深度剖析HashMap的数据存储实现原理（看完必懂篇）
具体的原理分析可以参考一下两篇文章，有透彻的分析！本文在此基础上加入个人理解和完善部分纰漏！！！
参考资料：
1. https://www.jianshu.com/p/17177c12f849 [JDK8中的HashMap实现原理及源码分析]
2. https://tech.meituan.com/java-hashmap.html [Java 8系列之重新认识HashMap]
1、关键字段：
/**
* The default initial capacity - MUST be a power of two.
*/
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // 2^4
/**
* 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.
*/
static final int MAXIMUM_CAPACITY = 1 << 30; // 2^30
/**
* The load factor used when none specified in constructor.
*/
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.
*
* 一个桶的树化阈值
* 当桶中元素个数超过这个值时，需要使用红黑树节点替换链表节点
* 这个值必须为 8，要不然频繁转换效率也不高
*/
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.
*
* 一个树的链表还原阈值
* 当扩容时，桶中元素个数小于这个值，就会把树形的桶元素 还原（切分）为链表结构
* 这个值应该比上面那个小，至少为 6，避免频繁转换
*/
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.
*
* 哈希表的最小树形化容量
* 当哈希表中的容量大于这个值时，表中的桶才能进行树形化
* 否则桶内元素太多时会扩容，而不是树形化
* 为了避免进行扩容、树形化选择的冲突，这个值不能小于 4 * TREEIFY_THRESHOLD
*/
static final int MIN_TREEIFY_CAPACITY = 64;
/* ---------------- 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.)
*
* 为了更好表示本文称之为桶数组
*/
transient Node<K,V>[] table;
/**
* Holds cached entrySet(). Note that AbstractMap fields are used
* for keySet() and values().
*/
transient Set<Map.Entry<K,V>> entrySet;
/**
* The number of key-value mappings contained in this map.
*/
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).
*/
transient int modCount;
/**
* The next size value at which to resize (capacity * load factor).
*
* @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.
*
* @serial
*/
final float loadFactor;
/**
* Constructs an empty <tt>HashMap</tt> with the specified initial
* capacity and load factor.
*
* @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).
*
* @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).
*/
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>.
*
* @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);
}
2、首先针对很多文章中的纰漏语句：如果一个桶中链表内的元素个数超过 TREEIFY_THRESHOLD(默认是8)，就使用红黑树来替换链表。
// 插入图片1张
图片中红色标记的地方个人理解是不够严谨的！！！数据插入HashMap的时候，如果当前桶中的元素个数 > TREEIFY_THRESHOLD时，则会进行桶的树形化处理（见代码片段1：treeifyBin()）。
注意这里只是进行桶的树形化处理，并不是把桶（如果是链表结构）直接转换为红黑树，这里面是有条件的！！！具体规则如下：
条件1. 如果当前桶数组为null或者桶数组的长度 < MIN_TREEIFY_CAPACITY，则进行扩容处理（见代码片段2：resize()）；
条件2. 当不满足条件1的时候则将桶中链表内的元素转换成红黑树！！！稍后再详细讨论红黑树。
3、再来分析下HashMap扩容机制的实现：
概念：
1. 扩容(resize)就是重新计算容量。当向HashMap对象里不停的添加元素，而HashMap对象内部的桶数组无法装载更多的元素时，HashMap对象就需要扩大桶数组的长度，以便能装入更多的元素。
2. capacity 就是数组的长度/大小，loadFactor 是这个数组填满程度的最大比比例。
3. size表示当前HashMap中已经储存的Node<key,value>的数量，包括桶数组和链表 / 红黑树中的的Node<key,value>。
4. threshold表示扩容的临界值，如果size大于这个值，则必需调用resize()方法进行扩容。
5. 在jdk1.7及以前，threshold = capacity * loadFactor，其中 capacity 为桶数组的长度。
这里需要说明一点，默认负载因子0.75是是对空间和时间(纵向横向)效率的一个平衡选择，建议大家不要修改。
jdk1.8对threshold值进行了改进，通过一系列位移操作算法最后得到一个power of two size的值，见代码片段4。
扩容过程：
1. 使用new Hashap<>()时，新桶数组初始容量设置为默认值DEFAULT_INITIAL_CAPACITY，默认容量下的阈值为DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY。
2. 使用new Hashap<>(int initialCapacity)或new HashMap(int initialCapacity, float loadFactor)时，newCap, newThr均重新计算。
3. 如果使用过程中HashMap中的数据过多，原始容量不够使用，那么需要扩容。扩容是以2^1为单位扩容的， newCap = oldCap << 1和newThr = oldThr << 1。
4. 如果原来的桶数组长度大于最大值MAXIMUM_CAPACITY时，扩容临界值提高到正无穷（Integer.MAX_VALUE），返回原来的数组，也就是系统已经管不了了，随便你怎么玩吧。
正常扩容之后需要将老的桶数组数据重新放到新的桶数组中，同时对每个桶上的链表进行了重排，再介绍重排之前先来看看代码片段5里面的hash()计算：
首先将得到key对应的哈希值：h = key.hashCode()，然后通过hashCode()的高16位异或低16位计算得到最终的key.hash值（(h = key.hashCode()) ^ (h >>> 16)）。
1. 取key的hashcode值：
① Object类的hashCode
