Class - java.util.HashMap
Created by : Mr Dk.
2019 / 11 / 16 16:20
Nanjing, Jiangsu, China
Definition
public class HashMap<K,V> extends AbstractMap<K,V>
implements Map<K,V>, Cloneable, Serializable {
}
基于 Hash 表的 Map 接口实现，提供了所有 Map 的可选操作，并允许 null 的 key 或 value。除了不同步和允许 null 以外，与 Hashtable 基本等价。
这个实现类对于 Map 中的顺序不作任何保证。特别地，不保证顺序会随着时间而保持不变。关于时间复杂度：
- 如果哈希函数将元素均匀分布在桶中，
put()和get()将达到O(1) - 遍历集合，将与
capacity+size的时间成比例
因此，如果迭代性能很重要。最好不要将初始的 capacity 设得过高；或者说，不要将 load factor 设置得过低。这两个因素影响着 HashMap 的性能：
capacity指 hash table 中桶的个数load factor指的是在 hash table 自动增长桶容量前，hash table 可以被装得多满
当 hash table 中的 capacity 和 load factor 超出，会进行 rehash：内部数据结构会重建，桶个数提升两倍。在通常情况下，保持 load factor 为 0.75 是时间开销和空间开销的一个折衷。如果有很多 entry 要被存储到 HashMap 中，设置足够的初始 capacity 比自动的 rehash 更有效。使用相同的 hashcode 作为 key 会降低性能。
该实现类不同步。如果有超过 1 个线程对集合进行结构性修改 (增删 entry)，必须在外部被同步
- 要么被封装集合的对象同步
- 如果没有，则
Map m = Collections.synchronizedMap(new HashMap(...));
在迭代器迭代期间，除了使用迭代器自身的 remove() 外，任何对集合的结构修改都会导致 ConcurrentModificationException。但不对非同步的并发修改作出完全保证，因此不能利用这种异常来保证正确性，仅用于检测 bug。
/**
* 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
*/
一些实现细节：
- 桶数组，每个桶可以存放 hash 相同的元素
- 当元素 hash 相同时 (hash 碰撞)
- 将 hash 相同的元素构造为链表
- 当链表长度超过阈值时，将链表转化为红黑树
Field
可以看到，默认的初始化容量为 16，最大的容量为 1 << 30。
- 当冲突的链表长度超过 8 时，将链表转化为树
- 当树的大小低于 6 时，将树转换为链表
- 当容量低于 64 时，hash 冲突不会被树化
/**
* 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;
Hash
计算指定 key 对应的 hash 值。HashMap 通过 hash 来判断一个 key 是否在容器内。
具体为什么要这么算没有看懂
/**
* 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);
}
Collection Status
记录了集合的当前状态
- 桶数组
- load factor
- 元素个数
- threshold - 下一次重新分配前的阈值
- ...
/**
* 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;
Table Size
返回一个给定容量的 2 的整幂，保证数组的容量总是 2 的幂 (方便通过与运算进行 hash)。
- 最后一次将 16 bit 右移，充斥整个 32-bit integer 数据
- 保证 integer 前面的 bit 都是 0，后面的 bit 都是 1，即
2^n - 1
那么 2^n - 1 + 1 一定是 2^n。
/**
* 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;
}
Constructor
可以指定初始的 capacity 和 load factor 构造集合。capacity 会被转化为对应的 2 的幂。
或者从另一个集合构造现集合：
- 使用默认的 load factor
0.75f - 然后将 entry 加入集合
/**
* 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);
}
Node Definition
基本的桶结点的定义。
/**
* 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;
}
}
Put All
将给定集合中的所有 entry 放入哈希表。
用于实现 拷贝构造函数 和 putAll()：
- 首先判断 table 是否已分配
- 如果还未分配，就计算分配的数组大小
size / loadFactor并转化为 2 的幂
- 再判断加入的 size 是否会超出空间阈值
- 如果超出阈值，就需要
resize()
- 如果超出阈值，就需要
- 最后将每一个元素放入集合
将元素放入集合使用的是接下来的 putVal() 函数。
/**
* Copies all of the mappings from the specified map to this map.
