使用LinkedList的常见情况是这样的:

假设您想要从一个字符串列表中删除许多特定的字符串，这些字符串的大小很大，比如100,000。要删除的字符串可以在HashSet dic中查找，字符串列表中应该包含30,000到60,000个这样的需要删除的字符串。

那么用于存储100,000个字符串的列表的最佳类型是什么?答案是LinkedList。如果它们存储在数组列表中，则遍历它并删除匹配的字符串将占用 到数十亿次操作，而使用迭代器和remove()方法只需要大约100,000次操作。

LinkedList<String> strings = readStrings();
HashSet<String> dic = readDic();
Iterator<String> iterator = strings.iterator();
while (iterator.hasNext()){
    String string = iterator.next();
    if (dic.contains(string))
    iterator.remove();
}

将链表视为列表可能会有一些误导。它更像一个链条。事实上，在。net中，LinkedList<T>甚至没有实现IList<T>。在链表中没有真正的索引概念，即使看起来有。当然，类中提供的方法都不接受索引。

链表可以是单链，也可以是双链。这是指链中的每个元素是否只与下一个元素有链接(单链)，还是与前一个/下一个元素都有链接(双链)。LinkedList<T>是双重链接。

在内部，List<T>由一个数组支持。这在内存中提供了一个非常紧凑的表示。相反，LinkedList<T>涉及额外的内存来存储连续元素之间的双向链接。因此LinkedList<T>的内存占用通常会比List<T>的内存占用大(需要注意的是，List<T>可以有未使用的内部数组元素，以提高追加操作期间的性能)。

它们也有不同的表现特征:

附加

LinkedList<T>.AddLast(item)常量时间 List<T>.Add(item)平摊常数时间，线性最坏情况

预谋

LinkedList<T>.AddFirst(item)常量时间 列表> < T。插入(0，项)线性时间

插入

LinkedList < T >。AddBefore(节点，项目)常量时间 LinkedList < T >。AddAfter(节点，项目)常量时间 列表> < T。插入(索引、项)线性时间

删除

删除(项目)线性时间 删除(节点)常量时间 列表<T>.删除(项目)线性时间 List<T>.RemoveAt(index)线性时间

数

LinkedList < T >。计算常数时间 列表> < T。计算常数时间

包含

包含(项目)线性时间 列表<T>.包含(项)线性时间

清晰的

LinkedList<T>.Clear()线性时间 List<T>.Clear()线性时间

如你所见，它们基本上是相等的。实际上，LinkedList<T>的API使用起来更麻烦，其内部需求的细节会泄漏到代码中。

然而，如果你需要在一个列表中做很多插入/删除，它提供了常数时间。List<T>提供线性时间，因为列表中的额外项必须在插入/删除之后重新排列。

在大多数情况下，List<T>更有用。LinkedList<T>在列表中间添加/删除项时成本更低，而list <T>只能在列表末尾添加/删除项。

LinkedList<T>只有在访问顺序数据(向前或向后)时才最有效-随机访问相对昂贵，因为它每次都必须遍历链(因此它没有索引器)。但是，因为List<T>本质上只是一个数组(带有包装器)，所以随机访问是可以的。

List<T>还提供了很多支持方法- Find, ToArray等;然而，这些也可以通过扩展方法用于。net 3.5/ c# 3.0的LinkedList<T> -所以这不是一个因素。

这是改编自Tono Nam的公认的答案，纠正了一些错误的测量。

测试:

static void Main()
{
    LinkedListPerformance.AddFirst_List(); // 12028 ms
    LinkedListPerformance.AddFirst_LinkedList(); // 33 ms

    LinkedListPerformance.AddLast_List(); // 33 ms
    LinkedListPerformance.AddLast_LinkedList(); // 32 ms

    LinkedListPerformance.Enumerate_List(); // 1.08 ms
    LinkedListPerformance.Enumerate_LinkedList(); // 3.4 ms

    //I tried below as fun exercise - not very meaningful, see code
    //sort of equivalent to insertion when having the reference to middle node

    LinkedListPerformance.AddMiddle_List(); // 5724 ms
    LinkedListPerformance.AddMiddle_LinkedList1(); // 36 ms
    LinkedListPerformance.AddMiddle_LinkedList2(); // 32 ms
    LinkedListPerformance.AddMiddle_LinkedList3(); // 454 ms

