how to invoke gcloud commands using terraform - redis

Since terraform does not have redis tls support yet, was planning to invoke gcloud command to create tls enabled redis through terraform. I am new to terraform, so was looking for some resource on web. but couldn't find much. Can some one please help with a working sample. any gcloud command invocation would work.
thanks

Terraform builds upon underlying REST APIs and does not use Cloud SDK (gcloud) directly. For this reason, it's not straightforward to invoke gcloud commands directly in the absence of a provider supporting the resource.
I'm unfamiliar with Terraform but I expect (!?) that invoking shell commands directly (e.g. gcloud redis instances create ...) is discouraged. And, while it's likely also possible to call REST APIs directly, you'll then need to take care authenticating.
That said, google-beta supports TLS.
transit_encryption_mode = "SERVER_AUTHENTICATION"
terraform {
required_providers {
google-beta = {
source = "hashicorp/google-beta"
version = "3.58.0"
}
}
}
variable "project" {}
variable "region" {}
variable "zone" {}
variable "key" {}
variable "instance" {}
provider "google-beta" {
credentials = file(var.key)
project = var.project
region = var.region
zone = var.zone
}
resource "google_redis_instance" "cache" {
provider = google-beta
name = var.instance
tier = "BASIC"
memory_size_gb = 1
location_id = var.zone
transit_encryption_mode = "SERVER_AUTHENTICATION"
}
Then to confirm TLS-enabled:
gcloud redis instances describe ${INSTANCE} \
--region=${REGION} \
--project=${PROJECT} \
--format="value(transitEncryptionMode)"
SERVER_AUTHENTICATION

Related

How to configure my credentials s3 in heroku [duplicate]

On boto I used to specify my credentials when connecting to S3 in such a way:
import boto
from boto.s3.connection import Key, S3Connection
S3 = S3Connection( settings.AWS_SERVER_PUBLIC_KEY, settings.AWS_SERVER_SECRET_KEY )
I could then use S3 to perform my operations (in my case deleting an object from a bucket).
With boto3 all the examples I found are such:
import boto3
S3 = boto3.resource( 's3' )
S3.Object( bucket_name, key_name ).delete()
I couldn't specify my credentials and thus all attempts fail with InvalidAccessKeyId error.
How can I specify credentials with boto3?
You can create a session:
import boto3
session = boto3.Session(
aws_access_key_id=settings.AWS_SERVER_PUBLIC_KEY,
aws_secret_access_key=settings.AWS_SERVER_SECRET_KEY,
)
Then use that session to get an S3 resource:
s3 = session.resource('s3')
You can get a client with new session directly like below.
s3_client = boto3.client('s3',
aws_access_key_id=settings.AWS_SERVER_PUBLIC_KEY,
aws_secret_access_key=settings.AWS_SERVER_SECRET_KEY,
region_name=REGION_NAME
)
This is older but placing this here for my reference too. boto3.resource is just implementing the default Session, you can pass through boto3.resource session details.
Help on function resource in module boto3:
resource(*args, **kwargs)
Create a resource service client by name using the default session.
See :py:meth:`boto3.session.Session.resource`.
https://github.com/boto/boto3/blob/86392b5ca26da57ce6a776365a52d3cab8487d60/boto3/session.py#L265
you can see that it just takes the same arguments as Boto3.Session
import boto3
S3 = boto3.resource('s3', region_name='us-west-2', aws_access_key_id=settings.AWS_SERVER_PUBLIC_KEY, aws_secret_access_key=settings.AWS_SERVER_SECRET_KEY)
S3.Object( bucket_name, key_name ).delete()
I'd like expand on #JustAGuy's answer. The method I prefer is to use AWS CLI to create a config file. The reason is, with the config file, the CLI or the SDK will automatically look for credentials in the ~/.aws folder. And the good thing is that AWS CLI is written in python.
You can get cli from pypi if you don't have it already. Here are the steps to get cli set up from terminal
$> pip install awscli #can add user flag
$> aws configure
AWS Access Key ID [****************ABCD]:[enter your key here]
AWS Secret Access Key [****************xyz]:[enter your secret key here]
Default region name [us-west-2]:[enter your region here]
Default output format [None]:
After this you can access boto and any of the api without having to specify keys (unless you want to use a different credentials).
If you rely on your .aws/credentials to store id and key for a user, it will be picked up automatically.
For instance
session = boto3.Session(profile_name='dev')
s3 = session.resource('s3')
This will pick up the dev profile (user) if your credentials file contains the following:
[dev]
aws_access_key_id = AAABBBCCCDDDEEEFFFGG
aws_secret_access_key = FooFooFoo
region=op-southeast-2
There are numerous ways to store credentials while still using boto3.resource().
I'm using the AWS CLI method myself. It works perfectly.
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html?fbclid=IwAR2LlrS4O2gYH6xAF4QDVIH2Q2tzfF_VZ6loM3XfXsPAOR4qA-pX_qAILys
you can set default aws env variables for secret and access keys - that way you dont need to change default client creation code - though it is better to pass it as a parameter if you have non-default creds

