How are data consistency issues resolved in microservices?

In microservices architecture, maintaining data consistency is one of the key challenges due to distribution and asynchronous processes. The main approaches to solving this:

• Eventual Consistency: data may be temporarily inconsistent, but will eventually become consistent through event exchange.
• Using the Saga pattern: breaking transactions into a sequence of local transactions with compensating actions in case of errors.
• Idempotent operations: so that repeated calls do not lead to inconsistency.
• Service-level transactions: if possible, use distributed transactions (e.g., two-phase commit), but this reduces performance and scalability.

An example of the Saga pattern in Go (simplified):

func CreateOrder() error {
    err := ReserveInventory()
    if err != nil {
        return err
    }
    err = ChargePayment()
    if err != nil {
        CompensateInventory()
        return err
    }
    return nil
}

Thus, in case of an error at the payment step, the reservation of the product is compensated, which maintains data consistency.