我看到了和所有场景问题一样的问题，比如:当程序改变时，会发生什么?突然，这个小内存泄漏被调用了1000万次，而程序的结尾在不同的地方，所以它很重要?如果它在库中，那么请向库维护者记录错误，不要在您自己的代码中泄漏。

理论上没有，实际情况要视情况而定。

这实际上取决于程序处理了多少数据，程序运行的频率以及它是否持续运行。

如果我有一个快速程序，读取少量数据进行计算并退出，那么就永远不会注意到一个小的内存泄漏。因为程序不会运行很长时间，并且只使用少量的内存，所以当程序存在时，泄漏将很小并被释放。

另一方面，如果我有一个处理数百万条记录并运行很长时间的程序，一个小的内存泄漏可能会在足够的时间内使机器停机。

对于有泄漏的第三方库，如果它们导致问题，要么修复库，要么找到更好的替代方案。如果不造成问题，这真的重要吗?

只要您的内存利用率不随着时间的推移而增加，这取决于情况。如果你在服务器软件中做很多复杂的同步，比如启动阻塞系统调用的后台线程，那么完全关闭可能太复杂了。在这种情况下，备选方案可能是:

直到进程退出才清理内存的库。 您编写了额外的500行代码，并向类中添加了另一个互斥量和条件变量，以便它可以从测试中干净地关闭—但是这些代码从未在生产中使用，在生产中服务器只会因崩溃而终止。

我只看到了一个实际的缺点，那就是这些良性泄漏将在内存泄漏检测工具中显示为误报。

If I understood correctly, you don't explicitly free memory (which can be freed because you still have a pointer) and rely on OS to free it during process termination. Though this may seem okay for simple program, consider the situation where your code is moved into a library and becomes a part of some resident daemon process running 24/7. Say this daemon spawns a thread each time it needs to do something useful using your code and say it spawns thousands of threads every hour. In this case you will get real memory leak.

不幸的是，这种情况在现实生活中并非不可能，一致的内存管理技术可能会使您的生活更轻松。

除非“使用”的内存量不断增长，否则我不认为这是内存泄漏。有一些未释放的内存，虽然不是理想的，但不是一个大问题，除非所需的内存数量不断增长。

You have to first realize that there's a big difference between a perceived memory leak and an actual memory leak. Very frequently analysis tools will report many red herrings, and label something as having been leaked (memory or resources such as handles etc) where it actually isn't. Often times this is due to the analysis tool's architecture. For example, certain analysis tools will report run time objects as memory leaks because it never sees those object freed. But the deallocation occurs in the runtime's shutdown code, which the analysis tool might not be able to see.

尽管如此，仍然会有一些时候，您会遇到实际的内存泄漏，这些泄漏要么很难发现，要么很难修复。现在的问题是，是否可以将它们保留在代码中?

The ideal answer is, "no, never." A more pragmatic answer may be "no, almost never." Very often in real life you have limited number of resources and time to resolve and endless list of tasks. When one of the tasks is eliminating memory leaks, the law of diminishing returns very often comes in to play. You could eliminate say 98% of all memory leaks in an application in a week, but the remaining 2% might take months. In some cases it might even be impossible to eliminate certain leaks because of the application's architecture without a major refactoring of code. You have to weigh the costs and benefits of eliminating the remaining 2%.