Class - java.util.concurrent.ConcurrentHashMap
Created by : Mr Dk.
2020 / 10 / 14 16:47
Nanjing, Jiangsu, China
Definition
支持全量并发的读取操作和高并发量的更新操作。对于依赖 线程安全 特性而不是 同步细节 的程序来说，与 java.util.Hashtable 可以互相替换。后续需要对这两个线程安全的容器作一个比较。
/**
* A hash table supporting full concurrency of retrievals and
* high expected concurrency for updates. This class obeys the
* same functional specification as {@link java.util.Hashtable}, and
* includes versions of methods corresponding to each method of
* {@code Hashtable}. However, even though all operations are
* thread-safe, retrieval operations do <em>not</em> entail locking,
* and there is <em>not</em> any support for locking the entire table
* in a way that prevents all access. This class is fully
* interoperable with {@code Hashtable} in programs that rely on its
* thread safety but not on its synchronization details.
*
* <p>Retrieval operations (including {@code get}) generally do not
* block, so may overlap with update operations (including {@code put}
* and {@code remove}). Retrievals reflect the results of the most
* recently <em>completed</em> update operations holding upon their
* onset. (More formally, an update operation for a given key bears a
* <em>happens-before</em> relation with any (non-null) retrieval for
* that key reporting the updated value.) For aggregate operations
* such as {@code putAll} and {@code clear}, concurrent retrievals may
* reflect insertion or removal of only some entries. Similarly,
* Iterators, Spliterators and Enumerations return elements reflecting the
* state of the hash table at some point at or since the creation of the
* iterator/enumeration. They do <em>not</em> throw {@link
* java.util.ConcurrentModificationException ConcurrentModificationException}.
* However, iterators are designed to be used by only one thread at a time.
* Bear in mind that the results of aggregate status methods including
* {@code size}, {@code isEmpty}, and {@code containsValue} are typically
* useful only when a map is not undergoing concurrent updates in other threads.
* Otherwise the results of these methods reflect transient states
* that may be adequate for monitoring or estimation purposes, but not
* for program control.
*
* <p>The table is dynamically expanded when there are too many
* collisions (i.e., keys that have distinct hash codes but fall into
* the same slot modulo the table size), with the expected average
* effect of maintaining roughly two bins per mapping (corresponding
* to a 0.75 load factor threshold for resizing). There may be much
* variance around this average as mappings are added and removed, but
* overall, this maintains a commonly accepted time/space tradeoff for
* hash tables. However, resizing this or any other kind of hash
* table may be a relatively slow operation. When possible, it is a
* good idea to provide a size estimate as an optional {@code
* initialCapacity} constructor argument. An additional optional
* {@code loadFactor} constructor argument provides a further means of
* customizing initial table capacity by specifying the table density
* to be used in calculating the amount of space to allocate for the
* given number of elements. Also, for compatibility with previous
* versions of this class, constructors may optionally specify an
* expected {@code concurrencyLevel} as an additional hint for
* internal sizing. Note that using many keys with exactly 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>A {@link Set} projection of a ConcurrentHashMap may be created
* (using {@link #newKeySet()} or {@link #newKeySet(int)}), or viewed
* (using {@link #keySet(Object)} when only keys are of interest, and the
* mapped values are (perhaps transiently) not used or all take the
* same mapping value.
*
* <p>A ConcurrentHashMap can be used as scalable frequency map (a
* form of histogram or multiset) by using {@link
* java.util.concurrent.atomic.LongAdder} values and initializing via
* {@link #computeIfAbsent computeIfAbsent}. For example, to add a count
* to a {@code ConcurrentHashMap<String,LongAdder> freqs}, you can use
* {@code freqs.computeIfAbsent(k -> new LongAdder()).increment();}
*
* <p>This class and its views and iterators implement all of the
* <em>optional</em> methods of the {@link Map} and {@link Iterator}
* interfaces.
*
* <p>Like {@link Hashtable} but unlike {@link HashMap}, this class
* does <em>not</em> allow {@code null} to be used as a key or value.
*
* <p>ConcurrentHashMaps support a set of sequential and parallel bulk
* operations that, unlike most {@link Stream} methods, are designed
* to be safely, and often sensibly, applied even with maps that are
* being concurrently updated by other threads; for example, when
* computing a snapshot summary of the values in a shared registry.
