Have there been performance issues or was there a need to optimize something?

Yes, performance optimization tasks often arise in Go projects, especially when working with large volumes of data or high load.

Typical problems and optimization approaches:

• Memory leaks — it is important to use profiling (pprof) to identify and fix leaks.
• Locks and data races — using race detector tools and optimizing synchronization.
• GC optimization — reducing allocations, using object pools (sync.Pool).
• Parallelism and concurrency — effective use of goroutines and channels to distribute load.
• Database operation optimization — caching, prepared statements, batch operations.

Example: when calculating payments for a large number of employees, you can use a goroutine pool to limit concurrent computations and reduce system load.

var wg sync.WaitGroup
sem := make(chan struct{}, 100) // limit to 100 parallel tasks

for _, emp := range employees {
    wg.Add(1)
    sem <- struct{}{}
    go func(e Employee) {
        defer wg.Done()
        defer func() { <-sem }()
        calculatePayment(e)
    }(emp)
}
wg.Wait()

This approach helps avoid overload and improves system stability.