Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

Latest commit

 

History

History
History
2462 lines (2321 loc) · 97.7 KB

File metadata and controls

2462 lines (2321 loc) · 97.7 KB
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/*
* Copyright (c) 1997, 2013, Oracle and/or its affiliates. All rights reserved.
* ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*/
package java.util;
import java.io.IOException;
import java.io.InvalidObjectException;
import java.io.Serializable;
import java.lang.reflect.ParameterizedType;
import java.lang.reflect.Type;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.Consumer;
import java.util.function.Function;
/**
* Hash table based implementation of the <tt>Map</tt> interface. This
* implementation provides all of the optional map operations, and permits
* <tt>null</tt> values and the <tt>null</tt> key. (The <tt>HashMap</tt>
* class is roughly equivalent to <tt>Hashtable</tt>, except that it is
* unsynchronized and permits nulls.) This class makes no guarantees as to
* the order of the map; in particular, it does not guarantee that the order
* will remain constant over time.
* 实现 Map 的接口,允许 null 值,不同于于 Hashtable,是线程不安全的
*
* <p>This implementation provides constant-time performance for the basic
* operations (<tt>get</tt> and <tt>put</tt>), assuming(假设) the hash function
* disperses the elements properly among the buckets. Iteration over
* collection views requires time proportional to the "capacity" of the
* <tt>HashMap</tt> instance (the number of buckets) plus its size (the number
* of key-value mappings). Thus, it's very important not to set the initial
* capacity too high (or the load factor too low) if iteration performance is
* important.
* get、put 的实现达到了常数的时间
* 要达到好的迭代效果,initial capacity 不要太高,load factor 不要太低
*
* <p>An instance of <tt>HashMap</tt> has two parameters that affect its
* performance: <i>initial capacity</i> and <i>load factor</i>. The
* <i>capacity</i> is the number of buckets in the hash table, and the initial
* capacity is simply the capacity at the time the hash table is created. The
* <i>load factor</i> is a measure(测量) of how full the hash table is allowed to
* get before its capacity is automatically increased. When the number of
* entries in the hash table exceeds the product of the load factor and the
* current capacity, the hash table is <i>rehashed</i> (that is, internal data
* structures are rebuilt) so that the hash table has approximately twice the
* number of buckets.
*
* initial capacity 和 load factor 两个参数能影响到 HashMap 的性能
* capacity:是 hash table 的可用 buckets(桶) 的数量
* initial capacity:是 hash table 初始大小。
* load factor:是测量 hash table 该多满的时候才去扩容。
*
* <p>as a general rule, the default load factor (.75) offers a good
* tradeoff between time and space costs. Higher values decrease the
* space overhead but increase the lookup cost (reflected in most of
* the operations of the <tt>HashMap</tt> class, including
* <tt>get</tt> and <tt>put</tt>). The expected number of entries in
* the map and its load factor should be taken into account when
* setting its initial capacity, so as to minimize the number of
* rehash operations. If the initial capacity is greater than the
* maximum number of entries divided by the load factor, no rehash
* operations will ever occur.(0.75太大太小的危害)
*
* load factor 默认值 0.75 是均衡了时间和空间
* 较高的值会减少空间开销(扩容减少),但增加了查找成本(hashcode 增加,链表长度变长)
* 不扩容的条件:initial capacity > 需要的数组大小 / load factor
*
* <p>If many mappings are to be stored in a <tt>HashMap</tt>
* instance, creating it with a sufficiently large capacity will allow
* the mappings to be stored more efficiently than letting it perform
* automatic rehashing as needed to grow the table. Note that using
* many keys with 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.
*
* 如果有很多数据需要储存到 HashMap 中,建议 HashMap 的容量一开始就设置成足够的大小,
* 这样可以防止在其过程中不断的扩容,影响性能。
*
* <p><strong>Note that this implementation is not synchronized.</strong>
* If multiple threads access a hash map concurrently, and at least one of
* the threads modifies the map structurally, it <i>must</i> be
* synchronized externally. (A structural modification is any operation
* that adds or deletes one or more mappings; merely changing the value
* associated with a key that an instance already contains is not a
* structural modification.) This is typically accomplished by
* synchronizing on some object that naturally encapsulates the map.
