how does the race detector handle long-running goroutines

1. 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.
- This means that if you have a long-running goroutine that is periodically issuing `defer` and `recover` calls, the program memory usage may grow without bound.

2. Memory Usage:
- The race detector can increase the memory usage of a program by 5-10 times.
- This is because the race detector needs to track memory accesses and synchronization events, which requires additional memory.

3. Performance Impact:
- The race detector can introduce significant performance overhead, especially for long-running goroutines.
- This overhead can make it difficult to accurately measure the performance of a program when the race detector is enabled.

4. 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.
- It is also important to properly manage memory usage and to avoid memory leaks in your program.

Here is an example of how the race detector handles long-running goroutines:

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`.