Building a Microservice with AWS Lambda and API Gateway is a great experience, especially in these aspects:

1. Testing
2. Deployment
3. Microservice size
4. Monitoring/Logging
5. Web UI / CLI

First of all, every Lambda function is pretty small. (My image processing module only has 200 LOC in total.)

If a service only has ~100 lines of code, it would be super easy to develop/read/debug. (You can read more about small functions' advantages in my previous post Sandi Metz's Rules for OOP - dsdshome)

Amazon has provided docker images for all its available Lambda environments: lambci/docker-lambda: Docker images and test runners that replicate the live AWS Lambda environment

When I develop a Lambda function on local, I can just use these docker images to create containers that are almost identical to the production environments, and use them to test my function.

docker run \
       -v "$PWD":/var/task \
       lambci/lambda:nodejs6.10 index.handler '{"queryStringParameters": { "key": "100x100/test_image.jpg" }}'

(Although in this project, my function need to manipulate images, so it's a bit hard to write unit/integration test for it.)

Amazon also provided a Makefile to build a lambda function zip file, and we can deploy this zip file via aws-cli easily.

.PHONY: all image package dist clean

all: package

image:
  docker build --tag amazonlinux:nodejs .

package: image
  docker run --rm --volume ${PWD}/lambda:/build amazonlinux:nodejs npm install --production

dist: package
  cd lambda && zip -FS -q -r ../dist/function.zip *

clean:
  rm -r lambda/node_modules
  docker rmi --force amazonlinux:nodejs

After the function.zip is generated, we can deploy it to AWS Lambda via aws-cli:

1. Deploy it to an existing function:

   aws lambda update-function-code --function-name ServerlessImageResize-ResizeFunction-1CUW6FR1XJJEL --zip-file fileb://dist/function.zip
   

2. Setup the whole Microservice architecture via CloudFormation

   aws cloudformation deploy \
     --template-file=deploy/output.yaml \
     --stack-name="${stack_name}" \
     --capabilities=CAPABILITY_NAMED_IAM
   

   CloudFormation is like docker-compose for AWS services, it can setup AWS services like S3, API Gateway, etc. based on a YAML template file.

   For this image resizing lambda function's CloudFormation template, you can check it here.

Amazon also provided builtin monitoring and logging support for Lambda.

Every lambda function comes with a monitoring console like this:

We can even go to the CloudWatch console for this Lambda function to see the logs.

All the actions mentioned in this post, can all be done via AWS's web user interface or through AWS's command line tool aws-cli. This gives developers enough choices to choose the one that suits them the best.

So, what's the ideal Microservice development experience?

1. The service codebase should be small enough.
2. It has a way to reproduce the production environment on local. (e.g. Docker image)
3. Developer can run tests in that environment by a single line of shell command (or a editor/IDE command)
4. Developer can deploy changes using only a single command
5. All the actions can be done via Web UI or CLI