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Commit 1be7dbe3 by brianhw
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Merge pull request #1495 from edx/brian/remove-celery-group 

Remove the use of celery.group from bulk email subtasks.
parents d2e0f27a 5b48ed84
Showing with 312 additions and 278 deletions
CHANGELOG.rst
......@@ -5,6 +5,9 @@ These are notable changes in edx-platform. This is a rolling list of changes,
in roughly chronological order, most recent first. Add your entries at or near
the top. Include a label indicating the component affected.
LMS: Change bulk email implementation to use less memory, and to better handle
duplicate tasks in celery.
LMS: Improve forum error handling so that errors in the logs are
clearer and HTTP status codes from the comments service indicating
client error are correctly passed through to the client.
......
lms/djangoapps/bulk_email/tasks.py
......@@ -2,11 +2,9 @@
This module contains celery task functions for handling the sending of bulk email
to a course.
"""
import math
import re
import random
import json
from uuid import uuid4
from time import sleep
from dogapi import dog_stats_api
......@@ -24,7 +22,7 @@ from boto.ses.exceptions import (
)
from boto.exception import AWSConnectionError
from celery import task, current_task, group
from celery import task, current_task
from celery.utils.log import get_task_logger
from celery.states import SUCCESS, FAILURE, RETRY
from celery.exceptions import RetryTaskError
......@@ -42,11 +40,10 @@ from courseware.access import _course_staff_group_name, _course_instructor_group
from courseware.courses import get_course, course_image_url
from instructor_task.models import InstructorTask
from instructor_task.subtasks import (
update_subtask_status,
create_subtask_status,
increment_subtask_status,
initialize_subtask_info,
SubtaskStatus,
queue_subtasks_for_query,
check_subtask_is_valid,
update_subtask_status,
)
log = get_task_logger(__name__)
......@@ -152,60 +149,11 @@ def _get_course_email_context(course):
return email_context
def _generate_subtasks(create_subtask_fcn, recipient_qset):
"""
Generates a list of subtasks to send email to a given set of recipients.
Arguments:
`create_subtask_fcn` : a function whose inputs are a list of recipients and a subtask_id
to assign to the new subtask. Returns the subtask that will send email to that
list of recipients.
`recipient_qset` : a query set that defines the recipients who should receive emails.
Returns: a tuple, containing:
* A list of subtasks that will send emails to all recipients.
* A list of subtask_ids corresponding to those subtasks.
* A count of the total number of emails being sent.
"""
total_num_emails = recipient_qset.count()
num_queries = int(math.ceil(float(total_num_emails) / float(settings.BULK_EMAIL_EMAILS_PER_QUERY)))
last_pk = recipient_qset[0].pk - 1
num_emails_queued = 0
task_list = []
subtask_id_list = []
for _ in range(num_queries):
recipient_sublist = list(recipient_qset.order_by('pk').filter(pk__gt=last_pk).values('profile__name', 'email', 'pk')[:settings.BULK_EMAIL_EMAILS_PER_QUERY])
last_pk = recipient_sublist[-1]['pk']
num_emails_this_query = len(recipient_sublist)
num_tasks_this_query = int(math.ceil(float(num_emails_this_query) / float(settings.BULK_EMAIL_EMAILS_PER_TASK)))
chunk = int(math.ceil(float(num_emails_this_query) / float(num_tasks_this_query)))
for i in range(num_tasks_this_query):
to_list = recipient_sublist[i * chunk:i * chunk + chunk]
subtask_id = str(uuid4())
subtask_id_list.append(subtask_id)
new_subtask = create_subtask_fcn(to_list, subtask_id)
task_list.append(new_subtask)
num_emails_queued += num_emails_this_query
# Sanity check: we expect the chunking to be properly summing to the original count:
if num_emails_queued != total_num_emails:
error_msg = "Task {}: number of emails generated by chunking {} not equal to original total {}".format(num_emails_queued, total_num_emails)
log.error(error_msg)
raise ValueError(error_msg)
return task_list, subtask_id_list, total_num_emails
def perform_delegate_email_batches(entry_id, course_id, task_input, action_name):
"""
Delegates emails by querying for the list of recipients who should
get the mail, chopping up into batches of settings.BULK_EMAIL_EMAILS_PER_TASK size,
and queueing up worker jobs.
Returns the number of batches (workers) kicked off.
get the mail, chopping up into batches of no more than settings.BULK_EMAIL_EMAILS_PER_TASK
in size, and queueing up worker jobs.
"""
entry = InstructorTask.objects.get(pk=entry_id)
# Get inputs to use in this task from the entry.
......@@ -252,42 +200,41 @@ def perform_delegate_email_batches(entry_id, course_id, task_input, action_name)
log.exception("Task %s: course not found: %s", task_id, course_id)
raise
# Get arguments that will be passed to every subtask.
to_option = email_obj.to_option
recipient_qset = _get_recipient_queryset(user_id, to_option, course_id, course.location)
global_email_context = _get_course_email_context(course)
def _create_send_email_subtask(to_list, subtask_id):
def _create_send_email_subtask(to_list, initial_subtask_status):
"""Creates a subtask to send email to a given recipient list."""
subtask_status = create_subtask_status(subtask_id)
subtask_id = initial_subtask_status.task_id
new_subtask = send_course_email.subtask(
(
entry_id,
email_id,
to_list,
global_email_context,
subtask_status,
initial_subtask_status.to_dict(),
),
task_id=subtask_id,
routing_key=settings.BULK_EMAIL_ROUTING_KEY,
)
return new_subtask
log.info("Task %s: Preparing to generate subtasks for course %s, email %s, to_option %s",
task_id, course_id, email_id, to_option)
task_list, subtask_id_list, total_num_emails = _generate_subtasks(_create_send_email_subtask, recipient_qset)
recipient_qset = _get_recipient_queryset(user_id, to_option, course_id, course.location)
recipient_fields = ['profile__name', 'email']
