这里有权衡，您可以选择与您想要的匹配的解决方案。我能想到的是:

初始大小

vector和unique_ptr<T[]>允许在运行时指定大小 数组只允许在编译时指定大小

调整

array和unique_ptr<T[]>不允许调整大小 向量是

存储

vector和unique_ptr<T[]>将数据存储在对象之外(通常在堆上) 数组将数据直接存储在对象中

复制

数组和向量允许复制 unique_ptr<T[]>不允许复制

交换/移动

vector和unique_ptr<T[]>有O(1)次交换和移动操作 数组有O(n)次交换和移动操作，其中n是数组中元素的数量

指针/引用/迭代器失效

array ensures pointers, references and iterators will never be invalidated while the object is live, even on swap() unique_ptr<T[]> has no iterators; pointers and references are only invalidated by swap() while the object is live. (After swapping, pointers point into to the array that you swapped with, so they're still "valid" in that sense.) vector may invalidate pointers, references and iterators on any reallocation (and provides some guarantees that reallocation can only happen on certain operations).

概念和算法的兼容性

array和vector都是容器 unique_ptr<T[]>不是容器

我不得不承认，对于基于策略的设计来说，这似乎是一个重构的机会。

这里有权衡，您可以选择与您想要的匹配的解决方案。我能想到的是:

初始大小

vector和unique_ptr<T[]>允许在运行时指定大小 数组只允许在编译时指定大小

调整

array和unique_ptr<T[]>不允许调整大小 向量是

存储

vector和unique_ptr<T[]>将数据存储在对象之外(通常在堆上) 数组将数据直接存储在对象中

复制

数组和向量允许复制 unique_ptr<T[]>不允许复制

交换/移动

vector和unique_ptr<T[]>有O(1)次交换和移动操作 数组有O(n)次交换和移动操作，其中n是数组中元素的数量

指针/引用/迭代器失效

array ensures pointers, references and iterators will never be invalidated while the object is live, even on swap() unique_ptr<T[]> has no iterators; pointers and references are only invalidated by swap() while the object is live. (After swapping, pointers point into to the array that you swapped with, so they're still "valid" in that sense.) vector may invalidate pointers, references and iterators on any reallocation (and provides some guarantees that reallocation can only happen on certain operations).

概念和算法的兼容性

array和vector都是容器 unique_ptr<T[]>不是容器

我不得不承认，对于基于策略的设计来说，这似乎是一个重构的机会。

出于二进制兼容性的考虑，您需要结构只包含一个指针。 你需要使用一个API来返回用new[]分配的内存 例如，您的公司或项目有一个禁止使用std::vector的一般规则，以防止粗心的程序员不小心引入副本 您希望防止粗心的程序员在这种情况下意外地引入副本。

有一个普遍的规则，c++容器比使用指针滚动自己的容器更受欢迎。这是一个普遍规律;它有例外。有更多的;这些只是例子。

为了回答人们认为你“必须”使用vector而不是unique_ptr，我在GPU上的CUDA编程中有一个案例，当你在Device中分配内存时，你必须使用一个指针数组(使用cudaMalloc)。 然后，当在Host中检索该数据时，必须再次寻找指针，unique_ptr可以很容易地处理指针。 将double*转换为vector<double>的额外成本是不必要的，并且会导致性能损失。

在一些Windows Win32 API调用中可以找到一个常见的模式，其中使用std::unique_ptr<T[]>可以派上用场，例如，当你调用一些Win32 API(将在该缓冲区中写入一些数据)时，不知道输出缓冲区应该有多大:

// Buffer dynamically allocated by the caller, and filled by some Win32 API function.
// (Allocation will be made inside the 'while' loop below.)
std::unique_ptr<BYTE[]> buffer;

// Buffer length, in bytes.
// Initialize with some initial length that you expect to succeed at the first API call.
UINT32 bufferLength = /* ... */;

LONG returnCode = ERROR_INSUFFICIENT_BUFFER;
while (returnCode == ERROR_INSUFFICIENT_BUFFER)
{
    // Allocate buffer of specified length
    buffer.reset( BYTE[bufferLength] );
    //        
    // Or, in C++14, could use make_unique() instead, e.g.
    //
    // buffer = std::make_unique<BYTE[]>(bufferLength);
    //

    //
    // Call some Win32 API.
    //
    // If the size of the buffer (stored in 'bufferLength') is not big enough,
    // the API will return ERROR_INSUFFICIENT_BUFFER, and the required size
    // in the [in, out] parameter 'bufferLength'.
    // In that case, there will be another try in the next loop iteration
    // (with the allocation of a bigger buffer).
    //
    // Else, we'll exit the while loop body, and there will be either a failure
    // different from ERROR_INSUFFICIENT_BUFFER, or the call will be successful
    // and the required information will be available in the buffer.
    //
    returnCode = ::SomeApiCall(inParam1, inParam2, inParam3, 
                               &bufferLength, // size of output buffer
                               buffer.get(),  // output buffer pointer
                               &outParam1, &outParam2);
}

if (Failed(returnCode))
{
    // Handle failure, or throw exception, etc.
    ...
}

// All right!
// Do some processing with the returned information...
...

I have used unique_ptr<char[]> to implement a preallocated memory pools used in a game engine. The idea is to provide preallocated memory pools used instead of dynamic allocations for returning collision requests results and other stuff like particle physics without having to allocate / free memory at each frame. It's pretty convenient for this kind of scenarios where you need memory pools to allocate objects with limited life time (typically one, 2 or 3 frames) that do not require destruction logic (only memory deallocation).