返回对象的经过处理后的内存地址，由于每个对象的内存地址都不一样，所以哈希码也不一样。这个是native方法，取决于JVM的内部设计，一般是某种C地址的偏移。
② String类的hashCode
根据String类包含的字符串的内容，根据一种特殊算法返回哈希码，只要字符串的内容相同，返回的哈希码也相同。
③ Integer等包装类
返回的哈希码就是Integer对象里所包含的那个整数的数值，例如Integer i1=new Integer(100)，i1.hashCode的值就是100。
由此可见，2个一样大小的Integer对象，返回的哈希码也一样。
④ int，char这样的基础类
它们不需要hashCode，如果需要存储时，将进行自动装箱操作，计算方法包装类。
2. hashCode()的高16位异或低16位
在JDK1.8的实现中，优化了高位运算的算法，通过hashCode()的高16位异或低16位实现的：key.hash = (h = k.hashCode()) ^ (h >>> 16)，
主要是从速度、功效、质量来考虑的，这么做可以在数组table的length比较小的时候，也能保证考虑到高低Bit都参与到Hash的计算中，同时不会有太大的开销。
3. key.hash & (n - 1) 取模运算
这个n我们说过是table的长度，那么n-1就是table数组元素应有的下表。这个方法非常巧妙，它通过 key.hash & (table.length - 1) 来得到该对象的保存位，
而HashMap底层数组的长度总是2的n次方，这是HashMap在速度上的优化。当length总是2的n次方时，key.hash & (table.length - 1) 运算等价于对length取模，也就是key.hash % length，但是&比%具有更高的效率。
链表重排：
1. 如果原桶上只有一个节点，并且该节点不是红黑树节点，那么直接放到新桶原索引key.hash & (table.length - 1)下；
2. 如果原桶上的节点是红黑树节点，那么则对该树进行分割split()；
3. 如果原桶上的节点是一个链表，则进行链表重排算法:
由于桶数组的容量是按2次幂的扩展(指容量扩为原来2倍)，所以，元素的位置要么是在“原索引”，要么是在“原索引 + oldCap”的位置。
所以，只需要看看原来key.hash值新增的那个bit是1还是0就好了，是0的话索引没变，是1的话索引变成“原索引 + oldCap”。
// 插入图片2张
4、HashMap的数据存储实现原理
流程：
1. 根据key计算得到key.hash = (h = k.hashCode()) ^ (h >>> 16)；
2. 根据key.hash计算得到桶数组的索引index = key.hash & (table.length - 1)，这样就找到该key的存放位置了：
① 如果该位置没有数据，用该数据新生成一个节点保存新数据，返回null；
② 如果该位置有数据是一个红黑树，那么执行相应的插入 / 更新操作，稍后再详细讨论红黑树；
③ 如果该位置有数据是一个链表，分两种情况一是该链表没有这个节点，另一个是该链表上有这个节点，注意这里判断的依据是key.hash是否一样：
如果该链表没有这个节点，那么采用尾插法新增节点保存新数据，返回null；
如果该链表已经有这个节点了，那么找到該节点并更新新数据，返回老数据。
注意：
HashMap的put会返回key的上一次保存的数据，比如：
HashMap<String, String> map = new HashMap<String, String>();
System.out.println(map.put("a", "A")); // 打印null
System.out.println(map.put("a", "AA")); // 打印A
System.out.println(map.put("a", "AB")); // 打印AA
5、红黑树
上面的讨论中对于红黑树并没有深入分析，HashMap的数据存储中主要有两种场景用到红黑树的操作：
1. 当满足一定条件（条件2，见上文）时，单链表内的数据会转换为红黑树存储（见代码片段2：treeifyBin()）。
2. 当HashMap桶结构由链表转换为红黑树后，再往里put数据将变成往红黑树插入 / 更新数据，这和链表又不太一样了。
下面进行逐一详细分析：
未完待续。。。。。。
源码片段1：
/**
* 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.
*
* @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 1st
treeifyBin(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 key
V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
e.value = value;
afterNodeAccess(e);
return oldValue;
}
}
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
源码片段2：
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);
}
}
源码片段3：
/**
* 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.
*
* @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 threshold
newCap = oldThr;
else { // zero initial threshold signifies using defaults
newCap = 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 order
Node<K,V> loHead = null, loTail = null;
Node<K,V> hiHead = null, hiTail = null;
Node<K,V> next;
do {
next = e.next;
// 原索引
if ((e.hash & oldCap) == 0) {
if (loTail == null)
loHead = e;
else
loTail.next = e;
loTail = e;
}
// 原索引 + oldCap
else {
if (hiTail == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
}
} while ((e = next) != null);
// 原索引放到桶数组里
if (loTail != null) {
loTail.next = null;
newTab[j] = loHead;
}
// 原索引 + oldCap放到桶数组里
if (hiTail != null) {
hiTail.next = null;
newTab[j + oldCap] = hiHead;
}
}
}
}
}
return newTab;
}
源码片段4：
/**
* Returns a power of two size for the given target capacity.
*/
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;
}
源码片段5：
/**
* 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.
*/
static final int hash(Object key) {
int h;
return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}
谢谢！欢迎批评指正！！！
