modCount;++size;afterNodeInsertion(true);}return v;}@Overridepublic V merge(K key, V value,BiFunction<? super V, ? super V, ? extends V> remappingFunction) {if (value == null)throw new NullPointerException();if (remappingFunction == null)throw new NullPointerException();int hash = hash(key);Node<K,V>[] tab; Node<K,V> first; int n, i;int binCount = 0;TreeNode<K,V> t = null;Node<K,V> old = null;if (size > threshold || (tab = table) == null ||(n = tab.length) == 0)n = (tab = resize()).length;if ((first = tab[i = (n - 1) & hash]) != null) {if (first instanceof TreeNode)old = (t = (TreeNode<K,V>)first).getTreeNode(hash, key);else {Node<K,V> e = first; K k;do {if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k)))) {old = e;break;}++binCount;} while ((e = e.next) != null);}}if (old != null) {V v;if (old.value != null)v = remappingFunction.apply(old.value, value);elsev = value;if (v != null) {old.value = v;afterNodeAccess(old);}elseremoveNode(hash, key, null, false, true);return v;}if (value != null) {if (t != null)t.putTreeVal(this, tab, hash, key, value);else {tab[i] = newNode(hash, key, value, first);if (binCount >= TREEIFY_THRESHOLD - 1)treeifyBin(tab, hash);}++modCount;++size;afterNodeInsertion(true);}return value;}@Overridepublic void forEach(BiConsumer<? super K, ? super V> action) {Node<K,V>[] tab;if (action == null)throw new NullPointerException();if (size > 0 && (tab = table) != null) {int mc = modCount;for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next)action.accept(e.key, e.value);}if (modCount != mc)throw new ConcurrentModificationException();}}@Overridepublic void replaceAll(BiFunction<? super K, ? super V, ? extends V> function) {Node<K,V>[] tab;if (function == null)throw new NullPointerException();if (size > 0 && (tab = table) != null) {int mc = modCount;for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next) {e.value = function.apply(e.key, e.value);}}if (modCount != mc)throw new ConcurrentModificationException();}}/* ------------------------------------------------------------ */// Cloning and serialization//克隆并且序列化/*** Returns a shallow copy of this <tt>HashMap</tt> instance: the keys and* values themselves are not cloned.* 返回一个对象的浅拷贝** @return a shallow copy of this map*/@SuppressWarnings("unchecked")@Overridepublic Object clone() {HashMap<K,V> result;try {result = (HashMap<K,V>)super.clone();} catch (CloneNotSupportedException e) {// this shouldn't happen, since we are Cloneablethrow new InternalError(e);}result.reinitialize();result.putMapEntries(this, false);return result;}// These methods are also used when serializing HashSets//这些方法在序列化hashset的时候也会被使用final float loadFactor() { return loadFactor; }final int capacity() {return (table != null) ? table.length :(threshold > 0) ? threshold :DEFAULT_INITIAL_CAPACITY;}/*** Save the state of the <tt>HashMap</tt> instance to a stream (i.e.,* serialize it).** 序列化** @serialData The <i>capacity</i> of the HashMap (the length of the*             bucket array) is emitted (int), followed by the*             <i>size</i> (an int, the number of key-value*             mappings), followed by the key (Object) and value (Object)*             for each key-value mapping.  The key-value mappings are*             emitted in no particular order.*/private void writeObject(java.io.ObjectOutputStream s)throws IOException {int buckets = capacity();// Write out the threshold, loadfactor, and any hidden stuffs.defaultWriteObject();s.writeInt(buckets);s.writeInt(size);internalWriteEntries(s);}/*** Reconstitute the {@code HashMap} instance from a stream (i.e.,* deserialize it).