sickn33 GCP Cloud Run
Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.
Detailed Guide
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Calculate memory including /tmp usage
# cloudbuild.yaml
steps:
- name: 'gcr.io/cloud-builders/gcloud'
args:
- 'run'
- 'deploy'
- 'my-service'
- '--memory=1Gi' # Include /tmp overhead
- '--image=gcr.io/$PROJECT_ID/my-service'
Monitor memory usage
import psutil
import logging
def log_memory():
memory = psutil.virtual_memory()
logging.info(f"Memory: {memory.percent}% used, "
f"{memory.available / 1024 / 1024:.0f}MB available")
Concurrency=1 Causes Scaling Bottlenecks
Severity: HIGH
Situation: Setting concurrency to 1 for request isolation
Symptoms: Auto-scaling creates many container instances. High latency during traffic spikes. Increased cold starts. Higher costs from more instances.
Why this breaks: Setting concurrency to 1 means each container handles only one request at a time. During traffic spikes:
- 100 concurrent requests = 100 container instances
- Each instance has cold start overhead
- More instances = higher costs
- Scaling takes time, requests queue up
This should only be used when:
- Processing is truly single-threaded
- Memory-heavy per-request processing
- Using thread-unsafe libraries
Recommended fix:
When to Use
Use this skill when the request clearly matches the capabilities and patterns described above.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.