集合的源码分析 - wtdig/study GitHub Wiki
哈希表：将存储的对象，通过hash函数，算出为一个位置后，将存储的对象放入进去；可能存在哈希碰撞（此时，使用数组+链表的方式进行解决）
二叉树：
二叉树的优化：插入过程中对二叉排序树进行调整，从而提升性能;比如：平衡二叉树、红黑树；
红黑树是JDK中TreeMap、TreeSet的底层数据结构，在JDK1.8中HashMap也用到了红黑树
数组是一个连续的内存空间，所以需要容量大小的确认；
创建一个无参的ArrayList，首先后创建一个空的数组，当添加第一个元素时，会默认扩容到默认的大小10，该过程涉及到数组的复
制，会导致效率降低；
创建一个指定大小的ArrayList，会直接初始化一个指定大小的数组，会一直占用空间；可以在添加元素的时候，手动进行容器的扩
容，使用ensureCapacity(int minCapacity)，在插入前先扩容；
进行元素的删除时，先先进行数组的复制，以便gc的回收
/**
* Appends the specified element to the end of this list.
*
* @param e element to be appended to this list
* @return <tt>true</tt> (as specified by {@link Collection#add})
*/
public boolean add(E e) {
//添加元素时，进行容器大小初始化
ensureCapacityInternal(size + 1); // Increments modCount!!
elementData[size++] = e;
return true;
}
private void ensureCapacityInternal(int minCapacity) {
//如果容器为空
if (elementData == DEFAULTCAPACITY_EMPTY_ELEMENTDATA) {
//进行大小的初始化：如果指定的大小超过默认值的10，就取指定大小，否则为默认大小10
minCapacity = Math.max(DEFAULT_CAPACITY, minCapacity);
}
//按照大小进行容器初始化
ensureExplicitCapacity(minCapacity);
}
private void ensureExplicitCapacity(int minCapacity) {
modCount++;
// overflow-conscious code
if (minCapacity - elementData.length > 0)
//进行容器的扩容
grow(minCapacity);
}
/**
* Increases the capacity to ensure that it can hold at least the
* number of elements specified by the minimum capacity argument.
*
* @param minCapacity the desired minimum capacity
*/
private void grow(int minCapacity) {
// overflow-conscious code
int oldCapacity = elementData.length;
int newCapacity = oldCapacity + (oldCapacity >> 1);
if (newCapacity - minCapacity < 0)
newCapacity = minCapacity;
if (newCapacity - MAX_ARRAY_SIZE > 0)
newCapacity = hugeCapacity(minCapacity);
// minCapacity is usually close to size, so this is a win:
//进行数据的复制
elementData = Arrays.copyOf(elementData, newCapacity);
}
private static int hugeCapacity(int minCapacity) {
if (minCapacity < 0) // overflow
throw new OutOfMemoryError();
return (minCapacity > MAX_ARRAY_SIZE) ?
Integer.MAX_VALUE :
MAX_ARRAY_SIZE;
}
LinkedList采用链表的方式进行数据的存储：在介绍链表结构时提到过，其数据单元分为数据域和指针域,使用node节点进行维护，而node节点是一个对象，在内存空间中不是连续的，没有空间大小的初始化问题，所以数据的增和删比较快，而进行数据查询时，就比较慢了。
private static class Node<E> {
E item;//存储数据
Node<E> next;//下一个节点
Node<E> prev;//先前的一个节点
Node(Node<E> prev, E element, Node<E> next) {
this.item = element;
this.next = next;
this.prev = prev;
}
}
数组与链表在处理数据时各有优缺点，数组查询速度很快而插入很慢，链表在插入时表现优秀但查询无力。哈希表则整合了数组与
链表的优点，能在插入和查找等方面都有不错的速度。
HashMap是哈希表的数据结构，底层是数组+链表的结构，jdk1.8之后，加上了红黑树的优化；
首先初始化一个数组，下标存储的是key的hash值，当下标的hash冲突时，以链表的形式挂载起来，当链表挂载的元素超过8个元素后，链表将后转换成红黑树进行存储；容量的扩容，默认初始容量是16，默认的负载因子是0.75,当存储的数据超过16*0.75=12后，进行容量的扩容，直接扩大2倍，就是32了。
构造函数
public HashMap(int initialCapacity, float loadFactor) {
//指定容量大小，负载因子
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;
//寻找最近的2次幂
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) {
//指定容量大小
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() {
//无参构造，采用默认的负载因子0.75
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
/**
* Returns a power of two size for the given target capacity.
