问题描述

我遇到了这个好问题，它很相似，但完全不同，因为它讨论了 Java，它具有不同的哈希表实现，因为它具有同步访问器/mutators:Java 中的 HashMap 和 Hashtable 有什么区别?

I came across this good question, which is similar but not at all same since it talks about Java, which has different implementation of hash-tables, by virtue of having synchronized accessor /mutators: What are the differences between a HashMap and a Hashtable in Java?

那么set和unordered_set的C++实现有什么区别?这个问题当然可以扩展到map vs unordered_map 等其他C++容器.

So what is the difference in C++ implementation of set and unordered_set? This question can be of course extended to map vs unordered_map and so on for other C++ containers.

这是我的初步评估:

set:虽然标准没有明确要求将其实现为树，但要求其查找/插入操作的时间复杂度约束意味着它将始终作为树实现.通常作为高度平衡的 RB 树(如 GCC 4.8 中所见).由于它们是高度平衡的，它们对于 find()

set: While the standard doesn't explicitly ask it to be implemented as trees, the time-complexity constraint asked for its operations for find/insert, means it will always be implemented as a tree. Usually as RB tree (as seen in GCC 4.8), which is height-balanced. Since they are height balanced, they have predictable time-complexity for find()

优点:紧凑(与其他 DS 相比)

Pros: Compact (compared to other DS in comparison)

缺点:访问时间复杂度为 O(lg n)

Con: Access time complexity is O(lg n)

unordered_set:虽然标准没有明确要求将其实现为树，但要求其查找/插入操作的时间复杂度约束意味着它将始终作为哈希实现-表.

unordered_set: While the standard doesn't explicitly asks it to be implemented as trees, the time-complexity constraint asked for its operations for find/insert, means it will always be implemented as a hash-table.

优点:

1. 更快(承诺为搜索摊销 O(1))
2. 与 tree-DS 相比，易于将基本原语转换为线程安全

缺点:

1. 查找不保证是 O(1).理论上最坏的情况是 O(n).
2. 不像树那么紧凑(实际上，负载因子永远不会是 1).

注意:哈希表的 O(1) 来自没有冲突的假设.即使负载因子为 0.5，每插入一秒变量都会导致冲突.可以看出，哈希表的负载因子与访问其中元素所需的操作数成反比.更多我们减少#operations，更稀疏的哈希表.当存储的元素大小与指针相当时，开销就相当可观.

Note: The O(1), for hashtable comes from the assumption that there are no collision. Even with load-factor of .5, every second variable insertion is leading to collision. It could be observed that the load-factor of hash-table is inversely proportional to the number of operations required for accessing a element in it. More we reduce #operations, sparser hash-table. When the element stored are of size comparable to pointer, then overhead is quite significant.

我是否错过了应该知道的性能分析地图/集合之间的任何区别?

推荐答案

我认为您通常已经回答了您自己的问题，但是，这个:

I think you've generally answered your own question, however, this:

不像树那么紧凑.(出于实际目的，负载因子永远不会是 1)

Not as compact as tree. (for practical purposes load factors is never 1)

不一定正确.T 类型的树的每个节点(我们假设它是红黑树)使用的空间至少等于 2 * pointer_size + sizeof(T) + sizeof(布尔).这可能是 3 * 指针大小，具体取决于树是否包含每个树节点的 parent 指针.

is not necessarily true. Each node of a tree (we'll assume it's a red-black tree) for a type T utilizes space that is equal to at least 2 * pointer_size + sizeof(T) + sizeof(bool). This may be 3 * pointer size depending on whether the tree contains a parent pointer for each tree node.

将其与哈希映射进行比较:由于 加载因子 <的事实，每个哈希映射都会浪费数组空间.1 正如你所说的.然而，假设哈希映射使用单向链表进行链接(实际上，没有真正的理由不这样做)，插入的每个元素仅采用 sizeof(T) + 指针大小.

Compare this to a hash-map: there will be wasted array space for each hash map due to the fact that load factor < 1 as you've said. However, assuming the hash map uses singly linked lists for chaining (and really, there's no real reason not to), each element inserted take only sizeof(T) + pointer size.

请注意，此分析忽略了可能来自对齐使用的额外空间的任何开销.

Note that this analysis ignores any overhead which may come from extra space used by alignment.

对于任何具有小尺寸的元素T(因此，任何基本类型)，指针的大小和其他开销占主导地位.在 > 的负载系数下；0.5(例如)std::unordered_set 可能确实比等效的 std::set 使用更少的内存.

For any element T which has a small size (so, any basic type), the size of the pointers and other overhead dominates. At a load factor of > 0.5 (for example) the std::unordered_set may indeed use up less memory than the equivalent std::set.

另一个重要的缺失点是，基于给定的比较函数，遍历 std::set 保证产生从小到大的排序，同时遍历 std::set>std::unordered_set 将以随机"顺序返回值.

The other big missing point is the fact that iterating through a std::set is guaranteed to produce an ordering from smallest to largest, based on the given comparison function, while iterating through an std::unordered_set will return the values in a "random" order.

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