* These mappings will replace any mappings that this map had for
* any of the keys currently in the specified map.
*
* @param m mappings to be stored in this map
* @throws NullPointerException if the specified map is null
*/
public void putAll(Map<? extends K, ? extends V> m) {
putMapEntries(m, true);
}
/**
* 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();
if (s > 0) {
if (table == null) { // pre-size
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);
}
}
}
Put
将 entry 加入哈希表：
- 首先判断哈希表是否为空，为空则调
resize()初始化 - 如果没有碰撞的函数 (没有桶)，则初始化新的桶结点
- 否则，桶中已发生碰撞 (hash 相同)
- 如果 key 相同，记录原元素
- 如果 key 不同，且已经是红黑树，那么直接调
putTreeVal()插入红黑树 - 否则就是链表
- 循环到达链表尾部，并实例化新结点插入；如果超过树化阈值，则构建红黑树
- 如果循环过程中发现 key 相同的元素，则记录原元素
- 如果记录元素不为空 (说明发生 hash 碰撞且 key 也相同的元素)
- 将旧的 value 记录下来
- 用新的 value 替代旧的 value
- 返回旧 value
- 添加结束后，如果超过了扩容阈值，则调
resize()扩容
n 为哈希表的长度。哈希表的实际下标为 [0, n - 1]，所以将元素的 hash 值映射到哈希表上就是 (n - 1) & hash。
/**
* 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;
}
Get
这两个函数都要根据给定的 key 取得对应的 entry：
/**
* 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;
}
/**
* Returns <tt>true</tt> if this map contains a mapping for the
* specified key.
*
* @param key The key whose presence in this map is to be tested
* @return <tt>true</tt> if this map contains a mapping for the specified
* key.
*/
public boolean containsKey(Object key) {
return getNode(hash(key), key) != null;
}
调 getNode() 实现：
- 首先检查桶是否为空，为空则返回
null - 若不为空，则检查第一个元素
- 如果第一个元素的 key 与给定的 key 相等，则直接返回
- 如果不相等
- 如果是红黑树结构，则调
getTreeNode()从树中找结点 - 如果是链表结构，则遍历直至找到 key 相同的结点或到结尾
- 如果是红黑树结构，则调
/**
* 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;
}
Resize
根据现在的容量和扩容阈值，计算新的容量和扩容阈值。一般情况下，是扩容两倍。未初始化的哈希表也会调这个函数，将哈希表数组初始化为默认长度。
用新计算出的扩容阈值替代 threshold，用新计算出的容量重新分配哈希表数组。如果原哈希表不为空，就把 entry 搬到新的哈希表中：
- 如果数组中的某一项不为
null，则将该项置null便于 GC- 如果只有该结点一个，则直接复制
- 如果是红黑树，调红黑树的
split到新哈希表 - 如果是链表，这里操作很骚：
xxxxx01001 & 0000011111
在扩容两倍后，变成：
xxxxX01001 & 0000111111
之前的容量中，以
01001结尾的元素都会被映射到同一个位置扩容后，以
01001结尾的元素会根据X位为 0 或 1 而被映射到两个位置一个位置为
01001对应的原位置，即下面的x，另一个位置是x+ 原容量- 遍历原链表，将映射到
x和x + 原容量的元素分别串成两个链表 - 将这两个链表分别放入新哈希表
x和x + 原容量的位置
- 遍历原链表，将映射到
/**
* 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;
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;
}
Remove
调 removeNode() 删除指定 key 的 entry，从哈希表中删除指定 key 的元素。另外还可以指定匹配特定的 value，如果不指定则忽略。
显然，首先判断哈希表数组是否存在，不然删个鸡毛：
- 如果指定 hash 位置上正好存在 key 相等的 entry，则直接记录该结点
- 如果指定 hash 位置上是红黑树或是链表
- 如果是红黑树，则调红黑树的
getTreeNode()找到指定 key 的结点 - 如果是链表，则遍历链表直到对应 key 的结点被找到
- 记录找到的结点为
node，前一个结点为p
- 如果是红黑树，则调红黑树的
如果找到的结点不为空：
- 如果是红黑树结点，调红黑树的
removeTreeNode()删除该结点 - 如果是单独的结点，则直接将该结点置空
- 如果是链表，那么将结点从链表中删除
/**
* 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;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
node = p;
else if ((e = p.next) != null) {
if (p instanceof TreeNode)
node = ((TreeNode<K,V>)p).getTreeNode(hash, key);
else {
do {
if (e.hash == hash &&
((k = e.key) == key ||
(key != null && key.equals(k)))) {
node = e;
break;
}
p = e;