* 反序列化*/private void readObject(java.io.ObjectInputStream s)throws IOException, ClassNotFoundException {// Read in the threshold (ignored), loadfactor, and any hidden stuffs.defaultReadObject();reinitialize();if (loadFactor <= 0 || Float.isNaN(loadFactor))throw new InvalidObjectException("Illegal load factor: " +loadFactor);s.readInt();                // Read and ignore number of bucketsint mappings = s.readInt(); // Read number of mappings (size)if (mappings < 0)throw new InvalidObjectException("Illegal mappings count: " +mappings);else if (mappings > 0) { // (if zero, use defaults)// Size the table using given load factor only if within// range of 0.25...4.0float lf = Math.min(Math.max(0.25f, loadFactor), 4.0f);float fc = (float)mappings / lf + 1.0f;int cap = ((fc < DEFAULT_INITIAL_CAPACITY) ?DEFAULT_INITIAL_CAPACITY :(fc >= MAXIMUM_CAPACITY) ?MAXIMUM_CAPACITY :tableSizeFor((int)fc));float ft = (float)cap * lf;threshold = ((cap < MAXIMUM_CAPACITY && ft < MAXIMUM_CAPACITY) ?(int)ft : Integer.MAX_VALUE);// Check Map.Entry[].class since it's the nearest public type to// what we're actually creating.SharedSecrets.getJavaOISAccess().checkArray(s, Map.Entry[].class, cap);@SuppressWarnings({"rawtypes","unchecked"})Node<K,V>[] tab = (Node<K,V>[])new Node[cap];table = tab;// Read the keys and values, and put the mappings in the HashMapfor (int i = 0; i < mappings; i++) {@SuppressWarnings("unchecked")K key = (K) s.readObject();@SuppressWarnings("unchecked")V value = (V) s.readObject();putVal(hash(key), key, value, false, false);}}}/* ------------------------------------------------------------ */// iterators//迭代器abstract class HashIterator {Node<K,V> next;        // next entry to returnNode<K,V> current;     // current entryint expectedModCount;  // for fast-failint index;             // current slotHashIterator() {expectedModCount = modCount;Node<K,V>[] t = table;current = next = null;index = 0;if (t != null && size > 0) { // advance to first entrydo {} while (index < t.length && (next = t[index++]) == null);}}public final boolean hasNext() {return next != null;}final Node<K,V> nextNode() {Node<K,V>[] t;Node<K,V> e = next;if (modCount != expectedModCount)throw new ConcurrentModificationException();if (e == null)throw new NoSuchElementException();if ((next = (current = e).next) == null && (t = table) != null) {do {} while (index < t.length && (next = t[index++]) == null);}return e;}public final void remove() {Node<K,V> p = current;if (p == null)throw new IllegalStateException();if (modCount != expectedModCount)throw new ConcurrentModificationException();current = null;K key = p.key;removeNode(hash(key), key, null, false, false);expectedModCount = modCount;}}final class KeyIterator extends HashIteratorimplements Iterator<K> {public final K next() { return nextNode().key; }}final class ValueIterator extends HashIteratorimplements Iterator<V> {public final V next() { return nextNode().value; }}final class EntryIterator extends HashIteratorimplements Iterator<Map.Entry<K,V>> {public final Map.Entry<K,V> next() { return nextNode(); }}/* ------------------------------------------------------------ */// spliterators//并行迭代器static class HashMapSpliterator<K,V> {final HashMap<K,V> map;Node<K,V> current;          // current nodeint index;                  // current index, modified on advance/splitint fence;                  // one past last indexint est;                    // size estimateint expectedModCount;       // for comodification checksHashMapSpliterator(HashMap<K,V> m, int origin,int fence, int est,int expectedModCount) {this.map = m;this.index = origin;this.fence = fence;this.est = est;this.expectedModCount = expectedModCount;}final int getFence() { // initialize fence and size on first useint hi;if ((hi = fence) < 0) {HashMap<K,V> m = map;est = m.size;expectedModCount = m.modCount;Node<K,V>[] tab = m.table;hi = fence = (tab == null) ? 