深度剖析HashMap的数据存储实现原理（看完必懂篇） 具体的原理分析可以参考一下两篇文章，有透彻的分析！ 参考资料： 1. https://www.jianshu.com/p/17177c12f849 [JDK8中的HashMap实现原理及源码分析] 2. https://tech.meituan.com/java-hashmap.html [Java 8系列之重新认识HashMap] 1、关键字段： /** * The default initial capacity - MUST be a power of two. */ static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // 2^4 /** * 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; // 2^30 /** * 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. * * 一个桶的树化阈值 * 当桶中元素个数超过这个值时，需要使用红黑树节点替换链表节点 * 这个值必须为 8，要不然频繁转换效率也不高 */ 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. * * 一个树的链表还原阈值 * 当扩容时，桶中元素个数小于这个值，就会把树形的桶元素 还原（切分）为链表结构 * 这个值应该比上面那个小，至少为 6，避免频繁转换 */ 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. * * 哈希表的最小树形化容量 * 当哈希表中的容量大于这个值时，表中的桶才能进行树形化 * 否则桶内元素太多时会扩容，而不是树形化 * 为了避免进行扩容、树形化选择的冲突，这个值不能小于 4 * TREEIFY_THRESHOLD */ static final int MIN_TREEIFY_CAPACITY = 64; /* ---------------- Fields -------------- */ /** * The table, initialized on first use, and resized as * necessary. When allocated, length is always a power of two. * (We also tolerate length zero in some operations to allow * bootstrapping mechanics that are currently not needed.) * * 为了更好表示本文称之为桶数组 */ 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; /** * 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); } 2、首先针对很多文章中的纰漏语句：如果一个桶中的元素个数超过 TREEIFY_THRESHOLD(默认是8)，就使用红黑树来替换链表。 插入图片！！！ 图片中红色标记的地方个人理解是不够严谨的！！！数据插入HashMap的时候，如果当前桶中的元素个数 > TREEIFY_THRESHOLD时，则会进行桶的树形化处理（见代码片段1：treeifyBin()）。 注意这里只是进行桶的树形化处理，并不是把桶（如果是链表结构）直接转换为红黑树，这里面是有条件的！！！具体规则如下： 条件1. 如果当前桶数组为null或者桶数组的长度 < MIN_TREEIFY_CAPACITY，则进行扩容处理（见代码片段2：resize()）； 条件2. 当不满足条件1的时候则将桶中链表内的元素转换成红黑树！！！稍后再详细讨论红黑树。 3、再来分析下HashMap扩容机制的实现： 概念： 1. 扩容(resize)就是重新计算容量。当向HashMap对象里不停的添加元素，而HashMap对象内部的桶数组无法装载更多的元素时，HashMap对象就需要扩大桶数组的长度，以便能装入更多的元素。 2. capacity 就是数组的长度/大小，loadFactor 是这个数组填满程度的最大比比例。 3. size表示当前HashMap中已经储存的Node<key,value>的数量，包括桶数组和链表 / 红黑树中的的Node<key,value>。 4. threshold表示扩容的临界值，如果size大于这个值，则必需调用resize()方法进行扩容。 5. 在jdk1.7及以前，threshold = capacity * loadFactor，其中 capacity 为桶数组的长度。 这里需要说明一点，默认负载因子0.75是是对空间和时间(纵向横向)效率的一个平衡选择，建议大家不要修改。 jdk1.8对threshold值进行了改进，通过一系列位移操作算法最后得到一个power of two size的值，见代码片段4。 扩容过程： 1. 使用new Hashap<>()时，新桶数组初始容量设置为默认值DEFAULT_INITIAL_CAPACITY，默认容量下的阈值为DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY。 2. 使用new Hashap<>(int initialCapacity)或new HashMap(int initialCapacity, float loadFactor)时，newCap, newThr均重新计算。 3. 如果使用过程中HashMap中的数据过多，原始容量不够使用，那么需要扩容。扩容是以2^1为单位扩容的， newCap = oldCap << 1和newThr = oldThr << 1。 4. 如果原来的桶数组长度大于最大值MAXIMUM_CAPACITY时，扩容临界值提高到正无穷（Integer.MAX_VALUE），返回原来的数组，也就是系统已经管不了了，随便你怎么玩吧。 正常扩容之后需要将老的桶数组数据重新放到新的桶数组中，同时对每个桶上的链表进行了重排，再介绍重排之前先来看看代码片段5里面的hash()计算： 首先将得到key对应的哈希值：h = key.hashCode()，然后通过hashCode()的高16位异或低16位计算得到最终的key.hash值（(h = key.hashCode()) ^ (h >>> 16)）。 