    Environment.Exit(-1);
}

代码是:

using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;

namespace stackoverflow
{
    static class LinkedListPerformance
    {
        class Temp
        {
            public decimal A, B, C, D;

            public Temp(decimal a, decimal b, decimal c, decimal d)
            {
                A = a; B = b; C = c; D = d;
            }
        }



        static readonly int start = 0;
        static readonly int end = 123456;
        static readonly IEnumerable<Temp> query = Enumerable.Range(start, end - start).Select(temp);

        static Temp temp(int i)
        {
            return new Temp(i, i, i, i);
        }

        static void StopAndPrint(this Stopwatch watch)
        {
            watch.Stop();
            Console.WriteLine(watch.Elapsed.TotalMilliseconds);
        }

        public static void AddFirst_List()
        {
            var list = new List<Temp>();
            var watch = Stopwatch.StartNew();

            for (var i = start; i < end; i++)
                list.Insert(0, temp(i));

            watch.StopAndPrint();
        }

        public static void AddFirst_LinkedList()
        {
            var list = new LinkedList<Temp>();
            var watch = Stopwatch.StartNew();

            for (int i = start; i < end; i++)
                list.AddFirst(temp(i));

            watch.StopAndPrint();
        }

        public static void AddLast_List()
        {
            var list = new List<Temp>();
            var watch = Stopwatch.StartNew();

            for (var i = start; i < end; i++)
                list.Add(temp(i));

            watch.StopAndPrint();
        }

        public static void AddLast_LinkedList()
        {
            var list = new LinkedList<Temp>();
            var watch = Stopwatch.StartNew();

            for (int i = start; i < end; i++)
                list.AddLast(temp(i));

            watch.StopAndPrint();
        }

        public static void Enumerate_List()
        {
            var list = new List<Temp>(query);
            var watch = Stopwatch.StartNew();

            foreach (var item in list)
            {

            }

            watch.StopAndPrint();
        }

        public static void Enumerate_LinkedList()
        {
            var list = new LinkedList<Temp>(query);
            var watch = Stopwatch.StartNew();

            foreach (var item in list)
            {

            }

            watch.StopAndPrint();
        }

        //for the fun of it, I tried to time inserting to the middle of 
        //linked list - this is by no means a realistic scenario! or may be 
        //these make sense if you assume you have the reference to middle node

        //insertion to the middle of list
        public static void AddMiddle_List()
        {
            var list = new List<Temp>();
            var watch = Stopwatch.StartNew();

            for (var i = start; i < end; i++)
                list.Insert(list.Count / 2, temp(i));

            watch.StopAndPrint();
        }

        //insertion in linked list in such a fashion that 
        //it has the same effect as inserting into the middle of list
        public static void AddMiddle_LinkedList1()
        {
            var list = new LinkedList<Temp>();
            var watch = Stopwatch.StartNew();

            LinkedListNode<Temp> evenNode = null, oddNode = null;
            for (int i = start; i < end; i++)
            {
                if (list.Count == 0)
                    oddNode = evenNode = list.AddLast(temp(i));
                else
                    if (list.Count % 2 == 1)
                        oddNode = list.AddBefore(evenNode, temp(i));
                    else
                        evenNode = list.AddAfter(oddNode, temp(i));
            }

            watch.StopAndPrint();
        }

        //another hacky way
        public static void AddMiddle_LinkedList2()
        {
            var list = new LinkedList<Temp>();
            var watch = Stopwatch.StartNew();

            for (var i = start + 1; i < end; i += 2)
                list.AddLast(temp(i));
            for (int i = end - 2; i >= 0; i -= 2)
                list.AddLast(temp(i));

            watch.StopAndPrint();
        }

        //OP's original more sensible approach, but I tried to filter out
        //the intermediate iteration cost in finding the middle node.
        public static void AddMiddle_LinkedList3()
        {
            var list = new LinkedList<Temp>();
            var watch = Stopwatch.StartNew();

            for (var i = start; i < end; i++)
            {
                if (list.Count == 0)
                    list.AddLast(temp(i));
                else
                {
                    watch.Stop();
                    var curNode = list.First;
                    for (var j = 0; j < list.Count / 2; j++)
                        curNode = curNode.Next;
                    watch.Start();

                    list.AddBefore(curNode, temp(i));
                }
            }

            watch.StopAndPrint();
        }
    }
}

你可以看到结果与其他人在这里记录的理论性能是一致的。很清楚- LinkedList<T>在插入的情况下获得了很大的时间。我还没有测试从列表中间删除，但结果应该是相同的。当然，List<T>在其他方面表现得更好，比如O(1)随机访问。

