I am currently learning about boto3 and how it can interact with AWS to connect using both the client and resources methods. I was made to understand that it doesnt matter which one I use that I can still get access except in some cases where I would need to access some client features that are not available through the resources medium hence I would specifiy the created resource variable i.e from
import boto3
s3_resource = boto3.resource('s3')
Hence if there is a need for me to access some client features, I would simply specify
s3.resource.meta.client
But the main issue here is, I tried creating clients/resources first for EC2, S3, IAM, and Redshift so I did this
import boto3
ec2 = boto3.resource('ec2',
region_name='us-west-2',
aws_access_key_id=KEY,
aws_secret_access_key=SECRET)
s3 = boto3.resource('s3',
region_name='us-west-2',
aws_access_key_id=KEY,
aws_secret_access_key=SECRET)
iam = boto3.client('iam',
region_name='us-west-2',
aws_access_key_id=KEY,
aws_secret_access_key=SECRET)
redshift = boto3.resource('redshift',
region_name='us-west-2',
aws_access_key_id=KEY,
aws_secret_access_key=SECRET)
But I get this error
UnknownServiceError: Unknown service: 'redshift'. Valid service names are: cloudformation, cloudwatch, dynamodb, ec2, glacier, iam, opsworks, s3, sns, sqs
During handling of the above exception, another exception occurred:
...
- s3
- sns
- sqs
Consider using a boto3.client('redshift') instead of a resource for 'redshift'
Please why is that, I thought I could create using the commands that I specified, Please help
I suggest that you consult the Boto3 documentation for Amazon Redshift. It does, indeed, show that there is no resource method for Redshift (or Redshift Data API, or Redshift Serverless).
Also, I recommend against using aws_access_key_id and aws_secret_access_key in your code unless there is a specific need (such as extracting them from Environment Variables). It is better to use the AWS CLI aws configure command to store AWS credentials in a configuration file, which will be automatically accessed by AWS SDKs such as boto3.
Related
I have a scenario where I read data from S3 Bucket from Account 1. This works as expected as I use TemporaryAWSCredentialsProvider. I have another S3 bucket in AWS Account 2 which is where I am running Spark (Spark on EKS). How to configure credentials for Spark to access or write event logs to S3 bucket in Account 2? I was using InstanceProfileCredentialsProvider since credentials for both the buckets are different and the IAM role is configured to allow the EKS instances/pods to have access to the bucket in this account, which seems to not work as expected and I think this is because I have both InstanceProfileCredentialsProvider and TemporaryAWSCredentialsProvider in the same SparkConf which might not be allowed. Is there a way to achieve this?
I am trying to register a respository on AWS S3 to store ElasticSearch snapshots.
I am following guide and ran the very first command listed in the doc.
But I am getting the error Access Denied while executing that command.
The role that is being used to perform operations on S3 is the AmazonEKSNodeRole.
I have assigned the appropriate permissions to the role to perform operations on the S3 bucket.
Also, here is another doc which suggests to use kibana for ElasticSearch version > 7.2 but I am doing the same via cURL requests.
Below is trust Policy of the role through which I am making the request to register repository in the S3 bucket.
Also, below are the screenshots of the permissions of the trusting and trusted accounts respectively -
I'm trying to create a botocore session (that does not use my local AWS credentials on ~/.aws/credentials). In other words, I want to create a "burner AWS account". With that burner credentials/session, I want to setup an STS client and with that client, assume a role in order to access a DynamoDB database. Can someone provide some example code which accomplishes exactly this?
Because if I want my system to go into production environment, I CANNOT store the AWS credentials on Github because AWS will scan for it. I'm trying to implement a workaround such that we don't have to store ~/.aws/credentials file on Github.
The running a task in Amazon ECS, simply assign an IAM Role to the task.
Amazon ECS will then generate temporary credentials for that IAM Role. Any code that uses an AWS SDK (such as boto3 for Python) knows how to access those credentials via the metadata service.
The result is that your code using boto3 will automatically receive credentials that have the permissions associated with the IAM Role assigned to the task.
See: IAM roles for tasks - Amazon Elastic Container Service
I want to get the bucket policy for the various buckets. I tried the following code snippet(picked from the boto3 documentation):
conn = boto3.resource('s3')
bucket_policy=conn.BucketPolicy('demo-bucket-py')
print(bucket_policy)
But here's the output I get :
s3.BucketPolicy(bucket_name='demo-bucket-py')
What shall I rectify here ? Or is there some another way to get the access policy for s3 ?
Try print(bucket_policy.policy). More information on that here.
this worked for me
import boto3
# Create an S3 client
s3 = boto3.client('s3')
# Call to S3 to retrieve the policy for the given bucket
result = s3.get_bucket_policy(Bucket='my-bucket')
print(result)
to perform this you need to configure or mention your keys like this s3=boto3.client("s3",aws_access_key_id=access_key_id,aws_secret_access_key=secret_key). BUT there is much better way to do this is by using aws configure command and enter your credentials. for setting up docs. Once you set up you wont need to enter your keys again in your code, boto3 or aws cli will automatically fetch it behind the scenes .https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html.
you can even set different profiles to work with different accounts
i have created an aws account, launched ec2 instance and created buckets in s3. Also i have installed python, boto3 and aws cli. But i'm stuck on connecting python with aws step.
The first and foremost thing that you need to check is whether your EC2 instance has permissions to access the S3 bucket. This can be done in 2 ways:
Store the credentials in the EC2 instance (insecure)
Assign IAM roles to the EC2 instance that has S3 read and write permissions (secure)
In order to assign a role to your instance, follow this guide.
Once your permissions are set up, you can either use the AWS CLI or BOTO3 to access S3 from your EC2 instance.
1: If you are asking how to establish a connection for running your AWS-python codes then you must follow these steps on terminal:
aws configure (this ill ask you credentials which you will find in the .CSV file that is created initially)
Provide the credentials and try to run the code
For ex:
$ aws configure
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]: us-west-2
Default output format [None]: json
2: If your question is how you will use the boto3 API calls to run AWS functions then this might help you:If you are using boto3 SDK then you can make use of low-level clients and higher-level.
ec2 = boto3.resource('ec2')
client = boto3.client('ec2')
You can follo this link for more detailed info : http://boto3.readthedocs.io/en/latest/reference/services/ec2.html