*
* If no such object exists, the map should be "wrapped" using the
* {@link Collections#synchronizedMap Collections.synchronizedMap}
* method. This is best done at creation time, to prevent accidental
* unsynchronized access to the map:<pre>
* Map m = Collections.synchronizedMap(new HashMap(...));</pre>
*
* HashMap 是非线程安全的,我们可以自己在外部加锁,或者通过
* Collections#synchronizedMap 来实现。
*
* <p>The iterators returned by all of this class's "collection view methods"
* are <i>fail-fast</i>: if the map is structurally modified at any time after
* the iterator is created, in any way except through the iterator's own
* <tt>remove</tt> method, the iterator will throw a
* {@link ConcurrentModificationException}. Thus, in the face of concurrent
* modification, the iterator fails quickly and cleanly, rather than risking
* arbitrary, non-deterministic behavior at an undetermined time in the
* future.
* 在迭代过程中,如果 HashMap 的结构被修改,会快速失败。
*
* <p>Note that the fail-fast behavior of an iterator cannot be guaranteed
* as it is, generally speaking, impossible to make any hard guarantees in the
* presence of unsynchronized concurrent modification. Fail-fast iterators
* throw <tt>ConcurrentModificationException</tt> on a best-effort basis.
* Therefore, it would be wrong to write a program that depended on this
* exception for its correctness: <i>the fail-fast behavior of iterators
* should be used only to detect bugs.</i>
*
* <p>This class is a member of the
* <a href="{@docRoot}/../technotes/guides/collections/index.html">
* Java Collections Framework</a>.
*
* @param <K> the type of keys maintained by this map
* @param <V> the type of mapped values
*
* @author Doug Lea
* @author Josh Bloch
* @author Arthur van Hoff
* @author Neal Gafter
* @see Object#hashCode()
* @see Collection
* @see Map
* @see TreeMap
* @see Hashtable
* @since 1.2
*/
public class HashMap<K,V> extends AbstractMap<K,V>
implements Map<K,V>, Cloneable, Serializable {
private static final long serialVersionUID = 362498820763181265L;
/*
* Implementation notes.
*
* This map usually acts as a binned (bucketed) hash table, but
* when bins get too large, they are transformed into bins of
* TreeNodes, each structured similarly to those in
* java.util.TreeMap. Most methods try to use normal bins, but
* relay to TreeNode methods when applicable (simply by checking
* instanceof a node). Bins of TreeNodes may be traversed and
* used like any others, but additionally support faster lookup
* when overpopulated. However, since the vast majority of bins in
* normal use are not overpopulated, checking for existence of
* tree bins may be delayed in the course of table methods.
*
* Tree bins (i.e., bins whose elements are all TreeNodes) are
* ordered primarily by hashCode, but in the case of ties, if two
* elements are of the same "class C implements Comparable<C>",
* type then their compareTo method is used for ordering. (We
* conservatively check generic types via reflection to validate
* this -- see method comparableClassFor). The added complexity
* of tree bins is worthwhile in providing worst-case O(log n)
* operations when keys either have distinct hashes or are
* orderable, Thus, performance degrades gracefully under
* accidental or malicious usages in which hashCode() methods
* return values that are poorly distributed, as well as those in
* which many keys share a hashCode, so long as they are also
* Comparable. (If neither of these apply, we may waste about a
* factor of two in time and space compared to taking no
* precautions. But the only known cases stem from poor user
* programming practices that are already so slow that this makes
* little difference.)
