Lambda-Layers | A collection of AWS lambda layers for python | Cloud Functions library

In the end, I found no other ways to do so with Lambda Layers due to the 250MB size limit. The workaround for me, is to attach an EFS to my lambda function and the content of the EFS is basically JRE and JDBC Driver to allow to connect via JDBC Connection.

You can use --zip-file, instead of --content:

The path to the zip file of the content you are uploading. Specify --zip-file or --content, but not both. Example: fileb://content.zip

I already found the root cause of the issue. What I was actually doing is that I'm zipping the LambdaLayer folder as the top-level directory instead of nodejs.

What I thought I was doing:

My question is: how do I launch this stack with the required paramiko package?

You need proper deployment pipeline. If you are on AWS, you can deploy lambda functions using AWS CodePipeline. Since you have build dependencies, you would need a build stage that would actual fetch and build all that is needed for your lambda deployment package or layers.

Or you can use paraminco from some pre-build or public layers, such as this one. This way you are de-coupling your code from your dependencies.

Dependencies in Terraform are between resources as a whole, and not between individual instances of resources. This is because the count and for_each arguments can themselves create dependencies, and so Terraform can decide the instances for a resource only after it's begun walking the dependency graph.

With that said, it's not clear to me from your question why it's important to depend on specific instances in your case. Dependencies only affect the order of operations and not which operations Terraform will perform, and so a dependency on all of the instances of aws_lambda_layer_version.abc should be an acceptable substitute for a dependency on an individual instance: it'll still provide the same guarantee that the dependent will be processed only after the thing it depends on, it'll just also wait until some other unrelated objects were complete first.

As this bcrypt should be a library installed as a layer, you shouldn't need to add it adjacent to your lambda_handler.py file.

Also, you would probably need an amazon linux build (amazon linux 2 for python 3.8) to run it on this runtime.

One method that has helped me build unavailable packages in the past was this: https://github.com/lambci/docker-lambda

You should not combine the two. Details can be found here: AWS Lambda function handler in Node.js.

One key is this:

For non-async handlers, function execution continues until the event loop is empty or the function times out. The response isn't sent to the invoker until all event loop tasks are finished. If the function times out, an error is returned instead. You can configure the runtime to send the response immediately by setting context.callbackWaitsForEmptyEventLoop to false.

When you are NOT using async it will wait for the event loop to finish. It does not do this when you are using async, as it expects you to use async all the way through.

As I see the problem is that you are giving a path that doesn't exist

I found this answer on the internet:

ImportError: cannot import name _imaging, It can happen if you have PIL installed, then install Pillow on top of it. Go to /usr/local/lib/python2. 7/dist-packages/ and delete anything with "PIL" in the name (including directories). Then re-install Pillow. When I run from PIL import Image or from PIL import Image etc, I get the error: ImportError: cannot import name 'imaging' from 'PIL' (C:\Users\Pruthvi\AppData\Local\Programs\Python\Python38-32\Lib\site-packages\PIL_init.py) So odd since these same exact commands were working fine yesterday when I was exploring the PIL and pillow modules.

I believe that this issue is related to Pillow library.