本文讨論java集合容器中的幾種元素數量擷取的方式，命題很小，但是也足以讓我們思考一些東西。
所謂計數：即是給出所在容器的元素總數的方式。一般能想到的就是兩種方式：一是使用某個字段直接存儲該計數值，二是在請求計數值時臨時去計算所有元素數量。貌似本文的答案已經出來了。好吧，那我們還是從源碼的角度來驗證下想法吧：
一般在java的集合容器中，可以分為普通容器和并發容器，我們就姑且以這種方式來劃分下，驗證下其實作計數的方式吧！
1. 普通容器 --HashMap
一般非并發容器在進行增删改時，都會同時維護一個count值，如hashmap中的實作:
// HashMap 增加和修改都在此實作
/**
* 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;
// 直接對size進行增加1即可, 如果是更新key的值，則不會運作到此處，即不會進行相加
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
// 删除元素的實作，同時維護 size 大小
/**
* 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;
// 先查找node所在的位置
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;
// 直接減小size即可
--size;
afterNodeRemoval(node);
return node;
}
}
return null;
} 因為有了增删改時對計數器的維護，是以在想要擷取總數時，就容易許多了。隻需把size字段傳回即可。
// HashMap.size()
/**
* Returns the number of key-value mappings in this map.
*
* @return the number of key-value mappings in this map
*/
public int size() {
return size;
} 是以，在這種情況下，擷取計數值的方式非常簡單。但是不管怎麼樣，size字段對外部是不可見的，因為它是容器内部的一個實作邏輯，它完全在将來的某個時刻改變掉。即 size() != size .
2. 普通容器 --LinkedList
看完hash類的計數實作，咱們再來看另外一個類型的容器LinkedList:
// LinkedList.add(E) 添加一個元素
public boolean add(E e) {
linkLast(e);
return true;
}
/**
* Links e as last element.
*/
void linkLast(E e) {
final Node<E> l = last;
final Node<E> newNode = new Node<>(l, e, null);
last = newNode;
if (l == null)
first = newNode;
else
l.next = newNode;
// 同樣，直接使用一個 size 計數器統計即可
size++;
modCount++;
}
// 删除一個元素, 同時維護 size 字段
public E removeFirst() {
final Node<E> f = first;
if (f == null)
throw new NoSuchElementException();
return unlinkFirst(f);
}
/**
* Unlinks non-null first node f.
*/
private E unlinkFirst(Node<E> f) {
// assert f == first && f != null;
final E element = f.item;
final Node<E> next = f.next;
f.item = null;
f.next = null; // help GC
first = next;
if (next == null)
last = null;
else
next.prev = null;
// 元素計數減1
size--;
modCount++;
return element;
}
// 同樣,統計元素數量時,直接傳回size即可
public int size() {
return size;
} 可見，LinkedList 也同樣是簡單地維護一個計數器字段，進而實作了高效地計數方法。而這簡單地實作，則是基于單線程的通路的，它同時維護一個計數字段，基本沒有多少開銷，卻給取值時帶來了便利。
總結: 普通容器直接維護一個計數器字段，可以很友善地進行大小統計操作。
3. 并發容器 --ConcurrentHashMap
而對于并發容器，則可能會不一樣些，但也有一些情況是一樣的。比較，HashTable, 直接使用 synchronized 來保證線程安全，則它也同樣可以直接使用一個size即可完成元素大小的統計。事實上，有些版本的HashTable僅僅是在HashMap的上面加上了synchronizd鎖而已(有些版本則是 不一樣的哦)，細節咱們無需再看。
而稍微有點不一樣的如: ConcurrentHashMap.size(), 早期的 ConcurrentHashMap 使用分段鎖,則需要統計各segement的元素,相加起來然後得到整體元素大小. 而jdk1.8中,已經放棄使用分段鎖來實作高性能安全的hash容器了,而是直接使用 synchronized + CAS + 紅黑樹 實作. 那麼,我們來看看其實作元素統計這一功能的實作有何不同吧!
