更有效的方法是使用Enumerable。除了:

var inListButNotInList2 = list.Except(list2);
var inList2ButNotInList = list2.Except(list);

该方法是通过使用延迟执行实现的。这意味着你可以这样写:

var first10 = inListButNotInList2.Take(10);

它也很有效，因为它在内部使用Set<T>来比较对象。它的工作原理是首先从第二个序列中收集所有不同的值，然后将第一个序列的结果流式传输，检查它们是否之前没有出现过。

我比较了3种不同的方法来比较不同的数据集。下面的测试创建了一个包含从0到length - 1的所有数字的字符串集合，然后是另一个具有相同范围但包含偶数的集合。然后我从第一个集合中挑出奇数。

使用Linq除外

public void TestExcept()
{
    WriteLine($"Except {DateTime.Now}");
    int length = 20000000;
    var dateTime = DateTime.Now;
    var array = new string[length];
    for (int i = 0; i < length; i++)
    {
        array[i] = i.ToString();
    }
    Write("Populate set processing time: ");
    WriteLine(DateTime.Now - dateTime);
    var newArray = new string[length/2];
    int j = 0;
    for (int i = 0; i < length; i+=2)
    {
        newArray[j++] = i.ToString();
    }
    dateTime = DateTime.Now;
    Write("Count of items: ");
    WriteLine(array.Except(newArray).Count());
    Write("Count processing time: ");
    WriteLine(DateTime.Now - dateTime);
}

输出

Except 2021-08-14 11:43:03 AM
Populate set processing time: 00:00:03.7230479
2021-08-14 11:43:09 AM
Count of items: 10000000
Count processing time: 00:00:02.9720879

使用HashSet。添加

public void TestHashSet()
{
    WriteLine($"HashSet {DateTime.Now}");
    int length = 20000000;
    var dateTime = DateTime.Now;
    var hashSet = new HashSet<string>();
    for (int i = 0; i < length; i++)
    {
        hashSet.Add(i.ToString());
    }
    Write("Populate set processing time: ");
    WriteLine(DateTime.Now - dateTime);
    var newHashSet = new HashSet<string>();
    for (int i = 0; i < length; i+=2)
    {
        newHashSet.Add(i.ToString());
    }
    dateTime = DateTime.Now;
    Write("Count of items: ");
    // HashSet Add returns true if item is added successfully (not previously existing)
    WriteLine(hashSet.Where(s => newHashSet.Add(s)).Count());
    Write("Count processing time: ");
    WriteLine(DateTime.Now - dateTime);
}

输出

HashSet 2021-08-14 11:42:43 AM
Populate set processing time: 00:00:05.6000625
Count of items: 10000000
Count processing time: 00:00:01.7703057

特殊HashSet测试:

public void TestLoadingHashSet()
{
    int length = 20000000;
    var array = new string[length];
    for (int i = 0; i < length; i++)
    {
       array[i] = i.ToString();
    }
    var dateTime = DateTime.Now;
    var hashSet = new HashSet<string>(array);
    Write("Time to load hashset: ");
    WriteLine(DateTime.Now - dateTime);
}
> TestLoadingHashSet()
Time to load hashset: 00:00:01.1918160

使用.Contains

public void TestContains()
{
    WriteLine($"Contains {DateTime.Now}");
    int length = 20000000;
    var dateTime = DateTime.Now;
    var array = new string[length];
    for (int i = 0; i < length; i++)
    {
        array[i] = i.ToString();
    }
    Write("Populate set processing time: ");
    WriteLine(DateTime.Now - dateTime);
    var newArray = new string[length/2];
    int j = 0;
    for (int i = 0; i < length; i+=2)
    {
        newArray[j++] = i.ToString();
    }
    dateTime = DateTime.Now;
    WriteLine(dateTime);
    Write("Count of items: ");
    WriteLine(array.Where(a => !newArray.Contains(a)).Count());
    Write("Count processing time: ");
    WriteLine(DateTime.Now - dateTime);
}

输出

Contains 2021-08-14 11:19:44 AM
Populate set processing time: 00:00:03.1046998
2021-08-14 11:19:49 AM
Count of items: Hosting process exited with exit code 1.
(Didnt complete. Killed it after 14 minutes)

结论:

Linq Except在我的设备上运行大约比使用HashSets慢1秒(n=20,000,000)。 使用Where和Contains运行了很长时间

哈希集的总结:

独特的数据 确保为类类型重写GetHashCode(正确地) 如果您复制数据集，可能需要高达2倍的内存，这取决于实现 HashSet是为使用IEnumerable构造函数克隆其他HashSet而优化的，但是将其他集合转换为HashSet比较慢(参见上面的特殊测试)

