Code Chronicles #9: Enhancing Kafka Performance with Dynamic Retry Topics

Today, we're looking into an interesting aspect of messaging systems - retry logic. This will be a journey through the challenges of managing retries and the intelligent solution of dynamic retry topics. We'll explore how to implement this using C# with Confluent.Kafka library.

The Challenge of Retries

Even in the well-orchestrated world of messaging systems, things can go sideways. Service unavailability, network problems, and other temporary hiccups can disrupt the seamless flow of messages. When that happens, it's vital to have robust retry logic. Rather than discarding the problematic messages, they should be queued for reprocessing once the issue is resolved. This is where message retry becomes crucial.

"Retry", in this context, implies reattempting to process the message, which could be due to a temporary problem or because the initial processing took longer than expected. The common strategy here involves using queues and delay mechanisms to reintroduce the message into the queue after a certain period. However, managing retries without impacting your messaging system's performance can pose a challenge.

The Power of Dynamic Retry Topics

A reliable solution lies in dynamically creating retry topics. This strategy circumvents the stuck retry queues, ensuring seamless message processing. We'll explore how this is done using Kafka as your messaging tool of choice.

Let's set up a scenario: There's a temporary issue with the inventory management service. New orders continue to arrive and fail to get processed. The messages end up in a retry queue, which could potentially get blocked.

Implementing the Solution with Confluent.Kafka

Once the inventory management service recovers, our messages are still trapped in the queue. This is where the concept of dynamic retry topics comes in. Let's take a look at how to create these topics dynamically in C#:

var clientConfiguration = new AdminClientConfig
    { BootstrapServers = bootstrapServers };

using var adminClient =
    new AdminClientBuilder(clientConfiguration).Build();

// This step would be repeated for each retry
// period defined in your configuration.
adminClient.CreateTopicsAsync(new TopicSpecification[]
{
    new TopicSpecification
    {
        Name = "{5min|30min|1h}_retry",
        NumPartitions = 1,
        ReplicationFactor = 1
    }
});

Having created the topics, we need to publish messages to the correct retry queue:

// Retrieve the retry count from the message header
var retryCount = GetRetryCountFromMessageHeader(message);

// Calculate the next retry topic based on the retry count
var nextRetryTopic = GetTopicName(retryCount);

var producerConfig = new ProducerConfig
    { BootstrapServers = bootstrapServers };

using var producer =
  new ProducerBuilder<Null, string>(producerConfig).Build();

var message = new Message<Null, string> { Value = message };
producer.Produce(nextRetryTopic, message);

We also need to have a message consumer for each retry queue:

var consumerConfig = new ConsumerConfig
{
    GroupId = "retry_group",
    BootstrapServers = bootstrapServers
};

using var consumer =
    new ConsumerBuilder<Ignore, string>(consumerConfig).Build();

consumer.Subscribe("{5min|30min|1h}_retry");

// In the consumer itself, add the corresponding delay period.
Task.Delay(retryTimePeriod).Wait();

However, our problems don't end here. If you sleep the consumer thread for too long, the Kafka broker will not get a response from the consumer within the polling period and will drop it from the system.

To address this issue, remember to adjust max.poll.interval.ms accordingly in your configuration. It should always be greater than the longest wait period in your retry logic.

The Final Step - The Dead Letter Queue (DLQ)

If all your retry attempts get exhausted, you should always have one last topic linked, commonly known as the dead letter queue (DLQ). It's the final destination for all lost messages. Monitoring this queue or configuring a consumer to notify you of each message that lands there is crucial.

Conclusion

Through this exploration, we've learned how to leverage Confluent.Kafka's functionalities to enhance Kafka performance with dynamic retry topics. This approach provides an efficient way to manage retry queues and ensure smooth message processing even in scenarios where temporary issues could disrupt the system. As always, tailoring these solutions to fit your unique requirements and context is key to achieving the best results.