List 的 removeAll 方法的效率

時間 2020-04-15

標籤 list removeall 方法效率简体版

原文原文鏈接

List 的 removeAll 方法的效率低的緣由：要遍歷source,對dest進行contain操做，而contain又要遍歷dest進行equal比較。css

解決辦法：dest轉爲set，用set的contain方法，而後不包含的add到新的list。add的效率更高。 html

代碼：java

 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;
}

實測：android

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

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

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

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

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

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

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

下文轉自：https://blog.csdn.net/kangxingang/article/details/9033491

原文內容：

Java中，List是最經常使用到的一種集合類。咱們也常常對List進行操做，也沒有碰到什麼問題。可是剛剛在調用removeAll方法是，碰到了嚴重的性能問題。

問題是這樣的：

原集合：List<T> source，有大概200,000數據。

目標集合：List<T> destination，有大概150,000數據。

二者中均可能有重複的元素，二者中可能有相同的元素。已經實現了T中的hashCode()，equals()方法。我調用了source.removeAll(destination)，結果花費了15分鐘時間。這真是不可接受的。

下來就是本身瞎折騰，試圖實現一個與removeAll()方法功能同樣的方法，可是性能要有提升。

思路一：有資料代表，給List中add()數據的速度要比從List中remove()數據的快。試着實現了下，可是效果不明顯，與原來的removeAll()差異不大。源代碼以下：

[html] view plain copy

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：運用Set能夠去重這一特性。將source中的元素逐個添加到由destination生成的Set中，若是Set.add(e)爲true，說明e應該保留到結果中，不然放棄e。由於source中可能存在重複的元素，所以想到用Map來保存source中的元素與其在source中出現的次數。結果令我大跌眼鏡，太JB帥了。性能的提升有好幾個數量級。源代碼以下：

[html] view plain copy

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：比較下思路二和思路一的代碼實現，思路二優於思路一？爲何，爲何？難道Map.containsKey()方法要比List.contains()方法快幾何倍數。因此有了思路三。由於Map.containsKey()實際就是Set.contains()，因此對思路一的代碼作了少量修改。證明了想法，結果比思路二更好。源代碼以下：

[html] view plain copy

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;
}

那麼，讓我沒看看具體的下效果如何。爲了方便測試，T選用Integer，List中的元素用隨機數Random.nextInt()，隨機數的最大之爲1,000,000。

上述結果代表，隨着數據量的變大，思路二和思路三的表現很是出色。爲何會這樣呢？歸根到底，仍是由於Map.containsKey()和Set.contains()的速度快。

好了，這個問題到此先告一段落。以後，再分析下各思路的算法和時間複雜度。

你們能夠到http://download.csdn.net/detail/kangxingang/5549289下載完整代碼，修改count和maxNumber後，查看執行結果。

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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 2ArrayList.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 1ArrayList.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 3ArrayList.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 29ListUtil.removeAll: source size is 1000000, final size is 800000, use millis is 121ListUtil.removeAll: source size is 10000000, final size is 8000000, use millis is 2277

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