I am facing something similar to How to load file from custom hosted Minio s3 bucket into pandas using s3 URL format?
however, I already have an initialized s3 session (from boto3).
How can I get the credentials returned from it to feed these directly to pandas?
I.e. how can I extract the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY from the initialized boto3 s3 client?
You can use session.get_credentials
import boto3
session = boto3.Session()
credentials = session.get_credentials()
AWS_ACCESS_KEY_ID = credentials.access_key
AWS_SECRET_ACCESS_KEY = credentials.secret_key
AWS_SESSION_TOKEN = credentials.token
If you only have access to boto client (like the S3 client), you can find the credentials hidden here:
client = boto3.client("s3")
client._request_signer._credentials.access_key
client._request_signer._credentials.secret_key
client._request_signer._credentials.token
If you don't want to handle credentials (I assume you're using the SSO here), you can load the S3 object directly with pandas: pd.read_csv(s3_client.get_object(Bucket='Bucket', Key ='FileName').get('Body'))
Related
I am new to AWS S3. I did a lot of googling around how to connect S3 using python and found everyone is using Boto so its what I am using as the client. I used powershell to login and create the .aws/credentials . In that file, I was able to get the aws_access_key_id, aws_secret_access_key AND aws_session_token needed to establish the session. I understand that session is only about 8 hours so the next day when my python script runs to connect to S3 obviously the session is expired. How can i overcome this and how can I establish a new session daily? Below is my code.
s3_client = boto3.client(
"s3",
aws_access_key_id=id_,
aws_secret_access_key=secret,
aws_session_token=token,
region_name='r'
)
# Test it on a service (yours may be different)
# s3 = session.resource('s3')
# Print out bucket names
for bucket in s3.buckets.all():
# print(bucket.name)
bucket = 'automated-reports' # already created on S3
csv_buffer = StringIO()
all_active_scraper_counts_df.to_csv(csv_buffer, index=False)
# s3_resource = boto3.resource('s3')
put_response = s3_client.put_object(Bucket=bucket, Key="all_active_scrapers.csv", Body=csv_buffer.getvalue())
status = put_response.get("ResponseMetadata", {}).get("HTTPStatusCode")
if status == 200:
print(f"Successful S3 put_object response. Status - {status}")
else:
print(f"Unsuccessful S3 put_object response. Status - {status}")
You have three options:
Run a script that updates the credentials before you run your Python script. You can use AWS CLI sts assume-role to get a new set of credentials.
Add a try/catch statement inside your code to handle credentials expired error. Then, generate new credentials and re-initialize the S3 client.
Instead of using a Role, use a User. (IAM Identities). User credentials can be valid forever. You won't need to update the credentials in this case.
Looking for some guidance with regards to uploading files into AWS S3 bucket via a python script and an IAM role. I am able to upload files using BOTO3 and an aws_access_key_id & aws_secret_access_key for other scripts.
However, I have now been given an IAM role to login to a certain account. I have no issue using AWS CLI to authenticate and query the S3 data so I do believe that my .aws/credential and .aws/config files are correct. However I am not sure how to use the ARN value within my python code.
This is what I have put together so far, but get a variety of errors which all lead to denied access:
session = boto3.Session(profile_name='randomName')
session.client('sts').get_caller_identity()
assumed_role_session = boto3.Session(profile_name='randomNameAccount')
print(assumed_role_session.client('sts').get_caller_identity())
credentials = session.get_credentials()
aws_access_key_id = credentials.access_key
aws_secret_access_key = credentials.secret_key
s3 = boto3.client('s3',
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key)
bucket_name = 'bucketName'
This is a sample of what my credential and config files looks like as a referal.
.aws/config file:
[profile randomNameAccount]
role_arn = arn:aws:iam::12345678910:role/roleName
source_profile = randomName
aws/credentials file:
[randomName]
aws_access_key_id = 12345678910
aws_secret_access_key = 1234567-abcdefghijk
My question is help around the python code to be able to authenticate against AWS and navigate around a S3 bucket using an IAM role and then upload files when I call an upload function.
Thank you in advance.
You should create an entry for the IAM Role in ~/.aws/credentials that refers to a set of IAM User credentials that have permission to assume the role:
[my-user]
aws_access_key_id = AKIAxxx
aws_secret_access_key = xxx
[my-role]
source_profile = my-user
role_arn = arn:aws:iam::123456789012:role/the-role
Add an entry to ~/.aws/config to provide a default region:
[profile my-role]
region = ap-southeast-2
Then you can assume the IAM Role with this code:
import boto3
# Create a session by assuming the role in the named profile
session = boto3.Session(profile_name='my-role')
# Use the session to access resources via the role
s3_client = session.client('s3')
response = s3_client.list_objects(Bucket=...)
What would be the client alternate for this?
import boto3
s3 = boto3.resource('s3')
bucket = s3.Bucket('name')
I want to use client but that has a list_buckets function. Is there a way to pass a bucket name to client instead of getting the details from the result array??
This will give you the bucket names
import boto3
s3 = boto3.resource('s3')
buckets = s3.buckets.all()
for bucket in buckets:
print(bucket.name)
I don't think you can only get the names.
But there is a tricky way to use the client
import boto3
s3 = boto3.resource('s3')
s3.meta.client.list_buckets()['Buckets']
where it gives the client responses.
I am using python and jupyter notebook to read files from an aws s3 bucket, and I am getting the error 'No Credentials Error:Unable to locate credentials' when running the following code:
import boto3
s3 = boto3.resource('s3')
bucket = s3.Bucket('my-bucket')
for obj in bucket.objects.all():
key = obj.key
body = obj.get()['Body'].read()
I believe I need to put my access key somewhere, but I am not sure where. Thank you!
If you have AWS CLI installed, just do a simple aws configure. Then you will be good to go.
I'm using a container that simulate a S3 server running on http://127.0.0.1:4569 (with no authorization or credentials needed)
and I'm trying to simply connect and print a list of all the bucket names using python and boto3
here's my docker-compose:
s3:
image: andrewgaul/s3proxy
environment:
S3PROXY_AUTHORIZATION: none
hostname: s3
ports:
- 4569:80
volumes:
- ./data/s3:/data
here's my code:
s3 = boto3.resource('s3', endpoint_url='http://127.0.0.1:4569')
for bucket in s3.buckets.all():
print(bucket.name)enter code here
here's the error message that I received:
botocore.exceptions.NoCredentialsError: Unable to locate credentials
I tried this solution => How do you use an HTTP/HTTPS proxy with boto3?
but still not working, I don't understand what I'm doing wrong
First, boto3 always try to handshake with S3 server with AWS API key. Even your simulation server don't need password, you still need to specify them either in your .aws/credentials or inside your program. e.g.
[default]
aws_access_key_id = x
aws_secret_access_key = x
hardcoded dummy access key example
import boto3
session = boto3.session(
aws_access_key_id = 'x',
aws_secret_access_key = 'x')
s3 = session.resource('s3', endpoint_url='http://127.0.0.1:4569')
Second, I don't know how reliable and what kind of protocol is implemented by your "s3 simulation container". To make life easier, I always suggest anyone that wants to simulate S3 load test or whatever to use fake-s3