很明显,泛型HashSet<T>类的搜索性能要高于泛型List<T>类。只需将基于哈希的键与List<T>类中的线性方法进行比较。
然而,计算哈希键本身可能需要一些CPU周期,因此对于少量的项,线性搜索可以成为HashSet<T>的真正替代方法。
我的问题是:盈亏平衡在哪里?
为了简化场景(公平起见),让我们假设List<T>类使用元素的Equals()方法来标识一个项。
很明显,泛型HashSet<T>类的搜索性能要高于泛型List<T>类。只需将基于哈希的键与List<T>类中的线性方法进行比较。
然而,计算哈希键本身可能需要一些CPU周期,因此对于少量的项,线性搜索可以成为HashSet<T>的真正替代方法。
我的问题是:盈亏平衡在哪里?
为了简化场景(公平起见),让我们假设List<T>类使用元素的Equals()方法来标识一个项。
当前回答
我只是想用一些不同场景的基准来说明前面的答案:
一些(12 - 20)小字符串(长度在5到10个字符之间) 许多(~10K)小字符串 一些长字符串(长度在200到1000个字符之间) 许多(~5K)长字符串 几个整数 许多(~10K)整数
对于每个场景,查找出现的值:
在列表的开头("start",索引0) 靠近列表开头("early", index 1) 在列表的中间("middle",索引计数/2) 接近列表末尾("late", index count-2) 在列表的末尾("end", index count-1)
在每个场景之前,我生成随机大小的随机字符串列表,然后将每个列表提供给一个哈希集。每个场景运行了10,000次,基本上是:
(测试伪代码)
stopwatch.start
for X times
exists = list.Contains(lookup);
stopwatch.stop
stopwatch.start
for X times
exists = hashset.Contains(lookup);
stopwatch.stop
样例输出
在Windows 7上测试,12GB Ram, 64位,Xeon 2.8GHz
---------- Testing few small strings ------------
Sample items: (16 total)
vgnwaloqf diwfpxbv tdcdc grfch icsjwk
...
Benchmarks:
1: hashset: late -- 100.00 % -- [Elapsed: 0.0018398 sec]
2: hashset: middle -- 104.19 % -- [Elapsed: 0.0019169 sec]
3: hashset: end -- 108.21 % -- [Elapsed: 0.0019908 sec]
4: list: early -- 144.62 % -- [Elapsed: 0.0026607 sec]
5: hashset: start -- 174.32 % -- [Elapsed: 0.0032071 sec]
6: list: middle -- 187.72 % -- [Elapsed: 0.0034536 sec]
7: list: late -- 192.66 % -- [Elapsed: 0.0035446 sec]
8: list: end -- 215.42 % -- [Elapsed: 0.0039633 sec]
9: hashset: early -- 217.95 % -- [Elapsed: 0.0040098 sec]
10: list: start -- 576.55 % -- [Elapsed: 0.0106073 sec]
---------- Testing many small strings ------------
Sample items: (10346 total)
dmnowa yshtrxorj vthjk okrxegip vwpoltck
...
Benchmarks:
1: hashset: end -- 100.00 % -- [Elapsed: 0.0017443 sec]
2: hashset: late -- 102.91 % -- [Elapsed: 0.0017951 sec]
3: hashset: middle -- 106.23 % -- [Elapsed: 0.0018529 sec]
4: list: early -- 107.49 % -- [Elapsed: 0.0018749 sec]
5: list: start -- 126.23 % -- [Elapsed: 0.0022018 sec]
6: hashset: early -- 134.11 % -- [Elapsed: 0.0023393 sec]
7: hashset: start -- 372.09 % -- [Elapsed: 0.0064903 sec]
8: list: middle -- 48,593.79 % -- [Elapsed: 0.8476214 sec]
9: list: end -- 99,020.73 % -- [Elapsed: 1.7272186 sec]
10: list: late -- 99,089.36 % -- [Elapsed: 1.7284155 sec]
---------- Testing few long strings ------------
Sample items: (19 total)
hidfymjyjtffcjmlcaoivbylakmqgoiowbgxpyhnrreodxyleehkhsofjqenyrrtlphbcnvdrbqdvji...