*/
//寻找最近的2次幂
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;
}
添加一个元素
public V put(K key, V value) {
//添加一个元素
return putVal(hash(key), key, value, false, true);
}
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab;
Node<K,V> p;
int n, i;
//开始为空，进行空间的分配；resize()
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
if ((p = tab[i = (n - 1) & hash]) == null)
// 将元素直接插进去
tab[i] = newNode(hash, key, value, null);
else {
//这时就需要链表或红黑树了
// e是用来查看是不是待插入的元素已经有了，有就替换
Node<K,V> e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
//要插入的元素就是p，这说明目的是修改值,key值相同进行值的覆盖
e = p;
else if (p instanceof TreeNode)
//将节点存储到树中
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
else {
//进行链表的处理
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
p.next = newNode(hash, key, value, null);
// 链表比较长，需要树化，
// 由于初始即为p.next，所以当插入第9个元素才会树化
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;
// size太大，达到了capacity的0.75，需要扩容
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
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)
//容量变成原先的2倍
newThr = oldThr << 1; // double threshold
}
else if (oldThr > 0) // initial capacity was placed in threshold
newCap = oldThr;
else {
// zero initial threshold signifies using defaults
//开始，分配默认大小空间16
newCap = DEFAULT_INITIAL_CAPACITY;
//上限临界值： 16*0.75 = 12 ，元素超过12时，进行2倍扩容，变成32
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"})
//常见一个指定大小的数组
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)
//数组的指定下标放入元素
newTab[e.hash & (newCap - 1)] = e;
else if (e instanceof TreeNode)
// 重新规划树，如果树的size很小，默认为6，就退化为链表
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else { // preserve order
// 处理链表的数据
// loXXX指的是在原表中出现的位置
Node<K,V> loHead = null, loTail = null;
// hiXXX指的是在原表中不包含的位置
Node<K,V> hiHead = null, hiTail = null;
Node<K,V> next;
do {
next = e.next;
//这里把hash值与oldCap按位与。
//oldCap是2的次幂，所以除了最高位为1以外其他位都是0
// 和它按位与的结果为0，说明hash比它小，原表有这个位置
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);
// 挂在原表相应位置
if (loTail != null) {
loTail.next = null;
newTab[j] = loHead;
}
// 挂在后边
if (hiTail != null) {
hiTail.next = null;
newTab[j + oldCap] = hiHead;
}
}
}
}
}
return newTab;
}
TreeMap是红黑树的java实现，底层是二叉树升级后的红黑树实现；
构造函数
public TreeMap() {
//无参构造，比较器为空，使用自然排序
comparator = null;
}
/**
* Constructs a new, empty tree map, ordered according to the given
* comparator. All keys inserted into the map must be <em>mutually
* comparable</em> by the given comparator: {@code comparator.compare(k1,
* k2)} must not throw a {@code ClassCastException} for any keys
* {@code k1} and {@code k2} in the map. If the user attempts to put
* a key into the map that violates this constraint, the {@code put(Object
* key, Object value)} call will throw a
* {@code ClassCastException}.
*
* @param comparator the comparator that will be used to order this map.
* If {@code null}, the {@linkplain Comparable natural
* ordering} of the keys will be used.
*/
public TreeMap(Comparator<? super K> comparator) {
//指定比较器，采用比较器的方法
this.comparator = comparator;
}
红黑树的存储实体类
/**
* Node in the Tree. Doubles as a means to pass key-value pairs back to
* user (see Map.Entry).
*/
//树的实体类
static final class Entry<K,V> implements Map.Entry<K,V> {
//键
K key;
//值
V value;
//左树节点
Entry<K,V> left;
//右树节点
Entry<K,V> right;
//父节点
Entry<K,V> parent;
//节点默认的颜色，黑色
boolean color = BLACK;
/**
* Make a new cell with given key, value, and parent, and with
* {@code null} child links, and BLACK color.
*/
Entry(K key, V value, Entry<K,V> parent) {
this.key = key;
this.value = value;
this.parent = parent;
}
/**
* Returns the key.
*
* @return the key
*/
public K getKey() {
return key;
}
/**
* Returns the value associated with the key.