# Update the InstructorTask with information about the subtasks we've defined.
log.info("Task %s: Preparing to update task for sending %d emails for course %s, email %s, to_option %s",
task_id, total_num_emails, course_id, email_id, to_option)
progress = initialize_subtask_info(entry, action_name, total_num_emails, subtask_id_list)
num_subtasks = len(subtask_id_list)
log.info("Task %s: Preparing to queue subtasks for sending emails for course %s, email %s, to_option %s",
task_id, course_id, email_id, to_option)
# Now group the subtasks, and start them running. This allows all the subtasks
# in the list to be submitted at the same time.
log.info("Task %s: Preparing to queue %d email tasks (%d emails) for course %s, email %s, to %s",
task_id, num_subtasks, total_num_emails, course_id, email_id, to_option)
task_group = group(task_list)
task_group.apply_async(routing_key=settings.BULK_EMAIL_ROUTING_KEY)
progress = queue_subtasks_for_query(
entry,
action_name,
_create_send_email_subtask,
recipient_qset,
recipient_fields,
settings.BULK_EMAIL_EMAILS_PER_QUERY,
settings.BULK_EMAIL_EMAILS_PER_TASK
)
# We want to return progress here, as this is what will be stored in the
# AsyncResult for the parent task as its return value.
......@@ -298,7 +245,7 @@ def perform_delegate_email_batches(entry_id, course_id, task_input, action_name)
@task(default_retry_delay=settings.BULK_EMAIL_DEFAULT_RETRY_DELAY, max_retries=settings.BULK_EMAIL_MAX_RETRIES) # pylint: disable=E1102
def send_course_email(entry_id, email_id, to_list, global_email_context, subtask_status):
def send_course_email(entry_id, email_id, to_list, global_email_context, subtask_status_dict):
"""
Sends an email to a list of recipients.
......@@ -312,7 +259,7 @@ def send_course_email(entry_id, email_id, to_list, global_email_context, subtask
* `global_email_context`: dict containing values that are unique for this email but the same
for all recipients of this email. This dict is to be used to fill in slots in email
template. It does not include 'name' and 'email', which will be provided by the to_list.
* `subtask_status` : dict containing values representing current status. Keys are:
* `subtask_status_dict` : dict containing values representing current status. Keys are:
'task_id' : id of subtask. This is used to pass task information across retries.
'attempted' : number of attempts -- should equal succeeded plus failed
......@@ -332,7 +279,8 @@ def send_course_email(entry_id, email_id, to_list, global_email_context, subtask
Emails are sent multi-part, in both plain text and html. Updates InstructorTask object
with status information (sends, failures, skips) and updates number of subtasks completed.
"""
current_task_id = subtask_status['task_id']
subtask_status = SubtaskStatus.from_dict(subtask_status_dict)
current_task_id = subtask_status.task_id
num_to_send = len(to_list)
log.info("Preparing to send email %s to %d recipients as subtask %s for instructor task %d: context = %s, status=%s",
email_id, num_to_send, current_task_id, entry_id, global_email_context, subtask_status)
......@@ -362,32 +310,33 @@ def send_course_email(entry_id, email_id, to_list, global_email_context, subtask
)
except Exception:
# Unexpected exception. Try to write out the failure to the entry before failing.
log.exception("Send-email task %s: failed unexpectedly!", current_task_id)
log.exception("Send-email task %s for email %s: failed unexpectedly!", current_task_id, email_id)
# We got here for really unexpected reasons. Since we don't know how far
# the task got in emailing, we count all recipients as having failed.
# It at least keeps the counts consistent.
new_subtask_status = increment_subtask_status(subtask_status, failed=num_to_send, state=FAILURE)
update_subtask_status(entry_id, current_task_id, new_subtask_status)
subtask_status.increment(failed=num_to_send, state=FAILURE)
update_subtask_status(entry_id, current_task_id, subtask_status)
raise
if send_exception is None:
# Update the InstructorTask object that is storing its progress.
log.info("Send-email task %s: succeeded", current_task_id)
log.info("Send-email task %s for email %s: succeeded", current_task_id, email_id)
update_subtask_status(entry_id, current_task_id, new_subtask_status)
elif isinstance(send_exception, RetryTaskError):
# If retrying, a RetryTaskError needs to be returned to Celery.
# We assume that the the progress made before the retry condition
# was encountered has already been updated before the retry call was made,
# so we only log here.
log.warning("Send-email task %s: being retried", current_task_id)
log.warning("Send-email task %s for email %s: being retried", current_task_id, email_id)
raise send_exception # pylint: disable=E0702
else:
log.error("Send-email task %s: failed: %s", current_task_id, send_exception)
log.error("Send-email task %s for email %s: failed: %s", current_task_id, email_id, send_exception)
update_subtask_status(entry_id, current_task_id, new_subtask_status)
raise send_exception # pylint: disable=E0702
log.info("Send-email task %s: returning status %s", current_task_id, new_subtask_status)
return new_subtask_status
# return status in a form that can be serialized by Celery into JSON:
log.info("Send-email task %s for email %s: returning status %s", current_task_id, email_id, new_subtask_status)
return new_subtask_status.to_dict()
def _filter_optouts_from_recipients(to_list, course_id):
......@@ -449,37 +398,20 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
* `global_email_context`: dict containing values that are unique for this email but the same
for all recipients of this email. This dict is to be used to fill in slots in email
template. It does not include 'name' and 'email', which will be provided by the to_list.
* `subtask_status` : dict containing values representing current status. Keys are:
'task_id' : id of subtask. This is used to pass task information across retries.
'attempted' : number of attempts -- should equal succeeded plus failed
'succeeded' : number that succeeded in processing
'skipped' : number that were not processed.
'failed' : number that failed during processing
'retried_nomax' : number of times the subtask has been retried for conditions that
should not have a maximum count applied
'retried_withmax' : number of times the subtask has been retried for conditions that
should have a maximum count applied
'state' : celery state of the subtask (e.g. QUEUING, PROGRESS, RETRY, FAILURE, SUCCESS)
* `subtask_status` : object of class SubtaskStatus representing current status.
Sends to all addresses contained in to_list that are not also in the Optout table.
Emails are sent multi-part, in both plain text and html.
Returns a tuple of two values:
* First value is a dict which represents current progress at the end of this call. Keys are
the same as for the input subtask_status.
* First value is a SubtaskStatus object which represents current progress at the end of this call.
* Second value is an exception returned by the innards of the method, indicating a fatal error.
