在Java中查找两个非稀疏集合的交集大小的最有效方法是什么？这是一个我将大量调用大型集合的操作，因此优化很重要。我无法修改原始集。
我看过Apache Commons CollectionUtils.intersection，看起来很慢。我目前的方法是取两组中较小的一组，克隆它，然后在两组中较大的一组上调用.retainAll。
public static int getIntersection(Set<Long> set1, Set<Long> set2) {
boolean set1IsLarger = set1.size() > set2.size();
Set<Long> cloneSet = new HashSet<Long>(set1IsLarger ? set2 : set1);
cloneSet.retainAll(set1IsLarger ? set1 : set2);
return cloneSet.size();
}
答案 0 :(得分：34)
使用已发布的方法运行一些测试，而不是构建新的HashSet。也就是说，让A为较小的集合，B为较大的集合，然后，对于A中的每个项目，如果它也存在于B中，则将其添加到C（一个新的HashSet） - 为了计算，可以跳过中间的C集。
与发布的方法一样，这应该是O(|A|)的成本，因为迭代是O(|A|)，并且对B的探测是O(1)。我不知道它将如何与克隆和删除方法进行比较。
快乐编码 - 并发布一些结果; - ）
实际上，在进一步思考时，我认为这比帖子中的方法有更好的界限：O(|A|) vs O(|A| + |B|)。我不知道这是否会在实际中产生任何不同（或改进），我只希望它在|A| <<< |B|时具有相关性。
好的，所以我真的很无聊。至少在JDK 7（Windows 7 x64）上，显示帖子中显示的方法较慢比上述方法 - 一个好的（尽管看起来似乎是主要是不变的因素。我的眼球猜测说，它比上面的建议慢了四倍只是在创建新的HashSet时使用计数器和两倍慢。这似乎在不同的初始集合大小上“大致一致”。
（请记住，正如Voo指出的那样，上面的数字和这个微基准假设正在使用HashSet！并且，一如既往，微基准存在危险。 YMMV。）
以下是丑陋的结果（以毫秒为单位的时间）：
Running tests for 1x1 IntersectTest$PostMethod@6cc2060e took 13.9808544 count=1000000 IntersectTest$MyMethod1@7d38847d took 2.9893732 count=1000000 IntersectTest$MyMethod2@9826ac5 took 7.775945 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@67fc9fee took 12.4647712 count=734000 IntersectTest$MyMethod1@7a67f797 took 3.1567252 count=734000 IntersectTest$MyMethod2@3fb01949 took 6.483941 count=734000 Running tests for 1x100 IntersectTest$PostMethod@16675039 took 11.3069326 count=706000 IntersectTest$MyMethod1@58c3d9ac took 2.3482693 count=706000 IntersectTest$MyMethod2@2207d8bb took 4.8687103 count=706000 Running tests for 1x1000 IntersectTest$PostMethod@33d626a4 took 10.28656 count=729000 IntersectTest$MyMethod1@3082f392 took 2.3478658 count=729000 IntersectTest$MyMethod2@65450f1f took 4.109205 count=729000 Running tests for 10x2 IntersectTest$PostMethod@55c4d594 took 10.4137618 count=736000 IntersectTest$MyMethod1@6da21389 took 2.374206 count=736000 IntersectTest$MyMethod2@2bb0bf9a took 4.9802039 count=736000 Running tests for 10x10 IntersectTest$PostMethod@7930ebb took 25.811083 count=4370000 IntersectTest$MyMethod1@47ac1adf took 6.9409306 count=4370000 IntersectTest$MyMethod2@74184b3b took 14.2603248 count=4370000 Running tests for 10x100 IntersectTest$PostMethod@7f423820 took 25.0577691 count=4251000 IntersectTest$MyMethod1@5472fe25 took 6.1376042 count=4251000 IntersectTest$MyMethod2@498b5a73 took 13.9880385 count=4251000 Running tests for 10x1000 IntersectTest$PostMethod@3033b503 took 25.0312716 count=4138000 IntersectTest$MyMethod1@12b0f0ae took 6.0932898 count=4138000 IntersectTest$MyMethod2@1e893918 took 13.8332505 count=4138000 Running tests for 100x1 IntersectTest$PostMethod@6366de01 took 9.4531628 count=700000 IntersectTest$MyMethod1@767946a2 took 2.4284762 count=700000 IntersectTest$MyMethod2@140c7272 took 4.7580235 count=700000 Running tests for 100x10 IntersectTest$PostMethod@3351e824 took 24.9788668 count=4192000 IntersectTest$MyMethod1@465fadce took 6.1462852 count=4192000 IntersectTest$MyMethod2@338bd37a took 13.1742654 