Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python, which allows Python developers to write software that makes use of services like Amazon S3 and Amazon EC2. Boto3 makes it easy to integrate your Python application, library, or script with AWS services.
Boto3 is vast and we will only cover a few popular services here, list of all available services
Here is an in-depth tutorial on using Boto3 with examples to give you a better understanding of how it works.
Installation
To install Boto3, simply use pip:
pip install boto3
You will also need to have an AWS account and set up your access keys in order to use Boto3. You can do this by going to the IAM (Identity and Access Management) section of the AWS Management Console and creating a new access key. Make sure to save the access key ID and secret access key in a secure location, as you will need them to authenticate your Boto3 scripts.
Importing Boto3 and Setting Up a Client
To use Boto3, you will first need to import it and create a client for the service you want to use. Here's an example of how to import Boto3 and create a client for the EC2 (Elastic Compute Cloud) service:
import boto3
ec2 = boto3.client('ec2')
You can use a client to make API calls to a specific service. In this example, the EC2 client will allow us to make calls to the EC2 API.
You can also use a resource to manage resources. A resource represents a collection of related actions you can perform. Here's an example of how to import Boto3 and create a resource for the S3 (Simple Storage Service) service:
import boto3
s3 = boto3.resource('s3')
Example: Listing EC2 Instances
Now that we have a client for the EC2 service, let's use it to list all the instances in our account. Here's the code to do that:
import boto3
ec2 = boto3.client('ec2')
response = ec2.describe_instances()
for reservation in response['Reservations']:
for instance in reservation['Instances']:
print(instance['InstanceId'])
This code will print the ID of each EC2 instance in your account.
Example: Creating an S3 Bucket
Now let's use Boto3 to create an S3 bucket. Here's the code to do that:
import boto3
s3 = boto3.client('s3')
response = s3.create_bucket(
ACL='private',
Bucket='my-new-bucket',
CreateBucketConfiguration={
'LocationConstraint': 'us-west-2'
}
)
print(response)
This code will create a new S3 bucket named "my-new-bucket" in the US West (Oregon) region.
Example: Uploading a File to S3
Now let's use Boto3 to upload a file to our S3 bucket. Here's the code to do that:
import boto3
s3 = boto3.client('s3')
response = s3.s3.upload_file( 'local/path/to/file.txt', 'my-new-bucket', 'remote/path/to/file.txt' )
Copy code This code will upload the file "file.txt" from your local machine to the "remote/path/to/file.txt" location in the "my-new-bucket" S3 bucket. ## Example: Downloading a File from S3 Now let's use Boto3 to download a file from our S3 bucket. Here's the code to do that:
import boto3
s3 = boto3.client('s3')
s3.download_file( 'my-new-bucket', 'remote/path/to/file.txt', 'local/path/to/file.txt' )
This code will download the file "file.txt" from the "remote/path/to/file.txt" location in the "my-new-bucket" S3 bucket to your local machine. ## Example: Listing S3 Buckets Now let's use Boto3 to list all the S3 buckets in our account. Here's the code to do that:
import boto3
s3 = boto3.client('s3')
response = s3.list_buckets()
for bucket in response['Buckets']: print(bucket['Name'])
This code will print the name of each S3 bucket in your account.
Example: Deleting an S3 Bucket Now let's use Boto3 to delete an S3 bucket. Here's the code to do that:
import boto3
s3 = boto3.client('s3')
response = s3.list_objects(Bucket='my-new-bucket')
for obj in response['Contents']: s3.delete_object(Bucket='my-new-bucket', Key=obj['Key'])
s3.delete_bucket(Bucket='my-new-bucket')
This code will delete the "my-new-bucket" S3 bucket, along with all the objects in the bucket. ## Conclusion I hope this tutorial has given you a good understanding of how to use Boto3 to interact with AWS services. Boto3 is a powerful Python library that can be used to automate a wide variety of AWS tasks, such as creating and managing EC2 instances, uploading and downloading files to S3, and much more. With Boto3, you can easily integrate your Python application, library, or script with AWS services.
Example: Listing RDS Instances
Let's say you want to use Boto3 to list all the RDS (Relational Database Service) instances in your account. Here's the code to do that:
import boto3
rds = boto3.client('rds')
response = rds.describe_db_instances()
for instance in response['DBInstances']:
print(instance['DBInstanceIdentifier'])
This code will print the identifier of each RDS instance in your account.
Example: Creating an RDS Instance
Now let's use Boto3 to create an RDS instance. Here's the code to do that:
import boto3
rds = boto3.client('rds')
response = rds.create_db_instance(
DBName='mydatabase',
DBInstanceIdentifier='mydbinstance',
AllocatedStorage=5,
DBInstanceClass='db.t2.micro',
Engine='mysql',
MasterUsername='admin',
MasterUserPassword='password',
VpcSecurityGroupIds=[
'sg-0123456789'
]
)
print(response)
This code will create a new RDS instance with the identifier "mydbinstance", using the MySQL engine and the "db.t2.micro" instance class. The instance will be associated with the VPC security group with the ID "sg-0123456789" and will have a master username and password of "admin" and "password" respectively.