* There are three kinds of operation, each with four forms, accepting
* functions with Keys, Values, Entries, and (Key, Value) arguments
* and/or return values. Because the elements of a ConcurrentHashMap
* are not ordered in any particular way, and may be processed in
* different orders in different parallel executions, the correctness
* of supplied functions should not depend on any ordering, or on any
* other objects or values that may transiently change while
* computation is in progress; and except for forEach actions, should
* ideally be side-effect-free. Bulk operations on {@link java.util.Map.Entry}
* objects do not support method {@code setValue}.
*
* <ul>
* <li> forEach: Perform a given action on each element.
* A variant form applies a given transformation on each element
* before performing the action.</li>
*
* <li> search: Return the first available non-null result of
* applying a given function on each element; skipping further
* search when a result is found.</li>
*
* <li> reduce: Accumulate each element. The supplied reduction
* function cannot rely on ordering (more formally, it should be
* both associative and commutative). There are five variants:
*
* <ul>
*
* <li> Plain reductions. (There is not a form of this method for
* (key, value) function arguments since there is no corresponding
* return type.)</li>
*
* <li> Mapped reductions that accumulate the results of a given
* function applied to each element.</li>
*
* <li> Reductions to scalar doubles, longs, and ints, using a
* given basis value.</li>
*
* </ul>
* </li>
* </ul>
*
* <p>These bulk operations accept a {@code parallelismThreshold}
* argument. Methods proceed sequentially if the current map size is
* estimated to be less than the given threshold. Using a value of
* {@code Long.MAX_VALUE} suppresses all parallelism. Using a value
* of {@code 1} results in maximal parallelism by partitioning into
* enough subtasks to fully utilize the {@link
* ForkJoinPool#commonPool()} that is used for all parallel
* computations. Normally, you would initially choose one of these
* extreme values, and then measure performance of using in-between
* values that trade off overhead versus throughput.
*
* <p>The concurrency properties of bulk operations follow
* from those of ConcurrentHashMap: Any non-null result returned
* from {@code get(key)} and related access methods bears a
* happens-before relation with the associated insertion or
* update. The result of any bulk operation reflects the
* composition of these per-element relations (but is not
* necessarily atomic with respect to the map as a whole unless it
* is somehow known to be quiescent). Conversely, because keys
* and values in the map are never null, null serves as a reliable
* atomic indicator of the current lack of any result. To
* maintain this property, null serves as an implicit basis for
* all non-scalar reduction operations. For the double, long, and
* int versions, the basis should be one that, when combined with
* any other value, returns that other value (more formally, it
* should be the identity element for the reduction). Most common
* reductions have these properties; for example, computing a sum
* with basis 0 or a minimum with basis MAX_VALUE.
*
* <p>Search and transformation functions provided as arguments
* should similarly return null to indicate the lack of any result
* (in which case it is not used). In the case of mapped
* reductions, this also enables transformations to serve as
* filters, returning null (or, in the case of primitive
* specializations, the identity basis) if the element should not
* be combined. You can create compound transformations and
* filterings by composing them yourself under this "null means
* there is nothing there now" rule before using them in search or
* reduce operations.
*
* <p>Methods accepting and/or returning Entry arguments maintain
* key-value associations. They may be useful for example when
* finding the key for the greatest value. Note that "plain" Entry
* arguments can be supplied using {@code new
* AbstractMap.SimpleEntry(k,v)}.
*
* <p>Bulk operations may complete abruptly, throwing an
* exception encountered in the application of a supplied
* function. Bear in mind when handling such exceptions that other
* concurrently executing functions could also have thrown
* exceptions, or would have done so if the first exception had
* not occurred.
*
* <p>Speedups for parallel compared to sequential forms are common
* but not guaranteed. Parallel operations involving brief functions
* on small maps may execute more slowly than sequential forms if the
* underlying work to parallelize the computation is more expensive
* than the computation itself. Similarly, parallelization may not
* lead to much actual parallelism if all processors are busy
* performing unrelated tasks.
*
* <p>All arguments to all task methods must be non-null.
*
* <p>This class is a member of the
* <a href="{@docRoot}/../technotes/guides/collections/index.html">
* Java Collections Framework</a>.