count=4192000 Running tests for 100x100 IntersectTest$PostMethod@297630d5 took 193.0121077 count=41047000 IntersectTest$MyMethod1@e800537 took 45.2652397 count=41047000 IntersectTest$MyMethod2@76d66550 took 120.8494766 count=41047000 Running tests for 100x1000 IntersectTest$PostMethod@33576738 took 199.6269531 count=40966000 IntersectTest$MyMethod1@2f39a7dd took 45.5255814 count=40966000 IntersectTest$MyMethod2@723bb663 took 122.1704975 count=40966000 Running tests for 1x1 IntersectTest$PostMethod@35e3bdb5 took 9.5598373 count=1000000 IntersectTest$MyMethod1@7abbd1b6 took 2.6359174 count=1000000 IntersectTest$MyMethod2@40c542ad took 6.1091794 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@3c33a0c5 took 9.4648528 count=733000 IntersectTest$MyMethod1@61800463 took 2.302116 count=733000 IntersectTest$MyMethod2@1ba03197 took 5.4803628 count=733000 Running tests for 1x100 IntersectTest$PostMethod@71b8da5 took 9.4971057 count=719000 IntersectTest$MyMethod1@21f04f48 took 2.2983538 count=719000 IntersectTest$MyMethod2@27e51160 took 5.3926902 count=719000 Running tests for 1x1000 IntersectTest$PostMethod@2fe7106a took 9.4702331 count=692000 IntersectTest$MyMethod1@6ae6b7b7 took 2.3013066 count=692000 IntersectTest$MyMethod2@51278635 took 5.4488882 count=692000 Running tests for 10x2 IntersectTest$PostMethod@223b2d85 took 9.5660879 count=743000 IntersectTest$MyMethod1@5b298851 took 2.3481445 count=743000 IntersectTest$MyMethod2@3b4ac99 took 4.8268489 count=743000 Running tests for 10x10 IntersectTest$PostMethod@51c700a0 took 23.0709476 count=4326000 IntersectTest$MyMethod1@5ffa3251 took 5.5460785 count=4326000 IntersectTest$MyMethod2@22fd9511 took 13.4853948 count=4326000 Running tests for 10x100 IntersectTest$PostMethod@46b49793 took 25.1295491 count=4256000 IntersectTest$MyMethod1@7a4b5828 took 5.8520418 count=4256000 IntersectTest$MyMethod2@6888e8d1 took 14.0856942 count=4256000 Running tests for 10x1000 IntersectTest$PostMethod@5339af0d took 25.1752685 count=4158000 IntersectTest$MyMethod1@7013a92a took 5.7978328 count=4158000 IntersectTest$MyMethod2@1ac73de2 took 13.8914112 count=4158000 Running tests for 100x1 IntersectTest$PostMethod@3d1344c8 took 9.5123442 count=717000 IntersectTest$MyMethod1@3c08c5cb took 2.34665 count=717000 IntersectTest$MyMethod2@63f1b137 took 4.907277 count=717000 Running tests for 100x10 IntersectTest$PostMethod@71695341 took 24.9830339 count=4180000 IntersectTest$MyMethod1@39d90a92 took 5.8467864 count=4180000 IntersectTest$MyMethod2@584514e9 took 13.2197964 count=4180000 Running tests for 100x100 IntersectTest$PostMethod@21b8dc91 took 195.1796213 count=41060000 IntersectTest$MyMethod1@6f98c4e2 took 44.5775162 count=41060000 IntersectTest$MyMethod2@16a60aab took 121.1754402 count=41060000 Running tests for 100x1000 IntersectTest$PostMethod@20b37d62 took 200.973133 count=40940000 IntersectTest$MyMethod1@67ecbdb3 took 45.4832226 count=40940000 IntersectTest$MyMethod2@679a6812 took 121.791293 count=40940000 Running tests for 1x1 IntersectTest$PostMethod@237aa07b took 9.2210288 