I need to automate task which is related to azure databricks.we have configured a job in azure databricks and suddenly my service prinicipal secrets get expired and my notebook failed. on the next day I created new secrets and unmount --> mount it again and job worked. I know the way to create new secret or get the alert before expiring using logic app and manually will change it.
But how to manage unmount --> mount step automatically? As SP can be used in multiple project.
This is how I am mounting and mount_point used across notebook.
Here is the sample code for Unmounting and mounting notebook.
Check if the path is mounted or not, If it is not mounted yet mount the path with the given condition.
def mount_blob_storage_from_sas(dbutils, storage_account_name, container_name, mount_path, sas_token, unmount_if_exists = True):
if([item.mountPoint for item in dbutils.fs.mounts()].count(mount_path) > 0):
if unmount_if_exists:
print('Mount point already taken - unmounting: '+mount_path)
dbutils.fs.unmount(mount_path)
else:
print('Mount point already taken - ignoring: '+mount_path)
return
print('Mounting external storage in: '+mount_path)
dbutils.fs.mount(
source = "wasbs://{0}#{1}.blob.core.windows.net".format(container_name, storage_account_name),
mount_point = mount_path,
extra_configs = {"fs.azure.sas.{0}.{1}.blob.core.windows.net".format(container_name, storage_account_name): sas_token })
Create a job to run the notebook with given specific time period.
Provision the notebook path and cluster to create the JOB.
Schedule a time to trigger the notebook in Add Schedule .
Select Scheduled path to provide time to trigger.
Job will trigger at given time to run the notebook.
Related
I'm currently exploring implementing hooks in some of my DAGs. For instance, in one dag, I'm trying to connect to s3 to send a csv file to a bucket, which then gets copied to a redshift table.
I have a custom module written which I import to run this process. I am trying to currently set up an S3Hook to undergo this process instead. But I'm a little confused in setting up the connection, and how everything works.
First, I input the hook
from airflow.hooks.S3_hook import S3Hook
Then I try to make the hook instance
s3_hook = S3Hook(aws_conn_id='aws-s3')
Next I try to set up the client
s3_client = s3_hook.get_conn()
However when I run the client line above, I received this error
OperationalError: (sqlite3.OperationalError)
no such table: connection
[SQL: SELECT connection.password AS connection_password, connection.extra AS connection_extra, connection.id AS connection_id, connection.conn_id AS connection_conn_id, connection.conn_type AS connection_conn_type, connection.description AS connection_description, connection.host AS connection_host, connection.schema AS connection_schema, connection.login AS connection_login, connection.port AS connection_port, connection.is_encrypted AS connection_is_encrypted, connection.is_extra_encrypted AS connection_is_extra_encrypted
FROM connection
WHERE connection.conn_id = ?
LIMIT ? OFFSET ?]
[parameters: ('aws-s3', 1, 0)]
(Background on this error at: http://sqlalche.me/e/13/e3q8)
I'm trying to diagnose the error, but the tracebook is long. I'm a little confused on why sqlite3 is involved here, when I'm trying to utilize s3 here. Can anyone unpack this? Why is this error being thrown when trying to set up the client?
Thanks
Airflow is not just a library - it's also an application.
To execute Airflow code you must have airflow instance running this mean also having a database with the needed schema.
To create the tables you must execute airflow init db.
Edit:
After the discussion in comments. Your issue is that you have working Airflow application inside docker but your DAGs are written on your local disk. Docker is closed environment if you want Airflow to recognize your dags you must move the files to the DAG folder in the docker.
I need to mount 2 separate directories as volumes to a newly creted container.
So far I've found the way to mount only one volume since there's no way to add a file share volume via withNewAzureFileShareVolume more than once.
Here's my code:
ContainerGroup containerGroup = azure.containerGroups()
.define(containerName)
.withRegion(Region.US_EAST)
.withExistingResourceGroup("myResourceGroup")
.withLinux()
.withPrivateImageRegistry("registry")
.withNewAzureFileShareVolume("aci-share", shareName)
.defineContainerInstance(containerName)
.withImage("image")
.withVolumeMountSetting("aci-share", "/usr/local/dir1/")
.withVolumeMountSetting("aci-share-2", "/usr/local/dir2/")
.attach()
.withDnsPrefix(team)
.create();
A new storage account gets created with a single file share and I get the following error:
Volumes 'aci-share-2' in container 'team44783530d' are not found
I really liked BigQuery's Data Transfer Service. I have flat files in the exact schema sitting to be loaded into BQ. It would have been awesome to just setup DTS schedule that picked up GCS files that match a pattern and load the into BQ. I like the built in option to delete source files after copy and email in case of trouble. But the biggest bummer is that the minimum interval is 60 minutes. That is crazy. I could have lived with a 10 min delay perhaps.
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
StartManualTransferRuns is part of the RPC library but does not have a REST API equivalent as of now. How to use that will depend on your environment. For instance, you can use the Python Client Library (docs).