} while ((e = e.next) != null);
}
}
if (node != null && (!matchValue || (v = node.value) == value ||
(value != null && value.equals(v)))) {
if (node instanceof TreeNode)
((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);
else if (node == p)
tab[index] = node.next;
else
p.next = node.next;
++modCount;
--size;
afterNodeRemoval(node);
return node;
}
}
return null;
}
Clear
将整个集合清除，这没啥问题。不过，将哈希表数组中的每个元素置
null就 ok 了吗？？？那些链表啊红黑树结点啊不用被置null吗？莫非会自动被 GC ？ 😶
/**
* 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;
}
}
Contains Value
判断某个 value 是否出现在集合中。第一层循环遍历哈希表数组中的每一个位置，对于每一个位置，第二层循环遍历每个位置上的链表。
有些位置虽然已经被构造为红黑树，但是在树化的过程中，始终保持着结点的 next 域的引用关系不变。所以即使被树化，还是可以通过 next 域遍历所有结点。遍历没有必要和树结构扯上关系。
/**
* 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;
}
Treeify
将某个 hash 值的链表组织为红黑树
- 如果哈希表数组低于树化阈值，就不整红黑树了，
resize()扩容一下拉倒 - 否则就将链表中的每个
Node<K,V>转化为TreeNode<K,V>；然后调链表头 TreeNode 的treeify()建红黑树
可以看到这个函数中，甚至是 TreeNode.treeify() 中，只使用了 next 域，但没有做任何修改。因此链表虽然被组织为红黑树，但是 next 依旧发挥着链表的作用。
/**
* 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;
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);
}
}
关于树化的具体细节：
// For treeifyBin
TreeNode<K,V> replacementTreeNode(Node<K,V> p, Node<K,V> next) {
return new TreeNode<>(p.hash, p.key, p.value, next);
}
实际上是用当前 Node 结点实例化了一个新的 TreeNode 结点。具体的定义如下：
/* ------------------------------------------------------------ */
// Tree bins
/**
* Entry for Tree bins. Extends LinkedHashMap.Entry (which in turn
* extends Node) so can be used as extension of either regular or
* linked node.
*/
static final class TreeNode<K,V> extends LinkedHashMap.Entry<K,V> {
TreeNode<K,V> parent; // red-black tree links
TreeNode<K,V> left;
TreeNode<K,V> right;
TreeNode<K,V> prev; // needed to unlink next upon deletion
boolean red;
TreeNode(int hash, K key, V val, Node<K,V> next) {
super(hash, key, val, next);
}
// ...
该结点继承自 LinkedHashMap，既是红黑树结点，也是个双向链表结点。
Tree Node Operation
以下是所有树节点的内部函数：
/**
* Forms tree of the nodes linked from this node.
*/
final void treeify(Node<K,V>[] tab) {
TreeNode<K,V> root = null;
for (TreeNode<K,V> x = this, next; x != null; x = next) {
next = (TreeNode<K,V>)x.next;
x.left = x.right = null;
if (root == null) {
x.parent = null;
x.red = false;
root = x;
}
else {
K k = x.key;
int h = x.hash;
Class<?> kc = null;
for (TreeNode<K,V> p = root;;) {
int dir, ph;
K pk = p.key;
if ((ph = p.hash) > h)
dir = -1;
else if (ph < h)
dir = 1;
else if ((kc == null &&
(kc = comparableClassFor(k)) == null) ||
(dir = compareComparables(kc, k, pk)) == 0)
dir = tieBreakOrder(k, pk);
TreeNode<K,V> xp = p;
if ((p = (dir <= 0) ? p.left : p.right) == null) {
x.parent = xp;
if (dir <= 0)
xp.left = x;
else
xp.right = x;
root = balanceInsertion(root, x);
break;
}
}
}
}
moveRootToFront(tab, root);
}
具体的红黑树操作不研究了。大体上，通过 next 遍历每一个元素，将所有结点构造为一颗以 root 为根的红黑树。但是 root 结点可能在链表的中间，所以要将 root 结点提到链表的最前面，放在哈希表数组一次就能引用到的地方。调下面的 moveRootToFront() 函数。
/**
* Ensures that the given root is the first node of its bin.
