aws-lambda-java-libs VS go

In today's world of cloud computing, AWS Lambda is a serverless, event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers. You can trigger Lambda from over 200 AWS services and software as a service (SaaS) applications, and only pay for what you use.

The first reason is that serverless architectures are inherently scalable and elastic. They automatically scale up or down based on the incoming workload without requiring manual intervention through serverless compute services like AWS Lambda, Azure Functions, or Google Cloud Functions.

On this day, we both first learned about Lambda. This was the world's first public Functions-as-a-Service platform, better known as FaaS. They told us that this was the next evolution in Cloud Computing. With Lambda, you could now host snippets of code on AWS. There were no more idle workers, and you could auto-scale with minimal additional configuration required. Also, these snippets were event-driven by nature. This was a fully serverless platform.

AWS Lambda simplifies composable applications by offering serverless execution, seamless integration with AWS services, automatic scaling, and cost efficiency without the need to manage servers.

Deploying Dart functions to AWS Lambda enables you to utilize them not only within AWS Lambda but also integrate them with services like Amazon API Gateway, allowing you to leverage them in Flutter applications as well. This unified codebase in Dart offers great convenience.

Event Producers: Generate streams of events, which can be implemented using straightforward microservices with AWS Lambda (for serverless computing), Amazon DynamoDB Streams (to captures changes to DynamoDB tables in real-time), Amazon S3 Event Notifications (Notify when certain events occur in S3 buckets) or AWS Fargate (a serverless compute engine for containers).

Amazon Web Services (AWS) Lambda is a serverless function-as-a-service (FaaS) platform that lets you deploy, run, and scale code in the cloud as self-contained functions without having to manually configure any infrastructure. Lambda runs your functions on demand in response to specific events, such as an HTTP request from the internet or activity in another AWS service.

FaaS is specifically focused on building and running applications as a set of independent functions or microservices. Major cloud providers like AWS (Lambda), Microsoft Azure (Functions), and Google Cloud (Cloud Functions) offer FaaS platforms that allow developers to write and deploy individual functions without managing the underlying infrastructure.

So AWS Lambda is basically a serverless computing service that is offered by AWS. It enables developers to run the code in response to various events. It protects the developers from the pain of managing the servers. Using a serverless execution model helps the developers to handle provision, manage and scale the servers automatically. Through this approach the developers can fully focus on writing the code instead of dealing with other aspects.

The first product that popularized the term “serverless” was AWS Lambda, which is both the prototypical and archetypical function as a service provider. It also has a great name, which pings back to its envisioned place in the cloud of the future. In computer programming, a lambda, often referred to as a lambda function or lambda expression, is a concise way to represent an anonymous function, which is a function without a name. The concept originates from lambda calculus in mathematical logic and has been adopted by many programming languages, each with its own syntax and characteristics.

The problem is that if you clone the Git repository, or view it on GitHub, you have no assurance that you're seeing the same code that the go command or the Go module proxy saw. The author of a malicious module could change the Git tag to point to a different, benign, commit after the Go module proxy caches the malicious copy. There are other tricks an attacker can play as well: https://github.com/golang/go/issues/66653

Ultimately, if you're doing a code audit, you have to compute the checksum of the code that you're looking at, and compare it against the entry in go.sum or the checksum database to make sure you're auditing the right copy.

must.Do proposal https://github.com/golang/go/issues/54297

Go instalado

This melding of the sync and the async is actually kinda interesting to me. I know that at least in lots of environments, the sync and async paths are effectively separate for things like I/O[1]. I wondered (and still do for some cases) how Go handles this.

For those curious I looked at Windows and Linux, but not much else.

Linux: no io_uring support. There's debate on even whether to use it as people are discussing security implications[2]. It looks like (from perusing this issue, but could be wrong) AIO wasn't used.

Windows: it looks like they're using IOCP everywhere. Seems sensible enough.

General case: there seems to be an open issue regarding this[3].

[1]: For example, Windows has IOCPs, Linux has io_uring, FreeBSD has kqueue, POSIX has... POSIX AIO, etc.

[2]: https://github.com/golang/go/issues/31908

[3]: https://github.com/golang/go/issues/6817

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> The Go database/sql package actually executes ROLLBACK in the SQL engine.

No: https://github.com/golang/go/blob/beaf7f3282c2548267d3c89441...

Nice write-up.

It's a shame that errors.Is is slow for general use, and at least some of that seems attributable to the Comparable change requiring reflection. Multi-errors seems to have bloated the switch. And of course the lack of a happy-path that was fixed in [1].

Since Go already has two ways of handling exceptional state: return or panic, it does feel like a stretch to also introduce a "not found" path too. All bets are off in tight inner loops, but I think as a general coding practice, it'll make the language (de facto) more complicated/ambiguous.

But my take away is that the question has been kicked off: can wrapped errors be made more efficient?

1. https://github.com/golang/go/commit/af43932c20d5b59cdffca454...

I know that at least two engineers from the runtime team have seen the post in the #darkarts channel of gopher slack. One of them left a fire emoji :).

I'll probably bring it up in the by-weekly Go runtime diagnostics sync [1] next Thursday, but my guess is that they'll have the same conclusion as me: Neat trick, but not a good idea for the runtime until hardware shadow stacks become widely available and accessible.

[1] https://github.com/golang/go/issues/57175