count=1000000 IntersectTest$MyMethod1@47bdfd6f took 2.3394042 count=1000000 IntersectTest$MyMethod2@a49a735 took 6.1688936 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@2b25ffba took 9.4103967 count=736000 IntersectTest$MyMethod1@4bb82277 took 2.2976994 count=736000 IntersectTest$MyMethod2@25ded977 took 5.3310813 count=736000 Running tests for 1x100 IntersectTest$PostMethod@7154a6d5 took 9.3818786 count=704000 IntersectTest$MyMethod1@6c952413 took 2.3014931 count=704000 IntersectTest$MyMethod2@33739316 took 5.3307998 count=704000 Running tests for 1x1000 IntersectTest$PostMethod@58334198 took 9.3831841 count=736000 IntersectTest$MyMethod1@d178f65 took 2.3071236 count=736000 IntersectTest$MyMethod2@5c7369a took 5.4062184 count=736000 Running tests for 10x2 IntersectTest$PostMethod@7c4bdf7c took 9.4040537 count=735000 IntersectTest$MyMethod1@593d85a4 took 2.3584088 count=735000 IntersectTest$MyMethod2@5610ffc1 took 4.8318229 count=735000 Running tests for 10x10 IntersectTest$PostMethod@46bd9fb8 took 23.004925 count=4331000 IntersectTest$MyMethod1@4b410d50 took 5.5678172 count=4331000 IntersectTest$MyMethod2@1bd125c9 took 14.6517184 count=4331000 Running tests for 10x100 IntersectTest$PostMethod@75d6ecff took 25.0114913 count=4223000 IntersectTest$MyMethod1@716195c9 took 5.798676 count=4223000 IntersectTest$MyMethod2@3db0f946 took 13.8064737 count=4223000 Running tests for 10x1000 IntersectTest$PostMethod@761d8e2a took 25.1910652 count=4292000 IntersectTest$MyMethod1@e60a3fb took 5.8621189 count=4292000 IntersectTest$MyMethod2@6aadbb1c took 13.8150282 count=4292000 Running tests for 100x1 IntersectTest$PostMethod@48a50a6e took 9.4141906 count=736000 IntersectTest$MyMethod1@4b4fe104 took 2.3507252 count=736000 IntersectTest$MyMethod2@693df43c took 4.7506854 count=736000 Running tests for 100x10 IntersectTest$PostMethod@4f7d29df took 24.9574096 count=4219000 IntersectTest$MyMethod1@2248183e took 5.8628954 count=4219000 IntersectTest$MyMethod2@2b2fa007 took 12.9836817 count=4219000 Running tests for 100x100 IntersectTest$PostMethod@545d7b6a took 193.2436192 count=40987000 IntersectTest$MyMethod1@4551976b took 44.634367 count=40987000 IntersectTest$MyMethod2@6fac155a took 119.2478037 count=40987000 Running tests for 100x1000 IntersectTest$PostMethod@7b6c238d took 200.4385174 count=40817000 IntersectTest$MyMethod1@78923d48 took 45.6225227 count=40817000 IntersectTest$MyMethod2@48f57fcf took 121.0602757 count=40817000 Running tests for 1x1 IntersectTest$PostMethod@102c79f4 took 9.0931408 count=1000000 IntersectTest$MyMethod1@57fa8a77 took 2.3309466 count=1000000 IntersectTest$MyMethod2@198b7c1 took 5.7627226 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@8c646d0 took 9.3208571 count=726000 IntersectTest$MyMethod1@11530630 took 2.3123797 count=726000 IntersectTest$MyMethod2@61bb4232 took 5.405318 count=726000 Running tests for 1x100 IntersectTest$PostMethod@1a137105 took 9.387384 count=710000 IntersectTest$MyMethod1@72610ca2 took 2.2938749 count=710000 IntersectTest$MyMethod2@41849a58 took 