*/
static <K,V> void moveRootToFront(Node<K,V>[] tab, TreeNode<K,V> root) {
int n;
if (root != null && tab != null && (n = tab.length) > 0) {
int index = (n - 1) & root.hash;
TreeNode<K,V> first = (TreeNode<K,V>)tab[index];
if (root != first) {
Node<K,V> rn;
tab[index] = root;
TreeNode<K,V> rp = root.prev;
if ((rn = root.next) != null)
((TreeNode<K,V>)rn).prev = rp;
if (rp != null)
rp.next = rn;
if (first != null)
first.prev = root;
root.next = first;
root.prev = null;
}
assert checkInvariants(root);
}
}
根据 hash 值，在哈希表数组中找到对应下标，将原第一个元素 first 记录，将 root 放到哈希表数组对应下标处，并将 root 从链表中间取出，将之前的链表和之后的链表串起来。最后将 first 接在 root 的后面。
Untreeify
返回非 TreeNode 版本的链表结点。
/**
* Returns a list of non-TreeNodes replacing those linked from
* this node.
*/
final Node<K,V> untreeify(HashMap<K,V> map) {
Node<K,V> hd = null, tl = null;
for (Node<K,V> q = this; q != null; q = q.next) {
Node<K,V> p = map.replacementNode(q, null);
if (tl == null)
hd = p;
else
tl.next = p;
tl = p;
}
return hd;
}
Find Tree Node
给定 hash 和 key，从 root 寻找结点。比较 hashcode，进入左子树或右子树。
/**
* 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;
}
/**
* Calls find for root node.
*/
final TreeNode<K,V> getTreeNode(int h, Object k) {
return ((parent != null) ? root() : this).find(h, k, null);
}
Put Tree Value
将 entry 放入树中：
- 首先寻找插入位置，如果结点已经存在，则直接返回结点位置。由调用该函数的
putVal()来记录 old value 并返回 - 如果没有找到结点，则新建结点
- 加入树中
- 加入链表中
- 调
moveRootToFront()将 root 结点调整到哈希表数组中
/**
* Tree version of putVal.
*/
final TreeNode<K,V> putTreeVal(HashMap<K,V> map, Node<K,V>[] tab,
int h, K k, V v) {
Class<?> kc = null;
boolean searched = false;
TreeNode<K,V> root = (parent != null) ? root() : this;
for (TreeNode<K,V> p = root;;) {
int dir, ph; K pk;
if ((ph = p.hash) > h)
dir = -1;
else if (ph < h)
dir = 1;
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
else if ((kc == null &&
(kc = comparableClassFor(k)) == null) ||
(dir = compareComparables(kc, k, pk)) == 0) {
if (!searched) {
TreeNode<K,V> q, ch;
searched = true;
if (((ch = p.left) != null &&
(q = ch.find(h, k, kc)) != null) ||
((ch = p.right) != null &&
(q = ch.find(h, k, kc)) != null))
return q;
}
dir = tieBreakOrder(k, pk);
}
TreeNode<K,V> xp = p;
if ((p = (dir <= 0) ? p.left : p.right) == null) {
Node<K,V> xpn = xp.next;
TreeNode<K,V> x = map.newTreeNode(h, k, v, xpn);
if (dir <= 0)
xp.left = x;
else
xp.right = x;
xp.next = x;
x.parent = x.prev = xp;
if (xpn != null)
((TreeNode<K,V>)xpn).prev = x;
moveRootToFront(tab, balanceInsertion(root, x));
return null;
}
}
}
Remove Tree Node
将结点从 next 和 pred 组织的链表中移出来。如果移除后长度过小，则将红黑树复原为链表并返回；否则就进行红黑树的删除结点操作，并将新的 root 移动到哈希表数组中。
/**
* Removes the given node, that must be present before this call.