*
* Because TreeNodes are about twice the size of regular nodes, we
* use them only when bins contain enough nodes to warrant use
* (see TREEIFY_THRESHOLD). And when they become too small (due to
* removal or resizing) they are converted back to plain bins. In
* usages with well-distributed user hashCodes, tree bins are
* rarely used. Ideally, under random hashCodes, the frequency of
* nodes in bins follows a Poisson distribution
* (http://en.wikipedia.org/wiki/Poisson_distribution) with a
* parameter of about 0.5 on average for the default resizing
* threshold of 0.75, although with a large variance because of
* resizing granularity. Ignoring variance, the expected
* occurrences of list size k are (exp(-0.5) * pow(0.5, k) /
* factorial(k)). The first values are:
*
* 0: 0.60653066
* 1: 0.30326533
* 2: 0.07581633
* 3: 0.01263606
* 4: 0.00157952
* 5: 0.00015795
* 6: 0.00001316
* 7: 0.00000094
* 8: 0.00000006
* more: less than 1 in ten million
*
* The root of a tree bin is normally its first node. However,
* sometimes (currently only upon Iterator.remove), the root might
* be elsewhere, but can be recovered following parent links
* (method TreeNode.root()).
*
* All applicable internal methods accept a hash code as an
* argument (as normally supplied from a public method), allowing
* them to call each other without recomputing user hashCodes.
* Most internal methods also accept a "tab" argument, that is
* normally the current table, but may be a new or old one when
* resizing or converting.
*
* When bin lists are treeified, split, or untreeified, we keep
* them in the same relative access/traversal order (i.e., field
* Node.next) to better preserve locality, and to slightly
* simplify handling of splits and traversals that invoke
* iterator.remove. When using comparators on insertion, to keep a
* total ordering (or as close as is required here) across
* rebalancings, we compare classes and identityHashCodes as
* tie-breakers.
*
* The use and transitions among plain vs tree modes is
* complicated by the existence of subclass LinkedHashMap. See
* below for hook methods defined to be invoked upon insertion,
* removal and access that allow LinkedHashMap internals to
* otherwise remain independent of these mechanics. (This also
* requires that a map instance be passed to some utility methods
* that may create new nodes.)
*
* The concurrent-programming-like SSA-based coding style helps
* avoid aliasing errors amid all of the twisty pointer operations.
*/
//初始容量
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16
//最大容量
static final int MAXIMUM_CAPACITY = 1 << 30;
//负载因子默认值
static final float DEFAULT_LOAD_FACTOR = 0.75f;
//bin(桶)容量大于等于8时,链表转化成红黑树
static final int TREEIFY_THRESHOLD = 8;
//bin(桶)容量小于等于6时,红黑树转化成链表
static final int UNTREEIFY_THRESHOLD = 6;
//容量最小64时才会转会成红黑树
static final int MIN_TREEIFY_CAPACITY = 64;
//用于fail-fast的,记录HashMap结构发生变化(数量变化或rehash)的数目
transient int modCount;
//HashMap 的实际大小,可能不准(因为当你拿到这个值的时候,可能又发生了变化)
transient int size;
// 扩容的门槛,如果初始化时,给定数组大小的话,通过tableSizeFor 方法计算,永远接近于 2 的幂次方
// 如果是通过 resize 方法进行扩容后,大小 = 数组容量 * 0.75
int threshold;
//存放数据的数组
transient Node<K,V>[] table;
//bin node 节点
static class Node<K,V> implements Map.Entry<K,V> {//Map.Entry是个接口
final int hash;//当前node的hash值