使用除外:

var firstNotSecond = list1.Except(list2).ToList();
var secondNotFirst = list2.Except(list1).ToList();

我怀疑有一些方法实际上会比这个稍微快一点，但即使是这个方法也会比O(N * M)方法快得多。

如果你想把它们结合起来，你可以用上面的方法创建一个方法，然后创建一个return语句:

return !firstNotSecond.Any() && !secondNotFirst.Any();

需要注意的一点是，问题中的原始代码和这里的解决方案之间的结果存在差异:在我的代码中，只在一个列表中出现的任何重复元素将只报告一次，而在原始代码中出现的次数与它们相同。

例如，对于[1,2,2,2,3]和[1]的列表，原始代码中的“元素在list1但不是list2”结果将是[2,2,2,3]。在我的代码中，它就是[2,3]。在许多情况下，这不是一个问题，但这是值得注意的。

更有效的方法是使用Enumerable。除了:

var inListButNotInList2 = list.Except(list2);
var inList2ButNotInList = list2.Except(list);

该方法是通过使用延迟执行实现的。这意味着你可以这样写:

var first10 = inListButNotInList2.Take(10);

它也很有效，因为它在内部使用Set<T>来比较对象。它的工作原理是首先从第二个序列中收集所有不同的值，然后将第一个序列的结果流式传输，检查它们是否之前没有出现过。

试试这个方法:

var difList = list1.Where(a => !list2.Any(a1 => a1.id == a.id))
            .Union(list2.Where(a => !list1.Any(a1 => a1.id == a.id)));

While Jon Skeet's answer is an excellent advice for everyday's practice with small to moderate number of elements (up to a few millions) it is nevertheless not the fastest approach and not very resource efficient. An obvious drawback is the fact that getting the full difference requires two passes over the data (even three if the elements that are equal are of interest as well). Clearly, this can be avoided by a customized reimplementation of the Except method, but it remains that the creation of a hash set requires a lot of memory and the computation of hashes requires time.

对于非常大的数据集(数十亿个元素)，考虑特定的情况通常是有好处的。这里有一些想法可能会给你一些启发: 如果元素可以比较(在实践中几乎总是这样)，那么对列表进行排序并应用以下zip方法是值得考虑的:

/// <returns>The elements of the specified (ascendingly) sorted enumerations that are
/// contained only in one of them, together with an indicator,
/// whether the element is contained in the reference enumeration (-1)
/// or in the difference enumeration (+1).</returns>
public static IEnumerable<Tuple<T, int>> FindDifferences<T>(IEnumerable<T> sortedReferenceObjects,
    IEnumerable<T> sortedDifferenceObjects, IComparer<T> comparer)
{
    var refs  = sortedReferenceObjects.GetEnumerator();
    var diffs = sortedDifferenceObjects.GetEnumerator();
    bool hasNext = refs.MoveNext() && diffs.MoveNext();
    while (hasNext)
    {
        int comparison = comparer.Compare(refs.Current, diffs.Current);
        if (comparison == 0)
        {
            // insert code that emits the current element if equal elements should be kept
            hasNext = refs.MoveNext() && diffs.MoveNext();

        }
        else if (comparison < 0)
        {
            yield return Tuple.Create(refs.Current, -1);
            hasNext = refs.MoveNext();
        }
        else
        {
            yield return Tuple.Create(diffs.Current, 1);
            hasNext = diffs.MoveNext();
        }
    }
}

例如，它可以以以下方式使用:

const int N = <Large number>;
const int omit1 = 231567;
const int omit2 = 589932;
IEnumerable<int> numberSequence1 = Enumerable.Range(0, N).Select(i => i < omit1 ? i : i + 1);
IEnumerable<int> numberSequence2 = Enumerable.Range(0, N).Select(i => i < omit2 ? i : i + 1);
var numberDiffs = FindDifferences(numberSequence1, numberSequence2, Comparer<int>.Default);

在我的计算机上对N = 1M进行基准测试，得到以下结果:

对于N = 100M:

请注意，这个示例当然得益于列表已经排序并且可以非常有效地比较整数的事实。但这正是关键所在:如果你确实有有利的环境，一定要好好利用它们。

A few further comments: The speed of the comparison function is clearly relevant for the overall performance, so it may be beneficial to optimize it. The flexibility to do so is a benefit of the zipping approach. Furthermore, parallelization seems more feasible to me, although by no means easy and maybe not worth the effort and the overhead. Nevertheless, a simple way to speed up the process by roughly a factor of 2, is to split the lists respectively in two halfs (if it can be efficiently done) and compare the parts in parallel, one processing from front to back and the other in reverse order.