Effortless AWS CloudFormation Deployment using Boto3

Introduction

Amazon Web Services (AWS) Python library Boto3 offers a programmatic interface for interfacing with many AWS services, including Amazon CloudFormation.

Users can model and deploy infrastructure resources using CloudFormation, an Amazon tool, in an automated, repeatable manner. With the capability to construct, update, and delete stacks through Python code, Boto3 CloudFormation offers a quick and flexible method for delivering infrastructure.

Additionally, substantial capability for managing stack resources, accessing stack data, and monitoring stack events is offered by Boto3 CloudFormation, making it a potent tool for controlling infrastructure on AWS.

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Steps to Deploy AWS CloudFormation using Boto3

AWS Console, AWS CLI, and other methods can create an AWS CloudFormation stack. Using AWS CLI, CodePipeline, and other tools, we can also automate the building of the CloudFormation stack. In this blog post, we’ll explore how to automate the building of an AWS CloudFormation Stack using the AWS SDK for Python.

To automate the building of a AWS CloudFormation stack, follow the procedures listed below.

Make that the Python version you wish to execute the Python script has the most recent updates installed. Macs already have Python installed by default.

boto

2. Install the AWS Toolkit for Visual Studio Code

Installing AWS Toolkit

3. To create a CloudFormation stack automatically, you need two files.

Your Python script, MyCFNStackCreation.py

Your parameter file is named MyStackParameter.json.

4. Maintain these two files in the same directory where you will run the Python script.

For reference, see the Python script below.

import boto3
from botocore.session import get_session
from os.path import dirname
boto3.setup_default_session()
script_dir= dirname(__file__)
print(script_dir)

sees= boto3.Session(region_name='us-west-2')
cfnclient= sees.client (‘cloudformation’)
stack=’ ’
with open(f"{script_dir}/ MyStackParameter.json",'r') as fd:
    stack = fd.read()
    print(stack)

params =[
    {
        "ParameterKey": 'HashKeyElementName ',
         "ParameterValue":'Product_id'
    },
]

cfnclient.create_stack(StackName="myfirststack1", TemplateBody=stack,Parameters=params)
                   print(cfnclient.describe_stacks())

import boto3

from botocore.session import get_session

from os.path import dirname

boto3.setup_default_session()

script_dir= dirname(__file__)

print(script_dir)

sees= boto3.Session(region_name='us-west-2')

cfnclient= sees.client (‘cloudformation’)

stack=’ ’

with open(f"{script_dir}/ MyStackParameter.json",'r') as fd:

stack = fd.read()

print(stack)

params =[

{

"ParameterKey": 'HashKeyElementName ',

"ParameterValue":'Product_id'

]

cfnclient.create_stack(StackName="myfirststack1", TemplateBody=stack,Parameters=params)

print(cfnclient.describe_stacks())

6. For reference, see the parameter JSON file below. This is the template parameter JSON file, which can be altered to suit the needs of the AWS CloudFormation build. In this case, I’m using this parameter file to call the master stack. To create a Amazon DynamoDB table, the master stack contains nested stacks.

{
  "AWSTemplateFormatVersion" : "2010-09-09",
 
  "Description" : "AWS CloudFormation Sample Template DynamoDB_Table.",
 
  "Parameters" : {
    "HashKeyElementName" : {
      "Description" : "HashType PrimaryKey Name",
      "Type" : "String",
      "AllowedPattern" : "[a-zA-Z0-9]*",
      "MinLength": "1",
      "MaxLength": "2048",
      "ConstraintDescription" : "must contain only alphanumberic characters"
    },

    "HashKeyElementType" : {
      "Description" : "HashType PrimaryKey Type",
      "Type" : "String",
      "Default" : "S",
      "AllowedPattern" : "[S|N]",
      "MinLength": "1",
      "MaxLength": "1",
      "ConstraintDescription" : "must be either S or N"
    },

    "ReadCapacityUnits" : {
      "Description" : "Provisioned read throughput",
      "Type" : "Number",
      "Default" : "5",
      "MinValue": "5",
      "MaxValue": "10000",
      "ConstraintDescription" : "must be between 5 and 10000"
    },

    "WriteCapacityUnits" : {
      "Description" : "Provisioned write throughput",
      "Type" : "Number",
      "Default" : "10",
      "MinValue": "5",
      "MaxValue": "10000",
      "ConstraintDescription" : "must be between 5 and 10000"
    }
  },
 