* This is messier than typical red-black deletion code because we
* cannot swap the contents of an interior node with a leaf
* successor that is pinned by "next" pointers that are accessible
* independently during traversal. So instead we swap the tree
* linkages. If the current tree appears to have too few nodes,
* the bin is converted back to a plain bin. (The test triggers
* somewhere between 2 and 6 nodes, depending on tree structure).
*/
final void removeTreeNode(HashMap<K,V> map, Node<K,V>[] tab,
boolean movable) {
int n;
if (tab == null || (n = tab.length) == 0)
return;
int index = (n - 1) & hash;
TreeNode<K,V> first = (TreeNode<K,V>)tab[index], root = first, rl;
TreeNode<K,V> succ = (TreeNode<K,V>)next, pred = prev;
if (pred == null)
tab[index] = first = succ;
else
pred.next = succ;
if (succ != null)
succ.prev = pred;
if (first == null)
return;
if (root.parent != null)
root = root.root();
if (root == null
|| (movable
&& (root.right == null
|| (rl = root.left) == null
|| rl.left == null))) {
tab[index] = first.untreeify(map); // too small
return;
}
TreeNode<K,V> p = this, pl = left, pr = right, replacement;
if (pl != null && pr != null) {
TreeNode<K,V> s = pr, sl;
while ((sl = s.left) != null) // find successor
s = sl;
boolean c = s.red; s.red = p.red; p.red = c; // swap colors
TreeNode<K,V> sr = s.right;
TreeNode<K,V> pp = p.parent;
if (s == pr) { // p was s's direct parent
p.parent = s;
s.right = p;
}
else {
TreeNode<K,V> sp = s.parent;
if ((p.parent = sp) != null) {
if (s == sp.left)
sp.left = p;
else
sp.right = p;
}
if ((s.right = pr) != null)
pr.parent = s;
}
p.left = null;
if ((p.right = sr) != null)
sr.parent = p;
if ((s.left = pl) != null)
pl.parent = s;
if ((s.parent = pp) == null)
root = s;
else if (p == pp.left)
pp.left = s;
else
pp.right = s;
if (sr != null)
replacement = sr;
else
replacement = p;
}
else if (pl != null)
replacement = pl;
else if (pr != null)
replacement = pr;
else
replacement = p;
if (replacement != p) {
TreeNode<K,V> pp = replacement.parent = p.parent;
if (pp == null)
root = replacement;
else if (p == pp.left)
pp.left = replacement;
else
pp.right = replacement;
p.left = p.right = p.parent = null;
}
TreeNode<K,V> r = p.red ? root : balanceDeletion(root, replacement);
if (replacement == p) { // detach
TreeNode<K,V> pp = p.parent;
p.parent = null;
if (pp != null) {
if (p == pp.left)
pp.left = null;
else if (p == pp.right)
pp.right = null;
}
}
if (movable)
moveRootToFront(tab, r);
}
Split
这个函数只会被 resize() 调用，将当前的红黑树拆分到新的哈希表数组中。如果切分后树的大小低于阈值，则复原为链表。同样，扩容后，原先映射到该桶 (红黑树) 的所有结点将可能映射到两个桶中：
- 一个是原位置的桶
- 一个是原位置 + 原容量的桶
利用 next 域对红黑树进行遍历，根据计算出的 hash 值，将映射到两个桶的结点串成两个链表 lo 和 hi：
- 若链表不为空且低于阈值，则转化为链表并放入对应桶中
- 若链表不为空且高于阈值，则将链表放入对应桶中，并对链表头调用
treeify()进行树化
/**
* Splits nodes in a tree bin into lower and upper tree bins,
* or untreeifies if now too small. Called only from resize;
* see above discussion about split bits and indices.
