I am using Python Shell Jobs under AWS Glue which has boto3 and a few other libraries built-in . I am facing issues trying to access the secrets manager to get credentials to my RDS instance running Mysql , the job keeps running forever without any (error/success) message nor does it time out .
Below is the simple code that runs even from my local or a lambda for Python3.7 but not in Python Shell GLUE ,
import boto3
import base64
from botocore.exceptions import ClientError
secret_name = "secret_name"
region_name = "eu-west-1"
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name
)
get_secret_value_response = client.get_secret_value(SecretId=secret_name)
print(get_secret_value_response)
Would be very helpful if someone could point out if anything needs to be done additionally in Python Shell jobs under AWS Glue in order to access the secret manager credentials .
Make sure the IAM role used by the Glue Job has the policy SecretsManagerReadWrite
Also AWSGlueServiceRole and AmazonS3FullAccess
According to the documentation
When you create a job without any VPC configuration , then glue tries to reach the secret manager through internet , if the policies allows to have internet route then we can connect to secret manager
But when a glue job is created with VPC configuration/connection then all the request are made from your VPC/subnet where the connection points to , if this is the case, make sure you have secret manager endpoint present in your route table of the subnet where glue launches the resources.
https://docs.aws.amazon.com/glue/latest/dg/setup-vpc-for-glue-access.html
https://docs.aws.amazon.com/secretsmanager/latest/userguide/vpc-endpoint-overview.html
Related
Using Azure Devops I'm developing a Cloud Run service on PROJECT_A that need to utilize a Service Account of a PROJECT_B to read logs from Stackdriver.
I've successfully deployed the Cloud Run using its gcloud commands.
gcloud run deploy [[SERVICE] --namespace=NAMESPACE] [--service-account=Service_Account#PROJECT_A.iam.gserviceaccount.com]
Since I'm storing my service account as secure file in Azure Devops, I've uploaded PROJECT_B service account to PROJECT_A GCP Secret Manager using
echo $(service_account_PROJECT_B.json) > SA_PROJECT_B.txt
gcloud secrets create SA_PROJECT_B --data-file=SA_PROJECT_B.txt --replication policy=user-managed --project=PROJECT_A
I'm finding Issues while accessing to the Service Account stored in the secret manager.
Locally, when I create the client, I use:
#config.py
if DEPLOY_ENVIRONMENT == "local":
SA_PROJECT_B = os.path.join(BASE_DIR / "SA_PROJECT_B.json")
os.environ["SA_PROJECT_B"] = str(SA_PROJECT_B)
.
#client.py
from google.cloud import logging
from config import SA_PROJECT_B
logging_client = logging.Client.from_service_account_json(
SA_PROJECT_B
)
And it works.
When I execute the code from the Cloud Run, I get an error message stating that it can't cannot import name 'SA_PROJECT_B'
So here is my question:
how should I reference to a secret stored in secrets manager from the code?
I've tried following this google cloud community tutorial that showed me that the issue happens when I upload the secret to secret manager.
secrets = secretmanager.SecretManagerServiceClient()
SA_PROJECT_B= secrets.access_secret_version(request={"name": "projects/"+"PROJECT_B"+"/secrets/PROJECT_B/versions/1"}).payload.data.decode("utf-8")
print(SA_PROJECT_B) returns '$(service_account_PROJECT_B.json)'
I can't understand what I'm doing wrong.
Is something related with uploading a service account to gcp or something related to accessing correctly the secret manager?
I am trying to connect to AWS Secret manage, on local it works fine on the python server it gives "botocore.exceptions.NoCredentialsError: Unable to locate credentials"
error
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name
)
So I had 2 ways to correct this :
First Method:
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name,aws_access_key_id=Xxxxxx,
aws_secret_access_key=xxxxxx
)
Second Method: To have this in a config file (Which will again expose keys)
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name,aws_access_key_id=confg.access,
aws_secret_access_key=confg.key
)
Arent, we exposing our key and access keys in github if we are specifying it here.?
What is the correct way to access Secret Manager without specifying it here
You are correct you shouldn't pass your access key and secret key to any running server or service in AWS to avoid exposing it. On your local machine, it worked because your environment is getting your user's permissions via AWS CLI.
What you need to do for a server is to add to the service role a policy allowing it to access the Secrets Manager, then you won't face permissions issues anymore
On Permissions policy examples - AWS Secrets Manager can find examples of how those policies need to be.
And on Assign an IAM Role to an EC2 Instance you can see how to attach a role with a specific policy to an EC2 instance.
My login to AWS console is MFA & for that I am using Google Authenticator.
I have S3 DEV bucket and to access that DEV bucket, I have to switch role and after switching i can access DEV bucket.
I need help how to achieve same in python with boto3.
There are many csv file that I need to open in dataframe and without that resolving access, I cannot proceed.
I tried configuring AWS credentials & config and using that in my python code but didn't helped.
AWS document is not clear about how to do switching role while using & doing in python.
import boto3
import s3fs
import pandas as pd
import boto.s3.connection
access_key = 'XXXXXXXXXXX'
secret_key = 'XXXXXXXXXXXXXXXXX'
# bucketName = 'XXXXXXXXXXXXXXXXX'
s3 = boto3.resource('s3')
for bucket in s3.buckets.all():
print(bucket.name)
Expected result should be to access that bucket after switching role in python code along with MFA.