*
* @since 1.5
* @author Doug Lea
* @param <K> the type of keys maintained by this map
* @param <V> the type of mapped values
*/
public class ConcurrentHashMap<K,V> extends AbstractMap<K,V>
implements ConcurrentMap<K,V>, Serializable {
}
Put
由于之前已经分析过 java.util.HashMap 的源代码，对于并发版的容器来说，我决定从 put() 函数出发，梳理其中的并发特性。
/**
* Maps the specified key to the specified value in this table.
* Neither the key nor the value can be null.
*
* <p>The value can be retrieved by calling the {@code get} method
* with a key that is equal to the original key.
*
* @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 {@code key}, or
* {@code null} if there was no mapping for {@code key}
* @throws NullPointerException if the specified key or value is null
*/
public V put(K key, V value) {
return putVal(key, value, false);
}
/** 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;
}
}
}
addCount(1L, binCount);
return null;
}
首先，既然要 put() 一个新元素，首先需要计算它的 hashcode，确定把它放到哪一个 slot 中。计算 hashcode 的函数如下所示。可以看到这里做了一个与运算，抹去了 hashcode 的最高位，保证了 hashcode 肯定是一个正数。
static final int HASH_BITS = 0x7fffffff; // usable bits of normal node hash
/**
* Spreads (XORs) higher bits of hash to lower and also forces top
* bit to 0. 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 spread(int h) {
return (h ^ (h >>> 16)) & HASH_BITS;
}
接下来就是对整个 HashMap 进行 for 循环遍历了。遍历的目标是核心数据结构，也就是 HashMap 对应的 key-value 数组：
/**
* The array of bins. Lazily initialized upon first insertion.
* Size is always a power of two. Accessed directly by iterators.
*/
transient volatile Node<K,V>[] table;
For 循环中的几个分支分别对应了几种情况：
table为空，则进行初始化 (懒初始化)，只允许一个线程初始化- hashcode 所在的槽为空，那么直接通过 CAS 操作把新结点放进去 (可能会有多个线程同时试图放进去，所以要 CAS)
- 该位置正被扩容，则当前线程也加入该位置的辅助扩容中
- 该位置所在槽不为空，且没有在扩容，那么通过
synchronized锁住这个位置，然后开始单线程操作
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;
}
}
第一步是初始化 key-value 数组 - 这件事由一个线程来完成就够了。在这个类中，由 sizeCtl 成员变量来确定由哪个线程负责初始化。一开始该变量的值为 0，多个线程通过 CAS 操作试图将其赋值为 -1，只有 CAS 成功了的那个线程可以负责进行初始化，其它线程全部自旋等待：
/**
* Table initialization and resizing control. When negative, the
* table is being initialized or resized: -1 for initialization,
* else -(1 + the number of active resizing threads). Otherwise,
* when table is null, holds the initial table size to use upon
* creation, or 0 for default. After initialization, holds the
* next element count value upon which to resize the table.
*/
private transient volatile int sizeCtl;
/**
* Initializes table, using the size recorded in sizeCtl.
*/
private final Node<K,V>[] initTable() {
Node<K,V>[] tab; int sc;
while ((tab = table) == null || tab.length == 0) {
if ((sc = sizeCtl) < 0) // 已经有一个线程将 sizeCtl 赋值为 -1 了，那么自旋等待
Thread.yield(); // lost initialization race; just spin
else if (U.compareAndSwapInt(this, SIZECTL, sc, -1)) { // CAS 将 sizeCtl 赋值为 -1
try {
if ((tab = table) == null || tab.length == 0) {
int n = (sc > 0) ? sc : DEFAULT_CAPACITY;
@SuppressWarnings("unchecked")
Node<K,V>[] nt = (Node<K,V>[])new Node<?,?>[n]; // 初始化数组
table = tab = nt;
sc = n - (n >>> 2);
}
} finally {
sizeCtl = sc;
}
break;
}
}
return tab;
}
第二步，数组中的 slot 为空。那没什么好说的，初始化一个新的 Node 然后放进去就可以了。需要注意的是可能有多个线程同时想往这个位置放新的 Node，因此放入的过程一定是 CAS 的：
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
}
@SuppressWarnings("unchecked")
static final <K,V> Node<K,V> tabAt(Node<K,V>[] tab, int i) {
return (Node<K,V>)U.getObjectVolatile(tab, ((long)i << ASHIFT) + ABASE);
}
