I'm working on a data processing service that processes input data and generates output in a Kubernetes pod, controlled through Airflow. The service is independent of cloud storage and solely interacts with the local filesystem, so I'm avoiding adding dependencies like boto3 for upload/download logic. For input, I utilize an initContainer to fetch data from S3 into a shared volume at '/opt/input'. However, I'm struggling with how to handle the output since Kubernetes doesn't support a 'finalizeContainer' concept. My output data can be substantial, reaching up to 50GB. What strategies can you recommend for managing this output upload effectively?
4 Answers
Another idea is to look into using a Container Storage Interface (CSI) that directly mounts S3 into your pod. This abstraction could simplify your setup as it will allow read/write operations without cluttering your main service with cloud-specific code. However, be cautious about performance, especially with larger files.
Since you want to avoid adding upload logic into your main container, I'd suggest considering a sidecar solution. The Ambassador pattern is great for this – it allows your main application to interact with the sidecar seamlessly, which can handle the data upload. You would essentially create an interface between your processing app and the sidecar, which executes the upload commands. This way, your service remains agnostic to cloud storage.
How's the performance with using CSI? I often deal with large files, and I'm concerned about how long read/writes will take.
One approach is to use a sidecar container that gets triggered by a preStop lifecycle hook. This way, when the pod is shutting down, it can execute a command to the sidecar to start the upload process. Just keep in mind that the grace period could be an issue, especially with large data uploads, as it may not allow sufficient time for everything to finish uploading before the pod terminates.
Exactly! I encountered the same issue. The preStop lifecycle hook works well for small files but becomes unreliable with larger datasets.
That sounds like a solid strategy. I did something similar, but faced challenges with larger data uploads. The preStop hook can be flaky for big uploads, even with extended grace periods.