how does the race detector manage memory allocation for long-running goroutines

Here are some key points about how the race detector manages memory allocation for long-running goroutines:

1. Extra Memory Allocation:
- The race detector allocates an extra 8 bytes per `defer` and `recover` statement.
- These extra allocations are not recovered until the goroutine exits.

2. Memory Usage Growth:
- If you have a long-running goroutine that is periodically issuing `defer` and `recover` calls, the program memory usage may grow without bound.

3. No Output in `runtime.ReadMemStats` or `runtime/pprof`:
- These memory allocations will not show up in the output of `runtime.ReadMemStats` or `runtime/pprof`.

4. Impact on Performance:
- The race detector can introduce significant performance overhead, especially for long-running goroutines.

5. Best Practices:
- To minimize the impact of the race detector on long-running goroutines, it is recommended to use the race detector only when necessary and to disable it in production environments.

Here is an example of how to use the race detector to detect data races in a Go program:

go
package main

import (
    "fmt"
    "runtime"
    "runtime/pprof"
)

func main() {
    f, err := os.Create("cpu.prof")
    if err != nil {
        log.Fatal(err)
    }
    defer f.Close()
    if err := pprof.StartCPUProfile(f); err != nil {
        log.Fatal(err)
    }
    defer pprof.StopCPUProfile()

    // Your application logic here
}

This code snippet enables CPU profiling at the beginning of the program and stops it when the program is done. The profiling data is saved in a file named `cpu.prof`.