Cold start behavior in GCP - Commands & Configuration

Introduction

Cold start behavior happens when a cloud service or function starts from zero and takes extra time to be ready. This delay can affect how fast your app responds when it gets a new request after being idle.

When you deploy a new version of a cloud function and it needs to start fresh.

When your serverless app has not received traffic for a while and needs to wake up.

When you want to understand why your app sometimes responds slower after inactivity.

When you want to optimize your cloud function to reduce startup delays.

When you monitor your app's performance and see spikes in response time.

Commands

This command deploys a new HTTP-triggered cloud function named example-function in the us-central1 region using Python 3.9 runtime. Deployment is the first step to create the function that will experience cold starts.

Terminal

gcloud functions deploy example-function --runtime python39 --trigger-http --region us-central1

Expected Output

Deploying function (may take a while)...

Deploying function (example-function)...done.

availableMemoryMb: 256

entryPoint: example_function

httpsTrigger:

  url: https://us-central1-your-project.cloudfunctions.net/example-function

ingressSettings: ALLOW_ALL

runtime: python39

serviceAccountEmail: [e-mail]

sourceUploadUrl: https://storage.googleapis.com/gcf-upload-us-central1/...

status: ACTIVE

timeout: 60s

updateTime: '2024-06-01T12:00:00Z'

versionId: '1'

→

--runtime - Specifies the language runtime environment for the function.

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--trigger-http - Sets the function to be triggered by HTTP requests.

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--region - Defines the geographic location where the function runs.

This command sends an HTTP request to the deployed cloud function to trigger it. The first request after deployment or inactivity may experience a cold start delay.

Terminal

curl https://us-central1-your-project.cloudfunctions.net/example-function

Expected Output

Hello from example-function!

This command fetches the last 5 log entries for example-function. Logs help you see cold start events and how long the function took to start and respond.

Terminal

gcloud functions logs read example-function --limit 5

Expected Output

2024-06-01 12:01:00 START RequestId: 12345678-1234-1234-1234-123456789abc

2024-06-01 12:01:02 Function execution took 2000 ms, finished with status code: 200

2024-06-01 12:05:00 START RequestId: 87654321-4321-4321-4321-cba987654321

2024-06-01 12:05:00 Function execution took 150 ms, finished with status code: 200

2024-06-01 12:05:30 START RequestId: abcdef12-3456-7890-abcd-ef1234567890

→

--limit - Limits the number of log entries returned.

Key Concept

If you remember nothing else from this pattern, remember: cold starts happen when a cloud function starts fresh and cause a delay in response time.

Common Mistakes

Ignoring cold start delays when testing cloud functions.

This leads to misunderstanding the real user experience and performance issues.

Test functions multiple times, including after idle periods, to observe cold start effects.

Deploying functions with large dependencies without optimization.

Large packages increase cold start time because the function takes longer to load.

Keep function code and dependencies small and optimized to reduce cold start delays.

Not checking logs to identify cold start durations.

Without logs, you cannot measure or troubleshoot cold start impact.

Use cloud function logs to monitor startup times and optimize accordingly.

Summary

Deploy a cloud function using gcloud to create a serverless app.

Trigger the function with an HTTP request to observe cold start behavior.

Check function logs to see startup times and diagnose cold start delays.