0 : tab.length;}return hi;}public final long estimateSize() {getFence(); // force initreturn (long) est;}}static final class KeySpliterator<K,V>extends HashMapSpliterator<K,V>implements Spliterator<K> {KeySpliterator(HashMap<K,V> m, int origin, int fence, int est,int expectedModCount) {super(m, origin, fence, est, expectedModCount);}public KeySpliterator<K,V> trySplit() {int hi = getFence(), lo = index, mid = (lo + hi) >>> 1;return (lo >= mid || current != null) ? null :new KeySpliterator<>(map, lo, index = mid, est >>>= 1,expectedModCount);}public void forEachRemaining(Consumer<? super K> action) {int i, hi, mc;if (action == null)throw new NullPointerException();HashMap<K,V> m = map;Node<K,V>[] tab = m.table;if ((hi = fence) < 0) {mc = expectedModCount = m.modCount;hi = fence = (tab == null) ? 0 : tab.length;}elsemc = expectedModCount;if (tab != null && tab.length >= hi &&(i = index) >= 0 && (i < (index = hi) || current != null)) {Node<K,V> p = current;current = null;do {if (p == null)p = tab[i++];else {action.accept(p.key);p = p.next;}} while (p != null || i < hi);if (m.modCount != mc)throw new ConcurrentModificationException();}}public boolean tryAdvance(Consumer<? super K> action) {int hi;if (action == null)throw new NullPointerException();Node<K,V>[] tab = map.table;if (tab != null && tab.length >= (hi = getFence()) && index >= 0) {while (current != null || index < hi) {if (current == null)current = tab[index++];else {K k = current.key;current = current.next;action.accept(k);if (map.modCount != expectedModCount)throw new ConcurrentModificationException();return true;}}}return false;}public int characteristics() {return (fence < 0 || est == map.size ? Spliterator.SIZED : 0) |Spliterator.DISTINCT;}}static final class ValueSpliterator<K,V>extends HashMapSpliterator<K,V>implements Spliterator<V> {ValueSpliterator(HashMap<K,V> m, int origin, int fence, int est,int expectedModCount) {super(m, origin, fence, est, expectedModCount);}public ValueSpliterator<K,V> trySplit() {int hi = getFence(), lo = index, mid = (lo + hi) >>> 1;return (lo >= mid || current != null) ? null :new ValueSpliterator<>(map, lo, index = mid, est >>>= 1,expectedModCount);}public void forEachRemaining(Consumer<? super V> action) {int i, hi, mc;if (action == null)throw new NullPointerException();HashMap<K,V> m = map;Node<K,V>[] tab = m.table;if ((hi = fence) < 0) {mc = expectedModCount = m.modCount;hi = fence = (tab == null) ? 0 : tab.length;}elsemc = expectedModCount;if (tab != null && tab.length >= hi &&(i = index) >= 0 && (i < (index = hi) || current != null)) {Node<K,V> p = current;current = null;do {if (p == null)p = tab[i++];else {action.accept(p.value);p = p.next;}} while (p != null || i < hi);if (m.modCount != mc)throw new ConcurrentModificationException();}}public boolean tryAdvance(Consumer<? super V> action) {int hi;if (action == null)throw new NullPointerException();Node<K,V>[] tab = map.table;if (tab != null && tab.length >= (hi = getFence()) && index >= 0) {while (current != null || index < hi) {if (current == null)current = tab[index++];else {V v = current.value;current = current.next;action.accept(v);if (map.modCount != expectedModCount)throw new ConcurrentModificationException();return true;}}}return false;}public int characteristics() {return (fence < 0 || est == map.size ? Spliterator.SIZED : 0);}}static final class EntrySpliterator<K,V>extends HashMapSpliterator<K,V>implements Spliterator<Map.Entry<K,V>> {EntrySpliterator(HashMap<K,V> m, int origin, int fence, int est,int expectedModCount) {super(m, origin, fence, est, expectedModCount);}public EntrySpliterator<K,V> trySplit() {int hi = getFence(), lo = index, mid = (lo + hi) >>> 1;return (lo >= mid || current != null) ? null :new EntrySpliterator<>(map, lo, index = mid, est >>>= 1,expectedModCount);}public void forEachRemaining(Consumer<? super Map.Entry<K,V>> action) {int i, hi, mc;if (action == null)throw new NullPointerException();HashMap<K,V> m = map;Node<K,V>[] tab = m.table;if ((hi = fence) < 0) {mc = expectedModCount = m.modCount;hi = fence = (tab == null) ? 0 : tab.length;}elsemc = expectedModCount;if (tab != null && tab.length >= hi &&(i = index) >= 0 && (i < (index = hi) || current != null)) {Node<K,V> p = current;current = null;do {if (p == null)p = tab[i++];else {action.accept(p);p = p.next;}} while (p != null || i < hi);if (m.modCount != mc)throw new ConcurrentModificationException();}}public boolean tryAdvance(Consumer<? super Map.Entry<K,V>> action) {int hi;if (action == null)throw new NullPointerException();Node<K,V>[] tab = map.table;if (tab != null && tab.length >= (hi = getFence()) && index >= 0) {while (current != null || index < hi) {if (current == null)current = tab[index++];else {Node<K,V> e = current;current = current.next;action.accept(e);if (map.modCount != expectedModCount)throw new ConcurrentModificationException();return true;}}}return false;}public int characteristics() {return (fence < 0 || est == map.size ? Spliterator.SIZED : 0) |Spliterator.DISTINCT;}}/* ------------------------------------------------------------ */// LinkedHashMap support/** The following package-protected methods are designed to be* overridden by LinkedHashMap, but not by any other subclass.* Nearly all other internal methods are also package-protected* but are declared final, so can be used by LinkedHashMap, view* classes, and HashSet.** 下面的包保护级别的方法是设计用来linkedhashmap重写的，但是不是被其他子类*  几乎所有的其他内部方法也是包级别的，但是被声明为final  ，所以可以被linkedhashmap hashset*  使用*/// Create a regular (non-tree) node//创建一个非树节点Node<K,V> newNode(int hash, K key, V value, Node<K,V> next) {return new Node<>(hash, key, value, next);}// For conversion from TreeNodes to plain nodes//转换树节点到链表节点Node<K,V> replacementNode(Node<K,V> p, Node<K,V> next) {return new Node<>(p.hash, p.key, p.value, next);}// Create a tree bin node//创建一个树节点TreeNode<K,V> newTreeNode(int hash, K key, V value, Node<K,V> next) {return new TreeNode<>(hash, key, value, next);}// For treeifyBin//treeifyBin 方法调用TreeNode<K,V> replacementTreeNode(Node<K,V> p, Node<K,V> next) {return new TreeNode<>(p.hash, p.key, p.value, next);}/*** Reset to initial default state.  Called by clone and readObject.* 重置来初始化到默认状态，被克隆和反序列化方法调用*/void reinitialize() {table = null;entrySet = null;keySet = null;values = null;modCount = 0;threshold = 0;size = 0;}// Callbacks to allow LinkedHashMap post-actions//提供给子类的回调函数来传递行为，这也叫钩子函数void afterNodeAccess(Node<K,V> p) { }void afterNodeInsertion(boolean evict) { }void afterNodeRemoval(Node<K,V> p) { }// Called only from writeObject, to ensure compatible ordering.