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0018266 sec]
2: list: start -- 115.76 % -- [Elapsed: 0.0021144 sec]
3: list: middle -- 143.44 % -- [Elapsed: 0.0026201 sec]
4: list: late -- 190.05 % -- [Elapsed: 0.0034715 sec]
5: list: end -- 193.78 % -- [Elapsed: 0.0035395 sec]
6: hashset: early -- 215.00 % -- [Elapsed: 0.0039271 sec]
7: hashset: end -- 248.47 % -- [Elapsed: 0.0045386 sec]
8: hashset: start -- 298.04 % -- [Elapsed: 0.005444 sec]
9: hashset: middle -- 325.63 % -- [Elapsed: 0.005948 sec]
10: hashset: late -- 431.62 % -- [Elapsed: 0.0078839 sec]
---------- Testing many long strings ------------
Sample items: (5000 total)
yrpjccgxjbketcpmnvyqvghhlnjblhgimybdygumtijtrwaromwrajlsjhxoselbucqualmhbmwnvnpnm
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0016211 sec]
2: list: start -- 132.73 % -- [Elapsed: 0.0021517 sec]
3: hashset: start -- 231.26 % -- [Elapsed: 0.003749 sec]
4: hashset: end -- 368.74 % -- [Elapsed: 0.0059776 sec]
5: hashset: middle -- 385.50 % -- [Elapsed: 0.0062493 sec]
6: hashset: late -- 406.23 % -- [Elapsed: 0.0065854 sec]
7: hashset: early -- 421.34 % -- [Elapsed: 0.0068304 sec]
8: list: middle -- 18,619.12 % -- [Elapsed: 0.3018345 sec]
9: list: end -- 40,942.82 % -- [Elapsed: 0.663724 sec]
10: list: late -- 41,188.19 % -- [Elapsed: 0.6677017 sec]
---------- Testing few ints ------------
Sample items: (16 total)
7266092 60668895 159021363 216428460 28007724
...
Benchmarks:
1: hashset: early -- 100.00 % -- [Elapsed: 0.0016211 sec]
2: hashset: end -- 100.45 % -- [Elapsed: 0.0016284 sec]
3: list: early -- 101.83 % -- [Elapsed: 0.0016507 sec]
4: hashset: late -- 108.95 % -- [Elapsed: 0.0017662 sec]
5: hashset: middle -- 112.29 % -- [Elapsed: 0.0018204 sec]
6: hashset: start -- 120.33 % -- [Elapsed: 0.0019506 sec]
7: list: late -- 134.45 % -- [Elapsed: 0.0021795 sec]
8: list: start -- 136.43 % -- [Elapsed: 0.0022117 sec]
9: list: end -- 169.77 % -- [Elapsed: 0.0027522 sec]
10: list: middle -- 237.94 % -- [Elapsed: 0.0038573 sec]
---------- Testing many ints ------------
Sample items: (10357 total)
370826556 569127161 101235820 792075135 270823009
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0015132 sec]
2: hashset: end -- 101.79 % -- [Elapsed: 0.0015403 sec]
3: hashset: early -- 102.08 % -- [Elapsed: 0.0015446 sec]
4: hashset: middle -- 103.21 % -- [Elapsed: 0.0015618 sec]
5: hashset: late -- 104.26 % -- [Elapsed: 0.0015776 sec]
6: list: start -- 126.78 % -- [Elapsed: 0.0019184 sec]
7: hashset: start -- 130.91 % -- [Elapsed: 0.0019809 sec]
8: list: middle -- 16,497.89 % -- [Elapsed: 0.2496461 sec]
9: list: end -- 32,715.52 % -- [Elapsed: 0.4950512 sec]
10: list: late -- 33,698.87 % -- [Elapsed: 0.5099313 sec]
其他回答
视情况而定。如果确切的答案真的很重要,那就做一些分析,找出答案。如果你确定你永远不会有超过一定数量的元素在集合中,使用List。如果数字是无界的,则使用HashSet。
很多人都说,一旦你达到了速度实际上是一个问题的大小,HashSet<T>将总是击败List<T>,但这取决于你在做什么。