In this case, the number of recipients that were not sent have already been added to the
'failed' count above.
"""
# Get information from current task's request:
task_id = subtask_status['task_id']
# collect stats on progress:
num_optout = 0
num_sent = 0
num_error = 0
task_id = subtask_status.task_id
try:
course_email = CourseEmail.objects.get(id=email_id)
......@@ -493,8 +425,9 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
# attempt. Anyone on the to_list on a retry has already passed the filter
# that existed at that time, and we don't need to keep checking for changes
# in the Optout list.
if (subtask_status['retried_nomax'] + subtask_status['retried_withmax']) == 0:
if subtask_status.get_retry_count() == 0:
to_list, num_optout = _filter_optouts_from_recipients(to_list, course_email.course_id)
subtask_status.increment(skipped=num_optout)
course_title = global_email_context['course_title']
subject = "[" + course_title + "] " + course_email.subject
......@@ -539,7 +472,7 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
# for a period of time between all emails within this task. Choice of
# the value depends on the number of workers that might be sending email in
# parallel, and what the SES throttle rate is.
if subtask_status['retried_nomax'] > 0:
if subtask_status.retried_nomax > 0:
sleep(settings.BULK_EMAIL_RETRY_DELAY_BETWEEN_SENDS)
try:
......@@ -557,13 +490,13 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
# This will fall through and not retry the message.
log.warning('Task %s: email with id %s not delivered to %s due to error %s', task_id, email_id, email, exc.smtp_error)
dog_stats_api.increment('course_email.error', tags=[_statsd_tag(course_title)])
num_error += 1
subtask_status.increment(failed=1)
except SINGLE_EMAIL_FAILURE_ERRORS as exc:
# This will fall through and not retry the message.
log.warning('Task %s: email with id %s not delivered to %s due to error %s', task_id, email_id, email, exc)
dog_stats_api.increment('course_email.error', tags=[_statsd_tag(course_title)])
num_error += 1
subtask_status.increment(failed=1)
else:
dog_stats_api.increment('course_email.sent', tags=[_statsd_tag(course_title)])
......@@ -571,7 +504,7 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
log.info('Email with id %s sent to %s', email_id, email)
else:
log.debug('Email with id %s sent to %s', email_id, email)
num_sent += 1
subtask_status.increment(succeeded=1)
# Pop the user that was emailed off the end of the list only once they have
# successfully been processed. (That way, if there were a failure that
......@@ -582,16 +515,9 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
dog_stats_api.increment('course_email.infinite_retry', tags=[_statsd_tag(course_title)])
# Increment the "retried_nomax" counter, update other counters with progress to date,
# and set the state to RETRY:
subtask_progress = increment_subtask_status(
subtask_status,
succeeded=num_sent,
failed=num_error,
skipped=num_optout,
retried_nomax=1,
state=RETRY
)
subtask_status.increment(retried_nomax=1, state=RETRY)
return _submit_for_retry(
entry_id, email_id, to_list, global_email_context, exc, subtask_progress, skip_retry_max=True
entry_id, email_id, to_list, global_email_context, exc, subtask_status, skip_retry_max=True
)
except LIMITED_RETRY_ERRORS as exc:
......@@ -601,16 +527,9 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
dog_stats_api.increment('course_email.limited_retry', tags=[_statsd_tag(course_title)])
# Increment the "retried_withmax" counter, update other counters with progress to date,
# and set the state to RETRY:
subtask_progress = increment_subtask_status(
subtask_status,
succeeded=num_sent,
failed=num_error,
skipped=num_optout,
retried_withmax=1,
state=RETRY
)
subtask_status.increment(retried_withmax=1, state=RETRY)
return _submit_for_retry(
entry_id, email_id, to_list, global_email_context, exc, subtask_progress, skip_retry_max=False
entry_id, email_id, to_list, global_email_context, exc, subtask_status, skip_retry_max=False
)
except BULK_EMAIL_FAILURE_ERRORS as exc:
......@@ -620,14 +539,8 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
task_id, email_id, num_pending)
# Update counters with progress to date, counting unsent emails as failures,
# and set the state to FAILURE:
subtask_progress = increment_subtask_status(
subtask_status,
succeeded=num_sent,
failed=(num_error + num_pending),
skipped=num_optout,
state=FAILURE
)
return subtask_progress, exc
subtask_status.increment(failed=num_pending, state=FAILURE)
return subtask_status, exc
except Exception as exc:
# Errors caught here cause the email to be retried. The entire task is actually retried
......@@ -639,30 +552,17 @@ def _send_course_email(entry_id, email_id, to_list, global_email_context, subtas
task_id, email_id)
# Increment the "retried_withmax" counter, update other counters with progress to date,
# and set the state to RETRY:
subtask_progress = increment_subtask_status(
subtask_status,
succeeded=num_sent,
failed=num_error,
skipped=num_optout,
retried_withmax=1,
state=RETRY
)
subtask_status.increment(retried_withmax=1, state=RETRY)
return _submit_for_retry(
entry_id, email_id, to_list, global_email_context, exc, subtask_progress, skip_retry_max=False
entry_id, email_id, to_list, global_email_context, exc, subtask_status, skip_retry_max=False
)
else:
# All went well. Update counters with progress to date,
# and set the state to SUCCESS:
subtask_progress = increment_subtask_status(
subtask_status,
succeeded=num_sent,
failed=num_error,
skipped=num_optout,
state=SUCCESS
)
subtask_status.increment(state=SUCCESS)
# Successful completion is marked by an exception value of None.
return subtask_progress, None
return subtask_status, None
finally:
# Clean up at the end.
connection.close()
......@@ -708,26 +608,26 @@ def _submit_for_retry(entry_id, email_id, to_list, global_email_context, current
successfully submitted, this value will be the RetryTaskError that retry() returns.
Otherwise, it (ought to be) the current_exception passed in.