5.3865938 count=710000 Running tests for 1x1000 IntersectTest$PostMethod@100001c8 took 9.4289031 count=696000 IntersectTest$MyMethod1@7074f9ac took 2.2977923 count=696000 IntersectTest$MyMethod2@fb3c4e2 took 5.3724119 count=696000 Running tests for 10x2 IntersectTest$PostMethod@52c638d6 took 9.4074124 count=775000 IntersectTest$MyMethod1@53bd940e took 2.3544881 count=775000 IntersectTest$MyMethod2@43434e15 took 4.9228549 count=775000 Running tests for 10x10 IntersectTest$PostMethod@73b7628d took 23.2110252 count=4374000 IntersectTest$MyMethod1@ca75255 took 5.5877838 count=4374000 IntersectTest$MyMethod2@3d0e50f0 took 13.5902641 count=4374000 Running tests for 10x100 IntersectTest$PostMethod@6d6bbba9 took 25.1999918 count=4227000 IntersectTest$MyMethod1@3bed8c5e took 5.7879144 count=4227000 IntersectTest$MyMethod2@689a8e0e took 13.9617882 count=4227000 Running tests for 10x1000 IntersectTest$PostMethod@3da3b0a2 took 25.1627329 count=4222000 IntersectTest$MyMethod1@45a17b4b took 5.8319523 count=4222000 IntersectTest$MyMethod2@6ca59ca3 took 13.8885479 count=4222000 Running tests for 100x1 IntersectTest$PostMethod@360202a5 took 9.5115367 count=705000 IntersectTest$MyMethod1@3dfbba56 took 2.3470254 count=705000 IntersectTest$MyMethod2@598683e4 took 4.8955489 count=705000 Running tests for 100x10 IntersectTest$PostMethod@21426d0d took 25.8234298 count=4231000 IntersectTest$MyMethod1@1005818a took 5.8832067 count=4231000 IntersectTest$MyMethod2@597b933d took 13.3676148 count=4231000 Running tests for 100x100 IntersectTest$PostMethod@6d59b81a took 193.676662 count=41015000 IntersectTest$MyMethod1@1d45eb0c took 44.6519088 count=41015000 IntersectTest$MyMethod2@594a6fd7 took 119.1646115 count=41015000 Running tests for 100x1000 IntersectTest$PostMethod@6d57d9ac took 200.1651432 count=40803000 IntersectTest$MyMethod1@2293e349 took 45.5311168 count=40803000 IntersectTest$MyMethod2@1b2edf5b took 120.1697135 count=40803000
这是丑陋（可能有缺陷）的微观基准：
import java.util.*;
public class IntersectTest {
static Random rng = new Random();
static abstract class RunIt {
public long count;
public long nsTime;
abstract int Run (Set<Long> s1, Set<Long> s2);
}
// As presented in the post
static class PostMethod extends RunIt {
public int Run(Set<Long> set1, Set<Long> set2) {
boolean set1IsLarger = set1.size() > set2.size();
Set<Long> cloneSet = new HashSet<Long>(set1IsLarger ? set2 : set1);
cloneSet.retainAll(set1IsLarger ? set1 : set2);
return cloneSet.size();
}
}
// No intermediate HashSet
static class MyMethod1 extends RunIt {
public int Run (Set<Long> set1, Set<Long> set2) {
Set<Long> a;
Set<Long> b;
if (set1.size() <= set2.size()) {
a = set1;
b = set2;
} else {
a = set2;
b = set1;
}
int count = 0;
for (Long e : a) {
if (b.contains(e)) {
count++;
}
}
return count;
}
}
// With intermediate HashSet
static class MyMethod2 extends RunIt {
public int Run (Set<Long> set1, Set<Long> set2) {
Set<Long> a;
Set<Long> b;
Set<Long> res = new HashSet<Long>();
if (set1.size() <= set2.size()) {
a = set1;
b = set2;
} else {
a = set2;
b = set1;
}
