Kafka Consumer Offset Commit: Addressing Message Loss Risks

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Hey guys! Today, we're diving deep into a critical systemic issue affecting Kafka consumers, specifically the risk of message loss due to how offset commits are handled during retry failures. This is a big deal, so let's break it down and see how we can fix it.

Problem Description: The Ghost Messages

The heart of the problem lies in the current implementation of KafkaConsumerBase<T>.ConsumeMessage. There are systemic issues lurking within that can impact all current and future consumers, potentially leading to messages disappearing into the void and even causing consumer crashes. Imagine your important data vanishing – not a fun thought, right?

The main issue is in how exceptions are handled. The exception handling pattern in KafkaConsumerBase<T>.ConsumeMessage has two major flaws that we need to address ASAP:

  1. Retry Function Vulnerability: The retryMessageFunc isn't wrapped in a try/catch block. This means if the retry function throws an exception, it can bubble up and prevent the offset from being committed. This could potentially fault the service task, leaving us in a bad state.
  2. Unconditional Offset Commits: The offset is committed regardless of whether the retry function actually managed to requeue the message successfully. This is like saying, "Yep, we got it!" even if we didn't.

Let's take a look at the problematic code snippet:

catch (Exception ex)
{
    await retryMessageFunc(message!);  // ← NOT wrapped in try/catch
    
    if (consumeResult != null)
    {
        _consumer.Commit(consumeResult);      // ← Unconditional commit
        _consumer.StoreOffset(consumeResult); // ← Unconditional store
    }
    
    _logger.LogError(ex, "...");
}

See the issue? This code commits the offset whether the retry was successful or not.

Failure Scenarios: When Things Go Wrong

Because IKafkaProducer.ProduceAsync returns false on failure (and never throws an exception), we have a few scenarios to worry about:

  • ✅ Retry succeeds: Everything's great! The offset is committed, and we're golden.
  • ❌ Retry returns false (publish failed): Uh oh! The offset is still committed, even though the publish failed. This means the message is effectively lost. Nobody wants lost messages!
  • ❌ Retry throws: Double uh oh! The offset is not committed, but the service task faults, leading to a consumer crash. Imagine the chaos!

Impacted Components: Who's at Risk?

Basically, anyone inheriting from KafkaConsumerBase<T> is potentially affected. Here's a breakdown of the main culprits:

  1. NotificationStatusConsumerBase<TConsumer, TResult>

    • Inheritors: EmailStatusConsumer, SmsStatusConsumer
    • The Problem: RetryStatus ignores the ProduceAsync result when republishing to the status topic. It's like blindly hoping for the best without checking if it actually worked.
    • File: src/Altinn.Notifications.Integrations/Kafka/Consumers/NotificationStatusConsumerBase.cs:98

  2. NotificationStatusRetryConsumerBase

    • Inheritors: EmailStatusRetryConsumer, (future) SmsStatusRetryConsumer
    • The Problem: Similar to the above, RetryStatus ignores the ProduceAsync result when republishing to the retry topic. Another case of wishful thinking instead of proper error handling.
    • File: src/Altinn.Notifications.Integrations/Kafka/Consumers/NotificationStatusRetryConsumerBase.cs:94

  3. PastDueOrdersRetryConsumer

    • The Problem: RetryOrder is a no-op (a Task.CompletedTask), which guarantees message loss on any exception. It's like having a "retry" button that does absolutely nothing. Seriously?
    • File: src/Altinn.Notifications.Integrations/Kafka/Consumers/PastDueOrdersConsumer.cs:58

  4. Future Consumers

    • The Bad News: Any new consumer using ConsumeMessage will inherit these problematic semantics unless we explicitly override them. We need to be proactive to prevent future headaches.

Potential Solutions: Let's Fix This!

Okay, enough doom and gloom. Let's talk about how we can solve this. We have a few options on the table:

Option 1: The Fortress Approach - Guard Retry Function in Base Class (Recommended)

The most robust solution is to wrap retryMessageFunc in a try/catch block within the base class and prevent offset commits on failure. This acts as a safety net, ensuring we don't commit offsets when things go south during the retry.

