You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to
BigQuery.
What should you do to fix the script?
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to
BigQuery.
What should you do to fix the script?
To fix the script, create a new service account with BigQuery access and execute your script using that user. Service accounts are used for server-to-server interactions, such as those between a virtual machine and BigQuery, and provide a way to authenticate applications to Google Cloud services. By assigning the appropriate BigQuery access role to the service account and using that account to run your script, the script will be able to connect to BigQuery and access the data it needs.
A - If client library was not installed, the python scripts won't run - since the question states the script reports "cannot connect" - the client library must have been installed. so it's B or C. B - https://cloud.google.com/bigquery/docs/authorization an access scope is how your client application retrieve access_token with access permission in OAuth when you want to access services via API call - in this case, it is possible that the python script use an API call instead of library, if this is true, then access scope is required. client library requires no access scope (as it does not go through OAuth) C - service account is Google Cloud's best practice So prefer C.
Might be an old version
agreed to comment here . C seems like a good option
Access scopes are the legacy method of specifying permissions for your instance. read from > https://cloud.google.com/compute/docs/access/service-accounts . So , I would go with C
agree access scope is enabled by default https://cloud.google.com/bigquery/docs/authorization#authenticate_with_oauth_20 If you use the BigQuery client libraries, you do not need this information, as this is done for you automatically.
Why not B? It looks better for me.
C is ok
Sorry, B is ok. You can create service account, add user to service account, and grant the user role as Service Account User. You still need to enable BigQuery scope to make the Python script running the instance to access BigQuery.
Stop confusing people, B) doesn't make any sense. Why would you use or create a whole new VM just because of a permission issue? If anything, just stop the instance and edit the scope of the default Compute Service Account and grant it the role through IAM. C) is the most appropriate answer since you can only set scopes of the default Compute Service Account, if you're using any other, there's no scope option - its access is dictated strictly by IAM in such scenario. So C) is the answer: Stop the VM, change the Service Account with the appropriate permissions and done. B) would still need to have permission the set through IAM & Admin, the scope isn't enough with the default Compute Service Account.
cloud guy1, relax. tartar is the hero for google cloud and if you read his answer, he explains the service account user's role granting on this one as that is the best practice
Configure the Python API to use a service account with relevant BigQuery access enabled. is the right answer. It is likely that this service account this script is running under does not have the permissions to connect to BigQuery and that could be causing issues. You can prevent these by using a service account that has the necessary roles to access BigQuery. Ref: https://cloud.google.com/bigquery/docs/reference/libraries#cloud-console A service account is a special kind of account used by an application or a virtual machine (VM) instance, not a person. Ref: https://cloud.google.com/iam/docs/service-accounts
C, no brainer. You need SA for using API period. Thats where your start your troubleshooting.
I stand corrected, B you need to have scope. It is union of Scope + Service Account. If scope is not there, you are screwed anyways.
Create a new service account with BigQuery access and execute your script with that user: If you want to run the script on an existing virtual machine, you can create a new service account with the necessary permissions to access BigQuery and then execute the script using that service account. This will allow the script to connect to BigQuery and access the data it needs.
Service accounts provide a way to authenticate your application to Google Cloud services. When you create a service account, you can assign it specific roles that dictate what resources the service account can interact with, and how it can interact with them. In this case, you would assign the BigQuery access role to the service account, which would then be used to authenticate your script to BigQuery.
You would need to create a new VM if you wanted to use the machines default service account as they can only be changed when the VM is created. But using a custom account with IAM roles is more inline with best practice so should be C.
Recomended best practice
C. Create a new service account with BigQuery access and execute your script with that user. Service accounts are used for server-to-server interactions, such as those between a virtual machine and BigQuery. You would need to create a service account that has the necessary permissions to access BigQuery, then download the service account key in JSON format. Once you have the key, you can set an environment variable (GOOGLE_APPLICATION_CREDENTIALS) to the path of the JSON key file before running your script, which will authenticate your requests to BigQuery.
better than creating and downloading a service account key would be to impersonate the service account
You don't need to create a new VM to have different access scopes: https://cloud.google.com/compute/docs/access/service-accounts#accesscopesiam This weakens answer B. When a user-managed service account is attached to the instance, the access scope defaults to cloud-platform: https://cloud.google.com/compute/docs/access/service-accounts#scopes_best_practice See Step 6 in: https://cloud.google.com/compute/docs/instances/change-service-account#changeserviceaccountandscopes These facts leave C as the valid answer.
Tricky question. However, as you can read in gcloud compute instances create documentation: --scopes=[SCOPE,…] If not provided, the instance will be assigned the default scopes, described below. However, if neither --scopes nor --no-scopes are specified and the project has no default service account, then the instance will be created with no scopes. Note that the level of access that a service account has is determined by a combination of access scopes and IAM roles so you must configure both access scopes and IAM roles for the service account to work properly. So, probably, B is the right one, as for the "new vm", I guess that this is because you don't want to stop the current one before having the working one ready...
you need service account to access any service in Google
Closest is C. https://cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances#gcloud The confusion part is that it should never use the word user to represent service account
B is silly because there's no need to create a new VM just to change the access scope. You can edit the existing VM's access scope, although you do have to stop it first.
C Best practice is that SA with least privilege from a CE should access BQ.
It should be B, Script cannot be run by user and user cannot be assigned with Service account, SA can be assigned to a VM
Connecting to BigQuery from a script requires proper authorization. Service accounts provide a secure way to grant access without sharing user credentials.
The answer is C https://cloud.google.com/bigquery/docs/authentication For most services, you must attach the service account when you create the resource that will run your code; you cannot add or replace the service account later. Compute Engine is an exception—it lets you attach a service account to a VM instance at any time.
C - service account is Google Cloud's best practice
I suppose all of them are correct, but we should choose the least effort, B is correct..
run script on a new vm, not create a new vm..