static final <K,V> boolean casTabAt(Node<K,V>[] tab, int i,
Node<K,V> c, Node<K,V> v) {
return U.compareAndSwapObject(tab, ((long)i << ASHIFT) + ABASE, c, v);
}
第三步，如果发现当前要放入 Node 的位置 (表头结点) 正在进行扩容，则当前线程也加入辅助扩容。正在进行扩容时，sizeCtl 用于记录正在进行扩容的并发线程数 - 所以每个线程要通过 CAS 把 sizeCtl 自增后才会进入扩容；扩容结束后，再将 sizeCtl 减下来。每个线程会负责一定量的 slot 迁移，具体数量由 CPU 核心数与 table 数组长度决定，每个线程至少要负责 16 个 slot 的迁移。
在扩容过程中：
- 还没开始迁移的 slot 可以正常进行
get()/put() - 正在进行迁移的 slot -
get()不被阻塞，put()操作被阻塞 - 迁移已完毕的 slot -
get()/put()操作会被转发到扩容后的新数组上
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
第四步，即要插入的位置已有元素 f，且未在迁移中，那么锁定该表头元素，然后当前线程独占这个位置，直到插入完成。插入分为链表和红黑树两种形式。在插入结束后，判断一次是否当前位置是否需要进行链表 -> 红黑树的转换。
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;
}
}
Get
关于 get() 操作，可以看到是全程无锁的。首先用 key 计算 hashcode，判断出结点所在的 slot。如果 key 和表头的 key 相等则直接返回，否则就需要对冲突链表进行遍历。
/**
* 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.equals(k)},
* then this method returns {@code v}; otherwise it returns
* {@code null}. (There can be at most one such mapping.)
*
* @throws NullPointerException if the specified key is null
*/
public V get(Object key) {
Node<K,V>[] tab; Node<K,V> e, p; int n, eh; K ek;
int h = spread(key.hashCode());
if ((tab = table) != null && (n = tab.length) > 0 &&
(e = tabAt(tab, (n - 1) & h)) != null) {
if ((eh = e.hash) == h) {
if ((ek = e.key) == key || (ek != null && key.equals(ek)))
return e.val;
}
else if (eh < 0)
return (p = e.find(h, key)) != null ? p.val : null;
while ((e = e.next) != null) {
if (e.hash == h &&
((ek = e.key) == key || (ek != null && key.equals(ek))))
return e.val;
}
}
return null;
}
Comparison with Collections.synchronizedMap()
Map m = Collections.synchronizedMap(new HashMap());
Collections 类对普通 HashMap 的并发封装具体做了什么工作呢？实际上，该类只是将 HashMap 包了一层，包装为 SynchronizedMap。
/**
* Returns a synchronized (thread-safe) map backed by the specified
* map. In order to guarantee serial access, it is critical that
* <strong>all</strong> access to the backing map is accomplished
* through the returned map.<p>
*
* It is imperative that the user manually synchronize on the returned
* map when iterating over any of its collection views:
* <pre>
* Map m = Collections.synchronizedMap(new HashMap());
* ...
* Set s = m.keySet(); // Needn't be in synchronized block
* ...
* synchronized (m) { // Synchronizing on m, not s!
* Iterator i = s.iterator(); // Must be in synchronized block
* while (i.hasNext())
* foo(i.next());
* }
* </pre>
* Failure to follow this advice may result in non-deterministic behavior.
*
* <p>The returned map will be serializable if the specified map is
* serializable.
*
* @param <K> the class of the map keys
* @param <V> the class of the map values
* @param m the map to be "wrapped" in a synchronized map.
* @return a synchronized view of the specified map.
*/
public static <K,V> Map<K,V> synchronizedMap(Map<K,V> m) {
return new SynchronizedMap<>(m);
}
而在这个包装出的 SynchronizedMap 中，额外维护了一个对象 mutex 作为锁。这个锁对象可以由用户通过构造函数指定，如不指定默认为当前的 SynchronizedMap 对象自身。
/**
* @serial include
*/
private static class SynchronizedMap<K,V>
implements Map<K,V>, Serializable {
private static final long serialVersionUID = 1978198479659022715L;
private final Map<K,V> m; // Backing Map
final Object mutex; // Object on which to synchronize
SynchronizedMap(Map<K,V> m) {
this.m = Objects.requireNonNull(m);
mutex = this;
}
SynchronizedMap(Map<K,V> m, Object mutex) {
this.m = m;
this.mutex = mutex;
}
// ...