假设你有一个List<T>,其中平均只有5个项目。在大量的循环中,如果每个循环添加或删除一个项目,那么使用List<T>可能会更好。
我在我的机器上做了一个测试,它必须非常非常小才能从List< t>中获得优势。对于一个短字符串列表,在大小为5之后,对于大小为20之后的对象,优势就消失了。
1 item LIST strs time: 617ms
1 item HASHSET strs time: 1332ms
2 item LIST strs time: 781ms
2 item HASHSET strs time: 1354ms
3 item LIST strs time: 950ms
3 item HASHSET strs time: 1405ms
4 item LIST strs time: 1126ms
4 item HASHSET strs time: 1441ms
5 item LIST strs time: 1370ms
5 item HASHSET strs time: 1452ms
6 item LIST strs time: 1481ms
6 item HASHSET strs time: 1418ms
7 item LIST strs time: 1581ms
7 item HASHSET strs time: 1464ms
8 item LIST strs time: 1726ms
8 item HASHSET strs time: 1398ms
9 item LIST strs time: 1901ms
9 item HASHSET strs time: 1433ms
1 item LIST objs time: 614ms
1 item HASHSET objs time: 1993ms
4 item LIST objs time: 837ms
4 item HASHSET objs time: 1914ms
7 item LIST objs time: 1070ms
7 item HASHSET objs time: 1900ms
10 item LIST objs time: 1267ms
10 item HASHSET objs time: 1904ms
13 item LIST objs time: 1494ms
13 item HASHSET objs time: 1893ms
16 item LIST objs time: 1695ms
16 item HASHSET objs time: 1879ms
19 item LIST objs time: 1902ms
19 item HASHSET objs time: 1950ms
22 item LIST objs time: 2136ms
22 item HASHSET objs time: 1893ms
25 item LIST objs time: 2357ms
25 item HASHSET objs time: 1826ms
28 item LIST objs time: 2555ms
28 item HASHSET objs time: 1865ms
31 item LIST objs time: 2755ms
31 item HASHSET objs time: 1963ms
34 item LIST objs time: 3025ms
34 item HASHSET objs time: 1874ms
37 item LIST objs time: 3195ms
37 item HASHSET objs time: 1958ms
40 item LIST objs time: 3401ms
40 item HASHSET objs time: 1855ms
43 item LIST objs time: 3618ms
43 item HASHSET objs time: 1869ms
46 item LIST objs time: 3883ms
46 item HASHSET objs time: 2046ms
49 item LIST objs time: 4218ms
49 item HASHSET objs time: 1873ms
下面是以图表形式显示的数据:
代码如下:
static void Main(string[] args)
{
int times = 10000000;
for (int listSize = 1; listSize < 10; listSize++)
{
List<string> list = new List<string>();
HashSet<string> hashset = new HashSet<string>();
for (int i = 0; i < listSize; i++)
{
list.Add("string" + i.ToString());
hashset.Add("string" + i.ToString());
}
Stopwatch timer = new Stopwatch();
timer.Start();
for (int i = 0; i < times; i++)
{
list.Remove("string0");
list.Add("string0");
}
timer.Stop();
Console.WriteLine(listSize.ToString() + " item LIST strs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
timer = new Stopwatch();
timer.Start();
for (int i = 0; i < times; i++)
{
hashset.Remove("string0");
hashset.Add("string0");
}
timer.Stop();
Console.WriteLine(listSize.ToString() + " item HASHSET strs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
Console.WriteLine();
}
for (int listSize = 1; listSize < 50; listSize+=3)
{
List<object> list = new List<object>();
HashSet<object> hashset = new HashSet<object>();
for (int i = 0; i < listSize; i++)
