聊聊java中的哪些Map：（一）HashMap(1.8)源码分析

- [1.类的结构及重要属性](https://cloud.tencent.com/developer)
    - [1.1 类的基本结构](https://cloud.tencent.com/developer)
    - [1.2 成员变量及常量](https://cloud.tencent.com/developer)
        - [1.2.1 常量](https://cloud.tencent.com/developer)
        - [1.2.2 成员变量](https://cloud.tencent.com/developer)
            - [1.2.2.1 table](https://cloud.tencent.com/developer)
            - [1.2.2.2 entrySet](https://cloud.tencent.com/developer)
            - [1.2.2.3 size](https://cloud.tencent.com/developer)
            - [1.2.2.4 modCount](https://cloud.tencent.com/developer)
            - [1.2.2.5 threshold](https://cloud.tencent.com/developer)
            - [1.2.2.6 loadFactor](https://cloud.tencent.com/developer)
- [1.3 重要的内部类](https://cloud.tencent.com/developer)
    - [1.3.1 Node](https://cloud.tencent.com/developer)
    - [1.3.2 TreeNode](https://cloud.tencent.com/developer)
    - [1.3.3 视图型内部类KeySet、Values、EntrySet](https://cloud.tencent.com/developer)
        - [1.3.3.1 KeySet](https://cloud.tencent.com/developer)
        - [1.3.3.2 Values](https://cloud.tencent.com/developer)
        - [1.3.3.2 EntrySet](https://cloud.tencent.com/developer)
    - [1.3.4 迭代器HashIterator及并行迭代器HashMapSpliterator](https://cloud.tencent.com/developer)
- [2.基本原理](https://cloud.tencent.com/developer)
    - [2.1 HashMap的基本结构](https://cloud.tencent.com/developer)
    - [2.2 HashMap中的位运算操作](https://cloud.tencent.com/developer)
        - [2.2.1 扩容](https://cloud.tencent.com/developer)
        - [2.2.2 bucket计算](https://cloud.tencent.com/developer)
        - [2.2.3 split](https://cloud.tencent.com/developer)
        - [2.2.4 为什么HashMap的DEFAULT\_INITIAL\_CAPACITY为16](https://cloud.tencent.com/developer)
- [3.构造函数](https://cloud.tencent.com/developer)
    - [3.1 HashMap()](https://cloud.tencent.com/developer)
    - [3.2 HashMap(int initialCapacity)](https://cloud.tencent.com/developer)
    - [3.3 HashMap(int initialCapacity, float loadFactor)](https://cloud.tencent.com/developer)
    - [3.4 HashMap(Map<? extends K, ? extends V> m)](https://cloud.tencent.com/developer)
- [4.重要方法](https://cloud.tencent.com/developer)
    - [4.1 tableSizeFor](https://cloud.tencent.com/developer)
    - [4.2 get](https://cloud.tencent.com/developer)
    - [4.3 hash](https://cloud.tencent.com/developer)
    - [4.4 put](https://cloud.tencent.com/developer)
    - [4.5 resize](https://cloud.tencent.com/developer)
    - [4.6 treeifyBin](https://cloud.tencent.com/developer)
    - [4.7 remove](https://cloud.tencent.com/developer)
    - [4.8 clear](https://cloud.tencent.com/developer)
    - [4.8 containsValue](https://cloud.tencent.com/developer)
- [5.总结](https://cloud.tencent.com/developer)

/**
 * 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.
 *
 * <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.
 *
 * <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.
 *
 * <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.
 *
 * <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.
 *
 * <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.
 *
 * 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>
 *
 * <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.
 *
 * <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>
 *
 * <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 {
    
    }

Map m = Collections.synchronizedMap(new HashMap(...));

 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.
     *
     * 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.)
     *
     * 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
     *
     * 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()).
     *
     * 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.
     *
     * 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.
     *
     * 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.)
     *
     * The concurrent-programming-like SSA-based coding style helps
     * avoid aliasing errors amid all of the twisty pointer operations.
     */

    /**
     * The default initial capacity - MUST be a power of two.
     */
    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.
     */
    static final int MAXIMUM_CAPACITY = 1 << 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.
     */
    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.
     */
    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.
     */
    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.)
 */
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;

/**
 * Basic hash bin node, used for most entries.  (See below for
 * TreeNode subclass, and in LinkedHashMap for its Entry subclass.)
 */
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;
    }
}