As an example, I used the following code (you'll need to run pip install google-cloud-bigquery-datatransfer for the depencencies):
import time
from google.cloud import bigquery_datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = bigquery_datatransfer_v1.DataTransferServiceClient()
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = '5e6...7bc' # alphanumeric ID you'll find in the UI
parent = client.project_transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = bigquery_datatransfer_v1.types.Timestamp(seconds=int(time.time() + 10))
response = client.start_manual_transfer_runs(parent, requested_run_time=start_time)
print(response)
Note that you'll need to use the right Transfer Config ID and the requested_run_time has to be of type bigquery_datatransfer_v1.types.Timestamp (for which there was no example in the docs). I set a start time 10 seconds ahead of the current execution time.
You should get a response such as:
runs {
name: "projects/PROJECT_NUMBER/locations/us/transferConfigs/5e6...7bc/runs/5e5...c04"
destination_dataset_id: "DATASET_NAME"
schedule_time {
seconds: 1579358571
nanos: 922599371
}
...
data_source_id: "google_cloud_storage"
state: PENDING
params {
...
}
run_time {
seconds: 1579358581
}
user_id: 28...65
}
and the transfer is triggered as expected (nevermind the error):
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
With this you can set a cron job to execute your function every ten minutes. As discussed in the comments, the minimum interval is 60 minutes so it won't pick up files less than one hour old (docs).
Apart from that, this is not a very robust solution and here come into play your follow-up questions. I think these might be too broad to address in a single StackOverflow question but I would say that, for on-demand refresh, Cloud Scheduler + Cloud Functions/Cloud Run can work very well.
Dataflow would be best if you needed ETL but it has a GCS connector that can watch a file pattern (example). With this you would skip the transfer, set the watch interval and the load job triggering frequency to write the files into BigQuery. VM(s) would be running constantly in a streaming pipeline as opposed to the previous approach but a 10-minute watch period is possible.
If you have complex workflows/dependencies, Airflow has recently introduced operators to start manual runs.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
You can use wildcards to match a file pattern when you create the transfer:
Also, this can be done on a file-by-file basis using Pub/Sub notifications for Cloud Storage to trigger a Cloud Function.
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
There is already a Feature Request here. Feel free to star it to show your interest and receive updates
Now your can easy manual run transfer Bigquery data use RESTApi:
HTTP request
POST https://bigquerydatatransfer.googleapis.com/v1/{parent=projects/*/locations/*/transferConfigs/*}:startManualRuns
About this part > {parent=projects//locations//transferConfigs/*}, check on CONFIGURATION of your Transfer then notice part like image bellow.
Here
More here:
https://cloud.google.com/bigquery-transfer/docs/reference/datatransfer/rest/v1/projects.locations.transferConfigs/startManualRuns
following the Guillem's answer and the API updates, this is my new code:
import time
from google.cloud.bigquery import datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = datatransfer_v1.DataTransferServiceClient()
config = '34y....654'
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = config
parent = client.transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = Timestamp(seconds=int(time.time()))
request = datatransfer_v1.types.StartManualTransferRunsRequest(
{ "parent": parent, "requested_run_time": start_time }
)
response = client.start_manual_transfer_runs(request, timeout=360)
print(response)
For this to work, you need to know the correct TRANSFER_CONFIG_ID.
In my case, I wanted to list all the BigQuery Scheduled queries, to get a specific ID. You can do it like that :
# Put your projetID here
PROJECT_ID = 'PROJECT_ID'
from google.cloud import bigquery_datatransfer_v1
bq_transfer_client = bigquery_datatransfer_v1.DataTransferServiceClient()
parent = bq_transfer_client.project_path(PROJECT_ID)
# Iterate over all results
for element in bq_transfer_client.list_transfer_configs(parent):
# Print Display Name for each Scheduled Query
print(f'[Schedule Query Name]:\t{element.display_name}')
# Print name of all elements (it contains the ID)
print(f'[Name]:\t\t{element.name}')
# Extract the IDs:
TRANSFER_CONFIG_ID= element.name.split('/')[-1]
print(f'[TRANSFER_CONFIG_ID]:\t\t{TRANSFER_CONFIG_ID}')
# You can print the entire element for debug purposes
print(element)
I created my first notebook instance on Amazon SageMaker.
Next I opened the Jupyter notebook and I used the SageMaker Example in the section Reinforcement Learning rl_deepracer_coach_robomaker.ipynb. The question is addressed principally to those who are familiar with this notebook.
There you can launch a training process and a RoboMaker simulation application to start the learning process for an autonomous car.
When a simulation job is launched, one can access to the log file, which is visualised by default in a CloudWatch console. Some of the informations that appear in the log file can be modified in the script deepracer_env.py in /src/robomaker/environments subdirectory.
I would like to "bypass" the CloudWatch console, saving the log file informations like episode, total reward, number of steps, coordinates of the car, steering and throttle etc. in a dataframe or csv file to be written somewhere on the S3 at the end of the simulation.
Something similar has been done in the main notebook rl_deepracer_coach_robomaker.ipynb to plot the metrics for a training job, namely the training reward per episode. There one can see that
csv_file_name = "worker_0.simple_rl_graph.main_level.main_level.agent_0.csv"
is called from the S3, but I simply cannot find where this csv is generated to mimic the process.
