1.HashMap简介
HashMap基于哈希表的Map接口实现。是以key-value存储形式存在。线程不安全。key和value都可以为null，无序
JDK1.8之前由数组+链表组成，数组是HashMap主体，链表则主要是为了解决哈希冲突（两个对象调用的hashCode方法计算的哈希码值一致导致计算的数组索引值相同）而存在的（“拉链法”解决冲突），JDK1.8之后，当链表长度大于阈值（或者红黑树的边界值，默认为8）并且当前数组的长度大于64时，此时此索引位置上的所有数据改为使用红黑树存储。加入红黑树可以使查询效率更高。
补充：为了提高效率，将链表转换为红黑树前会判断，即使阈值大于8，但是数组长度小于64，此时并不会将链表变为红黑树，而是选择进行数组扩容。
2.HashMap集合底层的数据结构
JDK1.8之前，数组+链表
JDK1.8之后，数组+链表+红黑树
问题1：
1、哈希表底层采用何种算法计算hash值？还有哪些算法可以计算出hash值？
底层采用的key的hashCode方法的值结合数组长度进行无符号右移（>>>)、按位异或(^)计算hash值，按位与(&)计算出索引
static final int hash(Object key) { int h; return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16); } //其中n为数组长度 (n - 1) & hash
还可以采用：平方取中法，取余数、伪随机数法
2.当两个对象的hashCode相等时会怎么样？
会产生哈希碰撞，通过调用equals方法比较key的内容是否相同，相同则替换旧的value，不然就连接到链表后面，链表长度超过阈值8转为红黑树。
3.在不断的添加数据的过程中，会涉及到扩容问题，当超出临界值时扩容，默认的扩容方式为扩充为原来的2倍，并将原有的数据复制过来。
4.1.8之后为什么引入红黑树，这样不是使结构更加复杂了吗？为什么阈值大于8转化成红黑树？
说明：
- size表示HashMap中K-V的实时数量，不是数组的长度
- threshold（临界值）=capacity（容量）*loadFactor（加载因子）。这个值是当前已占用数组长度的最大值。size超过这个临界值就重新resize（扩容），扩容后的HashMap容量是之前容量的两倍
3.HashMap继承关系
public class HashMap<K,V> extends AbstractMap<K,V> implements Map<K,V>, Cloneable, Serializable { private static final long serialVersionUID = 362498820763181265L;
public abstract class AbstractMap<K,V> implements Map<K,V> { /** * Sole constructor. (For invocation by subclass constructors, typically * implicit.) */ protected AbstractMap() { }
4.HashMap集合类的成员
4.1成员变量
1、序列化版本号
private static final long serialVersionUID = 362498820763181265L;
2、集合的初始化容量（必须是2的n次幂）
/** * The default initial capacity - MUST be a power of two. */ static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16
问题：为什么大小必须是2的n次幂？
存储高效，尽量减少碰撞，在（length-1)&hash求索引的时候更均匀。
问题：如果传入的容量默认不是2的幂，假如是10，会怎么样呢？
底层通过一些列的右移和或运算，把给定值变成比它大的最小的2的次数值，比如给10变成16，给17变成32。
//对传入容量进行右移位运算后进行或运算 //一共进行5次或运算，可以将当前数字中二进制最高位1的右边全部变成1，最后+1后返回 static final int tableSizeFor(int cap) { //这里-1的目的是使得找到的目标值大于或等于原值 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; }
完整例子：
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); }
3、默认的负载因子
static final float DEFAULT_LOAD_FACTOR = 0.75f;
4、集合最大容量
static final int MAXIMUM_CAPACITY = 1 << 30;
5、链表转红黑树的阈值
static final int TREEIFY_THRESHOLD = 8;
问题：为什么是8？
TreeNode占用空间是普通Node的两倍，空间和时间的权衡，同时如果为8，log（8）=3小于链表的平均8/2=4
/* 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)* /
还有一种解释方式：
6、红黑树转链表的阈值
static final int UNTREEIFY_THRESHOLD = 6;
7、链表转红黑树时数组的大小的阈值，即数组大小大于这个数字时，链表长度大于8才会转为红黑树
static final int MIN_TREEIFY_CAPACITY = 64;
8、table用来初始化数组（大小是2的n次幂）
transient Node<K,V>[] table;
9、用来存放缓存（遍历的时候使用）
transient Set<Map.Entry<K,V>> entrySet;
10、HashMap中存放元素的个数（重点）
transient int size;
11、记录HashMap的修改次数
transient int modCount;
12、临界值（如果存放元素大小大于该值，则进行扩容）
int threshold;
13、哈希表的加载因子（重点）
final float loadFactor
说明：
loadFactor加载因子，可以表示HashMap的舒米程度，影响hash操作到同一个数组位置的概率，默认0.75，不建议修改
4.2构造方法
1、构造一个空的HashMap，默认初始容量（16）和默认负载因子（0.75）
public HashMap() { this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted }
2、构造一个具有指定的出是容来那个和默认负载因子（0.75）的HashMap
public HashMap(int initialCapacity) { this(initialCapacity, DEFAULT_LOAD_FACTOR); }
3、构造一个具有指定初始容量和负载因子的HashMap
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; //根据初始值返回一个2的n次数字，赋给阈值，在put方法中会对此值进行重新运算 this.threshold = tableSizeFor(initialCapacity); }
4、包含另一个Map的构造函数
