def attempt_problem(user_data, user_exercise, problem_number, attempt_number,
attempt_content, sha1, seed, completed, count_hints, time_taken,
review_mode, exercise_non_summative, problem_type, ip_address):
if user_exercise and user_exercise.belongs_to(user_data):
dt_now = datetime.datetime.now()
exercise = user_exercise.exercise_model
old_graph = user_exercise.get_user_exercise_graph()
user_exercise.last_done = dt_now
user_exercise.seconds_per_fast_problem = exercise.seconds_per_fast_problem
user_exercise.summative = exercise.summative
user_data.record_activity(user_exercise.last_done)
# If a non-admin tries to answer a problem out-of-order, just ignore it
if problem_number != user_exercise.total_done + 1 and not user_util.is_current_user_developer():
# Only admins can answer problems out of order.
raise QuietException("Problem number out of order (%s vs %s) for user_id: %s submitting attempt content: %s with seed: %s" % (problem_number, user_exercise.total_done + 1, user_data.user_id, attempt_content, seed))
if len(sha1) <= 0:
raise Exception("Missing sha1 hash of problem content.")
if len(seed) <= 0:
raise Exception("Missing seed for problem content.")
if len(attempt_content) > 500:
raise Exception("Attempt content exceeded maximum length.")
# Build up problem log for deferred put
problem_log = models.ProblemLog(
key_name=models.ProblemLog.key_for(user_data, user_exercise.exercise, problem_number),
user=user_data.user,
exercise=user_exercise.exercise,
problem_number=problem_number,
time_taken=time_taken,
time_done=dt_now,
count_hints=count_hints,
hint_used=count_hints > 0,
correct=completed and not count_hints and (attempt_number == 1),
sha1=sha1,
seed=seed,
problem_type=problem_type,
count_attempts=attempt_number,
attempts=[attempt_content],
ip_address=ip_address,
review_mode=review_mode,
)
if exercise.summative:
problem_log.exercise_non_summative = exercise_non_summative
first_response = (attempt_number == 1 and count_hints == 0) or (count_hints == 1 and attempt_number == 0)
if user_exercise.total_done > 0 and user_exercise.streak == 0 and first_response:
bingo('hints_keep_going_after_wrong')
just_earned_proficiency = False
# Users can only attempt problems for themselves, so the experiment
# bucket always corresponds to the one for this current user
struggling_model = StrugglingExperiment.get_alternative_for_user(
user_data, current_user=True) or StrugglingExperiment.DEFAULT
if completed:
user_exercise.total_done += 1
if problem_log.correct:
proficient = user_data.is_proficient_at(user_exercise.exercise)
explicitly_proficient = user_data.is_explicitly_proficient_at(user_exercise.exercise)
suggested = user_data.is_suggested(user_exercise.exercise)
problem_log.suggested = suggested
problem_log.points_earned = points.ExercisePointCalculator(user_exercise, suggested, proficient)
user_data.add_points(problem_log.points_earned)
# Streak only increments if problem was solved correctly (on first attempt)
user_exercise.total_correct += 1
user_exercise.streak += 1
user_exercise.longest_streak = max(user_exercise.longest_streak, user_exercise.streak)
user_exercise.update_proficiency_model(correct=True)
bingo([
'struggling_problems_correct',
'suggested_activity_problems_correct',
])
if user_exercise.progress >= 1.0 and not explicitly_proficient:
bingo([
'hints_gained_proficiency_all',
'struggling_gained_proficiency_all',
'suggested_activity_gained_proficiency_all',
])
if not user_exercise.has_been_proficient():
bingo('hints_gained_new_proficiency')
if user_exercise.history_indicates_struggling(struggling_model):
bingo('struggling_gained_proficiency_post_struggling')
user_exercise.set_proficient(user_data)
user_data.reassess_if_necessary()
just_earned_proficiency = True
problem_log.earned_proficiency = True
util_badges.update_with_user_exercise(
user_data,
user_exercise,
include_other_badges=True,
action_cache=last_action_cache.LastActionCache.get_cache_and_push_problem_log(user_data, problem_log))
# Update phantom user notifications
util_notify.update(user_data, user_exercise)
bingo([
'hints_problems_done',
'struggling_problems_done',
'suggested_activity_problems_done',
])
else:
# Only count wrong answer at most once per problem
if first_response:
user_exercise.update_proficiency_model(correct=False)
bingo([
'hints_wrong_problems',
'struggling_problems_wrong',
'suggested_activity_problems_wrong',
])
if user_exercise.is_struggling(struggling_model):
bingo('struggling_struggled_binary')
# If this is the first attempt, update review schedule appropriately
if attempt_number == 1:
user_exercise.schedule_review(completed)
user_exercise_graph = models.UserExerciseGraph.get_and_update(user_data, user_exercise)
goals_updated = GoalList.update_goals(user_data,
lambda goal: goal.just_did_exercise(user_data, user_exercise,
just_earned_proficiency))
user_data.uservideocss_version += 1
if user_exercise.progress >= 1.0:
UserVideoCss.set_completed(user_data.key(), exercise.key(), user_data.uservideocss_version)
else:
UserVideoCss.set_started(user_data.key(), exercise.key(), user_data.uservideocss_version)
# Bulk put
db.put([user_data, user_exercise, user_exercise_graph.cache])