"""
task_id = subtask_status['task_id']
task_id = subtask_status.task_id
log.info("Task %s: Successfully sent to %s users; failed to send to %s users (and skipped %s users)",
task_id, subtask_status['succeeded'], subtask_status['failed'], subtask_status['skipped'])
task_id, subtask_status.succeeded, subtask_status.failed, subtask_status.skipped)
# Calculate time until we retry this task (in seconds):
# The value for max_retries is increased by the number of times an "infinite-retry" exception
# has been retried. We want the regular retries to trigger max-retry checking, but not these
# special retries. So we count them separately.
max_retries = _get_current_task().max_retries + subtask_status['retried_nomax']
max_retries = _get_current_task().max_retries + subtask_status.retried_nomax
base_delay = _get_current_task().default_retry_delay
if skip_retry_max:
# once we reach five retries, don't increase the countdown further.
retry_index = min(subtask_status['retried_nomax'], 5)
retry_index = min(subtask_status.retried_nomax, 5)
exception_type = 'sending-rate'
# if we have a cap, after all, apply it now:
if hasattr(settings, 'BULK_EMAIL_INFINITE_RETRY_CAP'):
retry_cap = settings.BULK_EMAIL_INFINITE_RETRY_CAP + subtask_status['retried_withmax']
retry_cap = settings.BULK_EMAIL_INFINITE_RETRY_CAP + subtask_status.retried_withmax
max_retries = min(max_retries, retry_cap)
else:
retry_index = subtask_status['retried_withmax']
retry_index = subtask_status.retried_withmax
exception_type = 'transient'
# Skew the new countdown value by a random factor, so that not all
......@@ -752,7 +652,7 @@ def _submit_for_retry(entry_id, email_id, to_list, global_email_context, current
email_id,
to_list,
global_email_context,
subtask_status,
subtask_status.to_dict(),
],
exc=current_exception,
countdown=countdown,
......@@ -773,8 +673,8 @@ def _submit_for_retry(entry_id, email_id, to_list, global_email_context, current
log.exception('Task %s: email with id %d caused send_course_email task to fail to retry. To list: %s',
task_id, email_id, [i['email'] for i in to_list])
num_failed = len(to_list)
new_subtask_progress = increment_subtask_status(subtask_status, failed=num_failed, state=FAILURE)
return new_subtask_progress, retry_exc
subtask_status.increment(subtask_status, failed=num_failed, state=FAILURE)
return subtask_status, retry_exc
def _statsd_tag(course_title):
......
lms/djangoapps/bulk_email/tests/test_email.py
......@@ -15,7 +15,7 @@ from student.tests.factories import UserFactory, GroupFactory, CourseEnrollmentF
from xmodule.modulestore.tests.django_utils import ModuleStoreTestCase
from xmodule.modulestore.tests.factories import CourseFactory
from bulk_email.models import Optout
from instructor_task.subtasks import increment_subtask_status
from instructor_task.subtasks import update_subtask_status
STAFF_COUNT = 3
STUDENT_COUNT = 10
......@@ -29,13 +29,13 @@ class MockCourseEmailResult(object):
"""
emails_sent = 0
def get_mock_increment_subtask_status(self):
def get_mock_update_subtask_status(self):
"""Wrapper for mock email function."""
def mock_increment_subtask_status(original_status, **kwargs): # pylint: disable=W0613
def mock_update_subtask_status(entry_id, current_task_id, new_subtask_status): # pylint: disable=W0613
"""Increments count of number of emails sent."""
self.emails_sent += kwargs.get('succeeded', 0)
return increment_subtask_status(original_status, **kwargs)
return mock_increment_subtask_status
self.emails_sent += new_subtask_status.succeeded
return update_subtask_status(entry_id, current_task_id, new_subtask_status)
return mock_update_subtask_status
@override_settings(MODULESTORE=TEST_DATA_MONGO_MODULESTORE)
......@@ -244,13 +244,13 @@ class TestEmailSendFromDashboard(ModuleStoreTestCase):
)
@override_settings(BULK_EMAIL_EMAILS_PER_TASK=3, BULK_EMAIL_EMAILS_PER_QUERY=7)
@patch('bulk_email.tasks.increment_subtask_status')
@patch('bulk_email.tasks.update_subtask_status')
def test_chunked_queries_send_numerous_emails(self, email_mock):
"""
Test sending a large number of emails, to test the chunked querying
"""
mock_factory = MockCourseEmailResult()
email_mock.side_effect = mock_factory.get_mock_increment_subtask_status()
email_mock.side_effect = mock_factory.get_mock_update_subtask_status()
added_users = []
for _ in xrange(LARGE_NUM_EMAILS):
user = UserFactory()
......
lms/djangoapps/bulk_email/tests/test_err_handling.py
......@@ -22,8 +22,8 @@ from bulk_email.models import CourseEmail, SEND_TO_ALL
from bulk_email.tasks import perform_delegate_email_batches, send_course_email
from instructor_task.models import InstructorTask
from instructor_task.subtasks import (
create_subtask_status,
initialize_subtask_info,
SubtaskStatus,
check_subtask_is_valid,
update_subtask_status,
DuplicateTaskException,
......@@ -75,7 +75,7 @@ class TestEmailErrors(ModuleStoreTestCase):
self.assertIsInstance(exc, SMTPDataError)
@patch('bulk_email.tasks.get_connection', autospec=True)
@patch('bulk_email.tasks.increment_subtask_status')
@patch('bulk_email.tasks.update_subtask_status')
@patch('bulk_email.tasks.send_course_email.retry')
def test_data_err_fail(self, retry, result, get_conn):
"""
......@@ -99,11 +99,11 @@ class TestEmailErrors(ModuleStoreTestCase):
# We shouldn't retry when hitting a 5xx error
self.assertFalse(retry.called)
# Test that after the rejected email, the rest still successfully send
((_initial_results), kwargs) = result.call_args
self.assertEquals(kwargs['skipped'], 0)
((_entry_id, _current_task_id, subtask_status), _kwargs) = result.call_args
self.assertEquals(subtask_status.skipped, 0)
expected_fails = int((settings.BULK_EMAIL_EMAILS_PER_TASK + 3) / 4.0)
self.assertEquals(kwargs['failed'], expected_fails)
self.assertEquals(kwargs['succeeded'], settings.BULK_EMAIL_EMAILS_PER_TASK - expected_fails)
self.assertEquals(subtask_status.failed, expected_fails)
self.assertEquals(subtask_status.succeeded, settings.BULK_EMAIL_EMAILS_PER_TASK - expected_fails)
@patch('bulk_email.tasks.get_connection', autospec=True)
@patch('bulk_email.tasks.send_course_email.retry')
......@@ -146,7 +146,7 @@ class TestEmailErrors(ModuleStoreTestCase):
exc = kwargs['exc']
self.assertIsInstance(exc, SMTPConnectError)
@patch('bulk_email.tasks.increment_subtask_status')
@patch('bulk_email.tasks.SubtaskStatus.increment')
@patch('bulk_email.tasks.log')
def test_nonexistent_email(self, mock_log, result):
"""
......@@ -216,10 +216,10 @@ class TestEmailErrors(ModuleStoreTestCase):
to_list = ['test@test.com']
global_email_context = {'course_title': 'dummy course'}
subtask_id = "subtask-id-value"
subtask_status = create_subtask_status(subtask_id)
subtask_status = SubtaskStatus.create(subtask_id)
email_id = 1001
with self.assertRaisesRegexp(DuplicateTaskException, 'unable to find subtasks of instructor task'):
send_course_email(entry_id, email_id, to_list, global_email_context, subtask_status)
send_course_email(entry_id, email_id, to_list, global_email_context, subtask_status.to_dict())
def test_send_email_missing_subtask(self):
