I am creating an Alexa skill and I am using Amazon Lambda to handle the intents. I found online several tutorials and decided to use NodeJs with the alexa-sdk. After installing the alexa-sdk with npm, the zipped archive occupied a disksize of ~6MB. If I upload it to amazon, it tells me
The deployment package of your Lambda function "..." is too large to enable inline code editing. However, you can still invoke your function right now.
My index.js has a size of < 4KB but the dependencies are large. If I want to change something, I have to zip it altogether (index.js and the folder with the depencencies "node_modules"), upload it to Amazon and wait till its processed, because online editing isn't available anymore. So every single change of the index.js wastes > 1 minute of my time to zip and upload it. Is there a possibility to use the alexa-sdk dependency (and other dependencies) without uploading the same code continually every time I am changing something? Is there a possibility to use the online-editing function though I am using large dependencies? I just want to edit the index.js.
If the size of your Lambda function's zipped deployment packages
exceeds 3MB, you will not be able to use the inline code editing
feature in the Lambda console. You can still use the console to invoke
your Lambda function.
Its mentioned here under AWS Lambda Deployment Limits
ASK-CLI
ASK Command Line Interface let you manage your Alexa skills and related AWS Lambda functions from your local machine. Once you set it up, you can make necessary changes in your Lambda code or skill and use deploy command to deploy a skill. The optional target will let you deploy the associated Lambda code.
ask deploy [--no-wait] [-t| --target <target>] [--force] [-p| --profile <profile>] [--debug]
More info about ASK CLI here and more about deploy command here
Related
Some context: Our Cloud Build process relies on manual triggers and about 8 substitutions to customize deploys to various firebase projects, hosting sites, and preview channels. Previously we used a bash script and gcloud to automate the selection of these substitution options, the "updating" of the trigger (via gcloud beta builds triggers import: our needs require us to use a single trigger, it's a long story), and the "running" of the trigger.
This bash script was hard to work with and improve, and through the import-run shenanigans actually led to some faulty deploys that caused all kinds of chaos: not great.
However, recently I found a way to pass substitution variables as part of a manual trigger operation using the Node.js library for Cloud Build (runTrigger with subs passed as part of the request)!
Problem: So I'm converting our build utility to Node, which is great, but as far as I can tell there isn't a native way to steam build logs from a running build in the console (except maybe with exec, but that feels hacky).
Am I missing something? Or should I be looking at one of the logging libraries?
I've tried my best scanning Google's docs and APIs (Cloud Build REST, the Node client library, etc.) but to no avail.
I've recently taken over a project that has AWS Serverless Image Handler version 5 implemented. And I've been sent various emails from the client they received about Node.js v12 hitting end of life and the Lambda functions needing to run on a new Node.js runtime.
Having a looking through the AWS account, I've seen I can just switch the runtime to Node.js v14 or v16, but do I need to do any code updates?
Sorry a complete noob to Lambda, and the CloudFormation stacks etc
Thought I would ask before I jump down the rabbit hole and look at setting up my own copy of Serverless Image Handler and doing some testing, or even trying to implement version 6.
This depends of the code / imported libraries in the Serverless Image Handler Lambda.
I would check if there are breaking changes in Node14, that could affect the code.
This does not have anything to do with Lambda / CF itself.
I created and initialized a Firebase the project on my machine and Firebase console. However during the CLI initialization process on my machine I did not tick/include Firebase-Functions feature during the feature selection process.
Halfway through the project I realized there where some features on my website that needed cloud functions and now I'm stuck trying to add firebase functions to the project.
Enabling Firebase functions on Firebase console is easy enough but it's making neccessary changes in the source code to enable it that are frustrating (e.g Creating a 'functions' folder in the code, creating a package.json etc). Is there a command to automatically generate this?
You should just be able to run firebase init in the same project again to add Cloud Functions. It will add extra information to your firebase.json for the new products you choose, but will not overwrite what you've already done for Hosting. If you don't trust that process, simply back up your files, run the comment, and revert the changes if you don't like them.
I've been using AWS for a while now but am wondering about how to go about developing with Lambda. I'm a big fan of having server-less functions and letting Amazon handle the maintenance and have been using it for a while. My question: Is there a recommended workflow for version control and development?
I understand there's the ability to publish a new version in Lambda. And that you can point to specific versions in a service that calls it, such as API Gateway. I see API Gateway also has some nice abilities to partition who calls which version. i.e. Having a test API and also slowly rolling updates to say 10% of production API calls and scaling up slowly.
However, this feels a bit clunky for an actual version control system. Perhaps the functions are coded locally and uploaded using the AWS CLI and then everything is managed through a third party version control system (Github, Bitbucket, etc)? Can I deploy to new or existing versions of the function this way? That way I can maintain a separation of test and production functions.
Development also doesn't feel as nice through the editor in Lambda. Not to mention using custom packages require to upload anyways. Seems local development is the better solution. Trying to understand others workflows so I can improve mine.
How have you approached this issue in your experience?
I wrote roughly a dozen lambda functions that trigger based on S3 file write event or time, and make a HTTP req to an API to kickstart data processing jobs.
I don't think there's any gold standard. From my research, there are various approaches and frameworks out there. I decided that I didn't want to depend on any kind of frameworks like Serverless nor Apex because I didn't want to learn how to use those things on top of learning about Lambda. Instead I built out improvements organically based on my needs as I was developing a function.
To answer your question, here's my workflow.
Develop locally and git commit changes.
Mock test data and test locally using mocha and chai.
Run a bash script that creates a zip file compressing files to be deployed to AWS lambda.
Upload the zip file to AWS lambda.
You can have version control on your lambda using aws CodeCommit (much simpler than using an external git repository system, although you can do either). Here is a tutorial for setting up a CodePipeline for commit/build/deploy stages: https://docs.aws.amazon.com/codepipeline/latest/userguide/tutorials-simple-codecommit.html
This example deploys an EC2 instance, so for the deploy portion for a lambda, see here
If you set up a pipeline you can have an initial commit stage, then a build stage that runs your unit tests and packages the code, and then a deploy stage (and potentially more stages if required). It's a very organized way of deploying lambda changes.
I would suggest you to have a look at SAM. SAM is a command line tool and a framework to help you to develop your serverless application. Using SAM, you can test your applications locally before to upload them to the cloud. It also support blue / green deployment and CI/CD workflows, starting automatically from github.
https://github.com/awslabs/aws-sam-cli
I have some problems with serverless deploy, because when I deploy my Lambda function, the Serverless Framework start packing my node_modules, but it takes a lot of time.
I mean why to upload node_modules again if it have not been updated. Maybe somebody know, how deploy only a Lambda function code without packing binaries?
You need to add packaging configuration.
In your serverless.yml file, add:
package:
exclude:
- node_modules/**
It is useful to remove the AWS-SDK modules (because if you don't upload them, Lambda will use what AWS offers - which is better) and to remove dev modules (like testing frameworks). However, all other modules are dependencies and will be needed to be uploaded for your function to work properly. So, configure the package settings to include/exclude exactly what you need.
Regarding your other question
why to upload node_modules again if it have not been updated
It is not a limitation of the Serverless Framework. It is a limitation of the AWS Lambda service. You can't make a partial upload of a Lambda function. Lambda always requires that the uploaded zip package contains the updated code and all required modules dependencies.
If your deploy is taking too long, maybe you should consider breaking this Lambda function into smaller units.