Removing S3 objects with delete marker replication causes spike of failed HeadObject calls (MethodNotAllowed) at midnight UTC

Greeting

Hi Peter,

Thanks for bringing up this intriguing and somewhat perplexing issue. It’s clear that the unexpected spikes in 4xx MethodNotAllowed errors tied to delete marker replication are causing a headache, particularly given the impact on your metrics around midnight UTC. Let’s dig into the details and explore a solution that minimizes this noise and restores the utility of your monitoring setup.

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Clarifying the Issue

From your description, it sounds like the behavior you're observing stems from how S3 handles delete marker replication in a replicated bucket setup. Specifically:

• You're seeing HeadObject calls that fail with a MethodNotAllowed status after objects are deleted from the source bucket.
• This behavior is tied to AWS’s internal processes rather than any batch jobs you’ve configured, as the replication of delete markers is immediate.
• These failures occur consistently for objects deleted in the last 24 hours and appear to align with a potential internal background process in AWS S3.

This pattern indeed suggests that the issue may be tied to how S3 manages consistency checks or validation across replicated buckets when delete markers are present. I'll guide you through a clearer understanding and actionable steps to address it.

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Why This Matters

Metrics are crucial for monitoring the health and performance of your application. Unintended spikes in 4xx errors can mask real issues, render a metric unusable, and lead to false alarms or delayed response times for legitimate problems. Addressing this ensures you maintain trust in your monitoring system and avoid unnecessary noise in your dashboards.

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Key Terms

• Delete Marker: A placeholder created when an object is deleted from a versioned S3 bucket. It enables versioned deletion without permanently removing older versions of the object.
• Replication: The process of automatically copying objects and associated metadata from one bucket to another within or across regions.
• HeadObject API: An S3 API call that retrieves metadata about an object without returning the object itself.
• 4xx Errors: Client-side HTTP errors indicating issues such as access being denied or an operation being disallowed.

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The Solution (Our Recipe)

Steps at a Glance:

1. Analyze the replication configuration and identify possible unintended API calls.
2. Update replication rules to reduce unnecessary HeadObject checks.
3. Adjust your monitoring strategy to filter or suppress noise from these errors.
4. Prepare specific logs and details to streamline communication with AWS Support.

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Step-by-Step Guide:

1. Examine Replication Rules
   Verify your S3 replication rules to ensure that only the necessary object metadata and delete markers are replicated. You can do this through the AWS Management Console or using the AWS CLI:

   aws s3api get-bucket-replication --bucket your-source-bucket-name

   Look for any configurations that might trigger unnecessary checks or validation. Ensure delete marker replication is enabled if required.

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2. Leverage Bucket Versioning Policies
   To avoid excessive HeadObject calls during delete marker replication, consider whether a lifecycle policy could clean up expired delete markers or non-current versions efficiently. For example:

   {
       "Rules": [
           {
               "ID": "DeleteExpiredMarkers",
               "Status": "Enabled",
               "NoncurrentVersionExpiration": {
                   "NoncurrentDays": 30
               },
               "AbortIncompleteMultipartUpload": {
                   "DaysAfterInitiation": 7
               }
           }
       ]
   }

   Apply this using:

   aws s3api put-bucket-lifecycle-configuration --bucket your-source-bucket-name --lifecycle-configuration file://policy.json

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3. Suppress Noise in Metrics
   Update your CloudWatch metric filters to suppress these specific 4xx errors from contributing to alarm thresholds. For example, you can exclude MethodNotAllowed errors for specific buckets using a metric filter:

   {
       "dimensions": {
           "BucketName": "your-bucket-name",
           "ErrorCode": "MethodNotAllowed"
       }
   }

   Implement this via the AWS Management Console or CLI to ensure these errors don’t disrupt meaningful insights.

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4. Prepare Logs for AWS Support
   Before contacting AWS Support, gather logs and metrics to streamline the investigation. Specifically:

   • Log entries for failed HeadObject calls, including request IDs and timestamps.
   • Details of the replication configuration for both the source and destination buckets.
   • Any patterns or anomalies you’ve observed, like the consistent timing of spikes at midnight UTC.

   These details will help AWS quickly determine whether this behavior is an expected part of their internal processes or something that can be adjusted.

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Closing Thoughts

The spikes you’re seeing are likely due to internal consistency checks or replication validations, which may not be fully documented in public AWS resources. By refining your replication rules, adjusting monitoring configurations, and providing detailed logs to AWS Support, you can minimize the impact of these errors while maintaining reliable metrics.

Documentation links:

• S3 Replication Overview
• S3 Lifecycle Policies
• CloudWatch Metric Filters

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Farewell

I hope this solution helps you tackle those spikes and regain control of your monitoring setup, Peter. Let me know if you have any further questions or need additional clarification. Best of luck resolving this issue! 🚀😊

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Cheers,

Aaron 😊