public HashMap(Map<? extends K, ? extends V> m) { this.loadFactor = DEFAULT_LOAD_FACTOR; putMapEntries(m, false); } final void putMapEntries(Map<? extends K, ? extends V> m, boolean evict) { int s = m.size(); if (s > 0) { if (table == null) { // pre-size //+1的目的是获取更大的容量，减少数组的扩容次数 float 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); } } }
4.3成员方法
增加方法（put）
1）先判断数组是否未初始化，如果没有初始化，则进行一次初始化操作（扩容），同时将数组大小赋给n
2）找到具体的桶，并判断此位置是否有元素，如果没有元素，则创建一个Node直接插入
3）如果出现冲突
1）如果为红黑树节点，调用红黑树方法插入数据
2）如果为普通节点，插入链表末尾，并且长度达到临界值时，将链表转为红黑树
4）如果桶中存在重复的键，将该键替换新值value
5）size大于阈值threshold，进行扩容
/** * 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); }
/** * 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); }
final V putVal(int hash, K key, V value, boolean onlyIfAbsent, boolean evict) { //初始化一个tab以及一个Node Node<K,V>[] tab; Node<K,V> p; int n, i; //此处才进行tab的初始化。tab为空或者数组大小为0，对数组进行初始化操作，并将数组大小赋给n if ((tab = table) == null || (n = tab.length) == 0) n = (tab = resize()).length; //通过hash与数组大小-1的与运算计算出所在桶位置的元素p，如果p为null，创建一个 //新节点直接插入，如果出现冲突，进入分支判断 if ((p = tab[i = (n - 1) & hash]) == null) tab[i] = newNode(hash, key, value, null); else { Node<K,V> e; K k; //如果插入的元素的hash值与p相等以及p的key与要插入的key相同，将p（原位置节点）赋给e； if (p.hash == hash && ((k = p.key) == key || (key != null && key.equals(k)))) e = p; //p为红黑树节点，则调用putTreeVal插入数据，如果为覆盖，则e为旧节点 else if (p instanceof TreeNode) e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value); else { //链表节点 for (int binCount = 0; ; ++binCount) { //找到链表的尾结点，此时e==null，p为链表的最后一个节点 if ((e = p.next) == null) { //在末尾处创建一个节点赋给p.next，此时e仍为null p.next = newNode(hash, key, value, null); //如果找到当前节点时已经循环了7次，即该链表在插入元素大小为8,将链表转为红黑树 if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st //链表转红黑树，传入tab数组以及该键的hash值（可计算出数组的具体索引） treeifyBin(tab, hash); break; } //如果找到了具有相同key的元素，也停止寻找 if (e.hash == hash && ((k = e.key) == key || (key != null && key.equals(k)))) break; p = e; } } //若此时e不为null，说明找到了一个具有相同key的值 if (e != null) { // existing mapping for key //保存一下旧节点的value值 V oldValue = e.value; //是否要改变之前存在值（默认为false）或者之前存在的值为null，将value进行一个覆盖 if (!onlyIfAbsent || oldValue == null) e.value = value; //回调相关方法，HashMap该方法默认实现为空，LinkedHashMap在此会进行一些处理 afterNodeAccess(e); //返回旧值，不会进行下面的修改次数以及元素个数增加操作 return oldValue; } } //记录下map的修改次数 ++modCount; //如果元素个数大于了阈值，进行扩容操作 if (++size > threshold) resize(); afterNodeInsertion(evict); return null; }
链表转红黑树（treeifyBin）
//tab为数组名，hash为hash值 if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st treeifyBin(tab, hash); break;
/** * Replaces all linked nodes in bin at index for given hash unless * table is too small, in which case resizes instead. */ final void treeifyBin(Node<K,V>[] tab, int hash) { int n, index; Node<K,V> e; //如果tab数组为空或者tab数组大小小于链表转红黑树的最小要求值，则进行扩容操作 if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY) resize(); //拿到当前要转换的桶的起始节点 else if ((e = tab[index = (n - 1) & hash]) != null) { //初始化头结点和尾结点 TreeNode<K,V> hd = null, tl = null; //循环将链表结点转化为红黑树结点 do { //利用链表结点来创建一个树结点 TreeNode<K,V> p = replacementTreeNode(e, null); //如果tl为null，表示红黑树还没有结点，将p赋给头结点 if (tl == null) hd = p; //将p节点与尾结点相连 else { p.prev = tl; tl.next = p; } //更新尾节点 tl = p; } while ((e = e.next) != null); if ((tab[index] = hd) != null) //将各个树结点转化为红黑树 hd.treeify(tab); } }
扩容方法（resize）
数组初始化以及数组元素个数大于阈值时进行扩容操作，一部分索引会增加原数组长度大小的长度（用到了高位1），一部分仍保持原索引（高位为0）
举个例子：