// ConcurrentHashMap.putVal() 新增或修改一個元素
/** Implementation for put and putIfAbsent */
final V putVal(K key, V value, boolean onlyIfAbsent) {
if (key == null || value == null) throw new NullPointerException();
int hash = spread(key.hashCode());
int binCount = 0;
for (Node<K,V>[] tab = table;;) {
Node<K,V> f; int n, i, fh;
if (tab == null || (n = tab.length) == 0)
tab = initTable();
else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {
if (casTabAt(tab, i, null,
new Node<K,V>(hash, key, value, null)))
break; // no lock when adding to empty bin
}
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
synchronized (f) {
if (tabAt(tab, i) == f) {
if (fh >= 0) {
binCount = 1;
for (Node<K,V> e = f;; ++binCount) {
K ek;
if (e.hash == hash &&
((ek = e.key) == key ||
(ek != null && key.equals(ek)))) {
oldVal = e.val;
if (!onlyIfAbsent)
e.val = value;
break;
}
Node<K,V> pred = e;
if ((e = e.next) == null) {
pred.next = new Node<K,V>(hash, key,
value, null);
break;
}
}
}
else if (f instanceof TreeBin) {
Node<K,V> p;
binCount = 2;
if ((p = ((TreeBin<K,V>)f).putTreeVal(hash, key,
value)) != null) {
oldVal = p.val;
if (!onlyIfAbsent)
p.val = value;
}
}
}
}
if (binCount != 0) {
if (binCount >= TREEIFY_THRESHOLD)
treeifyBin(tab, i);
if (oldVal != null)
return oldVal;
break;
}
}
}
// 主要是在進行新增成功時,再進行計數器的操作, 看起來不是 ++size 這麼簡單了
addCount(1L, binCount);
return null;
}
// 這個計數的相加看起來相當複雜
/**
* Adds to count, and if table is too small and not already
* resizing, initiates transfer. If already resizing, helps
* perform transfer if work is available. Rechecks occupancy
* after a transfer to see if another resize is already needed
* because resizings are lagging additions.
*
* @param x the count to add
* @param check if <0, don't check resize, if <= 1 only check if uncontended
*/
private final void addCount(long x, int check) {
CounterCell[] as; long b, s;
// 使用 CounterCell 來實作計數操作
// 使用 CAS 保證更新計數時隻會有一個線程成功
if ((as = counterCells) != null ||
!U.compareAndSwapLong(this, BASECOUNT, b = baseCount, s = b + x)) {
CounterCell a; long v; int m;
boolean uncontended = true;
if (as == null || (m = as.length - 1) < 0 ||
// 使用一個類似随機負載均衡的方式,将計數值随機添加到 CounterCell 的某個值下面,減少多線程競争的可能性
(a = as[ThreadLocalRandom.getProbe() & m]) == null ||
// 通過cas将計數值x添加到 CounterCell 的 value 字段中
!(uncontended =
U.compareAndSwapLong(a, CELLVALUE, v = a.value, v + x))) {
// 如果上面添加失敗,則使用 fullAddCount 進行重新添加該計數
fullAddCount(x, uncontended);
return;
}
if (check <= 1)
return;
// 基于 CounterCell 做一此彙總操作
s = sumCount();
}
// 在進行put值時, check的值都是大于等于0的
if (check >= 0) {
Node<K,V>[] tab, nt; int n, sc;
// rehash 處理
while (s >= (long)(sc = sizeCtl) && (tab = table) != null &&
(n = tab.length) < MAXIMUM_CAPACITY) {
int rs = resizeStamp(n);