{
list.Add(new object());
hashset.Add(new object());
}
object objToAddRem = list[0];
Stopwatch timer = new Stopwatch();
timer.Start();
for (int i = 0; i < times; i++)
{
list.Remove(objToAddRem);
list.Add(objToAddRem);
}
timer.Stop();
Console.WriteLine(listSize.ToString() + " item LIST objs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
timer = new Stopwatch();
timer.Start();
for (int i = 0; i < times; i++)
{
hashset.Remove(objToAddRem);
hashset.Add(objToAddRem);
}
timer.Stop();
Console.WriteLine(listSize.ToString() + " item HASHSET objs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
Console.WriteLine();
}
Console.ReadLine();
}
盈亏平衡将取决于计算散列的成本。哈希计算可以是微不足道的,或者不是…:-)总有System.Collections.Specialized.HybridDictionary类帮助你不必担心盈亏平衡点。
我只是想用一些不同场景的基准来说明前面的答案:
一些(12 - 20)小字符串(长度在5到10个字符之间) 许多(~10K)小字符串 一些长字符串(长度在200到1000个字符之间) 许多(~5K)长字符串 几个整数 许多(~10K)整数
对于每个场景,查找出现的值:
在列表的开头("start",索引0) 靠近列表开头("early", index 1) 在列表的中间("middle",索引计数/2) 接近列表末尾("late", index count-2) 在列表的末尾("end", index count-1)
在每个场景之前,我生成随机大小的随机字符串列表,然后将每个列表提供给一个哈希集。每个场景运行了10,000次,基本上是:
(测试伪代码)
stopwatch.start
for X times
exists = list.Contains(lookup);
stopwatch.stop
stopwatch.start
for X times
exists = hashset.Contains(lookup);
stopwatch.stop
样例输出
在Windows 7上测试,12GB Ram, 64位,Xeon 2.8GHz
---------- Testing few small strings ------------
Sample items: (16 total)
vgnwaloqf diwfpxbv tdcdc grfch icsjwk
...
Benchmarks:
1: hashset: late -- 100.00 % -- [Elapsed: 0.0018398 sec]
2: hashset: middle -- 104.19 % -- [Elapsed: 0.0019169 sec]
3: hashset: end -- 108.21 % -- [Elapsed: 0.0019908 sec]
4: list: early -- 144.62 % -- [Elapsed: 0.0026607 sec]
5: hashset: start -- 174.32 % -- [Elapsed: 0.0032071 sec]
6: list: middle -- 187.72 % -- [Elapsed: 0.0034536 sec]
7: list: late -- 192.66 % -- [Elapsed: 0.0035446 sec]
8: list: end -- 215.42 % -- [Elapsed: 0.0039633 sec]
9: hashset: early -- 217.95 % -- [Elapsed: 0.0040098 sec]
10: list: start -- 576.55 % -- [Elapsed: 0.0106073 sec]
---------- Testing many small strings ------------
Sample items: (10346 total)
dmnowa yshtrxorj vthjk okrxegip vwpoltck
...
Benchmarks:
1: hashset: end -- 100.00 % -- [Elapsed: 0.0017443 sec]
2: hashset: late -- 102.91 % -- [Elapsed: 0.0017951 sec]
3: hashset: middle -- 106.23 % -- [Elapsed: 0.0018529 sec]
4: list: early -- 107.49 % -- [Elapsed: 0.0018749 sec]
5: list: start -- 126.23 % -- [Elapsed: 0.0022018 sec]
6: hashset: early -- 134.11 % -- [Elapsed: 0.0023393 sec]
7: hashset: start -- 372.09 % -- [Elapsed: 0.0064903 sec]
8: list: middle -- 48,593.79 % -- [Elapsed: 0.8476214 sec]
9: list: end -- 99,020.73 % -- [Elapsed: 1.7272186 sec]
10: list: late -- 99,089.36 % -- [Elapsed: 1.7284155 sec]
---------- Testing few long strings ------------
Sample items: (19 total)
hidfymjyjtffcjmlcaoivbylakmqgoiowbgxpyhnrreodxyleehkhsofjqenyrrtlphbcnvdrbqdvji...