//序列化的时候才会被调用，来确保顺序void internalWriteEntries(java.io.ObjectOutputStream s) throws IOException {Node<K,V>[] tab;if (size > 0 && (tab = table) != null) {for (int i = 0; i < tab.length; ++i) {for (Node<K,V> e = tab[i]; e != null; e = e.next) {s.writeObject(e.key);s.writeObject(e.value);}}}}/* ------------------------------------------------------------ */// Tree bins/*** Entry for Tree bins. Extends LinkedHashMap.Entry (which in turn* extends Node) so can be used as extension of either regular or* linked node.** 树节点，继承了 LinkedHashMap.Entry（也就是继承了Node节点）* 所以可以用来扩展常规节点或者链表节点*/static final class TreeNode<K,V> extends LinkedHashMap.Entry<K,V> {TreeNode<K,V> parent;  // red-black tree linksTreeNode<K,V> left;TreeNode<K,V> right;TreeNode<K,V> prev;    // needed to unlink next upon deletionboolean red;TreeNode(int hash, K key, V val, Node<K,V> next) {super(hash, key, val, next);}/*** Returns root of tree containing this node.* 返回树节点的根节点*/final TreeNode<K,V> root() {for (TreeNode<K,V> r = this, p;;) {if ((p = r.parent) == null)return r;r = p;}}/*** Ensures that the given root is the first node of its bin.* 确定给定的root节点是 第一个节点*/static <K,V> void moveRootToFront(Node<K,V>[] tab, TreeNode<K,V> root) {int n;if (root != null && tab != null && (n = tab.length) > 0) {int index = (n - 1) & root.hash;TreeNode<K,V> first = (TreeNode<K,V>)tab[index];if (root != first) {Node<K,V> rn;tab[index] = root;TreeNode<K,V> rp = root.prev;if ((rn = root.next) != null)((TreeNode<K,V>)rn).prev = rp;if (rp != null)rp.next = rn;if (first != null)first.prev = root;root.next = first;root.prev = null;}assert checkInvariants(root);}}/*** Finds the node starting at root p with the given hash and key.* The kc argument caches comparableClassFor(key) upon first use* comparing keys.** 用知道的哈希码和key 找一个从根节点开始的节点* kc参数缓存了 comparableClassFor(key) 的结构，在第一次使用比较key的时候*/final TreeNode<K,V> find(int h, Object k, Class<?> kc) {TreeNode<K,V> p = this;do {int ph, dir; K pk;TreeNode<K,V> pl = p.left, pr = p.right, q;if ((ph = p.hash) > h)p = pl;else if (ph < h)p = pr;else if ((pk = p.key) == k || (k != null && k.equals(pk)))return p;else if (pl == null)p = pr;else if (pr == null)p = pl;else if ((kc != null ||(kc = comparableClassFor(k)) != null) &&(dir = compareComparables(kc, k, pk)) != 0)p = (dir < 0) ? pl : pr;else if ((q = pr.find(h, k, kc)) != null)return q;elsep = pl;} while (p != null);return null;}/*** Calls find for root node.* 调用查找根节点。*/final TreeNode<K,V> getTreeNode(int h, Object k) {return ((parent != null) ? root() : this).find(h, k, null);}/*** Tie-breaking utility for ordering insertions when equal* hashCodes and non-comparable. We don't require a total* order, just a consistent insertion rule to maintain* equivalence across rebalancings. Tie-breaking further than* necessary simplifies testing a bit.** 当哈希码相等还没法排序的时候，有序插入节点程序* 我们不是要求一个完整的顺序，仅仅是一个始终如一的插入规则来* 在重新平衡之间保持一致。* Tie-breaking 可以在必要的时候稍微简化测试*/static int tieBreakOrder(Object a, Object b) {int d;if (a == null || b == null ||(d = a.getClass().getName().compareTo(b.getClass().getName())) == 0)d = (System.identityHashCode(a) <= System.identityHashCode(b) ?-1 : 1);return d;}/*** Forms tree of the nodes linked from this node.* 将链表节点变为树* @return root of tree*/final void treeify(Node<K,V>[] tab) {TreeNode<K,V> root = null;for (TreeNode<K,V> x = this, next; x != null; x = next) {next = (TreeNode<K,V>)x.next;x.left = x.right = null;if (root == null) {x.parent = null;x.red = false;root = x;}else {K k = x.key;int h = x.hash;Class<?