  "Resources" : {
    "myDynamoDBTable" : {
      "Type" : "AWS::DynamoDB::Table",
      "Properties" : {
        "AttributeDefinitions": [ { 
          "AttributeName" : {"Ref" : "HashKeyElementName"},
          "AttributeType" : {"Ref" : "HashKeyElementType"}
        } ],
        "KeySchema": [
          { "AttributeName": {"Ref" : "HashKeyElementName"}, "KeyType": "HASH" }
        ],
        "ProvisionedThroughput" : {
          "ReadCapacityUnits" : {"Ref" : "ReadCapacityUnits"},
          "WriteCapacityUnits" : {"Ref" : "WriteCapacityUnits"}
        }                
      }
    }
  },
 
  "Outputs" : {
    "TableName" : {
      "Value" : {"Ref" : "myDynamoDBTable"},
      "Description" : "Table name of the newly created DynamoDB table"
    }
  }
}

{

"AWSTemplateFormatVersion" : "2010-09-09",

"Description" : "AWS CloudFormation Sample Template DynamoDB_Table.",

"Parameters" : {

"HashKeyElementName" : {

"Description" : "HashType PrimaryKey Name",

"Type" : "String",

"AllowedPattern" : "[a-zA-Z0-9]*",

"MinLength": "1",

"MaxLength": "2048",

"ConstraintDescription" : "must contain only alphanumberic characters"

"HashKeyElementType" : {

"Description" : "HashType PrimaryKey Type",

"Type" : "String",

"Default" : "S",

"AllowedPattern" : "[S|N]",

"MinLength": "1",

"MaxLength": "1",

"ConstraintDescription" : "must be either S or N"

"ReadCapacityUnits" : {

"Description" : "Provisioned read throughput",

"Type" : "Number",

"Default" : "5",

"MinValue": "5",

"MaxValue": "10000",

"ConstraintDescription" : "must be between 5 and 10000"

"WriteCapacityUnits" : {

"Description" : "Provisioned write throughput",

"Type" : "Number",

"Default" : "10",

"MinValue": "5",

"MaxValue": "10000",

"ConstraintDescription" : "must be between 5 and 10000"

}

"Resources" : {

"myDynamoDBTable" : {

"Type" : "AWS::DynamoDB::Table",

"Properties" : {

"AttributeDefinitions": [ {

"AttributeName" : {"Ref" : "HashKeyElementName"},

"AttributeType" : {"Ref" : "HashKeyElementType"}

} ],

"KeySchema": [

{ "AttributeName": {"Ref" : "HashKeyElementName"}, "KeyType": "HASH" }

"ProvisionedThroughput" : {

"ReadCapacityUnits" : {"Ref" : "ReadCapacityUnits"},

"WriteCapacityUnits" : {"Ref" : "WriteCapacityUnits"}

}

"Outputs" : {

"TableName" : {

"Value" : {"Ref" : "myDynamoDBTable"},

"Description" : "Table name of the newly created DynamoDB table"

}

7. Once the two necessary files (the parameter file and the Python script) have been saved in one directory, open the terminal and navigate to the two files’ directories.

8. Execute the Python Script

9. You can monitor the script’s progress on the console. You can use a Python script to deploy the CloudFormation stack using the above mentioned procedures.

Conclusion

Infrastructure management on Amazon can be substantially simplified by creating AWS CloudFormation stacks using Boto3. Boto3 facilitates deployment and lowers the possibility of human error by automating the development and configuration of resources.

Overall, Boto3’s deployment of AWS CloudFormation stacks has many advantages for Amazon users, including improved productivity, dependability, and scalability. Businesses may save time and money while constructing reliable and scalable infrastructure on AWS by utilizing the power of automation.

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FAQs

1. What is AWS CloudFormation?

ANS: – AWS CloudFormation is a tool that enables developers and companies to quickly assemble a group of linked AWS and outside resources, provision them, and manage them systematically and predictably.

2. What can developers do with AWS CloudFormation?

ANS: – In a straightforward, declarative manner that abstracts away the complexities of resource APIs, developers may deploy and upgrade compute, databases, and other resources. With the help of automatic rollbacks, automated state management, and resource management across accounts and regions, AWS CloudFormation enables the repeatable, predictable, and safe management of resource lifecycles. Recent improvements and options enable various resource creation methods, such as using the AWS CDK for programming in higher-level languages, configuration drift, and a new Registry that makes creating custom types that inherit many fundamental CloudFormation advantages simpler.