final K key;
V value;
Node<K,V> next;//指向当前node的下一个节点,构成单向链表
Node(int hash, K key, V value, Node<K,V> next) {
this.hash = hash;
this.key = key;
this.value = value;
this.next = next;
}
public final K getKey() { return key; }
public final V getValue() { return value; }
public final String toString() { return key + "=" + value; }
public final int hashCode() {
//Objects.hashCode 防止空指针
return Objects.hashCode(key) ^ Objects.hashCode(value);
}
public final V setValue(V newValue) {
V oldValue = value;
value = newValue;
return oldValue;
}
public final boolean equals(Object o) {
if (o == this)
return true;
if (o instanceof Map.Entry) {
Map.Entry<?,?> e = (Map.Entry<?,?>)o;
if (Objects.equals(key, e.getKey()) &&
Objects.equals(value, e.getValue()))
return true;
}
return false;
}
}
//Tree bins 红黑树
static final class TreeNode<K,V> extends LinkedHashMap.Entry<K,V> {
TreeNode<K,V> parent; // red-black tree links 红黑树父节点
TreeNode<K,V> left;//左节点
TreeNode<K,V> right;//右节点
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);
}
//找到红黑树的根节点,根据根节点没有父节点来判断
final TreeNode<K,V> root() {
for (TreeNode<K,V> r = this, p;;) {
if ((p = r.parent) == null)
return r;
r = p;
}
}
//把给定的root放到根节点上去
static <K,V> void moveRootToFront(Node<K,V>[] tab, TreeNode<K,V> root) {
int n;
if (root != null && tab != null && (n = tab.length) > 0) {
int index = (n - 1) & root.hash;
TreeNode<K,V> first = (TreeNode<K,V>)tab[index];//找到当前树的根节点
//如果root不是根节点,就把root放到根节点上去,分成2步
//1:解决root下面节点的问题
//2:把root放到当前根节点first的左边去
if (root != first) {
Node<K,V> rn;
tab[index] = root;
TreeNode<K,V> rp = root.prev;
//下面的两个if是为了解决步骤1。
// 把root的next挂在自己prev的后面即可
// 就是把自己摘掉,后面一位和前面一位连接起来
if ((rn = root.next) != null)
((TreeNode<K,V>)rn).prev = rp;
if (rp != null)
rp.next = rn;
//这个if解决了步骤2 把root当作根节点,并且设置prev为null
if (first != null)
first.prev = root;
root.next = first;
root.prev = null;
}
assert checkInvariants(root);
}
}
//根据hash和key查找红黑树中节点是否已经存在。策略如下
//1:从根节点递归查找
//2:根据 hashcode,比较查找节点,左边节点,右边节点之间的大小,查找节点大于左边节点,取左边节点,查找小于右边节点,取右边节点。
//3:判断查找节点和 2 步取的节点是否相等,相等返回,不等重复 2,3 两步。
//4:2,3不中的话,如果key实现了Comparable接口,使用compareTo进行比较大小,重复2
//5: 判断节点位置时,需要关心一边节点为空的情况
//6:1~5如果都没有命中,默认从右边开始往下递归
//7:这样查找的好处就是比较快,最大的循环次数是树的最大深度
//如果树比较平衡,查询还是很快的。
final TreeNode<K,V> find(int h, Object k, Class<?> kc) {
//得到当前红黑树
TreeNode<K,V> p = this;
do {
int ph, dir; K pk;
TreeNode<K,V> pl = p.left, pr = p.right, q;
//1:如果key的hash值小于当前节点,取当前节点左边的节点
if ((ph = p.hash) > h)
p = pl;
//2:如果key的hash值大于当前节点,取当前节点右边的节点
else if (ph < h)
p = pr;
//2.1:这里没有判断相等的情况,因为相等时,p就是当前节点,不需要判断
//3:如果key的hash值等于当前节点,直接返回当前节点,结束递归查找
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
//4:如果当前节点的左节点为空,说明左边已经查找完了,再去判断右节点
else if (pl == null)
p = pr;
//5:如果当前节点的右节点为空,说明右边已经查找完了,就再去判断左节点
//4和5防止左右一边为空,提前退出的情况
else if (pr == null)
p = pl;
//6: 不采用hashcode的判断大小的话,可以选择compareTo自定义的判断方法
//只需要自己实现key的Comparable就好了
else if ((kc != null ||
(kc = comparableClassFor(k)) != null) &&
(dir = compareComparables(kc, k, pk)) != 0)
p = (dir < 0) ? pl : pr;
//7:如果当前节点和key的hashcode相等,但是key的值不等,并且没有实现
//comparable的话,只能用最简单的方法,先匹配右边,匹配不到匹配左边
else if ((q = pr.find(h, k, kc)) != null)
return q;
//8:右节点找不到,再递归查找左节点
else
p = pl;
//如果p不为空需要一直递归循环
} while (p != null);
//如果找不到,返回null
return null;
}
/**
* Calls find for root node.