1. 取key的hashcode值： ① Object类的hashCode 返回对象的经过处理后的内存地址，由于每个对象的内存地址都不一样，所以哈希码也不一样。这个是native方法，取决于JVM的内部设计，一般是某种C地址的偏移。 ② String类的hashCode 根据String类包含的字符串的内容，根据一种特殊算法返回哈希码，只要字符串的内容相同，返回的哈希码也相同。 ③ Integer等包装类 返回的哈希码就是Integer对象里所包含的那个整数的数值，例如Integer i1=new Integer(100)，i1.hashCode的值就是100。 由此可见，2个一样大小的Integer对象，返回的哈希码也一样。 ④ int，char这样的基础类 它们不需要hashCode，如果需要存储时，将进行自动装箱操作，计算方法包装类。 2. hashCode()的高16位异或低16位 在JDK1.8的实现中，优化了高位运算的算法，通过hashCode()的高16位异或低16位实现的：key.hash = (h = k.hashCode()) ^ (h >>> 16)， 主要是从速度、功效、质量来考虑的，这么做可以在数组table的length比较小的时候，也能保证考虑到高低Bit都参与到Hash的计算中，同时不会有太大的开销。 3. key.hash & (n - 1) 取模运算 这个n我们说过是table的长度，那么n-1就是table数组元素应有的下表。这个方法非常巧妙，它通过 key.hash & (table.length - 1) 来得到该对象的保存位， 而HashMap底层数组的长度总是2的n次方，这是HashMap在速度上的优化。当length总是2的n次方时，key.hash & (table.length - 1) 运算等价于对length取模，也就是key.hash % length，但是&比%具有更高的效率。 链表重排： 1. 如果原桶上只有一个节点，并且该节点不是红黑树节点，那么直接放到新桶原索引key.hash & (table.length - 1)下； 2. 如果原桶上的节点是红黑树节点，那么则对该树进行分割split()； 3. 如果原桶上的节点是一个链表，则进行链表重排算法: 由于桶数组的容量是按2次幂的扩展(指容量扩为原来2倍)，所以，元素的位置要么是在“原索引”，要么是在“原索引 + oldCap”的位置。 所以，只需要看看原来key.hash值新增的那个bit是1还是0就好了，是0的话索引没变，是1的话索引变成“原索引 + oldCap”。 4、HashMap的数据存储实现原理 流程： 1. 根据key计算得到key.hash = (h = k.hashCode()) ^ (h >>> 16)； 2. 根据key.hash计算得到桶数组的索引index = key.hash & (table.length - 1)，这样就找到该key的存放位置了： ① 如果该位置没有数据，用该数据新生成一个节点保存新数据，返回null； ② 如果该位置有数据是一个红黑树，那么执行相应的插入 / 更新操作，稍后再详细讨论红黑树； ③ 如果该位置有数据是一个链表，分两种情况一是该链表没有这个节点，另一个是该链表上有这个节点，注意这里判断的依据是key.hash是否一样： 如果该链表没有这个节点，那么采用尾插法新增节点保存新数据，返回null； 如果该链表已经有这个节点了，那么找到該节点并更新新数据，返回老数据。 注意： HashMap的put会返回key的上一次保存的数据，比如： HashMap<String, String> map = new HashMap<String, String>(); System.out.println(map.put("a", "A")); // 打印null System.out.println(map.put("a", "AA")); // 打印A System.out.println(map.put("a", "AB")); // 打印AA 5、红黑树 上面的讨论中对于红黑树并没有深入分析，HashMap的数据存储中主要有两种场景用到红黑树的操作： 1. 当满足一定条件（条件2，见上文）时，单链表内的数据会转换为红黑树存储（见代码片段2：treeifyBin()）。 2. 当HashMap桶结构由链表转换为红黑树后，再往里put数据将变成往红黑树插入 / 更新数据，这和链表又不太一样了。 下面进行逐一详细分析： 未完待续。。。。。。 源码片段1： /** * 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; } 源码片段2： 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); } } 源码片段3： /** * 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; // 原索引 if ((e.hash & oldCap) == 0) { if (loTail == null) loHead = e; else loTail.next = e; loTail = e; } // 原索引 + oldCap else { if (hiTail == null) hiHead = e; else hiTail.next = e; hiTail = e; } } while ((e = next) != null); // 原索引放到桶数组里 if (loTail != null) { loTail.next = null; newTab[j] = loHead; } // 原索引 + oldCap放到桶数组里 if (hiTail != null) { hiTail.next = null; newTab[j + oldCap] = hiHead; } } } } } return newTab; } 源码片段4： /** * 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; } 源码片段5： /** * 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); } 谢谢！欢迎批评指正！！！