# test at a lower level, to ensure that the course gets checked down below too.
......@@ -230,10 +230,10 @@ class TestEmailErrors(ModuleStoreTestCase):
subtask_id = "subtask-id-value"
initialize_subtask_info(entry, "emailed", 100, [subtask_id])
different_subtask_id = "bogus-subtask-id-value"
subtask_status = create_subtask_status(different_subtask_id)
subtask_status = SubtaskStatus.create(different_subtask_id)
bogus_email_id = 1001
with self.assertRaisesRegexp(DuplicateTaskException, 'unable to find status for subtask of instructor task'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status.to_dict())
def test_send_email_completed_subtask(self):
# test at a lower level, to ensure that the course gets checked down below too.
......@@ -241,14 +241,14 @@ class TestEmailErrors(ModuleStoreTestCase):
entry_id = entry.id # pylint: disable=E1101
subtask_id = "subtask-id-value"
initialize_subtask_info(entry, "emailed", 100, [subtask_id])
subtask_status = create_subtask_status(subtask_id, state=SUCCESS)
subtask_status = SubtaskStatus.create(subtask_id, state=SUCCESS)
update_subtask_status(entry_id, subtask_id, subtask_status)
bogus_email_id = 1001
to_list = ['test@test.com']
global_email_context = {'course_title': 'dummy course'}
new_subtask_status = create_subtask_status(subtask_id)
new_subtask_status = SubtaskStatus.create(subtask_id)
with self.assertRaisesRegexp(DuplicateTaskException, 'already completed'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status.to_dict())
def test_send_email_running_subtask(self):
# test at a lower level, to ensure that the course gets checked down below too.
......@@ -256,14 +256,14 @@ class TestEmailErrors(ModuleStoreTestCase):
entry_id = entry.id # pylint: disable=E1101
subtask_id = "subtask-id-value"
initialize_subtask_info(entry, "emailed", 100, [subtask_id])
subtask_status = create_subtask_status(subtask_id)
subtask_status = SubtaskStatus.create(subtask_id)
update_subtask_status(entry_id, subtask_id, subtask_status)
check_subtask_is_valid(entry_id, subtask_id, subtask_status)
bogus_email_id = 1001
to_list = ['test@test.com']
global_email_context = {'course_title': 'dummy course'}
with self.assertRaisesRegexp(DuplicateTaskException, 'already being executed'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status.to_dict())
def test_send_email_retried_subtask(self):
# test at a lower level, to ensure that the course gets checked down below too.
......@@ -271,19 +271,19 @@ class TestEmailErrors(ModuleStoreTestCase):
entry_id = entry.id # pylint: disable=E1101
subtask_id = "subtask-id-value"
initialize_subtask_info(entry, "emailed", 100, [subtask_id])
subtask_status = create_subtask_status(subtask_id, state=RETRY, retried_nomax=2)
subtask_status = SubtaskStatus.create(subtask_id, state=RETRY, retried_nomax=2)
update_subtask_status(entry_id, subtask_id, subtask_status)
bogus_email_id = 1001
to_list = ['test@test.com']
global_email_context = {'course_title': 'dummy course'}
# try running with a clean subtask:
new_subtask_status = create_subtask_status(subtask_id)
new_subtask_status = SubtaskStatus.create(subtask_id)
with self.assertRaisesRegexp(DuplicateTaskException, 'already retried'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status.to_dict())
# try again, with a retried subtask with lower count:
new_subtask_status = create_subtask_status(subtask_id, state=RETRY, retried_nomax=1)
new_subtask_status = SubtaskStatus.create(subtask_id, state=RETRY, retried_nomax=1)
with self.assertRaisesRegexp(DuplicateTaskException, 'already retried'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, new_subtask_status.to_dict())
def dont_test_send_email_undefined_email(self):
# test at a lower level, to ensure that the course gets checked down below too.
......@@ -293,10 +293,10 @@ class TestEmailErrors(ModuleStoreTestCase):
global_email_context = {'course_title': 'dummy course'}
subtask_id = "subtask-id-value"
initialize_subtask_info(entry, "emailed", 100, [subtask_id])
subtask_status = create_subtask_status(subtask_id)
subtask_status = SubtaskStatus.create(subtask_id)
bogus_email_id = 1001
with self.assertRaises(CourseEmail.DoesNotExist):
# we skip the call that updates subtask status, since we've not set up the InstructorTask
# for the subtask, and it's not important to the test.
with patch('bulk_email.tasks.update_subtask_status'):
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status)
send_course_email(entry_id, bogus_email_id, to_list, global_email_context, subtask_status.to_dict())
lms/djangoapps/bulk_email/tests/test_tasks.py
......@@ -31,7 +31,7 @@ from django.core.management import call_command
from bulk_email.models import CourseEmail, Optout, SEND_TO_ALL
from instructor_task.tasks import send_bulk_course_email
from instructor_task.subtasks import update_subtask_status
from instructor_task.subtasks import update_subtask_status, SubtaskStatus
from instructor_task.models import InstructorTask
from instructor_task.tests.test_base import InstructorTaskCourseTestCase
from instructor_task.tests.factories import InstructorTaskFactory
......@@ -63,16 +63,9 @@ def my_update_subtask_status(entry_id, current_task_id, new_subtask_status):
entry = InstructorTask.objects.get(pk=entry_id)
subtask_dict = json.loads(entry.subtasks)
subtask_status_info = subtask_dict['status']
current_subtask_status = subtask_status_info[current_task_id]
def _get_retry_count(subtask_result):
"""Return the number of retries counted for the given subtask."""