for (Long e : a) {
if (b.contains(e)) {
res.add(e);
}
}
return res.size();
}
}
static Set<Long> makeSet (int count, float load) {
Set<Long> s = new HashSet<Long>();
for (int i = 0; i < count; i++) {
s.add((long)rng.nextInt(Math.max(1, (int)(count * load))));
}
return s;
}
// really crummy ubench stuff
public static void main(String[] args) {
int[][] bounds = {
{1, 1},
{1, 10},
{1, 100},
{1, 1000},
{10, 2},
{10, 10},
{10, 100},
{10, 1000},
{100, 1},
{100, 10},
{100, 100},
{100, 1000},
};
int totalReps = 4;
int cycleReps = 1000;
int subReps = 1000;
float load = 0.8f;
for (int tc = 0; tc < totalReps; tc++) {
for (int[] bound : bounds) {
int set1size = bound[0];
int set2size = bound[1];
System.out.println("Running tests for " + set1size + "x" + set2size);
ArrayList<RunIt> allRuns = new ArrayList<RunIt>(
Arrays.asList(
new PostMethod(),
new MyMethod1(),
new MyMethod2()));
for (int r = 0; r < cycleReps; r++) {
ArrayList<RunIt> runs = new ArrayList<RunIt>(allRuns);
Set<Long> set1 = makeSet(set1size, load);
Set<Long> set2 = makeSet(set2size, load);
while (runs.size() > 0) {
int runIdx = rng.nextInt(runs.size());
RunIt run = runs.remove(runIdx);
long start = System.nanoTime();
int count = 0;
for (int s = 0; s < subReps; s++) {
count += run.Run(set1, set2);
}
long time = System.nanoTime() - start;
run.nsTime += time;
run.count += count;
}
}
for (RunIt run : allRuns) {
double sec = run.nsTime / (10e6);
System.out.println(run + " took " + sec + " count=" + run.count);
}
}
}
}
}
答案 1 :(得分：30)
答案 2 :(得分：6)
您可以使用Set方法retainAll（）来避免所有手动工作。
来自docs：
s1.retainAll（s2） - 将s1转换为s1和s2的交集。 （两个集合的交集是仅包含两个集合共有的元素的集合。）
答案 3 :(得分：5)
这些集合的成员是否可以轻松映射到相对较小的整数范围？如果是这样，请考虑使用BitSet。然后，交叉点只是按位而且 - 一次是32个潜在成员。
答案 4 :(得分：4)
如果两个集合都可以排序，就像TreeSet运行两个迭代器一样，可以更快地计算共享对象的数量。
如果经常执行此操作，如果可以包装集合可能会带来很多因素，以便您可以缓存交集操作的结果，保留dirty标记以跟踪缓存结果的有效性，再次计算如果需要的话。
答案 5 :(得分：4)
使用Java 8流：
set1.stream().filter(s -> set2.contains(s)).collect(Collectors.toList());
答案 6 :(得分：2)
如果你只是为了计算集合中有多少元素来计算交集，我建议你只需要直接计算交集，而不是构建集合，然后调用size()。
我的计数功能：
/**
* Computes the size of intersection of two sets
* @param small first set. preferably smaller than the second argument
* @param large second set;
* @param <T> the type
* @return size of intersection of sets
*/
public <T> int countIntersection(Set<T> small, Set<T> large){
//assuming first argument to be smaller than the later;
//however double checking to be sure
if (small.size() > large.size()) {
//swap the references;
Set<T> tmp = small;
small = large;
large = tmp;
}
int result = 0;
for (T item : small) {
if (large.contains(item)){
//item found in both the sets
result++;
}
}
return result;
}
答案 7 :(得分：1)
这是一个很好的方法。您应该从当前的解决方案中获得O（n）性能。
答案 8 :(得分：0)
仅供参考，如果任何集合集合都使用相同的比较关系进行排序，那么您可以在时间N * M中迭代它们的交集，其中N是最小集合的大小，M是套数。
实施作为练习留给读者。 Here's an example
答案 9 :(得分：0)
通过流/减少的交集计数（假定您在调用它之前先找出哪个集合更大）：
public int countIntersect(Set<Integer> largerSet, Set<Integer> smallerSet){
return smallerSet.stream().reduce(0, (a,b) -> largerSet.contains(b)?a+1:a);
}
但是我在其他地方已经读到，没有Java代码可以比Set方法的Set方法更快，因为它们是作为本机代码而不是Java代码实现的。因此，我支持建议尝试BitSet以获得更快的结果。