deploy-cloud-functions VS libaws

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.

i print the error along with file and line number every time i return it. clunky, but it works.

in fact i print file and line with every log message.

https://github.com/nathants/libaws/blob/87fb45b4cae20abd1bb1...

cloud is so good now it’s hard to justify not doing something bespoke. ec2 spot is insanely cheaper than turnkey cicd, and better in almost every way.

i’m delighted to pay 30% over infra cost for convenience, but not 500%. and it better actually be convenient, not just have a good landing page and sales team.

this month i learned localzones have even better spot prices. losangeles-1 is half the spot price of us-west-2.

for a runner, do something like this, but react to an http call instead of a s3 put[1].

for a web ui do something like this[2].

s3, lambda, and ec2 spot are a perfect fit for cicd and a lot more.

1. https://github.com/nathants/libaws/tree/91b1c27fc947e067ed46...

2. https://github.com/nathants/aws-exec/tree/e68769126b5aae0e35...

like linux, cloud is a lot to learn, but worth it.

like linux, cloud is best kept simple, or it can become brittle and confusing.

like linux, cloud has a lot of cool things like zfs, that should be appreciated but rarely used.

like linux, using go makes your life a lot easier. the aws go sdk is the documentation.

like linux, you have to learn a lot and then find the core utility you actually care about. for me it is:

https://github.com/nathants/libaws

the good new is, for the 95% of projects that can tolerate it, aws the good parts are actually both simple and easy[1].

it’s hard to find things you can’t build on s3, dynamo, lambda, and ec2.

if either compliance or a 5% project demand it, complicated solutions should be explored.

1. https://github.com/nathants/libaws

i also wanted a good cli for aws, and built one:

https://github.com/nathants/libaws

companies like fly are fantastic.

they provide a good service, and they put market pressure on aws.

cool transition and fun writeup!

for low, intermittent traffic sites, go on lambda might be a better comparison:

https://github.com/nathants/libaws/tree/master/examples/simp...

lambda + s3. add ec2 spot if you need it.

just make sure you understand how billing works. mostly it’s just egress bandwidth is expensive.

do something like this:

https://github.com/nathants/aws-gocljs

or with less opinions:

https://github.com/nathants/libaws/tree/master/examples/simp...

welcome to cloud, glhf!

aws ux for retaining both hair and sanity.

https://github.com/nathants/libaws

build and scale systems with artificial load on aws! scaling the load testing will be just as interesting as scaling the system under test.

start with low bottlenecks, ie a cluster of c6i.large ec2 spot. how fast can you do this? have fast can you scale that? ec2 and s3 is all you need to build anything.

use ec2 spot, avoid network egress, avoid cross region/zone traffic, create and destroy ec2 instances as needed instead of letting them sit idle. you could grow system scaling intution for the price of your streaming subscriptions.

start with something like this:

https://github.com/nathants/libaws/tree/master/examples/comp...

maybe mess around with public datasets on aws, just make sure to be in the correct region to avoid data egress.

welcome systems friend. one accurate measurement is worth a thousand expert opinions. scaling is fun!

aws has too many knobs, presumably to satisfy the union of the needs of all the enterprise customers. that said, lambda+s3+dynamodb+ec2 are pretty good once you tape over all the knobs that aren't needed. i work with them like this[1].

these days i build on aws and r2. aws for the nuts and bolts, r2 for high bandwidth egress. it's a perfect match.

1. https://github.com/nathants/libaws