retry_count = subtask_result.get('retried_nomax', 0)
retry_count += subtask_result.get('retried_withmax', 0)
return retry_count
current_retry_count = _get_retry_count(current_subtask_status)
new_retry_count = _get_retry_count(new_subtask_status)
current_subtask_status = SubtaskStatus.from_dict(subtask_status_info[current_task_id])
current_retry_count = current_subtask_status.get_retry_count()
new_retry_count = new_subtask_status.get_retry_count()
if current_retry_count <= new_retry_count:
update_subtask_status(entry_id, current_task_id, new_subtask_status)
......
lms/djangoapps/instructor_task/subtasks.py
......@@ -3,6 +3,8 @@ This module contains celery task functions for handling the management of subtas
"""
from time import time
import json
from uuid import uuid4
import math
from celery.utils.log import get_task_logger
from celery.states import SUCCESS, READY_STATES, RETRY
......@@ -14,7 +16,7 @@ from instructor_task.models import InstructorTask, PROGRESS, QUEUING
TASK_LOG = get_task_logger(__name__)
# Lock expiration should be long enough to allow a send_course_email task to complete.
# Lock expiration should be long enough to allow a subtask to complete.
SUBTASK_LOCK_EXPIRE = 60 * 10 # Lock expires in 10 minutes
......@@ -23,51 +25,73 @@ class DuplicateTaskException(Exception):
pass
def create_subtask_status(task_id, succeeded=0, failed=0, skipped=0, retried_nomax=0, retried_withmax=0, state=None):
def _get_number_of_subtasks(total_num_items, items_per_query, items_per_task):
"""
Create and return a dict for tracking the status of a subtask.
Determines number of subtasks that would be generated by _generate_items_for_subtask.
Subtask status keys are:
This needs to be calculated before a query is executed so that the list of all subtasks can be
stored in the InstructorTask before any subtasks are started.
'task_id' : id of subtask. This is used to pass task information across retries.
'attempted' : number of attempts -- should equal succeeded plus failed
'succeeded' : number that succeeded in processing
'skipped' : number that were not processed.
'failed' : number that failed during processing
'retried_nomax' : number of times the subtask has been retried for conditions that
should not have a maximum count applied
'retried_withmax' : number of times the subtask has been retried for conditions that
should have a maximum count applied
'state' : celery state of the subtask (e.g. QUEUING, PROGRESS, RETRY, FAILURE, SUCCESS)
The number of subtask_id values returned by this should match the number of chunks returned
by the generate_items_for_subtask generator.
"""
total_num_tasks = 0
num_queries = int(math.ceil(float(total_num_items) / float(items_per_query)))
num_items_remaining = total_num_items
for _ in range(num_queries):
num_items_this_query = min(num_items_remaining, items_per_query)
num_items_remaining -= num_items_this_query
num_tasks_this_query = int(math.ceil(float(num_items_this_query) / float(items_per_task)))
total_num_tasks += num_tasks_this_query
Object must be JSON-serializable, so that it can be passed as an argument
to tasks.
return total_num_tasks
In future, we may want to include specific error information
indicating the reason for failure.
Also, we should count up "not attempted" separately from attempted/failed.
def _generate_items_for_subtask(item_queryset, item_fields, total_num_items, items_per_query, items_per_task):
"""
attempted = succeeded + failed
current_result = {
'task_id': task_id,
'attempted': attempted,
'succeeded': succeeded,
'skipped': skipped,
'failed': failed,
'retried_nomax': retried_nomax,
'retried_withmax': retried_withmax,
'state': state if state is not None else QUEUING,
}
return current_result
Generates a chunk of "items" that should be passed into a subtask.
Arguments:
`item_queryset` : a query set that defines the "items" that should be passed to subtasks.
`item_fields` : the fields that should be included in the dict that is returned.
These are in addition to the 'pk' field.
`total_num_items` : the result of item_queryset.count().
`items_per_query` : size of chunks to break the query operation into.
`items_per_task` : maximum size of chunks to break each query chunk into for use by a subtask.
def increment_subtask_status(subtask_result, succeeded=0, failed=0, skipped=0, retried_nomax=0, retried_withmax=0, state=None):
"""
Update the result of a subtask with additional results.
Returns: yields a list of dicts, where each dict contains the fields in `item_fields`, plus the 'pk' field.
Warning: if the algorithm here changes, the _get_number_of_subtasks() method should similarly be changed.
"""
num_queries = int(math.ceil(float(total_num_items) / float(items_per_query)))
last_pk = item_queryset[0].pk - 1
num_items_queued = 0
all_item_fields = list(item_fields)
all_item_fields.append('pk')
for _ in range(num_queries):
item_sublist = list(item_queryset.order_by('pk').filter(pk__gt=last_pk).values(*all_item_fields)[:items_per_query])
last_pk = item_sublist[-1]['pk']
num_items_this_query = len(item_sublist)
num_tasks_this_query = int(math.ceil(float(num_items_this_query) / float(items_per_task)))
chunk = int(math.ceil(float(num_items_this_query) / float(num_tasks_this_query)))
for i in range(num_tasks_this_query):
items_for_task = item_sublist[i * chunk:i * chunk + chunk]
yield items_for_task
num_items_queued += num_items_this_query
# Sanity check: we expect the chunking to be properly summing to the original count:
if num_items_queued != total_num_items:
error_msg = "Number of items generated by chunking {} not equal to original total {}".format(num_items_queued, total_num_items)
TASK_LOG.error(error_msg)
raise ValueError(error_msg)
class SubtaskStatus(object):
"""
Create and return a dict for tracking the status of a subtask.
Keys for input `subtask_result` and returned subtask_status are:
SubtaskStatus values are:
'task_id' : id of subtask. This is used to pass task information across retries.
