Design a worker for asynchronous processing of financial operations.

For asynchronous processing of financial operations, the worker must ensure reliability, scalability, and correctness of processing.

Main components:

• Task queue (e.g., RabbitMQ, Kafka, or built-in queue) for receiving operations.
• Goroutine pool for parallel processing with control over the maximum number of concurrently executing tasks.
• Mechanism for confirming successful processing (ack) and retrying in case of errors.
• Logging and monitoring of the worker's state.
• Transaction processing considering atomicity and idempotency.

Example of a simplified worker in Go:

package main

import (
    "fmt"
    "time"
)

type Operation struct {
    ID     string
    Amount float64
}

func processOperation(op Operation) error {
    // Simulate processing
    fmt.Printf("Processing operation %s for amount %.2f\n", op.ID, op.Amount)
    time.Sleep(500 * time.Millisecond)
    return nil // or error
}

func worker(jobs <-chan Operation, results chan<- error) {
    for op := range jobs {
        err := processOperation(op)
        results <- err
    }
}

func main() {
    jobs := make(chan Operation, 10)
    results := make(chan error, 10)

    // Launch 5 workers
    for w := 0; w < 5; w++ {
        go worker(jobs, results)
    }

    // Send tasks
    for i := 0; i < 20; i++ {
        jobs <- Operation{ID: fmt.Sprintf("op%d", i), Amount: float64(i) * 100}
    }
    close(jobs)

    // Read results
    for i := 0; i < 20; i++ {
        err := <-results
        if err != nil {
            fmt.Println("Processing error:", err)
        }
    }
}

In a real project, mechanisms for retries, deduplication, transactional integrity, and integration with external systems are added.