List 的 removeAll 方法的效率

List 的 removeAll 方法的效率低的原因： 要遍历source,对dest进行contain操作，而contain又要遍历dest进行equal比较。

解决办法：dest转为set，用set的contain方法，然后不包含的add到新的list。add的效率更高。 

代码：

 public static <T> List<T> removeAll(final List<T> source, final List<T> destination) {

    List<T> result = new ArrayList<>();
    Set<T> destinationSet = new HashSet<T>(destination);

    for(T t : source) {
        if (!destinationSet.contains(t)) {
            result.add(t);
        }
    }

    return result;
}

实测：

ArrayList.removeAll: source size is 100, final size is 80, use millis is 0

ListUtil.removeAll: source size is 100, final size is 80, use millis is 2

ArrayList.removeAll: source size is 1000, final size is 800, use millis is 4

ListUtil.removeAll: source size is 1000, final size is 800, use millis is 1

ArrayList.removeAll: source size is 10000, final size is 8000, use millis is 120

ListUtil.removeAll: source size is 10000, final size is 8000, use millis is 3

ArrayList.removeAll: source size is 100000, final size is 80000, use millis is 14424

ListUtil.removeAll: source size is 100000, final size is 80000, use millis is 29

ListUtil.removeAll: source size is 1000000, final size is 800000, use millis is 121

ListUtil.removeAll: source size is 10000000, final size is 8000000, use millis is 2277

下文转自：https://blog.csdn.net/kangxingang/article/details/9033491

 原文内容：

Java中，List是最常用到的一种集合类。我们也经常对List进行操作，也没有碰到什么问题。但是刚刚在调用removeAll方法是，碰到了严重的性能问题。

问题是这样的：

原集合：List<T> source，有大概200,000数据。

目标集合：List<T> destination，有大概150,000数据。

两者中都可能有重复的元素，两者中可能有相同的元素。已经实现了T中的hashCode()，equals()方法。我调用了source.removeAll(destination)，结果花费了15分钟时间。这真是不可接受的。

下来就是自己瞎折腾，试图实现一个与removeAll()方法功能一样的方法，但是性能要有提高。

思路一：有资料表明，给List中add()数据的速度要比从List中remove()数据的快。试着实现了下，但是效果不明显，与原来的removeAll()差别不大。源代码如下：

public List<T> removeAll_01(List<T> source, List<T> destination) {
    List<T> result = new LinkedList<T>();
    for(T t : source) {
        if (!destination.contains(t)) {
            result.add(t);
        }
    }
    return result;
}

思路二：运用Set可以去重这一特性。将source中的元素逐个添加到由destination生成的Set中，如果Set.add(e)为true，说明e应该保留到结果中，否则放弃e。因为source中可能存在重复的元素，因此想到用Map来保存source中的元素与其在source中出现的次数。结果令我大跌眼镜，太JB帅了。性能的提高有好几个数量级。源代码如下：

public List<T> removeAll_02(List<T> source, List<T> destination) {
    List<T> result = new LinkedList<T>();

    Map<T, Integer> sourceMap = new HashMap<T, Integer>();
    for (T t : source) {
        if (sourceMap.containsKey(t)) {
            sourceMap.put(t, sourceMap.get(t) + 1);
        } else {
            sourceMap.put(t, 1);
        }
    }

    Set<T> all = new HashSet<T>(destination);
    for (Map.Entry<T, Integer> entry : sourceMap.entrySet()) {
        T key = entry.getKey();
        Integer value = entry.getValue();
        if (all.add(key)) {
            for (int i = 0; i < value; i++) {
                result.add(key);
            }
        }
    }
    return result;
}

思路三：比较下思路二和思路一的代码实现，思路二优于思路一？为什么，为什么？难道Map.containsKey()方法要比List.contains()方法快几何倍数。所以有了思路三。因为Map.containsKey()实际就是Set.contains()，所以对思路一的代码做了少许修改。证实了想法，结果比思路二更好。源代码如下：

public List<T> removeAll_03(List<T> source, List<T> destination) {
    List<T> result = new LinkedList<T>();
    Set<T> destinationSet = new HashSet<T>(destination);
    for(T t : source) {
        if (!destinationSet.contains(t)) {
            result.add(t);
        }
    }
    return result;
}

那么，让我没看看具体的下效果如何。为了方便测试，T选用Integer，List中的元素用随机数Random.nextInt()，随机数的最大之为1,000,000。

上述结果表明，随着数据量的变大，思路二和思路三的表现非常出色。为什么会这样呢？归根到底，还是因为Map.containsKey()和Set.contains()的速度快。

好了，这个问题到此先告一段落。之后，再分析下各思路的算法和时间复杂度。

大家可以到http://download.csdn.net/detail/kangxingang/5549289下载完整代码，修改count和maxNumber后，查看执行结果。

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emp2uportal3rdjersey-server2.23.1jersey-server-2.23.1.jar;C:Usersz00199662.m2package2 emp2uportal3rdcom.springsource.org.apache.commons.lang2.6.0com.springsource.org.apache.commons.lang-2.6.0.jar;C:Usersz00199662.m2package2comhuaweiaigatewayase-snmp.0.1-SNAPSHOTase-snmp-0.0.1-20180419.172834-204.jar;C:Usersz00199662.m2package2 emp2uportal3rdsnmp4j2.2.2snmp4j-2.2.2.jar;C:Usersz00199662.m2package2comgithubpengglekaptcha2.3.2kaptcha-2.3.2.jar;C:Usersz00199662.m2package2javaxservletjavax.servlet-api3.1.0javax.servlet-api-3.1.0.jar;C:Usersz00199662.m2package2comjhlabsfilters2.0.235-1filters-2.0.235-1.jar" com.intellij.rt.execution.junit.JUnitStarter -ideVersion5 -junit4 com.huawei.ai.common.util.ListUtilTest,listRemoveAllArrayList.removeAll: source size is 100, final size is 80, use millis is 0ListUtil.removeAll: source size is 100, final size is 80, use millis is 2
ArrayList.removeAll: source size is 1000, final size is 800, use millis is 4ListUtil.removeAll: source size is 1000, final size is 800, use millis is 1
ArrayList.removeAll: source size is 10000, final size is 8000, use millis is 120ListUtil.removeAll: source size is 10000, final size is 8000, use millis is 3
ArrayList.removeAll: source size is 100000, final size is 80000, use millis is 14424ListUtil.removeAll: source size is 100000, final size is 80000, use millis is 29
ListUtil.removeAll: source size is 1000000, final size is 800000, use millis is 121
ListUtil.removeAll: source size is 10000000, final size is 8000000, use millis is 2277