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.

gcp-cloud-run