A digital media engineering team is planning the serverless compute architecture for a new video processing microservice on Google Cloud. The workload relies on a custom-compiled C++ binary with specific Linux OS-level dynamic libraries. Individual processing requests take up to 40 minutes to finish, and to maintain cost efficiency, each deployed instance must process up to 8 concurrent requests simultaneously. Which serverless compute option should the team select to meet these technical requirements?
- Deploy the application to Cloud Run using a custom container image that packages the binary and OS dependencies, setting the request timeout to 40 minutes and container concurrency to 8.Cevap
- BDeploy the application to Cloud Functions (1st gen) by embedding the C++ binary into a Python runtime package, configuring the function timeout to 40 minutes.
- CDeploy the application to Cloud Functions (2nd gen) using an Eventarc trigger, relying on standard language buildpacks without container customization.
- DDeploy the application to Compute Engine using an unmanaged Instance Group with CPU-based autoscaling rules.
Cevap
Deploy the application to Cloud Run using a custom container image that packages the binary and OS dependencies, setting the request timeout to 40 minutes and container concurrency to 8.
Cloud Run is the optimal serverless platform for containerized applications requiring custom system dependencies (like C++ binaries and Linux libraries), long request timeouts (up to 60 minutes for HTTP requests), and configurable multi-concurrency per instance.
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Anahtar Kavram
Selecting Cloud Run versus Cloud Functions based on container customization, execution timeout limits, and concurrency settings
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