我之前的回答不够准确。 D是正确答案 但现在我可以发布更有用和正确的答案。

我做了一些额外的检查您可以通过以下链接找到它的源代码，并在您自己的环境中通过https://github.com/ukushu/DataStructuresTestsAndOther.git重新检查它

短的结果:

Array need to use: So often as possible. It's fast and takes smallest RAM range for same amount information. If you know exact count of cells needed If data saved in array < 85000 b (85000/32 = 2656 elements for integer data) If needed high Random Access speed List need to use: If needed to add cells to the end of list (often) If needed to add cells in the beginning/middle of the list (NOT OFTEN) If data saved in array < 85000 b (85000/32 = 2656 elements for integer data) If needed high Random Access speed LinkedList need to use: If needed to add cells in the beginning/middle/end of the list (often) If needed only sequential access (forward/backward) If you need to save LARGE items, but items count is low. Better do not use for large amount of items, as it's use additional memory for links.

更多的细节:

有趣的是:

LinkedList<T> internally is not a List in .NET. It's even does not implement IList<T>. And that's why there are absent indexes and methods related to indexes. LinkedList<T> is node-pointer based collection. In .NET it's in doubly linked implementation. This means that prior/next elements have link to current element. And data is fragmented -- different list objects can be located in different places of RAM. Also there will be more memory used for LinkedList<T> than for List<T> or Array. List<T> in .Net is Java's alternative of ArrayList<T>. This means that this is array wrapper. So it's allocated in memory as one contiguous block of data. If allocated data size exceeds 85000 bytes, it will be moved to Large Object Heap. Depending on the size, this can lead to heap fragmentation(a mild form of memory leak). But in the same time if size < 85000 bytes -- this provides a very compact and fast-access representation in memory. Single contiguous block is preferred for random access performance and memory consumption but for collections that need to change size regularly a structure such as an Array generally need to be copied to a new location whereas a linked list only needs to manage the memory for the newly inserted/deleted nodes.

Edit

请阅读对这个答案的评论。人们说我没有 适当的测试。我同意这不应该是一个可以接受的答案。就像我一样 我做了一些测试，想和大家分享。

最初的回答…

我发现了有趣的结果:

// Temporary class to show the example
class Temp
{
    public decimal A, B, C, D;

    public Temp(decimal a, decimal b, decimal c, decimal d)
    {
        A = a;            B = b;            C = c;            D = d;
    }
}

链表(3.9秒)

        LinkedList<Temp> list = new LinkedList<Temp>();

        for (var i = 0; i < 12345678; i++)
        {
            var a = new Temp(i, i, i, i);
            list.AddLast(a);
        }

        decimal sum = 0;
        foreach (var item in list)
            sum += item.A;

列表(2.4秒)

        List<Temp> list = new List<Temp>(); // 2.4 seconds

        for (var i = 0; i < 12345678; i++)
        {
            var a = new Temp(i, i, i, i);
            list.Add(a);
        }

        decimal sum = 0;
        foreach (var item in list)
            sum += item.A;

即使你只是访问数据，本质上也要慢得多!!我说永远不要使用linkedList。

下面是另一个执行大量插入的比较(我们计划在列表中间插入一个项)

链表(51秒)

        LinkedList<Temp> list = new LinkedList<Temp>();

        for (var i = 0; i < 123456; i++)
        {
            var a = new Temp(i, i, i, i);

            list.AddLast(a);
            var curNode = list.First;

            for (var k = 0; k < i/2; k++) // In order to insert a node at the middle of the list we need to find it
                curNode = curNode.Next;

            list.AddAfter(curNode, a); // Insert it after
        }

        decimal sum = 0;
        foreach (var item in list)
            sum += item.A;

榜单(7.26秒)

        List<Temp> list = new List<Temp>();

        for (var i = 0; i < 123456; i++)
        {
            var a = new Temp(i, i, i, i);

            list.Insert(i / 2, a);
        }

        decimal sum = 0;
        foreach (var item in list)
            sum += item.A;

有插入位置引用的链表(。04秒)

        list.AddLast(new Temp(1,1,1,1));
        var referenceNode = list.First;

        for (var i = 0; i < 123456; i++)
        {
            var a = new Temp(i, i, i, i);

            list.AddLast(a);
            list.AddBefore(referenceNode, a);
        }

        decimal sum = 0;
        foreach (var item in list)
            sum += item.A;

因此，只有当你计划插入几个项目，并且你也在某个地方有你计划插入项目的引用时，才使用链表。只是因为你必须插入很多项，这并不会使它更快，因为搜索你想要插入的位置需要时间。