if (sc < 0) {
if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 ||
sc == rs + MAX_RESIZERS || (nt = nextTable) == null ||
transferIndex <= 0)
break;
if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1))
transfer(tab, nt);
}
else if (U.compareAndSwapInt(this, SIZECTL, sc,
(rs << RESIZE_STAMP_SHIFT) + 2))
// 輔助進行hash擴容
transfer(tab, null);
s = sumCount();
}
}
}
// fullAddCount 比較複雜, 它的目的是為了保證多線程可以快速進行添加完成, 目标很簡單, 即向數組 CounterCell 中添加一個值 x
// See LongAdder version for explanation
private final void fullAddCount(long x, boolean wasUncontended) {
int h;
if ((h = ThreadLocalRandom.getProbe()) == 0) {
ThreadLocalRandom.localInit(); // force initialization
h = ThreadLocalRandom.getProbe();
wasUncontended = true;
}
boolean collide = false; // True if last slot nonempty
for (;;) {
CounterCell[] as; CounterCell a; int n; long v;
if ((as = counterCells) != null && (n = as.length) > 0) {
if ((a = as[(n - 1) & h]) == null) {
if (cellsBusy == 0) { // Try to attach new Cell
CounterCell r = new CounterCell(x); // Optimistic create
if (cellsBusy == 0 &&
U.compareAndSwapInt(this, CELLSBUSY, 0, 1)) {
boolean created = false;
try { // Recheck under lock
CounterCell[] rs; int m, j;
if ((rs = counterCells) != null &&
(m = rs.length) > 0 &&
rs[j = (m - 1) & h] == null) {
rs[j] = r;
created = true;
}
} finally {
cellsBusy = 0;
}
if (created)
break;
continue; // Slot is now non-empty
}
}
collide = false;
}
else if (!wasUncontended) // CAS already known to fail
wasUncontended = true; // Continue after rehash
else if (U.compareAndSwapLong(a, CELLVALUE, v = a.value, v + x))
break;
else if (counterCells != as || n >= NCPU)
collide = false; // At max size or stale
else if (!collide)
collide = true;
else if (cellsBusy == 0 &&
U.compareAndSwapInt(this, CELLSBUSY, 0, 1)) {
try {
if (counterCells == as) {// Expand table unless stale
CounterCell[] rs = new CounterCell[n << 1];
for (int i = 0; i < n; ++i)
rs[i] = as[i];
counterCells = rs;
}
} finally {
cellsBusy = 0;
}
collide = false;
continue; // Retry with expanded table
}
h = ThreadLocalRandom.advanceProbe(h);
}
else if (cellsBusy == 0 && counterCells == as &&
U.compareAndSwapInt(this, CELLSBUSY, 0, 1)) {
boolean init = false;
try { // Initialize table
if (counterCells == as) {
CounterCell[] rs = new CounterCell[2];
rs[h & 1] = new CounterCell(x);
counterCells = rs;
init = true;
}
} finally {
cellsBusy = 0;
}
if (init)
break;
}
else if (U.compareAndSwapLong(this, BASECOUNT, v = baseCount, v + x))
break; // Fall back on using base
}
}
// ConcurrentHashMap.remove 删除元素
/**
* Implementation for the four public remove/replace methods:
* Replaces node value with v, conditional upon match of cv if
* non-null. If resulting value is null, delete.