*/
final TreeNode<K,V> getTreeNode(int h, Object k) {
return ((parent != null) ? root() : this).find(h, k, null);
}
/**
* Tie-breaking utility for ordering insertions when equal
* hashCodes and non-comparable. We don't require a total
* order, just a consistent insertion rule to maintain
* equivalence across rebalancings. Tie-breaking further than
* necessary simplifies testing a bit.
*/
static int tieBreakOrder(Object a, Object b) {
int d;
if (a == null || b == null ||
(d = a.getClass().getName().
compareTo(b.getClass().getName())) == 0)
d = (System.identityHashCode(a) <= System.identityHashCode(b) ?
-1 : 1);
return d;
}
/**
* Forms tree of the nodes linked from this node.
* @return root of tree
*/
final void treeify(Node<K,V>[] tab) {
TreeNode<K,V> root = null;
for (TreeNode<K,V> x = this, next; x != null; x = next) {
next = (TreeNode<K,V>)x.next;
x.left = x.right = null;
if (root == null) {
x.parent = null;
x.red = false;
root = x;
}
else {
K k = x.key;
int h = x.hash;
Class<?> kc = null;
for (TreeNode<K,V> p = root;;) {
int dir, ph;
K pk = p.key;
if ((ph = p.hash) > h)
dir = -1;
else if (ph < h)
dir = 1;
else if ((kc == null &&
(kc = comparableClassFor(k)) == null) ||
(dir = compareComparables(kc, k, pk)) == 0)
dir = tieBreakOrder(k, pk);
TreeNode<K,V> xp = p;
if ((p = (dir <= 0) ? p.left : p.right) == null) {
x.parent = xp;
if (dir <= 0)
xp.left = x;
else
xp.right = x;
root = balanceInsertion(root, x);
break;
}
}
}
}
moveRootToFront(tab, root);
}
/**
* Returns a list of non-TreeNodes replacing those linked from
* this node.
*/
final Node<K,V> untreeify(HashMap<K,V> map) {
Node<K,V> hd = null, tl = null;
for (Node<K,V> q = this; q != null; q = q.next) {
Node<K,V> p = map.replacementNode(q, null);
if (tl == null)
hd = p;
else
tl.next = p;
tl = p;
}
return hd;
}
//红黑树中加入节点
//在计算新增的节点挂在那个节点上,是线程不安全的,
//关键在于没有锁住tab,table可能是在动态的变化的
//1:首先判断新增的节点在红黑树上是不是已经存在。
//2:不在的话,根据hashcode,或者自定义的comparTo,递归找到要挂载的节点
//3:和要挂载的节点建立父子,前后关系
//4:判断是否需要着色,旋转。
//5:对红黑树的根节点进行校验
//h:key 的hash值
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;
//找到根节点
TreeNode<K,V> root = (parent != null) ? root() : this;
//自旋
for (TreeNode<K,V> p = root;;) {
int dir, ph; K pk;
// p hash 值大于 h,说明 p 在 h 的右边
if ((ph = p.hash) > h)
dir = -1;
// p hash 值小于 h,说明 p 在 h 的左边
else if (ph < h)
dir = 1;
//要放进去key在当前树中已经存在了(equals来判断)
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
//自己实现的Comparable的话,不能用hashcode比较了,需要用compareTo
else if ((kc == null &&
//得到key的Class类型,如果key没有实现Comparable就是null
(kc = comparableClassFor(k)) == null) ||
//当前节点pk和入参k不等
(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;
//找到和当前hashcode值相近的节点(当前节点的左右子节点其中一个为空即可)
if ((p = (dir <= 0) ? p.left : p.right) == null) {
Node<K,V> xpn = xp.next;
//生成新的节点
TreeNode<K,V> x = map.newTreeNode(h, k, v, xpn);
//把新节点放在当前子节点为空的位置上
if (dir <= 0)
xp.left = x;
else
xp.right = x;
//当前节点和新节点建立父子,前后关系
xp.next = x;
x.parent = x.prev = xp;
if (xpn != null)
((TreeNode<K,V>)xpn).prev = x;