Example: Deleting an RDS Instance
Now let's use Boto3 to delete an RDS instance. Here's the code to do that:
import boto3
rds = boto3.client('rds')
rds.delete_db_instance(
DBInstanceIdentifier='mydbinstance',
SkipFinalSnapshot=True
)
This code will delete the RDS instance with the identifier "mydbinstance", skipping the creation of a final snapshot.
Example: Listing SNS Topics
Now let's use Boto3 to list all the SNS (Simple Notification Service) topics in our account. Here's the code to do that:
import boto3
sns = boto3.client('sns')
response = sns.list_topics()
for topic in response['Topics']:
print(topic['TopicArn'])
This code will print the Amazon Resource Name (ARN) of each SNS topic in your account.
Example: Sending a Text Message with SNS
Now let's use Boto3 to send a text message using SNS. Here's the code to do that:
import boto3
sns = boto3.client('sns')
response = sns.publish(
PhoneNumber='+1234567890',
Message='Hello, world!'
)
print(response)
This code will send the text message "Hello, world!" to the phone number "+1234567890".
Example: Listing SQS Queues
Now let's use Boto3 to list all the SQS (Simple Queue Service) queues in our account. Here's the code to do that:
import boto3
sqs = boto3.client('sqs')
response = sqs.list_queues()
for queue_url in response['QueueUrls']:
print(queue_url)
This code will print the URL of each SQS queue in your account.
Example: Sending a Message to an SQS Queue
Now let's use Boto3 to send a message to an SQS queue. Here's the code to do that:
import boto3
sqs = boto3.client('sqs')
response = sqs.send_message(
QueueUrl='https://sqs.us-west-2.amazonaws.com/123456789012/my-queue',
MessageBody='Hello, world!'
)
print(response)
This code will send the message "Hello, world!" to the SQS queue with the URL "https://sqs.us-west-2.amazonaws.com/123456789012/my-queue".
Example: Receiving a Message from an SQS Queue
Now let's use Boto3 to receive a message from an SQS queue. Here's the code to do that:
import boto3
sqs = boto3.client('sqs')
response = sqs.receive_message(
QueueUrl='https://sqs.us-west-2.amazonaws.com/123456789012/my-queue',
MaxNumberOfMessages=1
)
if 'Messages' in response:
message = response['Messages'][0]
body = message['Body']
receipt_handle = message['ReceiptHandle']
sqs.delete_message(
QueueUrl='https://sqs.us-west-2.amazonaws.com/123456789012/my-queue',
ReceiptHandle=receipt_handle
)
This code will receive a single message from the SQS queue with the URL "https://sqs.us-west-2.amazonaws.com/123456789012/my-queue". If a message is received, the code will do something with the message and then delete it from the queue.
DynamoDB
DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability.
Here is an example of how you can use Boto3 to interact with DynamoDB in Python:
import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.create_table(
TableName='users',
KeySchema=[
{
'AttributeName': 'username',
'KeyType': 'HASH'
},
{
'AttributeName': 'last_name',
'KeyType': 'RANGE'
}
],
AttributeDefinitions=[
{
'AttributeName': 'username',
'AttributeType': 'S'
},
{
'AttributeName': 'last_name',
'AttributeType': 'S'
},
],
ProvisionedThroughput={
'ReadCapacityUnits': 5,
'WriteCapacityUnits': 5
}
)
table.meta.client.get_waiter('table_exists').wait(TableName='users')
print(table.item_count)
This example creates a new DynamoDB table called "users" with a composite primary key made up of a partition key (username) and a sort key (last_name). It sets the provisioned throughput for reads and writes to 5 capacity units each.
To add an item to the table, you can use the put_item method:
table.put_item(
Item={
'username': 'johndoe',
'last_name': 'Doe',
'age': 25,
'account_type': 'standard_user',
}
)
To retrieve an item from the table, you can use the get_item method:
response = table.get_item(
Key={
'username': 'johndoe',
'last_name': 'Doe'
}
)
item = response['Item']
print(item)
This will return the item with the primary key (username = "johndoe" and last_name = "Doe").
You can also use the query method to retrieve items based on the values of secondary index keys:
response = table.query(
IndexName='age-index',
KeyConditionExpression='age = :age',
ExpressionAttributeValues={
':age': 25
}
)
items = response['Items']
print(items)
This will return all items with an "age" attribute of 25, assuming that you have created a secondary index called "age-index" on the "age" attribute.
I hope this helps! Let me know if you have any questions.
