awslogs VS serverless-graphql

Using the official CLI (aws logs get-log-events) or https://github.com/jorgebastida/awslogs is pretty close to SSH-ing and grepping.

cw is a native executable targeting your OS, and not needed external dependencies such as pip and npm. Compared to awslogs which is famous helpful tool for CloudWatch Logs1, cw is written in golang and faster.

Its a command line tool but some people I know also use awslogs

Not a full solution, but when I was doing this I really got to love the awslogs utility:

https://github.com/jorgebastida/awslogs

It allows you to stream Cloudwatch logs from the command line, so you can grep them, save them to files, etc... (The web based Cloudwatch interface is terrible.)

Another suggestion is to try to modularize the core business logic in your lambda such that you separate the lambda-centric stuff from the rest of it. Obviously, though, if "the rest of it" is hitting other AWS services, you're going to hit the same testing roadblock.

Or you can try mocking, which may or may not provide much value for you. There's a python library for that, (moto), but it's not 100% up to date wrt AWS services/interfaces, last I had checked. Might be worth a try though.

https://github.com/spulec/moto

Cloudwatch Logs is pretty meh visually, but awslogs can give you a pretty good `tail -f`-like experience, and Insights is pretty good. Cloudwatch Metric Filters give you a 'StatsD'-like experience, in that you can log out a certain message or code and then use its appearance as a metric.

Check out https://github.com/jorgebastida/awslogs , you can define a `--start`, and it also has a `--watch`, and can be piped the `grep` or whatever you want. It's a pretty flexible tool.

📖 awslogs - a simple command-line tool for querying groups, streams, and events from Amazon CloudWatch logs.

In this article, we will use Serverless Framework and Docker Desktop to deploy Dart functions to AWS Lambda. If you haven't installed them yet, please complete the installation using the following commands or links.

Since then, the ecosystem has changed. Using the Serverless Framework makes deployment simpler. We released the managed Tracetest App making any serverless-based systems simpler to instrument and test. You can now test public-facing apps with no infra overhead!

We see some great results from using these in conjunction with frameworks such as SST or Serverless, and also some real spaghetti from people who organically proliferate 100’s of functions over time and lose track of how they relate to each other or how to update them safely across time and service. Buyer beware!

When we think about AWS serverless service, the first thing that comes to our mind is Lambda function. Yes, the quickest way to deploy this backend Express JS app to AWS is to deploy it as a Lambda function. The easiest way is using Serverless Framework.

Serverless Framework: https://www.serverless.com/

Github | Website

In this example, we're using the serverless framework to quickly set up the Lambda function along with an API gateway for the entry point. The lambda function is a simple Koa REST API with a few functional endpoints.

Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.

1 - Serverless + AWS CDK + Lift: An integration that amps up the traditional Serverless Framework with Lift's static frontend construct and CDK's robust infra definition.

It's a bit easier in Python if you use tools like https://www.serverless.com/. I'm not sure if Rust has something similar yet.