//balanceInsertion 对红黑树进行着色或旋转,以达到更多的查找效率,着色或旋转的几种场景如下
//着色:新节点总是为红色;如果新节点的父亲是黑色,则不需要重新着色;如果父亲是红色,那么必须通过重新
//着色或者旋转的方法,再次达到红黑树的5个约束条件
//旋转: 父亲是红色,叔叔是黑色时(前提是当前)
//如果当前节点是父亲的右节点,则进行左旋
//如果当前节点是父亲的左节点,则进行右旋
//moveRootToFront 方法是把算出来的root放到根节点上
moveRootToFront(tab, balanceInsertion(root, x));
return null;
}
}
}
/**
* Removes the given node, that must be present before this call.
* This is messier than typical red-black deletion code because we
* cannot swap the contents of an interior node with a leaf
* successor that is pinned by "next" pointers that are accessible
* independently during traversal. So instead we swap the tree
* linkages. If the current tree appears to have too few nodes,
* the bin is converted back to a plain bin. (The test triggers
* somewhere between 2 and 6 nodes, depending on tree structure).
*/
final void removeTreeNode(HashMap<K,V> map, Node<K,V>[] tab,
boolean movable) {
int n;
if (tab == null || (n = tab.length) == 0)
return;
int index = (n - 1) & hash;
TreeNode<K,V> first = (TreeNode<K,V>)tab[index], root = first, rl;
TreeNode<K,V> succ = (TreeNode<K,V>)next, pred = prev;
if (pred == null)
tab[index] = first = succ;
else
pred.next = succ;
if (succ != null)
succ.prev = pred;
if (first == null)
return;
if (root.parent != null)
root = root.root();
if (root == null || root.right == null ||
(rl = root.left) == null || rl.left == null) {
tab[index] = first.untreeify(map); // too small
return;
}
TreeNode<K,V> p = this, pl = left, pr = right, replacement;
if (pl != null && pr != null) {
TreeNode<K,V> s = pr, sl;
while ((sl = s.left) != null) // find successor
s = sl;
boolean c = s.red; s.red = p.red; p.red = c; // swap colors
TreeNode<K,V> sr = s.right;
TreeNode<K,V> pp = p.parent;
if (s == pr) { // p was s's direct parent
p.parent = s;
s.right = p;
}
else {
TreeNode<K,V> sp = s.parent;
if ((p.parent = sp) != null) {
if (s == sp.left)
sp.left = p;
else
sp.right = p;
}
if ((s.right = pr) != null)
pr.parent = s;
}
p.left = null;
if ((p.right = sr) != null)
sr.parent = p;
if ((s.left = pl) != null)
pl.parent = s;
if ((s.parent = pp) == null)
root = s;
else if (p == pp.left)
pp.left = s;
else
pp.right = s;
if (sr != null)
replacement = sr;
else
replacement = p;
}
else if (pl != null)
replacement = pl;
else if (pr != null)
replacement = pr;
else
replacement = p;
if (replacement != p) {
TreeNode<K,V> pp = replacement.parent = p.parent;
if (pp == null)
root = replacement;
else if (p == pp.left)
pp.left = replacement;
else
pp.right = replacement;
p.left = p.right = p.parent = null;
}
TreeNode<K,V> r = p.red ? root : balanceDeletion(root, replacement);
if (replacement == p) { // detach
TreeNode<K,V> pp = p.parent;
p.parent = null;
if (pp != null) {
if (p == pp.left)
pp.left = null;
else if (p == pp.right)
pp.right = null;
}
}
if (movable)
moveRootToFront(tab, r);
}
/**
* Splits nodes in a tree bin into lower and upper tree bins,
* or untreeifies if now too small. Called only from resize;
* see above discussion about split bits and indices.
