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.
Related
I used Workload Identity from AWS EC2 to GCP Bigquery by using assigned role on EC2, and it worked fine.
However when I use Workload Identity from AWS Fargete to GCP Bigquery by using fargate task role, it does not work.
How should I set up the Workload Identity on this case?
I used the libraries below.
implementation(platform("com.google.cloud:libraries-bom:20.9.0"))
implementation("com.google.cloud:google-cloud-bigquery")
Stacktrace has messages below
com.google.cloud.bigquery.BigQueryException: Failed to retrieve AWS IAM role.
at com.google.cloud.bigquery.spi.v2.HttpBigQueryRpc.translate(HttpBigQueryRpc.java:115) ~[google-cloud-bigquery-1.137.1.jar!/:1.137.1]
…
at java.base/java.lang.Thread.run(Unknown Source) ~[na:na]
Caused by: java.io.IOException: Failed to retrieve AWS IAM role.
at com.google.auth.oauth2.AwsCredentials.retrieveResource(AwsCredentials.java:217) ~[google-auth-library-oauth2-http-0.26.0.jar!/:na]
…
at com.google.cloud.bigquery.spi.v2.HttpBigQueryRpc.getDataset(HttpBigQueryRpc.java:126) ~[google-cloud-bigquery-1.137.1.jar!/:1.137.1]
... 113 common frames omitted
Caused by: java.net.ConnectException: Invalid argument (connect failed)
at java.base/java.net.PlainSocketImpl.socketConnect(Native Method) ~[na:na]
at com.google.auth.oauth2.AwsCredentials.retrieveResource(AwsCredentials.java:214) ~[google-auth-library-oauth2-http-0.26.0.jar!/:na]
... 132 common frames omitted
I faced a similar issue with Google Cloud Storage (GCS).
As Peter mentioned, retrieving the credentials on an AWS Farage task is not the same as if the code is running on an EC2 instance, therefore Google SDK fails to compose the correct AWS credentials for exchange with Google Workload Identity Federation.
I came up with a workaround that saved the trouble of editing core files in "../google/auth/aws.py" by doing 2 things:
Get session credentials with boto3
import boto3
task_credentials = boto3.Session().get_credentials().get_frozen_credentials()
Set the relevant environment variables
from google.auth.aws import environment_vars
os.environ[environment_vars.AWS_ACCESS_KEY_ID] = task_credentials.access_key
os.environ[environment_vars.AWS_SECRET_ACCESS_KEY] = task_credentials.secret_key
os.environ[environment_vars.AWS_SESSION_TOKEN] = task_credentials.token
Explanation:
I am using Python3.9 with boto3 and google-cloud==2.4.0, however it should work for other versions of google SDK if the following code is in the function "_get_security_credentials" under the class "Credentials" in "google.auth.aws" package:
# Check environment variables for permanent credentials first.
# https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html
env_aws_access_key_id = os.environ.get(environment_vars.AWS_ACCESS_KEY_ID)
env_aws_secret_access_key = os.environ.get(
environment_vars.AWS_SECRET_ACCESS_KEY
)
# This is normally not available for permanent credentials.
env_aws_session_token = os.environ.get(environment_vars.AWS_SESSION_TOKEN)
if env_aws_access_key_id and env_aws_secret_access_key:
return {
"access_key_id": env_aws_access_key_id,
"secret_access_key": env_aws_secret_access_key,
"security_token": env_aws_session_token,
}
Caveat:
When running code inside an ECS task the credentials that are being used are temporary (ECS assumes the task's role), therefore you can't generate temporary credentials via AWS STS as it is usually recommended.
Why is it a problem? Well since a task is running with temporary credentials it is subjected to expire & refresh. In order to solve that you can set up a background function that will do the operation again every 5 minutes or so (Haven't faced a problem where the temporary credentials expired).
I had the same issue but for Python code, anyway I think it should be the same.
You're getting this as getting the AWS IAM role at AWS Fargate is different from AWS EC2, where EC2 you can get them from instance metadata, as shown here:
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/s3access
While in AWS Faragte:
curl 169.254.170.2$AWS_CONTAINER_CREDENTIALS_RELATIVE_URI
So to get around that, the following need to be done:
Change GCP Workload Identity Federation Credential file content [wif_cred_file] as the following:
wif_cred_file["credential_source"]["url"]=f"http://169.254.170.2{AWS_CONTAINER_CREDENTIALS_RELATIVE_URI}"
In the "python3.8/site-packages/google/auth/aws.py" file in the library [Try to find the similar file in Java], I've updated this code as the following:
Comment this line:
# role_name = self._get_metadata_role_name(request)
Remove role_name from _get_metadata_security_credentials function args.