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0018266 sec]
2: list: start -- 115.76 % -- [Elapsed: 0.0021144 sec]
3: list: middle -- 143.44 % -- [Elapsed: 0.0026201 sec]
4: list: late -- 190.05 % -- [Elapsed: 0.0034715 sec]
5: list: end -- 193.78 % -- [Elapsed: 0.0035395 sec]
6: hashset: early -- 215.00 % -- [Elapsed: 0.0039271 sec]
7: hashset: end -- 248.47 % -- [Elapsed: 0.0045386 sec]
8: hashset: start -- 298.04 % -- [Elapsed: 0.005444 sec]
9: hashset: middle -- 325.63 % -- [Elapsed: 0.005948 sec]
10: hashset: late -- 431.62 % -- [Elapsed: 0.0078839 sec]
---------- Testing many long strings ------------
Sample items: (5000 total)
yrpjccgxjbketcpmnvyqvghhlnjblhgimybdygumtijtrwaromwrajlsjhxoselbucqualmhbmwnvnpnm
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0016211 sec]
2: list: start -- 132.73 % -- [Elapsed: 0.0021517 sec]
3: hashset: start -- 231.26 % -- [Elapsed: 0.003749 sec]
4: hashset: end -- 368.74 % -- [Elapsed: 0.0059776 sec]
5: hashset: middle -- 385.50 % -- [Elapsed: 0.0062493 sec]
6: hashset: late -- 406.23 % -- [Elapsed: 0.0065854 sec]
7: hashset: early -- 421.34 % -- [Elapsed: 0.0068304 sec]
8: list: middle -- 18,619.12 % -- [Elapsed: 0.3018345 sec]
9: list: end -- 40,942.82 % -- [Elapsed: 0.663724 sec]
10: list: late -- 41,188.19 % -- [Elapsed: 0.6677017 sec]
---------- Testing few ints ------------
Sample items: (16 total)
7266092 60668895 159021363 216428460 28007724
...
Benchmarks:
1: hashset: early -- 100.00 % -- [Elapsed: 0.0016211 sec]
2: hashset: end -- 100.45 % -- [Elapsed: 0.0016284 sec]
3: list: early -- 101.83 % -- [Elapsed: 0.0016507 sec]
4: hashset: late -- 108.95 % -- [Elapsed: 0.0017662 sec]
5: hashset: middle -- 112.29 % -- [Elapsed: 0.0018204 sec]
6: hashset: start -- 120.33 % -- [Elapsed: 0.0019506 sec]
7: list: late -- 134.45 % -- [Elapsed: 0.0021795 sec]
8: list: start -- 136.43 % -- [Elapsed: 0.0022117 sec]
9: list: end -- 169.77 % -- [Elapsed: 0.0027522 sec]
10: list: middle -- 237.94 % -- [Elapsed: 0.0038573 sec]
---------- Testing many ints ------------
Sample items: (10357 total)
370826556 569127161 101235820 792075135 270823009
...
Benchmarks:
1: list: early -- 100.00 % -- [Elapsed: 0.0015132 sec]
2: hashset: end -- 101.79 % -- [Elapsed: 0.0015403 sec]
3: hashset: early -- 102.08 % -- [Elapsed: 0.0015446 sec]
4: hashset: middle -- 103.21 % -- [Elapsed: 0.0015618 sec]
5: hashset: late -- 104.26 % -- [Elapsed: 0.0015776 sec]
6: list: start -- 126.78 % -- [Elapsed: 0.0019184 sec]
7: hashset: start -- 130.91 % -- [Elapsed: 0.0019809 sec]
8: list: middle -- 16,497.89 % -- [Elapsed: 0.2496461 sec]
9: list: end -- 32,715.52 % -- [Elapsed: 0.4950512 sec]
10: list: late -- 33,698.87 % -- [Elapsed: 0.5099313 sec]
您没有考虑到的一个因素是GetHashcode()函数的健壮性。有了完美的哈希函数,HashSet显然会有更好的搜索性能。但是随着哈希函数的减少,HashSet搜索时间也会减少。