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3 仔细分析java的Arrays.sort(version 1.71, 04/21/06)后发现，java对primitive（int，float等原型数据）数组采用快速排序，对Object对象数组采用归并排序。
4 对这一区别，sun在<<The Java Tutorial>>中做出的解释是：
5 The sort operation uses a slightly optimized merge sort algorithm that is fast and stable:
6 * Fast: It is guaranteed to run in n log(n) time and runs substantially faster on nearly sorted lists. Empirical tests showed it to be as fast as a highly optimized quicksort. A quicksort is generally considered to be faster than a merge sort but isn't stable and doesn't guarantee n log(n) performance.
7 * Stable: It doesn't reorder equal elements. This is important if you sort the same list repeatedly on different attributes. If a user of a mail program sorts the inbox by mailing date and then sorts it by sender, the user naturally expects that the now-contiguous list of messages from a given sender will (still) be sorted by mailing date. This is guaranteed only if the second sort was stable.
8 也就是说，优化的归并排序既快速（nlog(n)）又稳定。
9 对于对象的排序，稳定性很重要。比如成绩单,一开始可能是按人员的学号顺序排好了的,现在让我们用成绩排,那么你应该保证,本来张三在李四前面,即使他们成绩相同,张三不能跑到李四的后面去。
10 而快速排序是不稳定的，而且最坏情况下的时间复杂度是O(n^2)。
11 另外，对象数组中保存的只是对象的引用，这样多次移位并不会造成额外的开销，但是，对象数组对比较次数一般比较敏感，有可能对象的比较比单纯数的比较开销大很多。归并排序在这方面比快速排序做得更好，这也是选择它作为对象排序的一个重要原因之一。
12 排序优化：实现中快排和归并都采用递归方式，而在递归的底层，也就是待排序的数组长度小于7时, 直接使用冒泡排序，而不再递归下去。
13 分析：长度为6的数组冒泡排序总比较次数最多也就1+2+3+4+5+6=21次，最好情况下只有6次比较。而快排或归并涉及到递归调用等的开销，其时间效率在n较小时劣势就凸显了，因此这里采用了冒泡排序，这也是对快速排序极重要的优化。
14 /*快速排序*/
15 private static void sort1(int x[], int off, int len) {
16 // Insertion sort on smallest arrays
17 if (len < 7) {
18 for (int i=off; i<len+off; i++)
19 for (int j=i; j>off && x[j-1]>x[j]; j--)
20 swap(x, j, j-1);
21 return;
22 }
23 // Choose a partition element, v
24 int m = off + (len >> 1); // Small arrays, middle element
25 if (len > 7) {
26 int l = off;
27 int n = off + len - 1;
28 if (len > 40) { // Big arrays, pseudomedian of 9
29 int s = len/8;
30 l = med3(x, l, l+s, l+2*s);//取后三个参数中的中间值
31 m = med3(x, m-s, m, m+s);
32 n = med3(x, n-2*s, n-s, n);
33 }
34 m = med3(x, l, m, n); // Mid-size, med of 3
35 }
36 int v = x[m];
37 // Establish Invariant: v* (<v)* (>v)* v*
38 int a = off, b = a, c = off + len - 1, d = c;
39 while(true) {
40 while (b <= c && x[b] <= v) {
41 if (x[b] == v)
42 swap(x, a++, b);
43 b++;
44 }
45 while (c >= b && x[c] >= v) {
46 if (x[c] == v)
47 swap(x, c, d--);
48 c--;
49 }
50 if (b > c)
51 break;
52 swap(x, b++, c--);
53 }
54 // Swap partition elements back to middle
55 int s, n = off + len;
56 s = Math.min(a-off, b-a ); vecswap(x, off, b-s, s);
57 s = Math.min(d-c, n-d-1); vecswap(x, b, n-s, s);
58 // Recursively sort non-partition-elements
59 if ((s = b-a) > 1)
60 sort1(x, off, s);
61 if ((s = d-c) > 1)
62 sort1(x, n-s, s);
63 }
64 /*归并排序*/
65 private static void mergeSort(Object[] src,
66 Object[] dest,
67 int low,
68 int high,
69 int off) {
70 int length = high - low;
71 // Insertion sort on smallest arrays
72 if (length < INSERTIONSORT_THRESHOLD) {
73 for (int i=low; i<high; i++)
74 for (int j=i; j>low &&
75 ((Comparable) dest[j-1]).compareTo(dest[j])>0; j--)
76 swap(dest, j, j-1);
77 return;
78 }
79 // Recursively sort halves of dest into src
80 int destLow = low;
81 int destHigh = high;
82 low += off;
83 high += off;
84 int mid = (low + high) >>> 1;
85 mergeSort(dest, src, low, mid, -off);
86 mergeSort(dest, src, mid, high, -off);
87 // If list is already sorted, just copy from src to dest. This is an
88 // optimization that results in faster sorts for nearly ordered lists.
89 if (((Comparable)src[mid-1]).compareTo(src[mid]) <= 0) {
90 System.arraycopy(src, low, dest, destLow, length);
91 return;
92 }
93 // Merge sorted halves (now in src) into dest
94 for(int i = destLow, p = low, q = mid; i < destHigh; i++) {
95 if (q >= high || p < mid && ((Comparable)src[p]).compareTo(src[q])<=0)
96 dest[i] = src[p++];
97 else
98 dest[i] = src[q++];
99 }
100 }
