Boto 3: Basic and setup

Boto : Boto is a SDK designed to improve the use of the python programming language in aws.

Setup requirement:

  • Aws signup
  • Python version 2.7
  • Pycharm 3.3 [ide]
  • Pip setup

For programmatic access: We need to enable the access in the aws iam:

For programmatically user access , secret key and access id.

Setup awscli on windows

  • Awscli
  • Prerequires:
    • Check your system has Python 2.7
    • Pip is configured

Configure awscli

Configure the access key and id

C:\Users\amitm>aws configure

AWS Access Key ID [****************Z2FA]:

AWS Secret Access Key [****************V270]:

Default region name [ap-south-1]:

Default output format [test]:

Check setup?

C:\Users\amitm>aws s3 ls

2018-10-15 13:08:20 cf-templates-106h68kzl5m34-us-east-2

2018-11-08 23:39:48 openwriteup

2018-11-08 23:46:12 openwriteup-1

2018-11-09 00:16:44 test-openwriteup

What is awscli??

  • This is a command line tool
  • If we are writing script we can use it
  • Testing purpose or want to use shell or powershell it is useful that

Setup boto3

  • Pip install boto3
  • Test boto3
    • Python
    • Import boto3
    • Help(boto3)


  • A low-level interface to a growing number of Amazon Web Services. The botocore package is the foundation for the AWS CLIas well as boto3.
  • Botocore provides the low level clients, session, and credential & configuration data. Boto 3 builds on top of Botocore by providing its own session, resources and collections.
  • botocore does not provide higher-level abstractions on top of these services, operations and responses. That is left to the application layer. The goal of botocore is to handle all of the low-level details of making requests and getting results from a service

Core concepts of boto3


  • higher-level, object-oriented API
  • generated from resource description
  • uses identifiers and attributes
  • has actions (operations on resources)
  • exposes subresources and collections


import boto3

s3 = boto3.resource('s3')

bucket = s3.Bucket('mybucket')

for obj in bucket.objects.all():

print(obj.key, obj.last_modified)

Boto Client:

  • low-level service access
  • generated from service description
  • exposes botocore client to the developer
  • typically maps 1:1 with the service API
  • snake-cased method names (e.g. ListBuckets API => list_buckets method)


import boto3

client = boto3.client('s3')

response = client.list_objects(Bucket='mybucket')

for content in response['Contents']:

obj_dict = client.get_object(Bucket='mybucket', Key=content['Key'])

print(content['Key'], obj_dict['LastModified'])


Difference Between resource and client:

Resource object is very high level object, every operation with resource object would be high level operation. We may not have all the operation with resource.

Client is low level object, so whatever operation we want to perform its always be available. Client operations are mostly dictionary operation.


  • stores configuration information (primarily credentials and selected region)
  • allows you to create service clients and resources

Simple object to get it connected to particular aws account or iam account. If i want to connect any iam acocunt, session object will be used.


  • Automatically handles pagination
  • Yields individual pages
  • You must process each pages

Example: I have three thousand object in my s3 bucket, which i want to list. Boto3 Api can only list till a limit (1000 object). In such cases paginator can be used to list all the 3k objects. It will be using 3 pages to list .


Waiter are used for reach waiting to reach certain state

Example: I have ec2 instance, which i newly launched, it takes some time to reach running state. For that purpose we can use waiter


List all the running instance on amazon VPC

In my test environment, I have amazon VPC, which I am accessing using Linux server.

For performing all the activities on amazon vpc, I have used python script.For automation in amazon VPC, aws provides module boto3, which need to be installed using python pip. Using this module we list all the running instances.In below script I am creating a config file and then reading that config file.Following script perform all these steps:

  • Create a config file, which is required to connect aws vpc.
  • Read the config file, and list all the instance following details:
    • ‘Name’:name,
    • ‘Type’: instance.instance_type,
    • ‘State’:instance.state[‘Name’],
    • ‘Private IP’:instance.private_ip_address,
    • ‘Public IP’: instance.public_ip_address,
    • ‘Launch Time’: instance.launch_time



import ConfigParser,boto3,os,sys,paramiko
from collections import defaultdict
config = ConfigParser.RawConfigParser()
#When adding sections or items, add them in reverse order
config.set('USER','AWS_Profile','<aws user to login>')
config.set('EC2','Region','<aws region>')