/**
 * 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.
 *
 * @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();
        }
    }
}

 action.accept(e.key);

/**
 * 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.
 *
 * @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();
        }
    }
}

 action.accept(e.value);

/**
 * 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.
 *
 * @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();
        }
    }
}

action.accept(e);

public static void main(String[] args) {
	HashMap map = new HashMap();
	map.put(new CustomerKey(1),1);
	map.put(new CustomerKey(17),17);
	map.put(new CustomerKey(33),33);
	map.put(new CustomerKey(49),49);
	map.put(new CustomerKey(65),65);
	map.put(new CustomerKey(81),81);
	map.put(new CustomerKey(97),97);
	map.put(new CustomerKey(113),113);
	map.put(new CustomerKey(129),129);
	map.size();



}

private static class CustomerKey{
	int key;

	public CustomerKey(int key) {
		this.key = key;
	}

	@Override
	public int hashCode() {
		return 1;
	}


}

newCap = oldCap << 1

//当b为2的n次幂的时候
a % b = a & ( b - 1 ) 

first = tab[(n - 1) & hash]

if ((e.hash & bit) == 0)

/**
 * The default initial capacity - MUST be a power of two.
 */
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16

/**
 * 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 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 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 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);
}

/**
 * Implements Map.putAll and Map constructor.
 *
 * @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();
    //当插入的map不为空的时候
    if (s > 0) {
       //如果table为空
        if (table == null) { // pre-size
            //计算负载因子
            float ft = ((float)s / loadFactor) + 1.0F;
            int t = ((ft < (float)MAXIMUM_CAPACITY) ?
                     (int)ft : MAXIMUM_CAPACITY);
            if (t > threshold)
                threshold = tableSizeFor(t);
            //计算出阈值。
        }
        //如果插入的size大于阈值则扩容。
        else if (s > threshold)
            resize();
        //遍历插入
        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);
        }
    }
}

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;
}

/**
 * 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 &&
       //通过位运算计算bucket位置，这一点非常重要
        (first = tab[(n - 1) & hash]) != null) {
        if (first.hash == hash && // always check first node
            //如果hash相同，则判断key是否相等
            ((k = first.key) == key || (key != null && key.equals(k))))
            return first;
        //如果不等则遍历链表或者红黑树
        if ((e = first.next) != null) {
            //首先判断是否为红黑树，红黑树则用红黑树的检索方法
            if (first instanceof TreeNode)
                return ((TreeNode<K,V>)first).getTreeNode(hash, key);
            //遍历链表
            do {
                if (e.hash == hash &&
                    ((k = e.key) == key || (key != null && key.equals(k))))
                    return e;
            } while ((e = e.next) != null);
        }
    }
    return null;
}

