I am working on a databricks notebook running with ADLSV2 using service
priciple id but receive the following error after mounting my drive.
StatusCode=403
StatusDescription=This request is not authorized to perform this operation using this permission.
configs = {"dfs.adls.oauth2.access.token.provider.type":
"ClientCredential",
"dfs.adls.oauth2.client.id": "78jkj56-2ght-2345-3453-b497jhgj7587",
"dfs.adls.oauth2.credential": dbutils.secrets.get(scope =
"DBRScope", key = "AKVsecret"),
"dfs.adls.oauth2.refresh.url":
"https://login.microsoftonline.com/bdef8a20-aaac-4f80-b3a0-
d9a32f99fd33/oauth2/token"}
dbutils.fs.mount(source =
"adl://<accountname>.azuredatalakestore.net/tempfile",mount_point =
"/mnt/tempfile",extra_configs = configs)
%fs ls mnt/tempfile
The uri for your lake is a gen1 uri not gen2. Either way your service principal does not have permission to access the lake. As a test make it a resource owner, then remove it and work out what permissions are missing.
Related
We got error of Authentication fail, when we try to create an azure blob client from connection string, using python v12 sdk with Azure Blob Storage v12.5.0, and Azure core 1.8.2.
I used
azure-storate-blob == 12.5.0
azure-core == 1.8.2
I tried to access my blob storage account using connection string with Python v12 SDK and received the error above. The environment I'm running in is python venv in NixShell.
The code for calling the blob_upload is as following:
blob_service_client = BlobServiceClient(account_url=<>,credential=<>)
blob_client = blob_service_client.get_blob_client(container=container_name,
blob=file)
I printed out blob_client, and it looks normal. But the next line of upload_blob gives error.
with open(os.path.join(root,file), "rb") as data:
blob_client.upload_blob(data)
The error message is as follows
File "<local_address>/.venv/lib/python3.8/site-packages/azure/storage/blob/_upload_helpers.py", in upload_block_blob
return client.upload(
File "<local_address>/.venv/lib/python3.8/site-packages/azure/storage/blob/_generated/operations/_block_blob_operations.py", in upload
raise models.StorageErrorException(response, self._deserialize)
azure.storage.blob._generated.models._models_py3.StorageErrorException: Operation returned an invalid status 'Server failed to authenticate the request. Make sure the value of Authorization header is formed correctly including the signature.'
So I printed out the http put request to azure blob storage, and get the response value of [403]
I can work the following code well with the version the same as yours.
from azure.storage.blob import BlobServiceClient
blob=BlobServiceClient.from_connection_string(conn_str="your connect string in Access Keys")
with open("./SampleSource.txt", "rb") as data:
blob.upload_blob(data)
Please check your connect-string, and check your PC's time.
There is a similar issue about the error: AzureStorage Blob Server failed to authenticate the request. Make sure the value of Authorization header is formed correctly including the signature
UPDATE:
I tried with this code, and get the same error:
from azure.storage.blob import BlobServiceClient
from azure.identity import DefaultAzureCredential
token_credential = DefaultAzureCredential()
blob_service_client = BlobServiceClient(account_url="https://pamelastorage123.blob.core.windows.net/",credential=token_credential)
blob_client = blob_service_client.get_blob_client(container="pamelac", blob="New Text Document.txt")
with open("D:/demo/python/New Text Document.txt", "rb") as data:
blob_client.upload_blob(data)
Then I use AzureCliCredential() instead of DefaultAzureCredential(). I authenticate via the Azure CLI with az login. And it works.
If you use environment credential, you need to set the variables. Anyway, I recommend you to use the specific credentials instead DefaultAzureCredential.
For more details about Azure Identity, see here.