*
* @param map the map
* @param tab the table for recording bin heads
* @param index the index of the table being split
* @param bit the bit of hash to split on
*/
final void split(HashMap<K,V> map, Node<K,V>[] tab, int index, int bit) {
TreeNode<K,V> b = this;
// Relink into lo and hi lists, preserving order
TreeNode<K,V> loHead = null, loTail = null;
TreeNode<K,V> hiHead = null, hiTail = null;
int lc = 0, hc = 0;
for (TreeNode<K,V> e = b, next; e != null; e = next) {
next = (TreeNode<K,V>)e.next;
e.next = null;
if ((e.hash & bit) == 0) {
if ((e.prev = loTail) == null)
loHead = e;
else
loTail.next = e;
loTail = e;
++lc;
}
else {
if ((e.prev = hiTail) == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
++hc;
}
}
if (loHead != null) {
if (lc <= UNTREEIFY_THRESHOLD)
tab[index] = loHead.untreeify(map);
else {
tab[index] = loHead;
if (hiHead != null) // (else is already treeified)
loHead.treeify(tab);
}
}
if (hiHead != null) {
if (hc <= UNTREEIFY_THRESHOLD)
tab[index + bit] = hiHead.untreeify(map);
else {
tab[index + bit] = hiHead;
if (loHead != null)
hiHead.treeify(tab);
}
}
}
/* ------------------------------------------------------------ */
// Red-black tree methods, all adapted from CLR
static <K,V> TreeNode<K,V> rotateLeft(TreeNode<K,V> root,
TreeNode<K,V> p) {
TreeNode<K,V> r, pp, rl;
if (p != null && (r = p.right) != null) {
if ((rl = p.right = r.left) != null)
rl.parent = p;
if ((pp = r.parent = p.parent) == null)
(root = r).red = false;
else if (pp.left == p)
pp.left = r;
else
pp.right = r;
r.left = p;
p.parent = r;
}
return root;
}
static <K,V> TreeNode<K,V> rotateRight(TreeNode<K,V> root,
TreeNode<K,V> p) {
TreeNode<K,V> l, pp, lr;
if (p != null && (l = p.left) != null) {
if ((lr = p.left = l.right) != null)
lr.parent = p;
if ((pp = l.parent = p.parent) == null)
(root = l).red = false;
else if (pp.right == p)
pp.right = l;
else
pp.left = l;
l.right = p;
p.parent = l;
}
return root;
}
static <K,V> TreeNode<K,V> balanceInsertion(TreeNode<K,V> root,
TreeNode<K,V> x) {
x.red = true;
for (TreeNode<K,V> xp, xpp, xppl, xppr;;) {
if ((xp = x.parent) == null) {
x.red = false;
return x;
}
else if (!xp.red || (xpp = xp.parent) == null)
return root;
if (xp == (xppl = xpp.left)) {
if ((xppr = xpp.right) != null && xppr.red) {
xppr.red = false;
xp.red = false;
xpp.red = true;
x = xpp;
}
else {
if (x == xp.right) {
root = rotateLeft(root, x = xp);
xpp = (xp = x.parent) == null ? null : xp.parent;
}
if (xp != null) {
xp.red = false;
if (xpp != null) {
xpp.red = true;
root = rotateRight(root, xpp);
}
}
}
}
else {
if (xppl != null && xppl.red) {
xppl.red = false;
xp.red = false;
xpp.red = true;
x = xpp;
}
else {
if (x == xp.left) {
root = rotateRight(root, x = xp);
xpp = (xp = x.parent) == null ? null : xp.parent;
}
if (xp != null) {
xp.red = false;
if (xpp != null) {
xpp.red = true;
root = rotateLeft(root, xpp);
}
}
}
}
}
}
static <K,V> TreeNode<K,V> balanceDeletion(TreeNode<K,V> root,
TreeNode<K,V> x) {
for (TreeNode<K,V> xp, xpl, xpr;;) {
if (x == null || x == root)
return root;
else if ((xp = x.parent) == null) {
x.red = false;
return x;
}
else if (x.red) {
x.red = false;
return root;
}
else if ((xpl = xp.left) == x) {
if ((xpr = xp.right) != null && xpr.red) {
xpr.red = false;
xp.red = true;
root = rotateLeft(root, xp);
xpr = (xp = x.parent) == null ? null : xp.right;
}
if (xpr == null)
x = xp;
else {
TreeNode<K,V> sl = xpr.left, sr = xpr.right;
if ((sr == null || !sr.red) &&
(sl == null || !sl.red)) {
xpr.red = true;
x = xp;
}
else {
if (sr == null || !sr.red) {
if (sl != null)
sl.red = false;
xpr.red = true;
root = rotateRight(root, xpr);
xpr = (xp = x.parent) == null ?