/**
 * 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);
}

/**
 * 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) {
   //此处调用的是经过高位混淆的hash方法
    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)
       //如果table为null则直接扩容，这个地方可以通过new一个大小为0的HashMap验证
        n = (tab = resize()).length;
     //使用&计算bucket的索引，如果为空则当前创建的节点就是根节点
    if ((p = tab[i = (n - 1) & hash]) == null)
        tab[i] = newNode(hash, key, value, null);
    else {
       //反之就要进行链表或者红黑树处理
        Node<K,V> e; K k;
        //判断是否相等
        if (p.hash == hash &&
            ((k = p.key) == key || (key != null && key.equals(k))))
            e = p;
        //如果为红黑树则按红黑树的方式处理
        else if (p instanceof TreeNode)
            e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
        else {
        //反之则按链表的方式处理
            for (int binCount = 0; ; ++binCount) {
                if ((e = p.next) == null) {
                    p.next = newNode(hash, key, value, null);
                    //判断是否需要扩容
                    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;
            }
        }
        // 上边已经检查完map中是否存在对应key的Node节点，不存在的新创建节点，这里处理下存在对应key的节点数据
        if (e != null) { // existing mapping for key
            V oldValue = e.value;
            if (!onlyIfAbsent || oldValue == null)
                e.value = value;
            // 子类实现方法的话可以进行对应的后置操作
            afterNodeAccess(e);
            return oldValue;
        }
    }
    ++modCount;
    if (++size > threshold)
        resize();
    //插入之后的平衡操作
    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.
 *
 * @return the table
 */
final Node<K,V>[] resize() {
    Node<K,V>[] oldTab = table;
    //oldCap为table的size
    int oldCap = (oldTab == null) ? 0 : oldTab.length;
    int oldThr = threshold;
    int newCap, newThr = 0;
    //判断当oldCap大于0
    if (oldCap > 0) {
       //如果oldCap为最大值，则设置threshold也为最大值。
        if (oldCap >= MAXIMUM_CAPACITY) {
            threshold = Integer.MAX_VALUE;
            return oldTab;
        }
        //newCap 为oldCap的二倍。
        else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&
                 oldCap >= DEFAULT_INITIAL_CAPACITY)
            //newThr也增加2倍
            newThr = oldThr << 1; // double threshold
    }
    //如果oldCap为0但是oldThr大于0的话，说明此时为第一次put 将newCap的值改为oldThr
    else if (oldThr > 0) // initial capacity was placed in threshold
        newCap = oldThr;
    else { 
         //反之则设置默认值
         // zero initial threshold signifies using defaults
        newCap = DEFAULT_INITIAL_CAPACITY;
        newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
    }
   //如果呢问Thr为0 则根据默认值计算
    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"})
     //此时才重新创建了新的数组
    Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
    table = newTab;
    //如果旧数组不为空则进行copy
    if (oldTab != null) {
      //遍历旧数组
        for (int j = 0; j < oldCap; ++j) {
            Node<K,V> e;
            if ((e = oldTab[j]) != null) {
                oldTab[j] = null;
                //如果只有一个元素，则直接计算bucket在新数组中的index
                if (e.next == null)
                    newTab[e.hash & (newCap - 1)] = e;
               //反之则进行TreeNode判断，为TreeNode则拆分
                else if (e instanceof TreeNode)
                    ((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
                //反之则按链表遍历拆分
                else { // preserve order
                    Node<K,V> loHead = null, loTail = null;
                    Node<K,V> hiHead = null, hiTail = null;
                    Node<K,V> next;
                    //循环遍历链表
                    do {
                        next = e.next;
                        //通过&将链表进行高低位计算
                       //如果结果为0则在低位
                        if ((e.hash & oldCap) == 0) {
                            if (loTail == null)
                                loHead = e;
                            else
                                loTail.next = e;
                            loTail = e;
                        }
                        //反之在高位
                        else {
                            if (hiTail == null)
                                hiHead = e;
                            else
                                hiTail.next = e;
                            hiTail = e;
                        }
                    } while ((e = next) != null);
                    //判断高低位结果是否为空，并设置到正确的bucket
                    if (loTail != null) {
                        loTail.next = null;
                        newTab[j] = loHead;
                    }
                    if (hiTail != null) {
                        hiTail.next = null;
                        //高位为j+oldCap
                        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.
 */
final void treeifyBin(Node<K,V>[] tab, int hash) {
    int n, index; Node<K,V> e;
     //判断是否需要扩容
    if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY)
        resize();
    //否则计算槽位
    else if ((e = tab[index = (n - 1) & hash]) != null) {
        TreeNode<K,V> hd = null, tl = null;
        //遍历链表，重新替换为TreeNode
        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)
            //调用红黑树的树化方法
            hd.treeify(tab);
    }
}

// For treeifyBin
TreeNode<K,V> replacementTreeNode(Node<K,V> p, Node<K,V> next) {
    return new TreeNode<>(p.hash, p.key, p.value, next);
}

/**
 * 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;
    //通过前面混淆的hash方法来确定key的hash值
    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 &&
        //此处通过&计算bucket
        (p = tab[index = (n - 1) & hash]) != null) {
        Node<K,V> node = null, e; K k; V v;
        //判断是否相等
        if (p.hash == hash &&
            ((k = p.key) == key || (key != null && key.equals(k))))
            node = p;
        //如果不等则判断next是否为空
        else if ((e = p.next) != null) {
            //不为空的话判断是否红黑树，红黑树则采用红黑树的方法
            if (p instanceof TreeNode)
                node = ((TreeNode<K,V>)p).getTreeNode(hash, key);
            else {
            //反之遍历链表查找元素
                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;
            else
                p.next = node.next;
            ++modCount;
            --size;
            //后续操作 
            afterNodeRemoval(node);
            return node;
        }
    }
    return null;
}

/**
 * Removes all of the mappings from this map.
 * The map will be empty after this call returns.
 */
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.
 *
 * @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;
}

（exp（-0.5）* pow（0.5，k）/ factorial（k）