'attempted' : number of attempts -- should equal succeeded plus failed
......@@ -80,30 +104,76 @@ def increment_subtask_status(subtask_result, succeeded=0, failed=0, skipped=0, r
should have a maximum count applied
'state' : celery state of the subtask (e.g. QUEUING, PROGRESS, RETRY, FAILURE, SUCCESS)
Kwarg arguments are incremented to the corresponding key in `subtask_result`.
The exception is for `state`, which if specified is used to override the existing value.
"""
new_result = dict(subtask_result)
new_result['attempted'] += (succeeded + failed)
new_result['succeeded'] += succeeded
new_result['failed'] += failed
new_result['skipped'] += skipped
new_result['retried_nomax'] += retried_nomax
new_result['retried_withmax'] += retried_withmax
if state is not None:
new_result['state'] = state
return new_result
Object is not JSON-serializable, so to_dict and from_dict methods are provided so that
it can be passed as a serializable argument to tasks (and be reconstituted within such tasks).
def _get_retry_count(subtask_status):
"""
Calculate the total number of retries.
In future, we may want to include specific error information
indicating the reason for failure.
Also, we should count up "not attempted" separately from attempted/failed.
"""
count = 0
for keyname in ['retried_nomax', 'retried_withmax']:
count += subtask_status.get(keyname, 0)
return count
def __init__(self, task_id, attempted=None, succeeded=0, failed=0, skipped=0, retried_nomax=0, retried_withmax=0, state=None):
"""Construct a SubtaskStatus object."""
self.task_id = task_id
if attempted is not None:
self.attempted = attempted
else:
self.attempted = succeeded + failed
self.succeeded = succeeded
self.failed = failed
self.skipped = skipped
self.retried_nomax = retried_nomax
self.retried_withmax = retried_withmax
self.state = state if state is not None else QUEUING
@classmethod
def from_dict(self, d):
"""Construct a SubtaskStatus object from a dict representation."""
options = dict(d)
task_id = options['task_id']
del options['task_id']
return SubtaskStatus.create(task_id, **options)
@classmethod
def create(self, task_id, **options):
"""Construct a SubtaskStatus object."""
return self(task_id, **options)
def to_dict(self):
"""
Output a dict representation of a SubtaskStatus object.
Use for creating a JSON-serializable representation for use by tasks.
"""
return self.__dict__
def increment(self, succeeded=0, failed=0, skipped=0, retried_nomax=0, retried_withmax=0, state=None):
"""
Update the result of a subtask with additional results.
Kwarg arguments are incremented to the existing values.
The exception is for `state`, which if specified is used to override the existing value.
"""
self.attempted += (succeeded + failed)
self.succeeded += succeeded
self.failed += failed
self.skipped += skipped
self.retried_nomax += retried_nomax
self.retried_withmax += retried_withmax
if state is not None:
self.state = state
def get_retry_count(self):
"""Returns the number of retries of any kind."""
return self.retried_nomax + self.retried_withmax
def __repr__(self):
"""Return print representation of a SubtaskStatus object."""
return 'SubtaskStatus<%r>' % (self.to_dict(),)
def __unicode__(self):
"""Return unicode version of a SubtaskStatus object representation."""
return unicode(repr(self))
def initialize_subtask_info(entry, action_name, total_num, subtask_id_list):
......@@ -125,7 +195,7 @@ def initialize_subtask_info(entry, action_name, total_num, subtask_id_list):
The "subtasks" field also contains a 'status' key, that contains a dict that stores status
information for each subtask. The value for each subtask (keyed by its task_id)
is its subtask status, as defined by create_subtask_status().
is its subtask status, as defined by SubtaskStatus.to_dict().
This information needs to be set up in the InstructorTask before any of the subtasks start
running. If not, there is a chance that the subtasks could complete before the parent task
......@@ -152,7 +222,8 @@ def initialize_subtask_info(entry, action_name, total_num, subtask_id_list):
# Write out the subtasks information.
num_subtasks = len(subtask_id_list)
# Note that may not be necessary to store initial value with all those zeroes!
subtask_status = {subtask_id: create_subtask_status(subtask_id) for subtask_id in subtask_id_list}
# Write out as a dict, so it will go more smoothly into json.
subtask_status = {subtask_id: (SubtaskStatus.create(subtask_id)).to_dict() for subtask_id in subtask_id_list}
subtask_dict = {
'total': num_subtasks,
'succeeded': 0,
......@@ -166,6 +237,70 @@ def initialize_subtask_info(entry, action_name, total_num, subtask_id_list):
return task_progress
def queue_subtasks_for_query(entry, action_name, create_subtask_fcn, item_queryset, item_fields, items_per_query, items_per_task):
"""
Generates and queues subtasks to each execute a chunk of "items" generated by a queryset.
Arguments:
`entry` : the InstructorTask object for which subtasks are being queued.
`action_name` : a past-tense verb that can be used for constructing readable status messages.
`create_subtask_fcn` : a function of two arguments that constructs the desired kind of subtask object.
Arguments are the list of items to be processed by this subtask, and a SubtaskStatus
object reflecting initial status (and containing the subtask's id).
`item_queryset` : a query set that defines the "items" that should be passed to subtasks.
`item_fields` : the fields that should be included in the dict that is returned.
These are in addition to the 'pk' field.
`items_per_query` : size of chunks to break the query operation into.
`items_per_task` : maximum size of chunks to break each query chunk into for use by a subtask.
Returns: the task progress as stored in the InstructorTask object.
