deploy-cloud-functions VS go

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.

A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.

I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles

The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397

There used to be the GO FIPS branch :

https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...

But it looks dead.

And it looks like https://github.com/golang-fips/go as well.

I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:

- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644

- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412

Here are some excerpts from the latest Go survey [1]:

- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."

- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."

I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.

[1]: https://go.dev/blog/survey2024-h1-results

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.