Category Archives: Serverless

One good reason to use custom deployment buckets

When deploying serverless Lambda it will create a bucket with the code and CloudFormation template. It is totally fine, but the newly created bucket will be associated with the stack and will prevent us from purging the whole stack.

When do we want to delete a stack?

During development there could be many reasons to completely remove one, few ideas:

our Lambda’s type changed, thus it cannot be just updated
we had to experiment with roles and want to test them after clean serverless installation
none of the resources are required anymore

According to the default AWS policy an S3 bucket cannot be deleted if it is not empty, which exactly is preventing us from purging the stack.

What can one do?

It is really straightforward: create a bucket for serverless deployment. If we manually add a new S3 bucket and point our serverless installer to that, it won’t be interfering with our stack.

When the bucket is there we can add these two lines under provider to our serverless to make it use that for deployment:

  deploymentBucket:
    name: serverless-dep-bucket-dev

Updating the utc-time-teller’s serverless it looks like this:

service: BlogDavidPythonLambdaDeploy

provider:
  name: aws
  region: eu-west-1
  deploymentBucket:
    name: serverless-dep-bucket-dev

functions:
  utc-time-teller:
    name: utc-time-teller
    handler: src/lambda_handler.handler
    memorySize: 128
    timeout: 30
    runtime: python3.8
    events:
      - http:
          path: /{proxy+}
          method: any
      - http:
          path: /
          method: any

Deploying python function as Lambda with API Gateway

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Goal of this exercise is to put our previously developed python function behind a REST API.

Tools/prerequisites we are going to use: Visual Studio Code, NPM, python 3, AWS account, AWS credentials configured locally.

GitRepo is available here.

Api Gateways are important infrastructure elements, as a simple Lambda function is only available from inside AWS ecosystem. REST Api is a common, easy to implement standard to share resource.

To make our Lambda function publicly available we are going to update our previous code base with two things: tell the serverless to create the Api Gateway for our stack and upgrade the response of our utc-time-teller.

Let’s start with the python code changes. As we are going to communicate on HTTP channel JSON is a good model structure to respond with. Edit our handler’s code as following:

import datetime
import json

def handler(event, context) -> str:
    dt = datetime.datetime.now(datetime.timezone.utc)
    utc_time = dt.replace(tzinfo=datetime.timezone.utc)

    return {
        "statusCode": 200,
        "headers": {
            "Content-Type": "application/json"
        },
        "body": json.dumps({
            "utc ": str(utc_time)
        })
    }

We are returning a default HTTP response with statusCode of 200 (everything is OK), telling the receiver that the message is in JSON format (“Content-Type”: “application/json”) and of course adding the body itself.

The Lambda is still testable using the previously shown method:

We can continue with the serverless.yml:

Add these few lines to the end of our function:

    events:
      - http:
          path: /{proxy+}
          method: any
      - http:
          path: /
          method: any

This will generate the API Gateway on AWS. The whole serverless.yml looks like this:

service: BlogDavidPythonLambdaDeploy

provider:
  name: aws
  region: eu-west-1

functions:
  utc-time-teller:
    name: utc-time-teller
    handler: src/lambda_handler.handler
    memorySize: 128
    timeout: 30
    runtime: python3.8
    events:
      - http:
          path: /{proxy+}
          method: any
      - http:
          path: /
          method: any

Now call our deployment script from a command line tool:

serverless deploy

Serverless CLI should add new lines to the logs under the category “endpoints”. Copy the one without {proxy+} ending to the browser and enjoy our newly created API gateway response:

Deploying python function to AWS Lambda using serverless

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Goal of this exercise is to show how easy it is to deploy our python code to AWS.

Tools/prerequisites we are going to use: Visual Studio Code, NPM, python 3, AWS account, AWS credentials configured locally.

GitRepo is available here.

Serverless is a great tool to have the production code and required cloud computing configuration next to each other. To install serverless, use the following command in your favorite shell:

Note: you will need “npm” installed

npm install serverless -g

Create your folder structure for the project: we are going to need one python file, which will be the host of called function and add a file called serverless.yml, for configuration.

Let’s start with the python file:

import datetime

def handler(event, context) -> str:
    dt = datetime.datetime.now(datetime.timezone.utc)
    utc_time = dt.replace(tzinfo=datetime.timezone.utc)

    return str(utc_time)

It is a pretty simple code, which will return the UTC time formatted to string. Which we need to note is the two parameters of our function: event and context, both of them will be added by AWS. As in this exercise we don’t care about input parameters, just add them to our function.

Go to the serverless.yml and add these lines:

service: BlogDavidPythonLambdaDeploy

provider:
  name: aws
  region: eu-west-1

functions:
  utc-time-teller:
    name: utc-time-teller
    handler: src/lambda_handler.handler
    memorySize: 128
    timeout: 30
    runtime: python3.8

service: is the name of the group.
provider: configuration of cloud computing platform.
name: which cloud computing platform we are using.
region: AWS’ regions are available here.
functions: in the use case of AWS, functions are the Lambdas.
“utc-time-teller”: the identifier of our Lambda.
name: how the function will be named on AWS.
handler: path to our function in the form: path/file.functionName
memorySize: how large lambda we are planning to use. Measured in MB-s. Further details and prices can be found here.
timeout: maximum processing time, measured in seconds.
runtime: container type definition with the needed prerequisites like installed python3.8. Further AWS runtimes can be found here.

If we are done with our files, open up a command line tool and call this command:

serverless deploy

Serverless will pack our code and create the stack on AWS. After it finishes, go to AWS Console and in the search bar look for “lambda”:

Search for the name of our lambda “utc-time-teller”:

Click on it and navigate to the “Test” tab, then hit the “Test” button:

If everything went alright, we can see the result of our Lambda: