源码分析--HashMap(JDK1.8)
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2022-04-28 13:56:34
在JDK1.8中对HashMap的底层实现做了修改。本篇对HashMap源码从核心成员变量到常用方法进行分析。 HashMap数据结构如下: 先看成员变量: 1、底层存放数据的是Node[]数组,数组初始化大小为16。 2、Node[]数组最大容量 3、负载因子0.75。也就是如 ......
在jdk1.8中对hashmap的底层实现做了修改。本篇对hashmap源码从核心成员变量到常用方法进行分析。
hashmap数据结构如下:
先看成员变量:
1、底层存放数据的是node<k,v>[]数组,数组初始化大小为16。
/** * the default initial capacity - must be a power of two. */ static final int default_initial_capacity = 1 << 4; // aka 16
2、node<k,v>[]数组最大容量
/** * 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;
3、负载因子0.75。也就是如果默认初始化,hashmap在size = 16*0.75 = 12时,进行扩容。
/** * the load factor used when none specified in constructor. */ static final float default_load_factor = 0.75f;
4、将链表转化为红黑数的阀值。
/** * 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;
5、红黑树节点转换为链表的阀值
/** * 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;
6、转红黑树时,table的最小长度
/** * 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;
介绍一下hashmap用hash值定位数组index的过程:
//hahsmap中的静态方法
static final int hash(object key) { int h; return (key == null) ? 0 : (h = key.hashcode()) ^ (h >>> 16); }
//定位计算
int index = (table.length - 1) & hash
- 先得到key的hashcode值
- 再将hashcode值与hashcode无符号右移16位的值进行按位异或运算。得到hash值
- 将(table.length - 1) 与 hash值进行与运算。定位数组index
给一个长度为16的数组,以"testhash"为key,进行定位的过程实例:
hashmap中node就是放入的数据节点,代码定义为:
static class node<k,v> implements map.entry<k,v> { final int hash; final k key; v value; node<k,v> next; }
node节点保存key的hash值和k--v,借助next可实现链表。
红黑树封装为treenode节点:
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); }
介绍get()方法:
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; }
- index定位,得到该索引上的node节点,赋值给first
- 对first节点进行判断,如果是要找的元素,直接返回
- first节点的next不为空,继续找
- 如果first节点是红黑树,调用gettreenode()获取值。
- 不是红黑树,只能是链表。从头遍历,找到就返回。
上面对于红黑树取值的gettreenode()方法,看一下红黑树的遍历做法:
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; }
- 从do-while循环里的第一个if开始。如果当前节点的hash比传入的hash大,往p节点的左边遍历
- 如果当前节点的hash比传入的hash小,往p节点的右边遍历
- 如果key值相同,就找到节点了。返回
- 左节点为空,转到右边遍历
- 右节点为空,转到左边
- 如果传入key实现了comparable接口。就将传入key与p节点key进行比较,根据比较结果选择向左或向右遍历
- 没有实现接口,直接向右遍历,找到就返回
- 没找到,向左遍历
介绍put()方法:
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; }
- table为null,初始化
- 定位到数组index,若该位置为空,直接放
- 如果该位置上不为空,且hash和key与传入的值相同,说明key重复,直接将该节点赋值给e,结束循环
- 如果该index上的节点是红黑树,调用puttreeval()方法
- 不是红黑树,只能是链表,遍历整个链表
- 找到最后一个节点,在这个节点后面以k--v新增一个节点。
- 判断链表长度,bincount达到7,也就是长度达到8。转为红黑树。
- 遍历过程中,如果找到了相同key,就跳出循环。
- 如果e不为空,说明遍历结束后存在key重复的节点。做值覆盖
- 扩容判断
分析红黑树插入方法puttreeval():
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; } } }
- 查找root根节点
- 从root节点开始遍历
- 如果当前节点p的hash大于传入的hash值,记dir为-1,代表向左遍历。
- 小于,记1,代表向右遍历
- 如果key相同,直接返回
- 如果key所属的类实现comparable接口,或者key相等。先从p的左节点、右节点分别调用find(),找到就返回。
- 没找到,比较p和传入的key值,结果记为dir
- 根据dir选择向左或向右遍历
- 依次遍历,直到为null,表示达到最后一个节点,插入新节点
- 调整位置
分析hashmap扩容方法:
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; }
- 通过一系列判断,确认新table的容量
- 构造一个新容量的node数组,赋值给table
- 遍历旧table数组
- 如果节点是单节点,直接定位到新数组对应的index位置下
- 如果是红黑树,调用split方法
- 遍历链表。
- 如果e的hash值与老数组容量取与运算,值为0。索引位置不变
- 如果e的hash值与老数组容量取与运算,值为1。这在新数组中索引的位置为老数组索引 + 老数组容量。
- 链表放置
简要分析多线程下hashmap死循环问题:
jdk1.7hashmap扩容时,对于链表位置变化,采用头插法进行操作。多线程下容易形成环形链表,造成死循环。
jdk1.8时,会对于链表hash值与容量的计算结果。分成两部分,并改为插入到链表尾部。1.8以后不会再有死循环问题,只是有可能重复放置导致数据丢失。hashmap本身线程不安全的特性并没有改变。