*/
final V replaceNode(Object key, V value, Object cv) {
int hash = spread(key.hashCode());
for (Node<K,V>[] tab = table;;) {
Node<K,V> f; int n, i, fh;
if (tab == null || (n = tab.length) == 0 ||
(f = tabAt(tab, i = (n - 1) & hash)) == null)
break;
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
boolean validated = false;
synchronized (f) {
if (tabAt(tab, i) == f) {
if (fh >= 0) {
validated = true;
for (Node<K,V> e = f, pred = null;;) {
K ek;
if (e.hash == hash &&
((ek = e.key) == key ||
(ek != null && key.equals(ek)))) {
V ev = e.val;
if (cv == null || cv == ev ||
(ev != null && cv.equals(ev))) {
oldVal = ev;
if (value != null)
e.val = value;
// 删除元素
else if (pred != null)
pred.next = e.next;
else
setTabAt(tab, i, e.next);
}
break;
}
pred = e;
if ((e = e.next) == null)
break;
}
}
else if (f instanceof TreeBin) {
validated = true;
TreeBin<K,V> t = (TreeBin<K,V>)f;
TreeNode<K,V> r, p;
if ((r = t.root) != null &&
(p = r.findTreeNode(hash, key, null)) != null) {
V pv = p.val;
if (cv == null || cv == pv ||
(pv != null && cv.equals(pv))) {
oldVal = pv;
if (value != null)
p.val = value;
// 删除元素
else if (t.removeTreeNode(p))
setTabAt(tab, i, untreeify(t.first));
}
}
}
}
}
if (validated) {
if (oldVal != null) {
// value = null, 代表需要将元素删除，是以需要對計數器做減1操作
if (value == null)
addCount(-1L, -1);
return oldVal;
}
break;
}
}
}
return null;
} 同樣是由于在增删時，維護一個計數器（CounterCell數組）， 是以對于傳回計數值操作則會比較簡單化：
// ConcurrentHashMap.size()
public int size() {
long n = sumCount();
return ((n < 0L) ? 0 :
(n > (long)Integer.MAX_VALUE) ? Integer.MAX_VALUE :
(int)n);
}
// 直接将 CounterCell 中的值相加起來即可
final long sumCount() {
CounterCell[] as = counterCells; CounterCell a;
long sum = baseCount;
if (as != null) {
for (int i = 0; i < as.length; ++i) {
if ((a = as[i]) != null)
sum += a.value;
}
}
return sum;
} 雖然ConcurrentHash的元素本身沒有使用分段式存儲了，但是其計數值還是存在了多個 CounterCell 中，目的自然是為了減少多線程競争對計數器的更新成性能瓶頸。在進行 size() 計數時，并未有上鎖操作，整個 CounterCell 使用 volatile 修飾，保證其可見性，但是整個size 卻是不保證絕對準确的哦。
4. 并發容器 --ArrayBlockingQueue
下面我們再來看看另一各類型的并發容器: ArrayBlockingQueue
// ArrayBlockingQueue.offer()
/**
* Inserts the specified element at the tail of this queue if it is
* possible to do so immediately without exceeding the queue's capacity,
* returning {@code true} upon success and {@code false} if this queue
* is full. This method is generally preferable to method {@link #add},
* which can fail to insert an element only by throwing an exception.
*
* @throws NullPointerException if the specified element is null
*/
public boolean offer(E e) {
checkNotNull(e);
final ReentrantLock lock = this.lock;
// 直接上鎖操作
lock.lock();
try {
if (count == items.length)
return false;
else {
// 進行入隊操作
enqueue(e);
return true;
}
} finally {
lock.unlock();
}
}
/**
* Inserts element at current put position, advances, and signals.
* Call only when holding lock.
*/
private void enqueue(E x) {
// assert lock.getHoldCount() == 1;
// assert items[putIndex] == null;
final Object[] items = this.items;
items[putIndex] = x;
if (++putIndex == items.length)
putIndex = 0;
// 同樣,它還是通過一個 count 的計數器完成統計工作
count++;
notEmpty.signal();
}
// 移除動作時,也需要維護 count 的值
/**
* Deletes item at array index removeIndex.
* Utility for remove(Object) and iterator.remove.
* Call only when holding lock.
*/
void removeAt(final int removeIndex) {
// assert lock.getHoldCount() == 1;
// assert items[removeIndex] != null;
// assert removeIndex >= 0 && removeIndex < items.length;
final Object[] items = this.items;
if (removeIndex == takeIndex) {
// removing front item; just advance
items[takeIndex] = null;
if (++takeIndex == items.length)
takeIndex = 0;
// 移除成功, 将計數器減1
count--;
if (itrs != null)
itrs.elementDequeued();
} else {
// an "interior" remove
// slide over all others up through putIndex.
// 通過輪詢的方式, 必然有一個元素被删除
final int putIndex = this.putIndex;
for (int i = removeIndex;;) {
int next = i + 1;
if (next == items.length)
next = 0;
if (next != putIndex) {
items[i] = items[next];
i = next;
} else {
items[i] = null;
this.putIndex = i;
break;
}
// 計數器相減
count--;
if (itrs != null)
itrs.removedAt(removeIndex);
}
notFull.signal();
} 同樣是維護了一個計數器，但是因為有上鎖機制的保證，整個過程看起來就簡單了許多。在擷取元素大小時,自然也就簡單了.
