C++核心准则CP.2:避免数据竞争
C++核心准则CP.2:避免数据竞争
CP.2: Avoid data races
CP.2:避免数据竞争
Reason（原因）
Unless you do, nothing is guaranteed to work and subtle errors will persist.
除非你做到了，否则没有任何东西可以保证动作，微妙的错误还会继续存在。
Note（注意）
In a nutshell, if two threads can access the same object concurrently (without synchronization), and at least one is a writer (performing a non-const operation), you have a data race. For further information of how to use synchronization well to eliminate data races, please consult a good book about concurrency.
简而言之，如果两个线程可以（不进行任何同步）并发访问同一个对象，至少一个线程执行写操作（执行非常量操作），就会发生数据竞争。为了获得如何更好地使用同步以消除数据竞争的进一步信息，请查阅有关并发的经典书籍。
Example, bad（反面示例）
There are many examples of data races that exist, some of which are running in production software at this very moment. One very simple example:
有关数据竞争的例子非常多，有些就发生于正在运行的产品级软件。下面是很简单的例子：
int get_id()
{
static int id = 1;
return id++;
}
The increment here is an example of a data race. This can go wrong in many ways, including:
代码中的增量操作就是数据竞争的例子。出错的方式可以有很多种，包括：
- Thread A loads the value of id, the OS context switches A out for some period, during which other threads create hundreds of IDs. Thread A is then allowed to run again, and id is written back to that location as A's read of id plus one.
- 线程A获取id的值之后操作系统上下文从A中退出一段时间，这时另外的线程生成了几百个ID。接着线程A继续运行，这时id重新被写入，而值是A读取的局部变量加1之后的结果。
- Thread A and B load id and increment it simultaneously. They both get the same ID.
- 线程A和B同时获取id并加1。它们得到同样的ID。
Local static variables are a common source of data races.
局部静态变量是数据竞争的常见来源。
Example, bad（反面示例）:
void f(fstream& fs, regex pattern)
{
array<double, max> buf;
int sz = read_vec(fs, buf, max); // read from fs into buf
gsl::span<double> s {buf};
// ...
auto h1 = async([&] { sort(std::execution::par, s); }); // spawn a task to sort
// ...
auto h2 = async([&] { return find_all(buf, sz, pattern); }); // spawn a task to find matches
// ...
}
Here, we have a (nasty) data race on the elements of buf (sort will both read and write). All data races are nasty. Here, we managed to get a data race on data on the stack. Not all data races are as easy to spot as this one.
这里，保存在buf中的元素会发生（严重的）数据竞争（排序既包含读操作也包含写操作）。没有哪个数据竞争是不严重的。代码中的数据竞争发生在堆栈中的数据。不是所有的数据竞争都像本例这样容易被发现。
Example, bad（反面示例）:
// code not controlled by a lock
unsigned val;
if (val < 5) {
// ... other thread can change val here ...
switch (val) {
case 0: // ...
case 1: // ...
case 2: // ...
case 3: // ...
case 4: // ...
}
}
Now, a compiler that does not know that val can change will most likely implement that switch using a jump table with five entries. Then, a val outside the [0..4] range will cause a jump to an address that could be anywhere in the program, and execution would proceed there. Really, "all bets are off" if you get a data race. Actually, it can be worse still: by looking at the generated code you may be able to determine where the stray jump will go for a given value; this can be a security risk.
现在，编译器不知道val会被修改，因此编译结果很可能是一个带有五个分支的跳转表。那么一旦val的值超越了范围[0..4]，就有可能调转到程序的任何位置并从那里继续执行。真的，一旦发生了数据竞争，结果会怎么样谁也不知道。实际上，还有可能更坏：通过检查生成的代码，你或许可以准确算出针对一个给定的值，程序可以跳转到什么位置。这可能成为一个安全风险。
Enforcement（实施建议）
Some is possible, do at least something. There are commercial and open-source tools that try to address this problem, but be aware that solutions have costs and blind spots. Static tools often have many false positives and run-time tools often have a significant cost. We hope for better tools. Using multiple tools can catch more problems than a single one.
有一些是可能的，至少做点什么。有些商用和开源工具试图定位这些问题，但是需要注意的是：解决方案需要成本并存在盲区。静态工具会产生很多误报，而运行时工具通常需要巨大的成本。我们希望出现更好的工具。使用多种工具会比单一工具捕捉更多的错误。
There are other ways you can mitigate the chance of data races:
存在另外的方法可以降低数据竞争的可能性：
- Avoid global data
- 避免全局数据
- Avoid static variables
- 避免静态数据
- More use of value types on the stack (and don't pass pointers around too much)
- 在堆栈上更多地使用值类型（并且不要来回传递指针）
- More use of immutable data (literals, constexpr, and const)
- 更多地使用不可修改的数据（literals, constexpr, and const）
原文链接
https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md#cp2-avoid-data-races