arcane-flask 3.0.0

Utility functions for flask apps.

Arcane flask

This package help us authenticate users

Get Started

pip install arcane-flask

Example Usage

from arcane.flask import check_access_rights
from arcane.core import RightsLevelEnum, UserRightsEnum
from arcane.datastore import Client as DatastoreClient
from arcane.pubsub import Client as PubSubClient

datastore_client = DatastoreClient()
pubsub_client = PubSubClient()

@check_access_rights(
    service='my-service',
    required_rights=RightsLevelEnum.VIEWER,
    service_user_right=UserRightsEnum.MY_SERVICE,
    datastore_client=datastore_client,
    pubsub_client=pubsub_client,
    receive_rights_per_client=True,
    project='my-project',
    timeout=30  # Timeout in seconds
)
def function(params):
    pass

Timeout Configuration

The check_access_rights decorator includes a built-in timeout parameter that allows you to set a maximum execution time for the decorated function. If the function exceeds this timeout, the decorator will automatically return a timeout response.

import time

@check_access_rights(
    service='my-service',
    required_rights=RightsLevelEnum.VIEWER,
    service_user_right=UserRightsEnum.MY_SERVICE,
    datastore_client=datastore_client,
    pubsub_client=pubsub_client,
    project='my-project',
    timeout=2  # 2 seconds timeout
)
def slow_function():
    time.sleep(5)  # This will exceed the timeout
    return {'result': 'success'}

If the function exceeds the timeout, the decorator will return:

({'detail': 'Request Timeout'}, 504)

Note: Set the timeout parameter to a value a few seconds less than your Cloud Run or server timeout configuration to ensure proper error handling.

Structured logging labels

The arcane.flask.logs module lets you add structured labels to every log entry in a request. This is useful for attaching metadata such as component, module, function, or IDs like optimization_id.

First, set up logging once at application startup:

from arcane.flask import logs

logs.setup_logging(gcp_project="my-gcp-project-id")

Then, register a teardown handler to clear labels at the end of each request:

from arcane.flask import logs

@app.teardown_request
def clear_logging_labels(exc):
    logs.clear_labels()

Inside your handlers you can either attach labels imperatively:

from arcane.flask import logs

def validate_model_parallelism_post(optimization_id: str, job_prefix: str, ...):
    logs.attach_labels(
        component="feed-boost",
        module="api",
        function="validate_model_parallelism_post",
        optimization_id=optimization_id,
        job_prefix=job_prefix,
    )
    ...

or use the with_log_labels decorator for static labels and still add dynamic labels as needed:

from arcane.flask import logs

@logs.with_log_labels(
    component="feed-boost",
    module="api",
    function="validate_model_parallelism_post",
)
def validate_model_parallelism_post(optimization_id: str, job_prefix: str, ...):
    logs.attach_labels(
        optimization_id=optimization_id,
        job_prefix=job_prefix,
    )
    ...

All log records emitted during the request will then include a logging.googleapis.com/labels field with the JSON-encoded labels.