*
* @param map the map
* @param tab the table for recording bin heads
* @param index the index of the table being split
* @param bit the bit of hash to split on
*/
final void split(HashMap<K,V> map, Node<K,V>[] tab, int index, int bit) {
TreeNode<K,V> b = this;
// Relink into lo and hi lists, preserving order
TreeNode<K,V> loHead = null, loTail = null;
TreeNode<K,V> hiHead = null, hiTail = null;
int lc = 0, hc = 0;
for (TreeNode<K,V> e = b, next; e != null; e = next) {
next = (TreeNode<K,V>)e.next;
e.next = null;
if ((e.hash & bit) == 0) {
if ((e.prev = loTail) == null)
loHead = e;
else
loTail.next = e;
loTail = e;
++lc;
}
else {
if ((e.prev = hiTail) == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
++hc;
}
}
if (loHead != null) {
if (lc <= UNTREEIFY_THRESHOLD)
tab[index] = loHead.untreeify(map);
else {
tab[index] = loHead;
if (hiHead != null) // (else is already treeified)
loHead.treeify(tab);
}
}
if (hiHead != null) {
if (hc <= UNTREEIFY_THRESHOLD)
tab[index + bit] = hiHead.untreeify(map);
else {
tab[index + bit] = hiHead;
if (loHead != null)
hiHead.treeify(tab);
}
}
}
关于红黑树的操作函数就略了...
static <K,V> TreeNode<K,V> rotateLeft(TreeNode<K,V> root, TreeNode<K,V> p) {}
static <K,V> TreeNode<K,V> rotateRight(TreeNode<K,V> root, TreeNode<K,V> p) {}
static <K,V> TreeNode<K,V> balanceInsertion(TreeNode<K,V> root, TreeNode<K,V> x) {}
static <K,V> TreeNode<K,V> balanceDeletion(TreeNode<K,V> root, TreeNode<K,V> x) {}
static <K,V> boolean checkInvariants(TreeNode<K,V> t) {}
Comments
注释中的相关实现细节。
引入红黑树主要是为了 hashcode 分布特别不均匀时防止性能下降 (链表过长)，比如多个 key 共用同一个 hashcode 的情况。在一个分布较为均匀的 hashcode 下，基本上用不到树结点。理想情况下，使用随机 hashcode，每个桶中的结点数量满足泊松分布，概率为：
- 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
可以看到，超过树化阈值 (8) 的概率已经相当低了。
/*
* 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.
*/
Summary
把握 HashMap 具体实现的核心数据结构：哈希表数组 + 冲突链表 + 链表过长后转为红黑树。还要把握所有操作遵循的共同思想。
需要遍历所有元素的函数：
- 遍历哈希表数组的每一个位置
- 对于某一个位置，如果是链表 (或红黑树)，则直接用
next域遍历到底
插入元素的函数：
- 计算 hash，找到对应的位置
- 如果没有冲突，直接放入集合
- 如果发生冲突，且数据结构被组织为链表，则插入链表
- 如果发生冲突，且数据结构被组织为红黑树，则插入红黑树并重整 root 到链表头
- 如果插入完成后，元素个数超过了容量阈值，则进行 resize
- 容量扩容了两倍
- 对于同一个碰撞位置上的每一个元素 (链表或红黑树)，一定有了一个新的可能的坑位可以去 😅
- 重新计算 hash，将映射到老位置和新位置的元素分别串成两个链表，并放到对应位置
- 如果两个新链表超过了树化阈值，则进行树化
删除元素的函数：
- 计算 hash 值
- 如果元素不冲突，则直接在哈希表数组中删除
- 如果是链表，那么在链表中寻找对应元素删除
- 如果是红黑树，那么用红黑树的函数删掉元素
- 如果删除后树中元素过少，则将红黑树重新组织为链表
总体思想都是类似的，即：
- 计算 hash
- 分别用不同数据结构对应的操作实现功能
O(1)的数组随机访问O(n)的链表O(log(n))的红黑树
- 维护这几种数据结构之间的转化
- 维护总体的容量和阈值
HashMap 算是所有的容器中最复杂的一个了，专家不愧是专家，实现得很漂亮。👨💻