AWS Batch job, FFmpeg video encoding issues.

When dealing with AWS Batch jobs for video encoding, especially with larger files, there are several factors to consider for troubleshooting. Given that you've already checked timeouts, EBS storage, and resource allocation, here are some additional areas to investigate:

1. Container exit codes: Even if there are no explicit errors in the logs, check the container exit codes. These can provide clues about why the job is failing.

2. Memory usage: While 16 GB of memory might be sufficient for most videos, larger files could potentially exhaust this. Monitor the memory usage during the encoding process to ensure it's not hitting the limit.

3. Network bandwidth: Downloading and uploading large files could be impacted by network constraints. Ensure that your EC2 instances have sufficient network bandwidth for the data transfer.

4. S3 transfer acceleration: Consider enabling S3 transfer acceleration for faster uploads and downloads of large files.

5. Job definition: Review your job definition to ensure it's properly configured for handling larger files. You might need to adjust parameters like timeout settings or resource allocations specifically for larger videos.

6. Chunking strategy: For very large files, consider implementing a chunking strategy where you process the video in smaller segments. This can help manage memory usage and reduce the impact of potential failures.

7. EC2 instance type: The instance type you're using might not be optimized for memory-intensive tasks like video encoding. Consider using compute-optimized or memory-optimized instance types.

8. FFmpeg configuration: Review your FFmpeg command-line parameters. Some encoding options might be more memory-intensive than others. Adjusting these could potentially resolve issues with larger files.

9. Logging and monitoring: Implement more detailed logging in your container to capture any potential issues that aren't showing up in the standard logs. Also, consider using AWS CloudWatch for more comprehensive monitoring of your Batch jobs.

10. Dependencies: Ensure all necessary dependencies and libraries are up-to-date in your container image.

If the issue persists after checking these areas, you might want to consider running a test job with increased resources (e.g., more vCPUs and memory) to see if it resolves the problem. This could help determine if it's a resource constraint issue or something else in your processing pipeline.
Sources
Troubleshooting AWS Batch - AWS Batch
Common errors and troubleshooting - AWS Batch
When to use AWS Batch - AWS Batch

same issue here with files larger than 4GB, have you found any solution for this ?