// ArrayBlockingQueue.size()
/**
* Returns the number of elements in this queue.
*
* @return the number of elements in this queue
*/
public int size() {
final ReentrantLock lock = this.lock;
lock.lock();
try {
return count;
} finally {
lock.unlock();
}
} 但是它為了保證結果的準确性，在計數時，同樣進行了上鎖操作。可見,并發容器的實作思路也基本一緻.并無太多奇淫技巧. 咱們再來看一下并發容器的實作: CopyOnWriteArrayList
5. 并發容器 --CopyOnWriteArrayList
顧名思義，是在寫操作的時候，使用複制方式進行實作。
// CopyOnWriteArrayList.add()
/**
* Appends the specified element to the end of this list.
*
* @param e element to be appended to this list
* @return {@code true} (as specified by {@link Collection#add})
*/
public boolean add(E e) {
final ReentrantLock lock = this.lock;
// 同樣上鎖保證線程安全
lock.lock();
try {
Object[] elements = getArray();
int len = elements.length;
// 将元素copy出來, 但其并非維護一個len字段
Object[] newElements = Arrays.copyOf(elements, len + 1);
newElements[len] = e;
setArray(newElements);
return true;
} finally {
lock.unlock();
}
}
// CopyOnWriteArrayList, 删除一個字段, 同其名稱一樣, 還是使用寫時複制實作
public E remove(int index) {
final ReentrantLock lock = this.lock;
lock.lock();
try {
Object[] elements = getArray();
int len = elements.length;
E oldValue = get(elements, index);
int numMoved = len - index - 1;
if (numMoved == 0)
setArray(Arrays.copyOf(elements, len - 1));
else {
// 找到移除的字段位置, 依次複制其前後元素到新數組中,完成功能
Object[] newElements = new Object[len - 1];
System.arraycopy(elements, 0, newElements, 0, index);
System.arraycopy(elements, index + 1, newElements, index,
numMoved);
setArray(newElements);
}
return oldValue;
} finally {
lock.unlock();
}
}
// CopyOnWriteArrayList.size(), 直接使用數組長度字段
/**
* Returns the number of elements in this list.
*
* @return the number of elements in this list
*/
public int size() {
// 擷取元素大小時,直接擷取所有元素,取數組的長度即可. 借用jvm提供的數組長度元資訊實作
return getArray().length;
}
/**
* Gets the array. Non-private so as to also be accessible
* from CopyOnWriteArraySet class.
*/
final Object[] getArray() {
// 該array字段一定是要保證可見性的, 即至少得是 volatile 修飾的資料
return array;
} CopyOnWriteArrayList, 因為其語義決定，其在一定程度上是線程安全的，是以，在讀操作時，就不需要上鎖，進而性能在某些場景會比較好。
根據功能特性的不同, CopyOnWriteArrayList 采用了一個不同實作方式, 實作了元素的統計功能. 另外像 SynchronousQueue#size, 則永久傳回0, 因為它的定義是當被放一個元素後,必須等到有線程消費之後才可傳回,而其本身并不存儲元素. 是以, 雖然元素計數道理比較簡單通用, 但是還是要按照具體的場景進行相應的實作, 才能滿足具體的需求. 即不可脫離場景談技術.
6. 更多計數
類似資料庫類的産品，同樣的這樣的計數剛性需求，各自實作方式也有不同，但大體思路也差不多。比如 redis 的計數使用在計數時臨時周遊元素實作，mysql myisam 引擎使用一個表級的計數器等等。
不要害怕今日的苦，你要相信明天，更苦！
![01.JAVA并發程式設計-線程的使用-基本概念線程定義線程狀态線程優先級線程優勢線程問題守護線程（Daemon Thread）參考資料[圖]](data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs=)