*
* @return the value associated with the key
*/
public V getValue() {
return value;
}
/**
* Replaces the value currently associated with the key with the given
* value.
*
* @return the value associated with the key before this method was
* called
*/
public V setValue(V value) {
V oldValue = this.value;
this.value = value;
return oldValue;
}
public boolean equals(Object o) {
if (!(o instanceof Map.Entry))
return false;
Map.Entry<?,?> e = (Map.Entry<?,?>)o;
return valEquals(key,e.getKey()) && valEquals(value,e.getValue());
}
public int hashCode() {
int keyHash = (key==null ? 0 : key.hashCode());
int valueHash = (value==null ? 0 : value.hashCode());
return keyHash ^ valueHash;
}
public String toString() {
return key + "=" + value;
}
}
添加方法
public V put(K key, V value) {
//树的根节点
Entry<K,V> t = root;
//开始为空
if (t == null) {
compare(key, key); // type (and possibly null) check
//将第一个元素作为树的根节点
root = new Entry<>(key, value, null);
size = 1;
modCount++;
return null;
}
int cmp;
Entry<K,V> parent;
// split comparator and comparable paths
Comparator<? super K> cpr = comparator;
//进行key值的比较，比根节点key值小的，放到左树节点上，比根节点key值大的，放到右树节点上
//如果相等，进行值的覆盖
if (cpr != null) {
do {
parent = t;
cmp = cpr.compare(key, t.key);
if (cmp < 0)
t = t.left;
else if (cmp > 0)
t = t.right;
else
return t.setValue(value);
} while (t != null);
}
else {
if (key == null)
throw new NullPointerException();
@SuppressWarnings("unchecked")
Comparable<? super K> k = (Comparable<? super K>) key;
do {
parent = t;
cmp = k.compareTo(t.key);
if (cmp < 0)
t = t.left;
else if (cmp > 0)
t = t.right;
else
return t.setValue(value);
} while (t != null);
}
Entry<K,V> e = new Entry<>(key, value, parent);
if (cmp < 0)
parent.left = e;
else
parent.right = e;
//进行红黑树的调整，使其达到平衡
fixAfterInsertion(e);
size++;
modCount++;
return null;
}
红黑树的优化算法策略
/** From CLR */
//进行红黑树的调整，使其达到数的平衡，比如树节点的旋转，节点颜色的变换
private void fixAfterInsertion(Entry<K,V> x) {
x.color = RED;
while (x != null && x != root && x.parent.color == RED) {
if (parentOf(x) == leftOf(parentOf(parentOf(x)))) {
Entry<K,V> y = rightOf(parentOf(parentOf(x)));
if (colorOf(y) == RED) {
setColor(parentOf(x), BLACK);
setColor(y, BLACK);
setColor(parentOf(parentOf(x)), RED);
x = parentOf(parentOf(x));
} else {
if (x == rightOf(parentOf(x))) {
x = parentOf(x);
rotateLeft(x);
}
setColor(parentOf(x), BLACK);
setColor(parentOf(parentOf(x)), RED);
rotateRight(parentOf(parentOf(x)));
}
} else {
Entry<K,V> y = leftOf(parentOf(parentOf(x)));
if (colorOf(y) == RED) {
setColor(parentOf(x), BLACK);
setColor(y, BLACK);
setColor(parentOf(parentOf(x)), RED);
x = parentOf(parentOf(x));
} else {
if (x == leftOf(parentOf(x))) {
x = parentOf(x);
rotateRight(x);
}
setColor(parentOf(x), BLACK);
setColor(parentOf(parentOf(x)), RED);
rotateLeft(parentOf(parentOf(x)));
}
}
}
root.color = BLACK;
}
LinkedHashMap还维护了一个双向链表，以保证通过Iterator遍历时顺序与插入顺序一致。除此之外，它还支持Access Order，即按
照元素被访问的顺序来排序，我们熟知的LRUCache底层就依赖于此; Linked内部含有一个private transient Entry header;来记录元素插入的顺序或者是元素被访问的顺序。利用这个线性结构的对象
，可以帮助记录entry加入的前后顺序或者记录entry被访问的频率(最少被访问的entry靠前，最近访问的entry靠后)
HashSet底层的实现是HashMap,存储的是key,value为默认的new Object()对象；
TreeMap底层的实现是TreeMap,存储的是key；