I have set up a clustered Druid with the configuration as mentioned in the Druid documentation
https://druid.apache.org/docs/latest/tutorials/cluster.html
I am using AWS S3 for deep storage. Following is the snippet of my common configuration file
druid.extensions.loadList=["druid-datasketches", "mysql-metadata-storage", "druid-s3-extensions", "druid-orc-extensions", "druid-lookups-cached-global"]
# For S3:
druid.storage.type=s3
druid.storage.bucket=bucket-name
druid.storage.baseKey=druid/segments
#druid.storage.disableAcl=true
druid.storage.sse.type=s3
#druid.s3.accessKey=...
#druid.s3.secretKey=...
# For S3:
druid.indexer.logs.type=s3
druid.indexer.logs.s3Bucket=bucket-name
druid.indexer.logs.s3Prefix=druid/stage/indexing-logs
While running any ingestion task I am getting Access denied error
Java.io.IOException: com.amazonaws.services.s3.model.AmazonS3Exception: Access Denied (Service: Amazon S3; Status Code: 403; Error Code: AccessDenied; Request ID: ; S3 Extended Request ID: ), S3 Extended Request ID:
at org.apache.druid.storage.s3.S3DataSegmentPusher.push(S3DataSegmentPusher.java:103) ~[?:?]
at org.apache.druid.segment.realtime.appenderator.AppenderatorImpl.lambda$mergeAndPush$4(AppenderatorImpl.java:791) ~[druid-server-0.19.0.jar:0.19.0]
at org.apache.druid.java.util.common.RetryUtils.retry(RetryUtils.java:87) ~[druid-core-0.19.0.jar:0.19.0]
at org.apache.druid.java.util.common.RetryUtils.retry(RetryUtils.java:115) ~[druid-core-0.19.0.jar:0.19.0]
at org.apache.druid.java.util.common.RetryUtils.retry(RetryUtils.java:105) ~[druid-core-0.19.0.jar:0.19.0]
I am using s3 for two purposes
read data from s3 and ingest it. This connection is working fine and data is being from s3 location
for deep storage. I am getting error over here.
I am using Profile information authentication method to provide s3 credential. So I already have configured aws cli with appropriate credentials. Also, s3 data is encrypted by AES256 so i have added druid.storage.sse.type=s3 in config file.
Can someone help me out here as I am not able to debug the issue.
You asked how to approach debugging this. Normally I would:
Ssh onto the ec2 instance and run aws sts get-caller-identity. This will tell you what principal your requests are sent from. Then, I would confirm that principal has the S3 access that is expected.
I would confirm that I can write to the bucket in your configuration.
druid.storage.type=s3
druid.storage.bucket=<bucket-name>
druid.storage.baseKey=druid/segments
I would try some of the other auth methods such as exporting the keys into the environment mentioned in the third option since that is a simple test. Then I would run step 1 again to confirm my principal reflects those keys. And then I would try running your code again.
In attempt to setup airflow logging to localstack s3 buckets, for local and kubernetes dev environments, I am following the airflow documentation for logging to s3. To give a little context, localstack is a local AWS cloud stack with AWS services including s3 running locally.
I added the following environment variables to my airflow containers similar to this other stack overflow post in attempt to log to my local s3 buckets. This is what I added to docker-compose.yaml for all airflow containers:
- AIRFLOW__CORE__REMOTE_LOGGING=True
- AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER=s3://local-airflow-logs
- AIRFLOW__CORE__REMOTE_LOG_CONN_ID=MyS3Conn
- AIRFLOW__CORE__ENCRYPT_S3_LOGS=False
I've also added my localstack s3 creds to airflow.cfg
[MyS3Conn]
aws_access_key_id = foo
aws_secret_access_key = bar
aws_default_region = us-east-1
host = http://localstack:4572 # s3 port. not sure if this is right place for it
Additionally, I've installed apache-airflow[hooks], and apache-airflow[s3], though it's not clear which one is really needed based on the documentation.
I've followed the steps in a previous stack overflow post in attempt verify if the S3Hook can write to my localstack s3 instance:
from airflow.hooks import S3Hook
s3 = S3Hook(aws_conn_id='MyS3Conn')
s3.load_string('test','test',bucket_name='local-airflow-logs')
But I get botocore.exceptions.NoCredentialsError: Unable to locate credentials.
