HashMap分析

目录

• 1.HashMap简介
• 2.HashMap集合底层的数据结构
• 3.HashMap继承关系
• 4.HashMap集合类的成员
  • 4.1成员变量
  • 4.2构造方法
  • 4.3成员方法

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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。

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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

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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() {
    }

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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