#Writing configruation to config file
name = raw_input("Enter the config file name::: ")
with open(name, 'wb') as configfile:
#Reading the config file
config1 = ConfigParser.ConfigParser()
ses = boto3.Session(profile_name = config1.get("USER", "AWS_Profile"))
ec2 = ses.resource('ec2')
key = paramiko.rsakey.RSAKey.from_private_key_file(filename=config1.get("USER","Private_Key")) 
running_instances = ec2.instances.filter(Filters=[{
'Name': 'instance-state-name',
'Values': ['running']}])
ec2info = defaultdict()
for instance in running_instances:
for tag in instance.tags:
if 'Name' in tag['Key']:
name = tag['Value']
ec2info[] = {
'Type': instance.instance_type,
'Private IP':instance.private_ip_address,
'Public IP': instance.public_ip_address,
'Launch Time': instance.launch_time
attributes = ['Name','Type','State','Private IP','Public IP','Launch Time']
for instance_id, instance in ec2info.items():
for key in attributes:


output of the script :

Private IP:
Public IP:None
Launch Time:2016-08-26 23:09:17+00:00



Aws cloudFormation

Aws cloudformation templates is provide to way  deploy the apps in aws cloud. Using this we can create template for apps or services, and can be easily deploy whenever require we can easily provision .

As part of amazon aws exploration, i found that my clients environment are based on cloud formulation script. I tried to explore the environment, and found its quite same as our workflows (not exactly same).

In a standard way, when we want to create a template, we need to create all the require resources. We should have all the resources information which is required for application. The deployment is automated by an AWS Cloudformation template. The template starts the installation process by creating all the required AWS resources such as the Amazon VPC, security groups, public and private subnets, Internet gateways, NAT gateways, and the Amazon S3 bucket.

In this section we will discuss some of the base commands, which can be executed on the jumpbox:

aws cloudformation list-stacks

It will list all the available stacks.

aws cloudformation describe-stacks –stack-name <name>

Lot of other commands are also available:

In my search, I got Docker datacenter cloudformation template,  Its good stuff to get understanding. PS: you should have knowledge of docker.

How to create Cloudformation Template


CloudFormation is described as a JSON (JavaScript Object Notation) template. It’s a model-driven template in that the AWS infrastructure is instantiated according to its own specification of proper order of execution.

aws cloudformation get-template –stack-name <name>

This command will give the complete details of template, with resources, output, subnet etc.

When we use cloudformation, we work with template and stack. Template describes the resources and properties, and when we create stack, we provision the resources using template.

Templates: An AWS CloudFormation template is a text file whose format complies with the JSON standard. You can save these files with any extension, such as .json, .template, or .txt. AWS CloudFormation uses these templates as blueprints for building your AWS resources. For example, in a template, you can describe an Amazon EC2 instance, such as the instance type, the AMI ID, block device mappings, and its Amazon EC2 key pair name. Whenever you create a stack, you also specify a template that AWS CloudFormation uses to create whatever you described in the template.

Stacks:When you use AWS CloudFormation, you manage related resources as a single unit called a stack. You create, update, and delete a collection of resources by creating, updating, and deleting stacks. All the resources in a stack are defined by the stack’s AWS CloudFormation template. Suppose you created a template that includes an Auto Scaling group, Elastic Load Balancing load balancer, and an Amazon Relational Database Service (Amazon RDS) database instance. To create those resources, you create a stack by submitting the template that you created, and AWS CloudFormation provisions all those resources for you.

aws instance listing using python sdk

This blog is for those, who are very new to aws and python. They want to start both of them together. Assuming they have setup boto3 environment in their test lab.

In lab setup type python: python

It will give python prompt, we can explore boto3.

>>> import boto3
>>> dir(boto3)
[‘DEFAULT_SESSION’, ‘NullHandler’, ‘Session’, ‘__author__’, ‘__builtins__’, ‘__doc__’, ‘__file__’, ‘__name__’, ‘__package__’, ‘__path__’, ‘__version__’, ‘_get_default_session’, ‘client’, ‘docs’, ‘exceptions’, ‘logging’, ‘resource’, ‘resources’, ‘session’, ‘set_stream_logger’, ‘setup_default_session’, ‘utils’]

Perform help (boto3) ,It will show the package content with this package..

docs (package)
dynamodb (package)
ec2 (package)
resources (package)
s3 (package)

Lets import the resources : from boto3 import resources

>>> dir (boto3.resources)
[‘__builtins__’, ‘__doc__’, ‘__file__’, ‘__name__’, ‘__package__’, ‘__path__’, ‘action’, ‘base’, ‘collection’, ‘factory’, ‘model’, ‘params’, ‘response’]



/*perform following on your python console or write a .py script
import boto3
#help(ec2) /*it will list all the available option with ec2*/
#help(ec2.instances) /*search for filter*/
#help(ec2.instances.filter /*list the filter option and list syntax
/* instance_iterator = ec2.instances.filter(
 | DryRun=True|False,
 | InstanceIds=[
 | 'string',
 | ],
 | Filters=[
 | {
 | 'Name': 'string',
 | 'Values': [
 | 'string',
 | ]
 | },
 | ]

import boto3
instances = ec2.instances.filter(
    Filters=[{'Name': 'instance-state-name', 'Values': ['running']}])
for instance in instances:
    print(, instance.instance_type)

Automation of Aws instances using python sdk

Boto is python sdk in amazon, which can be used for automation purpose for ec2,s3 etc.

