如果有用的话，这里有一个表格视图。

从性能角度看:

+------------------+---------+----------+--------+----------+----------+---------+
| Collection       | Indexed | Keyed    | Value  | Addition |  Removal | Memory  |
|                  | lookup  | lookup   | lookup |          |          |         |
+------------------+---------+----------+--------+----------+----------+---------+
| SortedList       | O(1)    | O(log n) | O(n)   | O(n)*    | O(n)     | Lesser  |
| SortedDictionary | O(n)**  | O(log n) | O(n)   | O(log n) | O(log n) | Greater |
+------------------+---------+----------+--------+----------+----------+---------+

  * Insertion is O(log n) for data that are already in sort order, so that each 
    element is added to the end of the list. If a resize is required, that element
    takes O(n) time, but inserting n elements is still amortized O(n log n).
    list.
** Available through enumeration, e.g. Enumerable.ElementAt.

从实现的角度来看:

+------------+---------------+----------+------------+------------+------------------+
| Underlying | Lookup        | Ordering | Contiguous | Data       | Exposes Key &    |
| structure  | strategy      |          | storage    | access     | Value collection |
+------------+---------------+----------+------------+------------+------------------+
| 2 arrays   | Binary search | Sorted   | Yes        | Key, Index | Yes              |
| BST        | Binary search | Sorted   | No         | Key        | Yes              |
+------------+---------------+----------+------------+------------+------------------+

粗略地解释一下，如果需要原始性能，SortedDictionary可能是更好的选择。如果需要较少的内存开销和索引检索，SortedList更适合。关于何时使用哪个，请参阅这个问题。

你可以在这里，这里，这里，这里和这里阅读更多。

关于这个话题已经说得够多了，但是为了简单起见，这里是我的看法。

排序字典应该使用时-

需要更多的插入和删除操作。 无序数据。 键访问就足够了，不需要索引访问。 内存不是瓶颈。

另一方面，当-时应该使用Sorted List

需要更多的查找和更少的插入和删除操作。 数据已经排序(如果不是全部，也是大部分)。 需要索引访问。 内存是一种开销。

希望这能有所帮助!!

是的，它们的表现特征有很大不同。将它们命名为SortedList和SortedTree可能更好，因为这样可以更准确地反映实现。

查看它们各自的MSDN文档(SortedList, SortedDictionary)，了解不同情况下不同操作的性能细节。下面是一个很好的总结(来自SortedDictionary文档):

The SortedDictionary<TKey, TValue> generic class is a binary search tree with O(log n) retrieval, where n is the number of elements in the dictionary. In this, it is similar to the SortedList<TKey, TValue> generic class. The two classes have similar object models, and both have O(log n) retrieval. Where the two classes differ is in memory use and speed of insertion and removal: SortedList<TKey, TValue> uses less memory than SortedDictionary<TKey, TValue>. SortedDictionary<TKey, TValue> has faster insertion and removal operations for unsorted data, O(log n) as opposed to O(n) for SortedList<TKey, TValue>. If the list is populated all at once from sorted data, SortedList<TKey, TValue> is faster than SortedDictionary<TKey, TValue>.

(SortedList实际上维护一个排序的数组，而不是使用树。它仍然使用二进制搜索来查找元素。)

如果有用的话，这里有一个表格视图。

从性能角度看:

+------------------+---------+----------+--------+----------+----------+---------+
| Collection       | Indexed | Keyed    | Value  | Addition |  Removal | Memory  |
|                  | lookup  | lookup   | lookup |          |          |         |
+------------------+---------+----------+--------+----------+----------+---------+
| SortedList       | O(1)    | O(log n) | O(n)   | O(n)*    | O(n)     | Lesser  |
| SortedDictionary | O(n)**  | O(log n) | O(n)   | O(log n) | O(log n) | Greater |
+------------------+---------+----------+--------+----------+----------+---------+

  * Insertion is O(log n) for data that are already in sort order, so that each 
    element is added to the end of the list. If a resize is required, that element
    takes O(n) time, but inserting n elements is still amortized O(n log n).
    list.
** Available through enumeration, e.g. Enumerable.ElementAt.

从实现的角度来看:

+------------+---------------+----------+------------+------------+------------------+
| Underlying | Lookup        | Ordering | Contiguous | Data       | Exposes Key &    |
| structure  | strategy      |          | storage    | access     | Value collection |
+------------+---------------+----------+------------+------------+------------------+
| 2 arrays   | Binary search | Sorted   | Yes        | Key, Index | Yes              |
| BST        | Binary search | Sorted   | No         | Key        | Yes              |
+------------+---------------+----------+------------+------------+------------------+

粗略地解释一下，如果需要原始性能，SortedDictionary可能是更好的选择。如果需要较少的内存开销和索引检索，SortedList更适合。关于何时使用哪个，请参阅这个问题。

你可以在这里，这里，这里，这里和这里阅读更多。

我打开Reflector来看看这个，因为似乎有一些关于SortedList的困惑。它实际上不是一个二叉搜索树，它是一个由键-值对排序的数组。还有一个TKey[]键变量，它与键值对同步排序，用于二进制搜索。

这里有一些源代码(针对。net 4.5)来备份我的声明。

私有成员

// Fields
private const int _defaultCapacity = 4;
private int _size;
[NonSerialized]
private object _syncRoot;
private IComparer<TKey> comparer;
private static TKey[] emptyKeys;
private static TValue[] emptyValues;
private KeyList<TKey, TValue> keyList;
private TKey[] keys;
private const int MaxArrayLength = 0x7fefffff;
private ValueList<TKey, TValue> valueList;
private TValue[] values;
private int version;

SortedList。ctor (IDictionary IComparer)

public SortedList(IDictionary<TKey, TValue> dictionary, IComparer<TKey> comparer) : this((dictionary != null) ? dictionary.Count : 0, comparer)
{
    if (dictionary == null)
    {
        ThrowHelper.ThrowArgumentNullException(ExceptionArgument.dictionary);
    }
    dictionary.Keys.CopyTo(this.keys, 0);
    dictionary.Values.CopyTo(this.values, 0);
    Array.Sort<TKey, TValue>(this.keys, this.values, comparer);
    this._size = dictionary.Count;
}

SortedList。Add(TKey, TValue): void

public void Add(TKey key, TValue value)
{
    if (key == null)
    {
        ThrowHelper.ThrowArgumentNullException(ExceptionArgument.key);
    }
    int num = Array.BinarySearch<TKey>(this.keys, 0, this._size, key, this.comparer);
    if (num >= 0)
    {
        ThrowHelper.ThrowArgumentException(ExceptionResource.Argument_AddingDuplicate);
    }
    this.Insert(~num, key, value);
}

SortedList.RemoveAt(int): void

public void RemoveAt(int index)
{
    if ((index < 0) || (index >= this._size))
    {
        ThrowHelper.ThrowArgumentOutOfRangeException(ExceptionArgument.index, ExceptionResource.ArgumentOutOfRange_Index);
    }
    this._size--;
    if (index < this._size)
    {
        Array.Copy(this.keys, index + 1, this.keys, index, this._size - index);
        Array.Copy(this.values, index + 1, this.values, index, this._size - index);
    }
    this.keys[this._size] = default(TKey);
    this.values[this._size] = default(TValue);
    this.version++;
}

这是性能之间相互比较的可视化表示。