> kc = null;for (TreeNode<K,V> p = root;;) {int dir, ph;K pk = p.key;if ((ph = p.hash) > h)dir = -1;else if (ph < h)dir = 1;else if ((kc == null &&(kc = comparableClassFor(k)) == null) ||(dir = compareComparables(kc, k, pk)) == 0)dir = tieBreakOrder(k, pk);TreeNode<K,V> xp = p;if ((p = (dir <= 0) ? p.left : p.right) == null) {x.parent = xp;if (dir <= 0)xp.left = x;elsexp.right = x;root = balanceInsertion(root, x);break;}}}}moveRootToFront(tab, root);}/*** Returns a list of non-TreeNodes replacing those linked from* this node.**  把这个节点指向的所有节点返回为一个非树节点的链表，去除树化*/final Node<K,V> untreeify(HashMap<K,V> map) {Node<K,V> hd = null, tl = null;for (Node<K,V> q = this; q != null; q = q.next) {Node<K,V> p = map.replacementNode(q, null);if (tl == null)hd = p;elsetl.next = p;tl = p;}return hd;}/*** Tree version of putVal.* 树版本的putval*/final TreeNode<K,V> putTreeVal(HashMap<K,V> map, Node<K,V>[] tab,int h, K k, V v) {Class<?> kc = null;boolean searched = false;TreeNode<K,V> root = (parent != null) ? root() : this;for (TreeNode<K,V> p = root;;) {int dir, ph; K pk;if ((ph = p.hash) > h)dir = -1;else if (ph < h)dir = 1;else if ((pk = p.key) == k || (k != null && k.equals(pk)))return p;else if ((kc == null &&(kc = comparableClassFor(k)) == null) ||(dir = compareComparables(kc, k, pk)) == 0) {if (!searched) {TreeNode<K,V> q, ch;searched = true;if (((ch = p.left) != null &&(q = ch.find(h, k, kc)) != null) ||((ch = p.right) != null &&(q = ch.find(h, k, kc)) != null))return q;}dir = tieBreakOrder(k, pk);}TreeNode<K,V> xp = p;if ((p = (dir <= 0) ? p.left : p.right) == null) {Node<K,V> xpn = xp.next;TreeNode<K,V> x = map.newTreeNode(h, k, v, xpn);if (dir <= 0)xp.left = x;elsexp.right = x;xp.next = x;x.parent = x.prev = xp;if (xpn != null)((TreeNode<K,V>)xpn).prev = x;moveRootToFront(tab, balanceInsertion(root, x));return null;}}}/*** Removes the given node, that must be present before this call.* This is messier than typical red-black deletion code because we* cannot swap the contents of an interior node with a leaf* successor that is pinned by "next" pointers that are accessible* independently during traversal. So instead we swap the tree* linkages. If the current tree appears to have too few nodes,* the bin is converted back to a plain bin. (The test triggers* somewhere between 2 and 6 nodes, depending on tree structure).** 移除给定的节点（这个节点在这次调用之前必须存在）* 这个比传统的红黑树删除节点麻烦，因为我们不能将内部节点的内容与叶后继节点交换，* 叶后继节点由遍历期间可独立访问的“下一个”指针固定* 所以，替代的是，如果一个当前树包含节点太少，我们把树退化为链表（测试有时候是2个到6个节点，取决于树的结构）**/final void removeTreeNode(HashMap<K,V> map, Node<K,V>[] tab,boolean movable) {int n;if (tab == null || (n = tab.length) == 0)return;int index = (n - 1) & hash;TreeNode<K,V> first = (TreeNode<K,V>)tab[index], root = first, rl;TreeNode<K,V> succ = (TreeNode<K,V>)next, pred = prev;if (pred == null)tab[index] = first = succ;elsepred.next = succ;if (succ != null)succ.prev = pred;if (first == null)return;if (root.parent != null)root = root.root();if (root == null || root.right == null ||(rl = root.left) == null || rl.left == null) {tab[index] = first.untreeify(map);  // too smallreturn;}TreeNode<K,V> p = this, pl = left, pr = right, replacement;if (pl != null && pr != null) {TreeNode<K,V> s = pr, sl;while ((sl = s.left) != null) // find successors = sl;boolean c = s.red; s.red = p.red; p.red = c; // swap colorsTreeNode<K,V> sr = s.right;TreeNode<K,V> pp = p.parent;if (s == pr) { // p was s's direct parentp.parent = s;s.right = p;}else {TreeNode<K,V> sp = s.parent;if ((p.parent = sp) != null) {if (s == sp.left)sp.left = p;elsesp.right = p;}if ((s.right = pr) != null)pr.parent = s;}p.left = null;if ((p.right = sr) != null)sr.parent = p;if ((s.left = pl) != null)pl.parent = s;if ((s.parent = pp) == null)root = s;else if (p == pp.left)pp.left = s;elsepp.right = s;if (sr != null)replacement = sr;elsereplacement = p;}else if (pl != null)replacement = pl;else if (pr != null)replacement = pr;elsereplacement = p;if (replacement != p) {TreeNode<K,V> pp = replacement.parent = p.parent;if (pp == null)root = replacement;else if (p == pp.left)pp.left = replacement;elsepp.right = replacement;p.left = p.right = p.parent = null;}TreeNode<K,V> r = p.red ? root : balanceDeletion(root, replacement);if (replacement == p) {  // detachTreeNode<K,V> pp = p.parent;p.parent = null;if (pp != null) {if (p == pp.left)pp.left = null;else if (p == pp.right)pp.right = null;}}if (movable)moveRootToFront(tab, r);}/*** Splits nodes in a tree bin into lower and upper tree bins,* or untreeifies if now too small. Called only from resize;* see above discussion about split bits and indices.** 拆分一个树的节点到低位和高位两个树节点，如果节点太少的话，可以先不树化。* 只有resize 方法会调用** @param map the map* @param tab the table for recording bin heads* @param index the index of the table being split* @param bit the bit of hash to split on*/final void split(HashMap<K,V> map, Node<K,V>[] tab, int index, int bit) {TreeNode<K,V> b = this;// Relink into lo and hi lists, preserving orderTreeNode<K,V> loHead = null, loTail = null;TreeNode<K,V> hiHead = null, hiTail = null;int lc = 0, hc = 0;for (TreeNode<K,V> e = b, next; e != null; e = next) {next = (TreeNode<K,V>)e.next;e.next = null;if ((e.hash & bit) == 0) {if ((e.prev = loTail) == null)loHead = e;elseloTail.next = e;loTail = e;++lc;}else {if ((e.prev = hiTail) == null)hiHead = e;elsehiTail.next = e;hiTail = e;++hc;}}if (loHead != null) {if (lc <= UNTREEIFY_THRESHOLD)tab[index] = loHead.untreeify(map);else {tab[index] = loHead;if (hiHead != null) // (else is already treeified)loHead.treeify(tab);}}if (hiHead != null) {if (hc <= UNTREEIFY_THRESHOLD)tab[index + bit] = hiHead.untreeify(map);else {tab[index + bit] = hiHead;if (loHead != null)hiHead.treeify(tab);}}}/* ------------------------------------------------------------ */// Red-black tree methods, all adapted from CLR//左旋static <K,V> TreeNode<K,V> rotateLeft(TreeNode<K,V> root,TreeNode<K,V> p) {TreeNode<K,V> r, pp, rl;if (p != null && (r = p.right) != null) {if ((rl = p.right = r.left) != null)rl.parent = p;if ((pp = r.parent = p.parent) == null)(root = r).red = false;else if (pp.left == p)pp.left = r;elsepp.right = r;r.left = p;p.parent = r;}return root;}//右旋static <K,V> TreeNode<K,V> rotateRight(TreeNode<K,V> root,TreeNode<K,V> p) {TreeNode<K,V> l, pp, lr;if (p != null && (l = p.left) != null) {if ((lr = p.left = l.right) != null)lr.parent = p;if ((pp = l.parent = p.parent) == null)(root = l).red = false;else if (pp.right == p)pp.right = l;elsepp.left = l;l.right = p;p.parent = l;}return root;}//平衡插入static <K,V> TreeNode<K,V> balanceInsertion(TreeNode<K,V> root,TreeNode<K,V> x) {x.red = true;for (TreeNode<K,V> xp, xpp, xppl, xppr;;) {if ((xp = x.parent) == null) {x.red = false;return x;}else if (!xp.red || (xpp = xp.parent) == null)return root;if (xp == (xppl = xpp.left)) {if ((xppr = xpp.right) != null && xppr.red) {xppr.red = false;xp.red = false;xpp.red = true;x = xpp;}else {if (x == xp.right) {root = rotateLeft(root, x = xp);xpp = (xp = x.parent) == null ? null : xp.parent;}if (xp != null) {xp.red = false;if (xpp != null) {xpp.red = true;root = rotateRight(root, xpp);}}}}else {if (xppl != null && xppl.red) {xppl.red = false;xp.red = false;xpp.red = true;x = xpp;}else {if (x == xp.left) {root = rotateRight(root, x = xp);xpp = (xp = x.parent) == null ? null : xp.parent;}if (xp != null) {xp.red = false;if (xpp != null) {xpp.red = true;root = rotateLeft(root, xpp);}}}}}}//平衡，删除static <K,V> TreeNode<K,V> balanceDeletion(TreeNode<K,V> root,TreeNode<K,V> x) {for (TreeNode<K,V> xp, xpl, xpr;;)  {if (x == null || x == root)return root;else if ((xp = x.parent) == null) {x.red = false;return x;}else if (x.red) {x.red = false;return root;}else if ((xpl = xp.left) == x) {if ((xpr = xp.right) != null && xpr.red) {xpr.red = false;xp.red = true;root = rotateLeft(root, xp);xpr = (xp = x.parent) == null ? null : xp.right;}if (xpr == null)x = xp;else {TreeNode<K,V> sl = xpr.left, sr = xpr.right;if ((sr == null || !sr.red) &&(sl == null || !sl.red)) {xpr.red = true;x = xp;}else {if (sr == null || !sr.red) {if (sl != null)sl.red = false;xpr.red = true;root = rotateRight(root, xpr);xpr = (xp = x.parent) == null ?null : xp.right;}if (xpr != null) {xpr.red = (xp == null) ? false : xp.red;if ((sr = xpr.right) != null)sr.red = false;}if (xp != null) {xp.red = false;root = rotateLeft(root, xp);}x = root;}}}else { // symmetricif (xpl != null && xpl.red) {xpl.red = false;xp.red = true;root = rotateRight(root, xp);xpl = (xp = x.parent) == null ? null : xp.left;}if (xpl == null)x = xp;else {TreeNode<K,V> sl = xpl.left, sr = xpl.right;if ((sl == null || !sl.red) &&(sr == null || !sr.red)) {xpl.red = true;x = xp;}else {if (sl == null || !sl.red) {if (sr != null)sr.red = false;xpl.red = true;root = rotateLeft(root, xpl);xpl = (xp = x.parent) == null ?null : xp.left;}if (xpl != null) {xpl.red = (xp == null) ? false : xp.red;if ((sl = xpl.left) != null)sl.red = false;}if (xp != null) {xp.red = false;root = rotateRight(root, xp);}x = root;}}}}}/*** Recursive invariant check** 递归的恒等校验*/static <K,V> boolean checkInvariants(TreeNode<K,V> t) {TreeNode<K,V> tp = t.parent, tl = t.left, tr = t.right,tb = t.prev, tn = (TreeNode<K,V>)t.next;if (tb != null && tb.next != t)return false;if (tn != null && tn.prev != t)return false;if (tp != null && t != tp.left && t != tp.right)return false;if (tl != null && (tl.parent != t || tl.hash > t.hash))return false;if (tr != null && (tr.parent != t || tr.hash < t.hash))return false;if (t.red && tl != null && tl.red && tr != null && tr.red)return false;if (tl != null && !checkInvariants(tl))return false;if (tr != null && !checkInvariants(tr))return false;return true;}}}

7.2 存储说明

JDK1.7中HashMap使用一个table数组来存储数据，用key的hashcode取模来决定key会被放到数组里的位置，如果hashcode相同，或者hashcode取模后的结果相同，那么这些key会被定位到Entry数组的同一个格子里，这些key会形成一个链表，在极端情况下比如说所有key的hashcode都相同，将会导致这个链表会很长，那么put/get操作需要遍历整个链表，那么最差情况下时间复杂度变为O（n）。

在这里插入图片描述

针对JDK1.7中的这个性能缺陷，JDK1.8中的table数组中可能存放的是链表结构，也可能存放的是红黑树结构，如果链表中节点数量不超过8个则使用链表存储，超过8个会调用treeifyBin函数，将链表转换为红黑树 。那么即使所有key的hashcode完全相同，由于红黑树的特点，查找某个特定元素，也只需要O（logn）的开销。

7.3 关注点

hashmap 初始化容量是2的4次方，最大容量是2的30次方
默认负载因子是0.75 ，可以在创建的时候指定，但是不建议
在链表节点大于8时，会转为红黑树，在节点小于6时会转化为链表
hashmap 不是线程安全的，里面维护了modcount ，多线程修改时，会报错
通过方法名也可以看出来KeySet 和 EntrySet 返回的是set集合，Values返回的是Collection集合
并行迭代器HashMapSpliterator是java8的新特性，提高并行处理效率
hashmap实现了红黑树相关操作，比如左旋，右旋，平衡插入等

以上，本人菜鸟一枚，如有错误，请不吝指正

06. 数据结构之散列表

前言