After adding credentials to airflow console under /admin/connection/edit as depicted:
this is the new exception, botocore.exceptions.ClientError: An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The AWS Access Key Id you provided does not exist in our records. is returned. Other people have encountered this same issue and it may have been related to networking.
Regardless, a programatic setup is needed, not a manual one.
I was able to access the bucket using a standalone Python script (entering AWS credentials explicitly with boto), but it needs to work as part of airflow.
Is there a proper way to set up host / port / credentials for S3Hook by adding MyS3Conn to airflow.cfg?
Based on the airflow s3 hooks source code, it seems a custom s3 URL may not yet be supported by airflow. However, based on the airflow aws_hook source code (parent) it seems it should be possible to set the endpoint_url including port, and it should be read from airflow.cfg.
I am able to inspect and write to my s3 bucket in localstack using boto alone. Also, curl http://localstack:4572/local-mochi-airflow-logs returns the contents of the bucket from the airflow container. And aws --endpoint-url=http://localhost:4572 s3 ls returns Could not connect to the endpoint URL: "http://localhost:4572/".
What other steps might be needed to log to localstack s3 buckets from airflow running in docker, with automated setup and is this even supported yet?
I think you're supposed to use localhost not localstack for the endpoint, e.g. host = http://localhost:4572.
In Airflow 1.10 you can override the endpoint on a per-connection basis but unfortunately it only supports one endpoint at a time so you'd be changing it for all AWS hooks using the connection. To override it, edit the relevant connection and in the "Extra" field put:
{"host": "http://localhost:4572"}
I believe this will fix it?
I managed to make this work by referring to this guide. Basically you need to create a connection using the Connection class and pass the credentials that you need, in my case I needed AWS_SESSION_TOKEN, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, REGION_NAME to make this work. Use this function as a python_callable in a PythonOperator which should be the first part of the DAG.
import os
import json
from airflow.models.connection import Connection
from airflow.exceptions import AirflowFailException
def _create_connection(**context):
"""
Sets the connection information about the environment using the Connection
class instead of doing it manually in the Airflow UI
"""
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
AWS_SESSION_TOKEN = os.getenv("AWS_SESSION_TOKEN")
REGION_NAME = os.getenv("REGION_NAME")
credentials = [
AWS_SESSION_TOKEN,
AWS_ACCESS_KEY_ID,
AWS_SECRET_ACCESS_KEY,
REGION_NAME,
]
if not credentials or any(not credential for credential in credentials):
raise AirflowFailException("Environment variables were not passed")
extras = json.dumps(
dict(
aws_session_token=AWS_SESSION_TOKEN,
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
region_name=REGION_NAME,
),
)
try:
Connection(
conn_id="s3_con",
conn_type="S3",
extra=extras,
)
except Exception as e:
raise AirflowFailException(
f"Error creating connection to Airflow :{e!r}",
)
I'm trying the Azure IoT Accelerators Remote Monitoring solution and trying to follow the instructions here:
https://learn.microsoft.com/en-us/azure/iot-accelerators/iot-accelerators-remote-monitoring-create-simulated-device
In it, I need to run the storage adapter microservice locally and for that to work, it seems that I need three environmental variables WHICH I DON'T KNOW HOW TO FIND THE VALUES FOR:
PCS_AAD_APPID = { Azure service principal id }
PCS_AAD_APPSECRET = { Azure service principal secret }
PCS_KEYVAULT_NAME = { Name of Key Vault resource that stores settings and configuration }
I can create those environmental variables but I have no idea what values I should put in there. Anyone?
FYI, right now when I'm running the storage adapter microservice locally, I get this error:
"{"Name":"StorageAdapter","Status":{"IsHealthy":false,"Message":"Storage check failed"}..."
...which is preceeded by a caught exception with this messae:
"AuthKey = '((Microsoft.Azure.Documents.Client.DocumentClient)this.client).AuthKey' threw an exception of type 'System.ArgumentNullException'"
We just started using Apache airflow in our project for our data pipelines .While exploring the features came to know about configuring remote folder as log destination in airflow .For that we
Created a google cloud bucket.