How to programmatically set up Airflow 1.10 logging with localstack s3 endpoint?

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}",
)

How do you SSH into a Google Compute Engine VM instance with Python rather than the CLI?

I want to SSH into a GCE VM instance using the google-api-client. I am able to start an instance using google-api-client with the following code:
from googleapiclient import discovery
from oauth2client.client import GoogleCredentials
credentials = GoogleCredentials.get_application_default()
service = discovery.build('compute', 'v1', credentials=credentials)
project = 'my_project'
zone = 'us-west2-a'
instance = 'my_instance'
request = service.instances().start(project=project, zone=zone, instance=instance)
response = request.execute()
In the command line the above code is rendered as:
gcloud compute instances start my_instance
Similarly, to SSH into a GCE VM instance with the command line one writes:
gcloud init && gcloud compute ssh my_instance --project my_project --verbosity=debug --zone=us-west2-a
I've already got the SSH keys set up and all that.
I want to know how to write the above command line in Google Api Client or Python.
There is no official REST API method to connect to a Compute Engine instance with SSH. But assuming you have the SSH keys configured as per the documentation, in theory, you could use a third-party tool such as Paramiko. Take a look at this post for more details.

Mount an individual file in Azure Container Instances?

I'm attempting to mount a single file in an azure container instance, in this case the ssh host key file as described in this docker image: https://github.com/atmoz/sftp
However from my experiments Azure Container Instances via ARM / Azure CLI seem to only support mounting folders.
If I attempt to mount as a file I suspect it's actually mounting as a folder, as the built in bash appears to miss the fact it already exists, and then errors when it tries to write to it.
Are there any undocumented features to mount individual files? I'm hoping not needing to resorting customising the docker image, as it would defeat my objective of using a ready made image. :-(
You can mount files using Key Vault. If you are deploying your ACI container group using an ARM template, you can integrate it with an instance of Azure Key Vault. It is possible to mount a key vault "secret" as a single file within a directory of your choosing. Refer to the ACI ARM template reference for more details.
You can do it via Azure Container Instance secrets.
Either azure cli:
az container create \
--resource-group myResourceGroup \
--name secret-volume-demo \
--image mcr.microsoft.com/azuredocs/aci-helloworld \
--secrets id_rsa.pub="<file-content>" \
--secrets-mount-path /home/foo/.ssh/keys
or with terraform:
resource "azurerm_container_group" "aci_container" {
name = ""
resource_group_name = ""
location = ""
ip_address_type = "public"
dns_name_label = "dns_endpoint"
os_type = "Linux"
container {
name = "sftp"
image = "docker.io/atmoz/sftp:alpine-3.7"
cpu = "1"
memory = "0.5"
ports {
port = 22
protocol = "TCP"
}
// option 1: mount key as Azure Container Instances secret volume
volume {
name = "user-pub-key"
mount_path = "/home/foo/.ssh/keys"
secret = {
"id_rsa.pub" = base64encode("<public-key-content>")
}
}
// option 2: mount ssh public key as Azure File share volume
// Note: This option will work for user keys to auth, but not for the host keys
// since atmoz/sftp logic is to change files permission,
// but Azure File share does not support this POSIX feature
volume {
name = "user-pub-key"
mount_path = "/home/foo/.ssh/keys"
read_only = true
share_name = "share-name"
storage_account_name = "storage-account-name"
storage_account_key = "storage-account-key"
}
}
In both cases, you will have a file /home/foo/.ssh/keys/id_rsa.pub with the given content.

Configuring Google cloud bucket as Airflow Log folder

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"