In general, it is a bad for security to put credentials in your program code. It is better to store them in a configuration file. You can do this by using the AWS Command-Line Interface (CLI) aws configure command.
Once the credentials are stored this way, any AWS SDK (eg boto3) will automatically retrieve the credentials without having to reference them in code.
See: Configuring the AWS CLI - AWS Command Line Interface
There is an additional capability with the configuration file, that allows you to store a role that you wish to assume. This can be done by specifying a profile with the Role ARN:
# In ~/.aws/credentials:
[development]
aws_access_key_id=foo
aws_access_key_id=bar
# In ~/.aws/config
[profile crossaccount]
role_arn=arn:aws:iam:...
source_profile=development
The source_profile points to the profile that contains credentials that will be used to make the AssumeRole() call, and role_arn specifies the Role to assume.
See: Assume Role Provider
Finally, you can tell boto3 to use that particular profile for credentials:
session = boto3.Session(profile_name='crossaccount')
# Any clients created from this session will use credentials
# from the [crossaccount] section of ~/.aws/credentials.
dev_s3_client = session.client('s3')
An alternative to all the above (which boto3 does for you) is to call assume_role() in your code, then use the temporary credentials that are returned to define a new session that you can use to connect to a service. However, the above method using profiles is a lot easier.
I am using python to send emails via an AWS Simple Email Service.
In attempt to have the best security possible I would like to
make a boto SES connection without exposing my access keys inside the code.
Right now I am establishing a connection like this
ses = boto.ses.connect_to_region(
'us-west-2',
aws_access_key_id='<ACCESS_KEY>',
aws_secret_access_key='<SECRET_ACCESS_KEY>'
)
Is there a way to do this without exposing my access keys inside the script?
The simplest solution is to use environment variables you may retrieve in your Python code with os.environ.
export AWS_ACCESS_KEY_ID=<YOUR REAL ACCESS KEY>
export AWS_SECRET_ACCESS_KEY=<YOUR REAL SECRET KEY>
And in the Python code:
from os import environ as os_env
ses = boto.ses.connect_to_region(
'us-west-2',
aws_access_key_id=os_env['AWS_ACCESS_KEY_ID'],
aws_secret_access_key=os_env['AWS_SECRET_ACCESS_KEY']'
)
To your EC2 instance attach an IAM role that has SES privileges, then you do not have to pass the credentials explicitly. Your script will get the credentials automatically from the metadata server.
See: Easily Replace or Attach an IAM Role to an Existing EC2 Instance by Using the EC2 Console. Then your code will be like:
ses = boto.ses.connect_to_region('us-west-2')
Preferred method of authentication is to use boto3's ability to read your AWS credential file.
Configure your AWS CLI using the aws configure command.
Then, in your script you can use the Session call to get the credentials:
session = boto3.Session(profile_name='default')
Two options are to set an environment variable named ACCESS_KEY and another named SECRET_ACCESS_KEY, then in your code you would have:
import os
ses = boto.ses.connect_to_region(
'us-west-2',
aws_access_key_id=os.environ['ACCESS_KEY'],
aws_secret_access_key=os.environ['SECRET_ACCESS_KEY']
)
or use a json file:
import json
path_to_json = 'your/path/here.json'
with open(path_to_json, 'r') as f:
keys = json.load(f)
ses = boto.ses.connect_to_region(
'us-west-2',
aws_access_key_id=keys['ACCESS_KEY'],
aws_secret_access_key=keys['SECRET_ACCESS_KEY']
)
the json file would contain:
{'ACCESS_KEY':<ACCESS_KEY>, 'SECRET_ACCESS_KEY':<SECRET_ACCESS_KEY>}
I have a test environment that mimics the S3 envrionment, and I want to write some test scripts using boto3. How can I connect to that service?
I tried:
client = boto3.client('s3', region_name="us-east-1", endpoint_url="http://mymachine")
client = boto3.client('iam', region_name="us-east-1", endpoint_url="http://mymachine")
Both fail to work.
The service is setup to use IAM authentication.
My error:
botocore.exceptions.NoCredentialsError: Unable to locate credentials
Any ideas?
Thanks
Please use as below :
import boto3
client = boto3.client( 's3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY,)
Please check this link for more ways to configure AWS credentials.
http://boto3.readthedocs.io/en/latest/guide/configuration.html
1.
boto API always look for credential to pass on to connecting services, there is no way you can access AWS resources using bot without a access key and password.
If you intended to use some other method, e.g. Temporary Security Credentials, your AWS admin must setup roles and etc , to allow the VM instance connect to AWS using AWS Security Token Service.
Otherwise, you must request a restricted credential key from your AWS account admin.
2.On the other hand, if you want to mimics S3 and test rapid upload/download huge amount of data for development, then you should setup FakeS3. It will take any dummy access key. However, there is few drawback of FakeS3 : you can't setup and test S3 bucket policy.
3.Even you configure your S3 bucket to allow anyone to take the file, it is only through the url, it is a file access permission, not bucket access permission.