Managing AWS S3 objects via Python & boto3

I recently had a task which required updating a large number of JSON manifest files housed within S3 folders, so that Quicksight could read and import the data.

boto3 makes this achievable with just a few lines of code. This blog post will show a few different ways to use this module effectively.

For reference, the boto3 documentation lives here;

Assume an IAM role which can access the S3 bucket

If you have a robust permissions model configured within AWS, it may be that you need to assume an IAM role in order to execute actions against a particular s3 bucket. Using the assume_role function allows this.

sess = boto3.Session(aws_access_key_id=ACCESS_KEY,
sts_connection = sess.client('sts')
assume_role_object = sts_connection.assume_role(RoleArn="arn:aws:iam::12345678901:role/s3userrole_admin",

Create a boto3 session

Create a boto3 session, passing the returned values from the assume_role_object variable, which was generated in the previous step.

session = boto3.Session(
	region_name = 'eu-west-2')
client = session.client('s3')

>>> client
<botocore.client.S3 object at 0x7f51dd378050>

Querying s3 folder structure

The function we are calling here is list_objects, but for buckets of unknown size, running it through the paginator means that if more than 1000 records exists, the pagnitator will abstract this process into a single request/response for querying.

paginator = client.get_paginator('list_objects')
s3_bucket_name = 'tg-master-bucket'
#Top Level
for result in paginator.paginate(Bucket=s3_bucket_name, Delimiter='/'):
    for prefix in result.get('CommonPrefixes'):
        a = prefix.get('Prefix')
        # 2nd level
        for result in paginator.paginate(Bucket=s3_bucket_name, Delimiter='/', Prefix=a):
            for prefix in result.get('CommonPrefixes'):
                b = prefix.get('Prefix')
>> /folder1
>> /folder1/folder2

Deleting S3 objects

A simple function where filename needs to be the full path, excluding the bucket name.

Note that to start working with specific services, we need to create a resource session as follows:

s3 = session.resource(‘s3’)

s3 = session.resource('s3')
s3_bucket_name = 'tg-master-bucket'
filename = '/folder1/folder2/example-file.json'

Reading a S3 JSON file into a usable python object

In this example, I needed to read the JSON file from the bucket into a python JSON object, which could then be manipulated before pushing back to the s3 bucket.

content_object = s3.Object(s3_bucket_name, json_file)
file_content = content_object.get()['Body'].read()
json_content = json.loads(file_content)
json_content['Name'] = 'New Name'

Dependant on the file format that is being read, the content_object.get call above (line 2) may need an extra argument adding of .decode(‘utf-8’).


Copying s3 files

A simple action to copy an existing file in a bucket.

s3_bucket_name = 'tg-master-bucket'
existing_file = '/folder1/folder2/example-file.json'
new_filename = '/folder1/folder2/folder3/new-file.json'
copy_source = {'Bucket': s3_bucket_name, 'Key': existing_file}

Upload local files to S3 bucket

Upload a file from a local path to a defined path within a bucket.

s3 = session.resource('s3')
s3_bucket_name = 'tg-master-bucket'
s3_file_name = "folder1/folder2/newjsonfile.json"
s3.meta.client.upload_file(Filename='./temp.json', Bucket=s3_bucket_name, Key=s3_file_name)

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