Here's how the code would look:

catch (Exception ex)
{
    bool retrySucceeded = false;
    try
    {
        await retryMessageFunc(message!);
        retrySucceeded = true;
    }
    catch (Exception retryEx)
    {
        _logger.LogError(retryEx, "Retry function failed. Offset will NOT be committed to allow reprocessing.");
        throw; // Prevent offset commit
    }
    
    if (retrySucceeded && consumeResult != null)
    {
        _consumer.Commit(consumeResult);
        _consumer.StoreOffset(consumeResult);
    }
    
    _logger.LogError(ex, "Error processing message.");
}

This approach ensures that if the retry fails, the offset won't be committed, and the message will be reprocessed. Much better!

Option 2: The Vigilant Approach - All Retry Functions Check ProduceAsync Result

Another way to tackle this is to make each retry function responsible for validating the publish result and throwing an exception if it fails. This puts the onus on the individual retry functions to ensure they're working correctly.

For example:

protected async Task RetryStatus(string message)
{
    var ok = await _kafkaProducer.ProduceAsync(_statusUpdatedRetryTopicName, message);
    if (!ok)
    {
        throw new InvalidOperationException({{content}}quot;Retry publish to '{_statusUpdatedRetryTopicName}' failed.");
    }
}

We'd need to apply this to:

  • NotificationStatusConsumerBase.RetryStatus
  • NotificationStatusRetryConsumerBase.RetryStatus
  • PastDueOrdersConsumer.RetryOrder (This one needs a serious upgrade – replace the no-op with actual retry logic or throw an exception!)

Option 3: The Signal Approach - Change Retry Function Signature

This option involves modifying the signature of the retry functions to return Task<bool> to explicitly signal success or failure. This makes the intent clear and allows the ConsumeMessage function to make informed decisions about committing offsets.

protected async Task ConsumeMessage(
    Func<string, Task> processMessageFunc,
    Func<string, Task<bool>> retryMessageFunc,  // ← returns bool
    CancellationToken stoppingToken)
{
    // ... existing code ...
    catch (Exception ex)
    {
        bool retrySuccess = await retryMessageFunc(message!);
        
        if (retrySuccess && consumeResult != null)
        {
            _consumer.Commit(consumeResult);
            _consumer.StoreOffset(consumeResult);
        }
        
        _logger.LogError(ex, "Error processing message.");
    }
}

This provides a clear signal for whether the retry was successful.

Recommended Approach: Defense in Depth

The best approach is to combine Option 1 (base class guard) and Option 2 (explicit checks in retry functions). This gives us a layered defense strategy:

  1. The base class prevents crashes from unhandled retry exceptions, acting as the first line of defense.
  2. Explicit checks in retry functions make the contract clear and ensure that each retry function is responsible for its own success or failure.
  3. This combo prevents silent message loss across all consumers, which is what we want!

Testing Requirements: Prove It Works!

Of course, any solution needs to be thoroughly tested. We need integration tests covering these scenarios:

  1. ProduceAsync returns false → offset not committed → message reprocessed (This proves our fix works!)
  2. Retry function throws → offset not committed → message reprocessed (Another crucial test)
  3. Processing throws, retry succeeds → offset committed → message processed once (Make sure we're not reprocessing unnecessarily)
  4. We also need tests for each affected consumer class to ensure everything's working as expected.

References: Dig Deeper

Want to learn more? Check out these resources:

Conclusion: No More Ghost Messages!

So, there you have it! We've identified a critical issue with Kafka consumer offset commits, explored the potential for message loss and crashes, and laid out a plan to fix it. By implementing the recommended approach and adding thorough testing, we can ensure our Kafka consumers are robust and reliable. Let's get those ghost messages back into the system where they belong!

Reported by: @Ahmed-Ghanam