Or if you like, you may change step 1 at the aws.py file, both ways should be fine.
And that should be it.
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}",
)
Getting below connection error when trying to validate S3 source endpoint of DMS.
Test Endpoint failed: Application-Status: 1020912, Application-Message: Failed to connect to database.
Followed all the steps listed in the below links but still maybe I am missing something...
https://aws.amazon.com/premiumsupport/knowledge-center/dms-connection-test-fail-s3/
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.S3.html
The role associated with the endpoint does have access to the S3 bucket of the endpoint, along with dms being listed as trusted entity.
I got this same error when trying to use S3 as a target.
The one thing not mentioned in the documentation, and which turned out to be the root cause for my error, is that the DMS Replication Instance and the Bucket need to be in the same region.
I am trying to put some files in S3 bucket through my Spring Boot app using AmazonS3Client. In AWS, I created an IAM user (test_user1) and granted S3 full access rights to this user. Also in S3, I granted "s3:*" actions to this user. The same user's credentials are specified for cloud.aws.credentials.accessKey and cloud.aws.credentials.secretKey in my config files.
When I run the app from my local computer, it works fine. I am able to put multiple files in S3 bucket and view the files.
But, when the same app is run from an AWS EC2 instance, I get bellow errors at application start:
Caused by: org.springframework.beans.BeanInstantiationException: Failed to instantiate org.springframework.cloud.aws.core.env.stack.config.StackResourceRegistryFactoryBean]: Factory method 'stackResourceRegistryFactoryBean' threw exception; nested exception is com.amazonaws.AmazonServiceException: User: arn:aws:iam::560600000009:user/test_user1 is not authorized to perform: cloudformation:DescribeStackResources (Service: AmazonCloudFormation; Status Code: 403; Error Code: AccessDenied;
Is there something else I have to set when accessing S3 from code running in EC2 instance? I am not using Amazon Cloud Formation.
Here is how my project looks like:
build.gradle :
compile 'org.springframework.cloud:spring-cloud-aws-autoconfigure:1.0.3.RELEASE'
compile 'org.springframework.cloud:spring-cloud-aws-context:1.0.3.RELEASE'
application.yml:
bucket: test-bucket-1
cloud.aws.credentials.accessKey: AxxxxxxxxxxxxxxA
cloud.aws.credentials.secretKey: jxxxxxxxxxxxxxxR
cloud.aws.credentials.instanceProfile: true
AmazonS3Client is autowired in my service class.
#Autowired
public FileService(AmazonS3Client s3Client) {..}
Spring Cloud AWS tries to autoconfigure CloudFormation (when the app runs in EC2).
I solved this error disabling autoconf in application.properties
cloud.aws.stack.auto=false
Read this for more info http://cloud.spring.io/spring-cloud-aws/spring-cloud-aws.html#_automatic_cloudformation_configuration.
According to the documents at http://www.elasticsearch.org/tutorials/2011/08/22/elasticsearch-on-ec2.html, I have included the respective API Access Key ID with its secret access key and Elasticsearch is able to write into the S3 bucket as follows:
[2012-08-02 04:21:38,793][DEBUG][gateway.s3] [Schultz, Herman] writing to gateway org.elasticsearch.gateway.shared.SharedStorageGateway$2#4e64f6fe ...
[2012-08-02 04:21:39,337][DEBUG][gateway.s3] [Schultz, Herman] wrote to gateway org.elasticsearch.gateway.shared.SharedStorageGateway$2#4e64f6fe, took 543ms
However when it comes to writing snapshots into the S3 bucket, out comes the following error:
[2012-08-02 04:25:37,303][WARN ][index.gateway.s3] [Schultz, Herman] [plumbline_2012.08.02][3] failed to read commit point [commit-i] java.io.IOException: Failed to get [commit-i]
Caused by: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: E084E2ED1E68E710, AWS Error Code: InvalidAccessKeyId, AWS Error Message: The AWS Access Key Id you provided does not exist in our records.
[2012-08-02 04:36:06,696][WARN ][index.gateway] [Schultz, Herman] [plumbline_2012.08.02][0] failed to snapshot (scheduled) org.elasticsearch.index.gateway.IndexShardGatewaySnapshotFailedException: [plumbline_2012.08.02][0] Failed to perform snapshot (index files)
Is there a reason why this is happening since the access keys I have provided is able to write metadata and not creating snapshots?