"""
task_id = entry.task_id
total_num_items = item_queryset.count()
# Calculate the number of tasks that will be created, and create a list of ids for each task.
total_num_subtasks = _get_number_of_subtasks(total_num_items, items_per_query, items_per_task)
subtask_id_list = [str(uuid4()) for _ in range(total_num_subtasks)]
# Update the InstructorTask with information about the subtasks we've defined.
TASK_LOG.info("Task %s: updating InstructorTask %s with subtask info for %s subtasks to process %s items.",
task_id, entry.id, total_num_subtasks, total_num_items) # pylint: disable=E1101
progress = initialize_subtask_info(entry, action_name, total_num_items, subtask_id_list)
# Construct a generator that will return the recipients to use for each subtask.
# Pass in the desired fields to fetch for each recipient.
item_generator = _generate_items_for_subtask(
item_queryset,
item_fields,
total_num_items,
items_per_query,
items_per_task
)
# Now create the subtasks, and start them running.
TASK_LOG.info("Task %s: creating %s subtasks to process %s items.",
task_id, total_num_subtasks, total_num_items)
num_subtasks = 0
for item_list in item_generator:
subtask_id = subtask_id_list[num_subtasks]
num_subtasks += 1
subtask_status = SubtaskStatus.create(subtask_id)
new_subtask = create_subtask_fcn(item_list, subtask_status)
new_subtask.apply_async()
# Sanity check: we expect the subtask to be properly summing to the original count:
if num_subtasks != len(subtask_id_list):
task_id = entry.task_id
error_fmt = "Task {}: number of tasks generated {} not equal to original total {}"
error_msg = error_fmt.format(task_id, num_subtasks, len(subtask_id_list))
TASK_LOG.error(error_msg)
raise ValueError(error_msg)
# Return the task progress as stored in the InstructorTask object.
return progress
def _acquire_subtask_lock(task_id):
"""
Mark the specified task_id as being in progress.
......@@ -202,7 +337,7 @@ def check_subtask_is_valid(entry_id, current_task_id, new_subtask_status):
Confirms that the current subtask is known to the InstructorTask and hasn't already been completed.
Problems can occur when the parent task has been run twice, and results in duplicate
subtasks being created for the same InstructorTask entry. This can happen when Celery
subtasks being created for the same InstructorTask entry. This maybe happens when Celery
loses its connection to its broker, and any current tasks get requeued.
If a parent task gets requeued, then the same InstructorTask may have a different set of
......@@ -216,6 +351,9 @@ def check_subtask_is_valid(entry_id, current_task_id, new_subtask_status):
The other worker is allowed to finish, and this raises an exception.
Raises a DuplicateTaskException exception if it's not a task that should be run.
If this succeeds, it requires that update_subtask_status() is called to release the lock on the
task.
"""
# Confirm that the InstructorTask actually defines subtasks.
entry = InstructorTask.objects.get(pk=entry_id)
......@@ -236,8 +374,8 @@ def check_subtask_is_valid(entry_id, current_task_id, new_subtask_status):
# Confirm that the InstructorTask doesn't think that this subtask has already been
# performed successfully.
subtask_status = subtask_status_info[current_task_id]
subtask_state = subtask_status.get('state')
subtask_status = SubtaskStatus.from_dict(subtask_status_info[current_task_id])
subtask_state = subtask_status.state
if subtask_state in READY_STATES:
format_str = "Unexpected task_id '{}': already completed - status {} for subtask of instructor task '{}': rejecting task {}"
msg = format_str.format(current_task_id, subtask_status, entry, new_subtask_status)
......@@ -249,8 +387,8 @@ def check_subtask_is_valid(entry_id, current_task_id, new_subtask_status):
if subtask_state == RETRY:
# Check to see if the input number of retries is less than the recorded number.
# If so, then this is an earlier version of the task, and a duplicate.
new_retry_count = _get_retry_count(new_subtask_status)
current_retry_count = _get_retry_count(subtask_status)
new_retry_count = new_subtask_status.get_retry_count()
current_retry_count = subtask_status.get_retry_count()
if new_retry_count < current_retry_count:
format_str = "Unexpected task_id '{}': already retried - status {} for subtask of instructor task '{}': rejecting task {}"
msg = format_str.format(current_task_id, subtask_status, entry, new_subtask_status)
......@@ -292,10 +430,10 @@ def update_subtask_status(entry_id, current_task_id, new_subtask_status):
The "subtasks" field also contains a 'status' key, that contains a dict that stores status
information for each subtask. At the moment, the value for each subtask (keyed by its task_id)
is the value of `status`, but could be expanded in future to store information about failure
messages, progress made, etc.
is the value of the SubtaskStatus.to_dict(), but could be expanded in future to store information
about failure messages, progress made, etc.
"""
TASK_LOG.info("Preparing to update status for email subtask %s for instructor task %d with status %s",
TASK_LOG.info("Preparing to update status for subtask %s for instructor task %d with status %s",
current_task_id, entry_id, new_subtask_status)
try:
......@@ -304,13 +442,13 @@ def update_subtask_status(entry_id, current_task_id, new_subtask_status):
subtask_status_info = subtask_dict['status']
if current_task_id not in subtask_status_info:
# unexpected error -- raise an exception
format_str = "Unexpected task_id '{}': unable to update status for email subtask of instructor task '{}'"
format_str = "Unexpected task_id '{}': unable to update status for subtask of instructor task '{}'"
msg = format_str.format(current_task_id, entry_id)
TASK_LOG.warning(msg)
raise ValueError(msg)
# Update status:
subtask_status_info[current_task_id] = new_subtask_status
subtask_status_info[current_task_id] = new_subtask_status.to_dict()
# Update the parent task progress.
# Set the estimate of duration, but only if it
......@@ -326,10 +464,10 @@ def update_subtask_status(entry_id, current_task_id, new_subtask_status):
# In future, we can make this more responsive by updating status
# between retries, by comparing counts that change from previous
# retry.
new_state = new_subtask_status['state']
new_state = new_subtask_status.state
if new_subtask_status is not None and new_state in READY_STATES:
for statname in ['attempted', 'succeeded', 'failed', 'skipped']:
task_progress[statname] += new_subtask_status[statname]
task_progress[statname] += getattr(new_subtask_status, statname)
# Figure out if we're actually done (i.e. this is the last task to complete).
# This is easier if we just maintain a counter, rather than scanning the
......@@ -349,7 +487,7 @@ def update_subtask_status(entry_id, current_task_id, new_subtask_status):
entry.subtasks = json.dumps(subtask_dict)
entry.task_output = InstructorTask.create_output_for_success(task_progress)
TASK_LOG.info("Task output updated to %s for email subtask %s of instructor task %d",
TASK_LOG.info("Task output updated to %s for subtask %s of instructor task %d",
entry.task_output, current_task_id, entry_id)
TASK_LOG.debug("about to save....")
entry.save()
......
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