}
有了这个锁对象后，对于所有的操作，首先要拿到这个锁，然后再进行相应的操作，从而实现了线程同步：
public int size() {
synchronized (mutex) {return m.size();}
}
public boolean isEmpty() {
synchronized (mutex) {return m.isEmpty();}
}
public boolean containsKey(Object key) {
synchronized (mutex) {return m.containsKey(key);}
}
public boolean containsValue(Object value) {
synchronized (mutex) {return m.containsValue(value);}
}
public V get(Object key) {
synchronized (mutex) {return m.get(key);}
}
public V put(K key, V value) {
synchronized (mutex) {return m.put(key, value);}
}
public V remove(Object key) {
synchronized (mutex) {return m.remove(key);}
}
public void putAll(Map<? extends K, ? extends V> map) {
synchronized (mutex) {m.putAll(map);}
}
public void clear() {
synchronized (mutex) {m.clear();}
}
private transient Set<K> keySet;
private transient Set<Map.Entry<K,V>> entrySet;
private transient Collection<V> values;
public Set<K> keySet() {
synchronized (mutex) {
if (keySet==null)
keySet = new SynchronizedSet<>(m.keySet(), mutex);
return keySet;
}
}
public Set<Map.Entry<K,V>> entrySet() {
synchronized (mutex) {
if (entrySet==null)
entrySet = new SynchronizedSet<>(m.entrySet(), mutex);
return entrySet;
}
}
public Collection<V> values() {
synchronized (mutex) {
if (values==null)
values = new SynchronizedCollection<>(m.values(), mutex);
return values;
}
}
public boolean equals(Object o) {
if (this == o)
return true;
synchronized (mutex) {return m.equals(o);}
}
public int hashCode() {
synchronized (mutex) {return m.hashCode();}
}
public String toString() {
synchronized (mutex) {return m.toString();}
}
// Override default methods in Map
@Override
public V getOrDefault(Object k, V defaultValue) {
synchronized (mutex) {return m.getOrDefault(k, defaultValue);}
}
@Override
public void forEach(BiConsumer<? super K, ? super V> action) {
synchronized (mutex) {m.forEach(action);}
}
@Override
public void replaceAll(BiFunction<? super K, ? super V, ? extends V> function) {
synchronized (mutex) {m.replaceAll(function);}
}
@Override
public V putIfAbsent(K key, V value) {
synchronized (mutex) {return m.putIfAbsent(key, value);}
}
@Override
public boolean remove(Object key, Object value) {
synchronized (mutex) {return m.remove(key, value);}
}
@Override
public boolean replace(K key, V oldValue, V newValue) {
synchronized (mutex) {return m.replace(key, oldValue, newValue);}
}
@Override
public V replace(K key, V value) {
synchronized (mutex) {return m.replace(key, value);}
}
@Override
public V computeIfAbsent(K key,
Function<? super K, ? extends V> mappingFunction) {
synchronized (mutex) {return m.computeIfAbsent(key, mappingFunction);}
}
@Override
public V computeIfPresent(K key,
BiFunction<? super K, ? super V, ? extends V> remappingFunction) {
synchronized (mutex) {return m.computeIfPresent(key, remappingFunction);}
}
@Override
public V compute(K key,
BiFunction<? super K, ? super V, ? extends V> remappingFunction) {
synchronized (mutex) {return m.compute(key, remappingFunction);}
}
@Override
public V merge(K key, V value,
BiFunction<? super V, ? super V, ? extends V> remappingFunction) {
synchronized (mutex) {return m.merge(key, value, remappingFunction);}
}
private void writeObject(ObjectOutputStream s) throws IOException {
synchronized (mutex) {s.defaultWriteObject();}
}
Compared with Hashtable
Hashtable 中基本所有的函数都被 synchronized 关键字修饰 (效率慢？)。
从 Java 8 开始，
synchronized关键字实际上会经历锁升级过程逐步重量化。所以到底是无锁的 CAS 更快还是synchronized更快好像也很难下定论。
References
知乎专栏 - 《进大厂系列》系列-ConcurrentHashMap & HashTable
知乎专栏 - ConcurrentHashMap 源码分析 (基于 JDK 1.8)