You can create a csv file in the /opt/ml/output/intermediate/ folder, and the file will be saved in the following directory:
s3://<s3_bucket>/<s3_prefix>/output/intermediate/<csv_file_name>
However, it is not clear to me where exactly you will create such a file. DeepRacer notebook uses two machines, one for training (SageMaker instance) and one for simulations (RoboMaker instance). The above method will only work in a SageMaker instance, but much of what you would like to log such as ("Total rewards" in an episode) is actually in RoboMaker instance. For RoboMaker instances, the intermediate folder feature doesn't exist, and you'll have to save the file to s3 yourself using the boto library. Here is an example of doing that: https://qiita.com/hengsokvisal/items/329924dd9e3f65dd48e7
There is a way to download the CloudWatch logs to a file. This way you can just print, save the logs and parse it. Assuming you are executing from a notebook cell:
STREAM_NAME= <your stream name as given by RoboMaker CloudWatch logs>
task = !aws logs create-export-task --task-name "copy_deepracer_logs" --log-group-name "/aws/robomaker/SimulationJobs" --log-stream-name-prefix $STREAM_NAME --destination "<s3_bucket>" --destination-prefix "<s3_prefix>" --from <unix timestamp in milliseconds> --to <unix timestamp in milliseconds>
task_id = json.loads(''.join(task))['taskId']
The export is an asynchronous call, so give it a few minutes to download. If you can print the task_id, then the export is done.
We are using the Beta Scheduled query feature of BigQuery.
Details: https://cloud.google.com/bigquery/docs/scheduling-queries
We have few ETL scheduled queries running overnight to optimize the aggregation and reduce query cost. It works well and there hasn't been much issues.
The problem arises when the person who scheduled the query using their own credentials leaves the organization. I know we can do "update credential" in such cases.
I read through the document and also gave it some try but couldn't really find if we can use a service account instead of individual accounts to schedule queries.
Service accounts are cleaner and ties up to the rest of the IAM framework and is not dependent on a single user.
So if you have any additional information regarding scheduled queries and service account please share.
Thanks for taking time to read the question and respond to it.
Regards
BigQuery Scheduled Query now does support creating a scheduled query with a service account and updating a scheduled query with a service account. Will these work for you?
While it's not supported in BigQuery UI, it's possible to create a transfer (including a scheduled query) using python GCP SDK for DTS, or from BQ CLI.
The following is an example using Python SDK:
r"""Example of creating TransferConfig using service account.
Usage Example:
1. Install GCP BQ python client library.
2. If it has not been done, please grant p4 service account with
iam.serviceAccout.GetAccessTokens permission on your project.
$ gcloud projects add-iam-policy-binding {user_project_id} \
--member='serviceAccount:service-{user_project_number}#'\
'gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com' \
--role='roles/iam.serviceAccountTokenCreator'
where {user_project_id} and {user_project_number} are the user project's
project id and project number, respectively. E.g.,
$ gcloud projects add-iam-policy-binding my-test-proj \
--member='serviceAccount:service-123456789#'\
'gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com'\
--role='roles/iam.serviceAccountTokenCreator'
3. Set environment var PROJECT to your user project, and
GOOGLE_APPLICATION_CREDENTIALS to the service account key path. E.g.,
$ export PROJECT_ID='my_project_id'
$ export GOOGLE_APPLICATION_CREDENTIALS=./serviceacct-creds.json'
4. $ python3 ./create_transfer_config.py
"""
import os
from google.cloud import bigquery_datatransfer
from google.oauth2 import service_account
from google.protobuf.struct_pb2 import Struct
PROJECT = os.environ["PROJECT_ID"]
SA_KEY_PATH = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
credentials = (
service_account.Credentials.from_service_account_file(SA_KEY_PATH))
client = bigquery_datatransfer.DataTransferServiceClient(
credentials=credentials)
# Get full path to project
parent_base = client.project_path(PROJECT)
params = Struct()
params["query"] = "SELECT CURRENT_DATE() as date, RAND() as val"
transfer_config = {
"destination_dataset_id": "my_data_set",
"display_name": "scheduled_query_test",
"data_source_id": "scheduled_query",
"params": params,
}
parent = parent_base + "/locations/us"
response = client.create_transfer_config(parent, transfer_config)
print response
As far as I know, unfortunately you can't use a service account to directly schedule queries yet. Maybe a Googler will correct me, but the BigQuery docs implicitly state this:
https://cloud.google.com/bigquery/docs/scheduling-queries#quotas
A scheduled query is executed with the creator's credentials and
project, as if you were executing the query yourself
If you need to use a service account (which is great practice BTW), then there are a few workarounds listed here. I've raised a FR here for posterity.
This question is very old and came on this thread while I was searching for same.
Yes, It is possible to use service account to schedule big query jobs.
While creating schedule query job, click on "Advance options", you will get option to select service account.
By default is uses credential of requesting user.
Image from bigquery "create schedule query"1