From Airflow UI created a new GS connection
I am not able to understand all the fields .I just created a sample GS Bucket under my project from google console and gave that project ID to this Connection.Left key file path and scopes as blank.
Then edited airflow.cfg file as follows
remote_base_log_folder = gs://my_test_bucket/
remote_log_conn_id = test_gs
After this changes restarted the web server and scheduler .But still my Dags is not writing logs to the GS bucket .I am able to see the logs which is creating logs in base_log_folder .But nothing is created in my bucket .
Is there any extra configuration needed from my side to get it working
Note: Using Airflow 1.8 .(Same issue I faced with AmazonS3 also. )
Updated on 20/09/2017
Tried the GS method attaching screenshot
Still I am not getting logs in the bucket
Thanks
Anoop R
I advise you to use a DAG to connect airflow to GCP instead of UI.
First, create a service account on GCP and download the json key.
Then execute this DAG (you can modify the scope of your access):
from airflow import DAG
from datetime import datetime
from airflow.operators.python_operator import PythonOperator
def add_gcp_connection(ds, **kwargs):
"""Add a airflow connection for GCP"""
new_conn = Connection(
conn_id='gcp_connection_id',
conn_type='google_cloud_platform',
)
scopes = [
"https://www.googleapis.com/auth/pubsub",
"https://www.googleapis.com/auth/datastore",
"https://www.googleapis.com/auth/bigquery",
"https://www.googleapis.com/auth/devstorage.read_write",
"https://www.googleapis.com/auth/logging.write",
"https://www.googleapis.com/auth/cloud-platform",
]
conn_extra = {
"extra__google_cloud_platform__scope": ",".join(scopes),
"extra__google_cloud_platform__project": "<name_of_your_project>",
"extra__google_cloud_platform__key_path": '<path_to_your_json_key>'
}
conn_extra_json = json.dumps(conn_extra)
new_conn.set_extra(conn_extra_json)
session = settings.Session()
if not (session.query(Connection).filter(Connection.conn_id ==
new_conn.conn_id).first()):
session.add(new_conn)
session.commit()
else:
msg = '\n\tA connection with `conn_id`={conn_id} already exists\n'
msg = msg.format(conn_id=new_conn.conn_id)
print(msg)
dag = DAG('add_gcp_connection', start_date=datetime(2016,1,1), schedule_interval='#once')
# Task to add a connection
AddGCPCreds = PythonOperator(
dag=dag,
task_id='add_gcp_connection_python',
python_callable=add_gcp_connection,
provide_context=True)
Thanks to Yu Ishikawa for this code.
Yes, you need to provide additional information for both, S3 and GCP connection.
S3
Configuration is passed via extra field as JSON. You can provide only profile
{"profile": "xxx"}
or credentials
{"profile": "xxx", "aws_access_key_id": "xxx", "aws_secret_access_key": "xxx"}
or path to config file
{"profile": "xxx", "s3_config_file": "xxx", "s3_config_format": "xxx"}
In case of the first option, boto will try to detect your credentials.
Source code - airflow/hooks/S3_hook.py:107
GCP
You can either provide key_path and scope (see Service account credentials) or credentials will be extracted from your environment in this order:
Environment variable GOOGLE_APPLICATION_CREDENTIALS pointing to a file with stored credentials information.
Stored "well known" file associated with gcloud command line tool.
Google App Engine (production and testing)
Google Compute Engine production environment.
Source code - airflow/contrib/hooks/gcp_api_base_hook.py:68
The reason for logs not being written to your bucket could be related to service account rather than config on airflow itself. Make sure it has access to the mentioned bucket. I had same problems in the past.
Adding more generous permissions to the service account, e.g. even project wide Editor and then narrowing it down. You could also try using gs client with that key and see if you can write to the bucket.
For me personally this scope works fine for writing logs: "https://www.googleapis.com/auth/cloud-platform"