deploy-cloud-functions VS deploy-cloudrun

One of the issues developers can encounter when developing in Cloud Functions is the time taken to deploy changes. You can help reduce this time by dynamically loading some of your Python classes. This allows you to make iterative changes to just the area of your application that you’re working on.

I've been looking at Google Secret Manager which sounds promising but I've not been able to find any examples or tutorials that help with the actual practical details of best practice or getting this working. I'm currently reading about Cloud Functions which also sound promising but again, I'm just going deeper and deeper into GCP without feeling like I'm gaining any useful insights.

Serverless computing was also introduced, where the developers focus on their code instead of server configuration.Google offers serverless technologies that include Cloud Functions and Cloud Run.Cloud Functions manages event-driven code and offers a pay-as-you-go service, while Cloud Run allows clients to deploy their containerized microservice applications in a managed environment.

Lambda is made for your use case :). It doesn’t have to be AWS there are plenty of other serverless computing services like: - Google cloud functions - Azure functions Etc

Once you have some basic familiarity with programming, try deploying one of your Python programs to the cloud. Start with Cloud Functions, because that doesn't require any knowledge of Linux server administration.

If the customer prefers making REST-like calls: Deploy a simple Cloud Function that the customer would invoke by making a regular HTTP call with some parameters. The Cloud Function would validate the customer's credentials, and then send a query to BigQuery using one of the client libraries. You can write Cloud Functions in Node.js, Python, Go, Java, C#, Ruby, or PHP. You are only charged when the function runs.

Cloud Functions: A serverless platform for event-driven functions

I propose starting with Cloud Functions. You can use your Python experience, you can do rapid prototyping by writing your code in a text-box in the Google Cloud Console, and there will be no server setup or maintenance.

AWS Lambda If you're in Azure, your equivalent service is Azure Functions. For Google, this is Google Functions (yes, AWS just HAD to be different). Regardless of its name, all of these services fulfill the same purpose - a small compute building block to house your business logic code. An AWS Lambda function is simply the code you want to run, written in your language of choice (I preference Python, but Typescript and Java are popular options). In your infrastructure code, you specify some lambda function basics, like name, path to the business logic code, security role, and what runtime you're using, and optionally have the ability to control more parameters like timeout, concurrency, aliases, and more. Lambda even has built in integrations to other AWS services, such as S3 and SQS (we'll get to these) to make application development even easier. Additionally, lambda functions are priced based on the number of times they're invoked and the duration of time they run, making them exceptionally affordable.

Examples for products in this category are: Google Cloud Run, AWS App Runner, Azure Container Apps. Each has different scalability, cost, and integration trade-offs.

Cloud Run is a managed platform that enables you to run container based workloads on top of Google infrastructure. Cloud Run automates many of the above steps and allows you to focus on developing and deploying updates to your application.

Serverless computing was also introduced, where the developers focus on their code instead of server configuration.Google offers serverless technologies that include Cloud Functions and Cloud Run.Cloud Functions manages event-driven code and offers a pay-as-you-go service, while Cloud Run allows clients to deploy their containerized microservice applications in a managed environment.

The quickest way is to deploy to Cloud Run. The service will use Dockerfile to build the production image. You can even omit the GOOGLE_APPLICATION_CREDENTIALS env var as these are in GCP’s projects by default.

In our company we use Google Cloud Run to deploy web applications, and every app is built into a docker image. For now we use the default memory limit by Cloud Run which is 256 MB per container. Recently we started to notice that the part of applications go beyond this limit, causing a container to restart and in some cases even resulting to downtime of a service.

It's perhaps a little fiddlier than other options, but you can probably host it on Google Cloud Run and it would fall within the free tier.

You can see more here on how to use the google cloud run github actions.

I wonder what the aversion is to using a plain old server / vps. It's really not that difficult to deploy nowadays [0][1][2][3] and I'd rather get an $8 bill every month as insurance than ever worry about shit like OP just went through. It'll probably be more performant anyway due to cold starts and "edge" still having to hit us-east-1 for data.. cache your static files with Cloud Flare/Front. People are always surprised by how much traffic a single VPS can take[4] and believe it all has to be serverless to be web scale. I believe HN still runs on a single core or something.

There's a ton of places to get cloud credits as well, too many to link, so just Bing™ it

[0] https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_...

[1] https://aws.amazon.com/apprunner/

[2] https://cloud.google.com/run

[3] https://render.com/

[4] https://news.ycombinator.com/item?id=34676186

Google Cloud Run