/** * 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() { //将旧数组进行保存 Node<K,V>[] oldTab = table; //保存旧数组的长度 int oldCap = (oldTab == null) ? 0 : oldTab.length; //保存旧数组的阈值 int oldThr = threshold; //定义新的长度和阈值 int newCap, newThr = 0; if (oldCap > 0) { //数组已经达到最大容量，直接返回 if (oldCap >= MAXIMUM_CAPACITY) { threshold = Integer.MAX_VALUE; return oldTab; } //新数组长度为旧数组长度*2 else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY && oldCap >= DEFAULT_INITIAL_CAPACITY) //阈值同样*2 newThr = oldThr << 1; // double threshold } else if (oldThr > 0) // initial capacity was placed in threshold newCap = oldThr; else { // zero initial threshold signifies using defaults，默认的初始化操作 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); } //将新的阈值赋给成员变量 threshold = newThr; //创建一个新的数组，大小为newCap @SuppressWarnings({"rawtypes","unchecked"}) Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap]; table = newTab; //将旧数组元素放入到新数组中 if (oldTab != null) { for (int j = 0; j < oldCap; ++j) { Node<K,V> e; if ((e = oldTab[j]) != null) { oldTab[j] = null; //如果当前索引只有一个节点 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; if ((e.hash & oldCap) == 0) { if (loTail == null) loHead = e; else loTail.next = e; loTail = e; } else { if (hiTail == null) hiHead = e; else hiTail.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;
删除方法（remove）
/** * Removes the mapping for the specified key from this map if present. * * @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 * * @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; //初始节点为要找的节点，赋值给node if (p.hash == hash && ((k = p.key) == key || (key != null && key.equals(k)))) node = p; //向下找节点 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); } } //删除操作 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; else p.next = node.next; ++modCount; --size; afterNodeRemoval(node); return node; } } return null; }
查找方法（get）
/** * Returns the value to which the specified key is mapped, * or {@code null} if this map contains no mapping for the key. * * <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. * * @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 * * @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; }
/** * Calls find for root node. */ final TreeNode<K,V> getTreeNode(int h, Object k) { return ((parent != null) ? root() : this).find(h, k, null); }
/** * Finds the node starting at root p with the given hash and key. * The kc argument caches comparableClassFor(key) upon first use * comparing keys. */ 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; else p = pl; } while (p != null); return null; }
遍历HashMap集合的几种方式
1、分别遍历Key和Values
for(String key:map.keySet()){ System.out.println(key); } for(Object value:map.values()){ System.out.println(value); }
2、迭代器（增强for循环）
Iterator<Map.Entry<String, Integer>> iterator = map.entrySet().iterator(); while(iterator.hasNext()){ Map.Entry<String, Integer> next = iterator.next(); System.out.println(next.getKey()+":"+next.getValue()); }
3、通过get方式（不建议使用）
Set<String> keySet=map.keySet(); for(String str:keySet){ System.out.println(str+"==="+map.get(str)) }
4、jdk8以后采用Map接口的默认方法forEach
map.forEach((k,v)->{ System.out.println(k+":"+v); });
HashMap的初始化设计
为了尽可能的避免hashmap的扩容操作，提高性能，如果明确知道存储的数据量大小I时，初始化值如下
Map<String,String> map=new HashMap<>(initialCapacity); initialCapacity=(需要存储的元素个数/负载因子)+1
