Harnessing Kafka for Asynchronous Email Sending in Go

Emails play a crucial role in keeping users updated. For example, in an e-commerce system, we may want to notify users about their transaction status. We use email to keep users informed about these transactions.

In this post, we will create an email system using Kafka and Go for sending emails asynchronously. We opt for an asynchronous system because users typically don’t want to wait for processes to complete. Instead, we can send an immediate response back to the user and allow the backend system to handle the email processing.

A common term for this system is “fire and forget,” which means that the developer initiates (“fires”) some background work but does not wait for it to finish (“forgets”).

Architecture

Assume that users have completed the transaction process, and the system sends the email data via an API. The API saves the data to the Kafka message queue and immediately responds to the user. However, the email has not been sent yet and is still in the Kafka messaging queue.

We create a worker using Go to subscribe to the Kafka message queue. Once the message is consumed by the worker, it processes the email and sends it to the user.

Producer

We receive email data from the API and then immediately send the data to Kafka. In other words, we create a Kafka producer. A producer is a client application that publishes (writes) messages to one or more Kafka topics. Producers send data to Kafka topics, which can then be consumed by consumers.

Consumer

We create a worker to read messages from the Kafka messaging system using the same topic as the producer. A consumer is a client application that reads messages from one or more Kafka topics. This consumer will process the data and send the email based on the data received by the consumer.

Conclusion

In summary, Kafka plays a crucial role in handling data streams through its producer and consumer architecture. Producers efficiently send messages to designated topics, while consumers read and process these messages. By utilizing Kafka for asynchronous email processing in a Go application, we can achieve scalable and reliable communication. The producer generates email data and sends it to a Kafka topic, while the consumer retrieves this data, processes it, and sends the emails. This approach allows for improved performance and responsiveness in email handling, making Kafka an ideal choice for applications that require real-time data processing and integration.