null : xp.right;
}
if (xpr != null) {
xpr.red = (xp == null) ? false : xp.red;
if ((sr = xpr.right) != null)
sr.red = false;
}
if (xp != null) {
xp.red = false;
root = rotateLeft(root, xp);
}
x = root;
}
}
}
else { // symmetric
if (xpl != null && xpl.red) {
xpl.red = false;
xp.red = true;
root = rotateRight(root, xp);
xpl = (xp = x.parent) == null ? null : xp.left;
}
if (xpl == null)
x = xp;
else {
TreeNode<K,V> sl = xpl.left, sr = xpl.right;
if ((sl == null || !sl.red) &&
(sr == null || !sr.red)) {
xpl.red = true;
x = xp;
}
else {
if (sl == null || !sl.red) {
if (sr != null)
sr.red = false;
xpl.red = true;
root = rotateLeft(root, xpl);
xpl = (xp = x.parent) == null ?
null : xp.left;
}
if (xpl != null) {
xpl.red = (xp == null) ? false : xp.red;
if ((sl = xpl.left) != null)
sl.red = false;
}
if (xp != null) {
xp.red = false;
root = rotateRight(root, xp);
}
x = root;
}
}
}
}
}
/**
* Recursive invariant check
*/
static <K,V> boolean checkInvariants(TreeNode<K,V> t) {
TreeNode<K,V> tp = t.parent, tl = t.left, tr = t.right,
tb = t.prev, tn = (TreeNode<K,V>)t.next;
if (tb != null && tb.next != t)
return false;
if (tn != null && tn.prev != t)
return false;
if (tp != null && t != tp.left && t != tp.right)
return false;
if (tl != null && (tl.parent != t || tl.hash > t.hash))
return false;
if (tr != null && (tr.parent != t || tr.hash < t.hash))
return false;
if (t.red && tl != null && tl.red && tr != null && tr.red)
return false;
if (tl != null && !checkInvariants(tl))
return false;
if (tr != null && !checkInvariants(tr))
return false;
return true;
}
}
/* ---------------- Static utilities -------------- */
/**
* 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);
}
/**
* Returns x's Class if it is of the form "class C implements
* Comparable<C>", else null.
*/
static Class<?> comparableClassFor(Object x) {
if (x instanceof Comparable) {
Class<?> c; Type[] ts, as; Type t; ParameterizedType p;
if ((c = x.getClass()) == String.class) // bypass checks
return c;
if ((ts = c.getGenericInterfaces()) != null) {
for (int i = 0; i < ts.length; ++i) {
if (((t = ts[i]) instanceof ParameterizedType) &&
((p = (ParameterizedType)t).getRawType() ==
Comparable.class) &&
(as = p.getActualTypeArguments()) != null &&
as.length == 1 && as[0] == c) // type arg is c
Morty Proxy This is a proxified and sanitized view of the page, visit original site.