def get_bucket(self, course_key=None, track=True):
"""
Return which bucket number the specified user is in.
The user may be force-bucketed if matching subordinate flags of the form
"main_flag.BUCKET_NUM" exist. Otherwise, they will be hashed into a default
bucket based on their username, the experiment name, and the course-run key.
If `self.use_course_aware_bucketing` is False, the course-run key will
be omitted from the hashing formula, thus making it so a given user
has the same default bucket across all course runs; however, subordinate
flags that match the course-run key will still apply.
If `course_key` argument is omitted altogether, then subordinate flags
will be evaluated outside of the course-run context, and the default bucket
will be calculated as if `self.use_course_aware_bucketing` is False.
Finally, Bucket 0 is assumed to be the control bucket and will be returned if the
experiment is not enabled for this user and course.
Arguments:
course_key (Optional[CourseKey])
track (bool):
Whether an analytics event should be generated if the user is
bucketed for the first time.
Returns: int
"""
# Keep some imports in here, because this class is commonly used at a module level, and we want to avoid
# circular imports for any models.
from lms.djangoapps.experiments.models import ExperimentKeyValue
from lms.djangoapps.courseware.masquerade import get_specific_masquerading_user
request = get_current_request()
if not request:
return 0
if not hasattr(request, 'user') or not request.user.id:
# We need username for stable bucketing and id for tracking, so just skip anonymous (not-logged-in) users
return 0
user = get_specific_masquerading_user(request.user, course_key)
if user is None:
user = request.user
masquerading_as_specific_student = False
else:
masquerading_as_specific_student = True
# If a course key is passed in, include it in the experiment name
# in order to separate caches and analytics calls per course-run.
# If we are using course-aware bucketing, then also append that course key
# to `bucketing_group_name`, such that users can be hashed into different
# buckets for different course-runs.
experiment_name = bucketing_group_name = self.namespaced_flag_name
if course_key:
experiment_name += ".{}".format(course_key)
if course_key and self.use_course_aware_bucketing:
bucketing_group_name += ".{}".format(course_key)
# Check if we have a cache for this request already
request_cache = RequestCache('experiments')
cache_response = request_cache.get_cached_response(experiment_name)
if cache_response.is_found:
return cache_response.value
# Check if the main flag is even enabled for this user and course.
if not self.is_experiment_on(
course_key): # grabs user from the current request, if any
return self._cache_bucket(experiment_name, 0)
# Check if the enrollment should even be considered (if it started before the experiment wants, we ignore)
if course_key and self.experiment_id is not None:
values = ExperimentKeyValue.objects.filter(
experiment_id=self.experiment_id).values('key', 'value')
values = {pair['key']: pair['value'] for pair in values}
if not self._is_enrollment_inside_date_bounds(
values, user, course_key):
return self._cache_bucket(experiment_name, 0)
# Determine the user's bucket.
# First check if forced into a particular bucket, using our subordinate bucket flags.
# If not, calculate their default bucket using a consistent hash function.
for i, bucket_flag in enumerate(self.bucket_flags):
if bucket_flag.is_enabled(course_key):
bucket = i
break
else:
bucket = stable_bucketing_hash_group(bucketing_group_name,
self.num_buckets,
user.username)
session_key = 'tracked.{}'.format(experiment_name)
if (track and hasattr(request, 'session')
and session_key not in request.session
and not masquerading_as_specific_student):
segment.track(user_id=user.id,
event_name='edx.bi.experiment.user.bucketed',
properties={
'site': request.site.domain,
'app_label': self.waffle_namespace.name,
'experiment': self.flag_name,
'course_id':
str(course_key) if course_key else None,
'bucket': bucket,
'is_staff': user.is_staff,
'nonInteraction': 1,
})
# Mark that we've recorded this bucketing, so that we don't do it again this session
request.session[session_key] = True
return self._cache_bucket(experiment_name, bucket)
def get_bucket(self, course_key=None, track=True):
"""
Return which bucket number the specified user is in.
Bucket 0 is assumed to be the control bucket and will be returned if the experiment is not enabled for
this user and course.
"""
# Keep some imports in here, because this class is commonly used at a module level, and we want to avoid
# circular imports for any models.
from experiments.models import ExperimentKeyValue
from lms.djangoapps.courseware.access import has_access
from lms.djangoapps.courseware.masquerade import (
setup_masquerade,
is_masquerading,
get_specific_masquerading_user,
)
request = get_current_request()
if not request:
return 0
if not hasattr(request, 'user') or not request.user.id:
# We need username for stable bucketing and id for tracking, so just skip anonymous (not-logged-in) users
return 0
user = get_specific_masquerading_user(request.user, course_key)
if user is None:
user = request.user
masquerading_as_specific_student = False
else:
masquerading_as_specific_student = True
# Use course key in experiment name to separate caches and segment calls per-course-run
experiment_name = self.namespaced_flag_name + ('.{}'.format(course_key)
if course_key else '')
# Check if we have a cache for this request already
request_cache = RequestCache('experiments')
cache_response = request_cache.get_cached_response(experiment_name)
if cache_response.is_found:
return cache_response.value
# Check if the main flag is even enabled for this user and course.
if not self.is_experiment_on(
course_key): # grabs user from the current request, if any
return self._cache_bucket(experiment_name, 0)
# Check if the enrollment should even be considered (if it started before the experiment wants, we ignore)
if course_key and self.experiment_id is not None:
values = ExperimentKeyValue.objects.filter(
experiment_id=self.experiment_id).values('key', 'value')
values = {pair['key']: pair['value'] for pair in values}
if not self._is_enrollment_inside_date_bounds(
values, user, course_key):
return self._cache_bucket(experiment_name, 0)
bucket = stable_bucketing_hash_group(experiment_name, self.num_buckets,
user.username)
# Now check if the user is forced into a particular bucket, using our subordinate bucket flags
for i, bucket_flag in enumerate(self.bucket_flags):
if bucket_flag.is_enabled(course_key):
bucket = i
break
session_key = 'tracked.{}'.format(experiment_name)
if track and hasattr(
request, 'session'
) and session_key not in request.session and not masquerading_as_specific_student:
segment.track(user_id=user.id,
event_name='edx.bi.experiment.user.bucketed',
properties={
'site': request.site.domain,
'app_label': self.waffle_namespace.name,
'experiment': self.flag_name,
'course_id':
str(course_key) if course_key else None,
'bucket': bucket,
'is_staff': user.is_staff,
'nonInteraction': 1,
})
# Mark that we've recorded this bucketing, so that we don't do it again this session
request.session[session_key] = True
return self._cache_bucket(experiment_name, bucket)