Installation: Boto can be installed using using pip or offline. for pip the command:    pip install boto3

Offline installation we need to download the offline bundle and install, it has the dependency , download and install it.

Configuration : Before we use boto we need to set the configuration, which can be done using : aws configure

Alternatively, you can create the credential file yourself. By default, its location is at ~/.aws/credentials:

aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY

You may also want to set a default region. This can be done in the configuration file. By default, its location is at ~/.aws/config:


Alternatively, you can pass a region_name when creating clients and resources.

Lets see some the sample code:

import boto3


if we print ec, it shows “ec2.ServiceResource()”

Just give help(ec),it will show up the complete description what we can do ,what methods are available. Below example we want to check all the instance status

# Boto 3
import boto3
#check the status of all the ec2 instances
for status in ec.meta.client.describe_instance_status()['InstanceStatuses']:


Amazon cloud vm : How to access in secure environment

Recently, One of my client they were doing the proof of concept for amazon aws. Till Now, I just used for trial purpose, took a single ec2 instance and assigned a public ip, was able to access easily using putty. Just download the .pem file and access using ssh by any linux box.

In my client case they were using vpc (virtual private cloud), in that they have defined the availability zone (subnet are defined). When I was selecting any vpc, it has shows mapped availability zone. Client has provided one jump server as well from where we will be doing ssh or can use aws command line to control the environment. The complete environment is access aws private ip ,since it has mapped to it.

Problem faced and applied solution:

During the phase of starting, initially i have created ec2 instance in my assigned vpc, when i was trying to access it was not pinging from jump box. After googling a lot I found that “Security group” which i am using, doesn’t mention Source ip range from which it has to communicate.

I created a new security group  mentioning source “” but it has started giving the security warning, so i went back and created the correct range “”. After that i was able to ping my ec2 instance from my jump server.

Next step i created a key pair and downloaded the .pem file, while i was using that .pem file in my environment somehow it was not able to do ssh from the jump server. I was able to ping the aws instance but not able to connect.

I have moved the .pem file to file format: mv <file.pem> <file>

ssh <file option>  <file> ec2-user@<instance private ip>

now i was running aws command from the jump server (which is already configured on the system)

aws ec2 describe-instances

It was giving error not configured. Tried the below command

aws configure

This command ask “Access key id”, “Secret access key”,”default region”, and “output format”.

Access key id and secret access key information will get from IAM service configuration (Identity and Access Management), In users segment select your user. In that section you can create access key and activate it. Download it, It has both the information access key and secret.

Region information you can find from aws web page, which region you have selected, and output format (text,json or xml..) , once you ready with this information, please use the same command “aws configure”, will able to configure. Now if you run any command ” aws help” It will work from command line…

It was overall a good experience, yet to have lot of information to fetch…will definitely share


Connect to linux amazon instance to your linux personal computer

In this blog, we will learn how to ssh to your linux amazon instance from your personal computer.


– Rhel ec2 instance creation access
– Linux personal computer with proper internet connection.

For connecting to amazon instance from your computer, we need to create a key pair. For doing this,we have to create key pair. A key pair can be created on you computer or from aws site as well.

– If we create key pair from your computer, then you need to import the public key in your aws,and while creating the instance use that key as part of security profile.

-if we create key pair from amazon then, we need to download the private key and copy to the
linux personal computer, and ssh to ec2 instance using that

Create key pair using the console:

– Open the Amazon EC2 console .
– Slect the region for the key pair.
– In the navigation, under Network & Security, choose key Pairs.
– Choose Create key Pair.
– Enter name for key pair,and create. It will generate a file with .pem extension, which save it.

Note: This file cannot be regenerate or get it again, so please save it at safe place.
– copy the <filename>.pem to your linux box, perform chmod.

chmod 400 <filename>.pem

Import you key pair to amazon EC2:

Instead of using amazon key pair , you can use create an RSA key pair and import the public key to Amazon EC2.

– Generate a key pair with a third party tool of your choice.
– In this case, we are using ssh-keygen. It will generate public and private key.
– Copy public key to a local file

Import the public key

-Open the amazon ec2 console.
-From the navigation, select the region.
-In navigation pane,under network & Security, choose key pair.
-Choose import key pair and browse the public key you have