Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Build and deploy event-driven serverless applications with Google Cloud Functions (Gen1 and Gen2).
gcloud) installed and authenticatedroles/cloudfunctions.developer (or roles/run.developer for Gen2)gcloud services enable cloudfunctions.googleapis.com cloudbuild.googleapis.com \
artifactregistry.googleapis.com run.googleapis.com eventarc.googleapis.com
| Feature | Gen1 | Gen2 (recommended) |
|---|---|---|
| Runtime | Cloud Functions infra | Built on Cloud Run |
| Max timeout | 9 minutes | 60 minutes |
| Max memory | 8 GB | 32 GB |
| Concurrency | 1 request/instance | Up to 1000/instance |
| Traffic splitting | No | Yes |
| Eventarc triggers | No | Yes |
# Python HTTP function
gcloud functions deploy hello-http \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-http --allow-unauthenticated \
--entry-point=hello_http \
--memory=256Mi --timeout=60s \
--min-instances=0 --max-instances=100 \
--set-env-vars=APP_ENV=production --source=.
# Node.js HTTP function
gcloud functions deploy hello-node \
--gen2 --region=us-central1 --runtime=nodejs20 \
--trigger-http --allow-unauthenticated \
--entry-point=helloNode --memory=256Mi --source=.
gcloud pubsub topics create order-events
gcloud functions deploy process-order \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-topic=order-events \
--entry-point=process_order \
--memory=512Mi --timeout=120s --retry \
--service-account=order-processor@${PROJECT_ID}.iam.gserviceaccount.com \
--source=.
gcloud functions deploy process-upload \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
--trigger-event-filters="bucket=my-upload-bucket" \
--entry-point=process_upload \
--memory=1Gi --timeout=300s --source=.
gcloud functions deploy daily-cleanup \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-http --no-allow-unauthenticated \
--entry-point=daily_cleanup --source=.
gcloud scheduler jobs create http daily-cleanup-job \
--schedule="0 2 * * *" \
--uri="https://us-central1-${PROJECT_ID}.cloudfunctions.net/daily-cleanup" \
--http-method=POST \
--oidc-service-account-email=scheduler-sa@${PROJECT_ID}.iam.gserviceaccount.com \
--location=us-central1
# main.py
import functions_framework
import base64, json
from flask import jsonify
from google.cloud import firestore
@functions_framework.http
def hello_http(request):
"""HTTP Cloud Function."""
name = request.args.get("name", "World")
return jsonify({"message": f"Hello, {name}!", "status": "ok"}), 200
@functions_framework.cloud_event
def process_order(cloud_event):
"""Triggered by a Pub/Sub message."""
data = base64.b64decode(cloud_event.data["message"]["data"]).decode("utf-8")
order = json.loads(data)
db = firestore.Client()
db.collection("orders").document(order["id"]).set({
"status": "processing", "items": order["items"], "total": order["total"],
})
@functions_framework.cloud_event
def process_upload(cloud_event):
"""Triggered when a file is uploaded to Cloud Storage."""
data = cloud_event.data
bucket_name, file_name = data["bucket"], data["name"]
if not file_name.lower().endswith((".png", ".jpg", ".jpeg")):
return
from google.cloud import vision
client = vision.ImageAnnotatorClient()
image = vision.Image(source=vision.ImageSource(
gcs_image_uri=f"gs://{bucket_name}/{file_name}"))
labels = [l.description for l in client.label_detection(image=image).label_annotations]
print(f"Labels for {file_name}: {labels}")
# requirements.txt
functions-framework==3.*
google-cloud-firestore==2.*
google-cloud-storage==2.*
google-cloud-vision==3.*
flask>=2.0
// index.js
const functions = require("@google-cloud/functions-framework");
functions.http("helloNode", (req, res) => {
const name = req.query.name || "World";
res.json({ message: `Hello, ${name}!`, status: "ok" });
});
functions.cloudEvent("processMessage", (cloudEvent) => {
const data = Buffer.from(cloudEvent.data.message.data, "base64").toString();
console.log(`Processing: ${JSON.parse(data)}`);
});
gcloud functions list --gen2 --region=us-central1
gcloud functions describe hello-http --gen2 --region=us-central1
gcloud functions logs read hello-http --gen2 --region=us-central1 --limit=50
gcloud functions delete hello-http --gen2 --region=us-central1 --quiet
# Update env vars without redeploying code
gcloud functions deploy hello-http --gen2 --region=us-central1 \
--update-env-vars=APP_ENV=staging
# Test locally before deploying
functions-framework --target=hello_http --port=8080
resource "google_cloudfunctions2_function" "api" {
name = "hello-http"
location = "us-central1"
build_config {
runtime = "python312"
entry_point = "hello_http"
source {
storage_source {
bucket = google_storage_bucket.source.name
object = google_storage_bucket_object.source.name
}
}
}
service_config {
min_instance_count = 0
max_instance_count = 100
available_memory = "256Mi"
timeout_seconds = 60
service_account_email = google_service_account.fn.email
environment_variables = { APP_ENV = "production" }
}
}
resource "google_cloud_run_service_iam_member" "invoker" {
location = google_cloudfunctions2_function.api.location
service = google_cloudfunctions2_function.api.name
role = "roles/run.invoker"
member = "allUsers"
}
resource "google_cloudfunctions2_function" "processor" {
name = "process-order"
location = "us-central1"
build_config {
runtime = "python312"
entry_point = "process_order"
source {
storage_source {
bucket = google_storage_bucket.source.name
object = google_storage_bucket_object.source.name
}
}
}
service_config {
max_instance_count = 50
available_memory = "512Mi"
timeout_seconds = 120
service_account_email = google_service_account.fn.email
}
event_trigger {
trigger_region = "us-central1"
event_type = "google.cloud.pubsub.topic.v1.messagePublished"
pubsub_topic = google_pubsub_topic.orders.id
retry_policy = "RETRY_POLICY_RETRY"
}
}
| Symptom | Cause | Fix |
|---|---|---|
PERMISSION_DENIED on deploy | Missing Cloud Build or Artifact Registry perms | Grant roles/cloudbuild.builds.builder to Cloud Build SA |
| Function deploys but returns 403 | Missing roles/run.invoker for Gen2 | Add --allow-unauthenticated or grant invoker role |
| Cold start latency > 5s | Large dependencies or no min instances | Set --min-instances=1; reduce deps; use lazy imports |
| Pub/Sub messages redelivered | Function errors or times out | Increase --timeout; fix error handling; add dead-letter topic |
Build failed during deploy | Syntax error or missing dependency | Check gcloud builds log; verify requirements.txt |
| Cannot connect to VPC resource | Function not on VPC connector | Add --vpc-connector=my-connector to deploy |
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Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
中文简介正在准备中。当前显示原文说明。
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
中文简介正在准备中。当前显示原文说明。
Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
中文简介正在准备中。当前显示原文说明。
Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
中文简介正在准备中。当前显示原文说明。
Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
中文简介正在准备中。当前显示原文说明。