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

yaml
# 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

python
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.