# Defer the put of ProblemLog for now, as we think it might be causing hot tablets
# and want to shift it off to an automatically-retrying task queue.
# http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/
deferred.defer(models.commit_problem_log, problem_log,
_queue="problem-log-queue")
if user_data is not None and user_data.coaches:
# Making a separate queue for the log summaries so we can clearly see how much they are getting used
deferred.defer(models.commit_log_summary_coaches, problem_log, user_data.coaches,
_queue="log-summary-queue",
)
return user_exercise, user_exercise_graph, goals_updated
def attempt_problem(user_data,
user_exercise,
problem_number,
attempt_number,
attempt_content,
sha1,
seed,
completed,
count_hints,
time_taken,
review_mode,
topic_mode,
problem_type,
ip_address,
async_problem_log_put=True):
if user_exercise and user_exercise.belongs_to(user_data):
dt_now = datetime.datetime.now()
exercise = user_exercise.exercise_model
old_graph = user_exercise.get_user_exercise_graph()
user_exercise.last_done = dt_now
user_exercise.seconds_per_fast_problem = exercise.seconds_per_fast_problem
user_data.record_activity(user_exercise.last_done)
# If a non-admin tries to answer a problem out-of-order, just ignore it
if problem_number != user_exercise.total_done + 1 and not user_util.is_current_user_developer(
):
# Only admins can answer problems out of order.
raise custom_exceptions.QuietException(
"Problem number out of order (%s vs %s) for user_id: %s submitting attempt content: %s with seed: %s"
% (problem_number, user_exercise.total_done + 1,
user_data.user_id, attempt_content, seed))
if len(sha1) <= 0:
raise Exception("Missing sha1 hash of problem content.")
if len(seed) <= 0:
raise Exception("Missing seed for problem content.")
if len(attempt_content) > 500:
raise Exception("Attempt content exceeded maximum length.")
# Build up problem log for deferred put
problem_log = exercise_models.ProblemLog(
key_name=exercise_models.ProblemLog.key_for(
user_data, user_exercise.exercise, problem_number),
user=user_data.user,
exercise=user_exercise.exercise,
problem_number=problem_number,
time_taken=time_taken,
time_done=dt_now,
count_hints=count_hints,
hint_used=count_hints > 0,
correct=completed and not count_hints and (attempt_number == 1),
sha1=sha1,
seed=seed,
problem_type=problem_type,
count_attempts=attempt_number,
attempts=[attempt_content],
ip_address=ip_address,
review_mode=review_mode,
topic_mode=topic_mode,
)
first_response = (attempt_number == 1
and count_hints == 0) or (count_hints == 1
and attempt_number == 0)
if user_exercise.total_done > 0 and user_exercise.streak == 0 and first_response:
bingo('hints_keep_going_after_wrong')
just_earned_proficiency = False
# Users can only attempt problems for themselves, so the experiment
# bucket always corresponds to the one for this current user
struggling_model = StrugglingExperiment.get_alternative_for_user(
user_data, current_user=True) or StrugglingExperiment.DEFAULT
if completed:
user_exercise.total_done += 1
if problem_log.correct:
proficient = user_data.is_proficient_at(user_exercise.exercise)
explicitly_proficient = user_data.is_explicitly_proficient_at(
user_exercise.exercise)
suggested = user_data.is_suggested(user_exercise.exercise)
problem_log.suggested = suggested
problem_log.points_earned = points.ExercisePointCalculator(
user_exercise, topic_mode, suggested, proficient)
user_data.add_points(problem_log.points_earned)
# Streak only increments if problem was solved correctly (on first attempt)
user_exercise.total_correct += 1
user_exercise.streak += 1
user_exercise.longest_streak = max(
user_exercise.longest_streak, user_exercise.streak)
user_exercise.update_proficiency_model(correct=True)
bingo([
'struggling_problems_correct',
])
if user_exercise.progress >= 1.0 and not explicitly_proficient:
bingo([
'hints_gained_proficiency_all',
'struggling_gained_proficiency_all',
])
if not user_exercise.has_been_proficient():
bingo('hints_gained_new_proficiency')
if user_exercise.history_indicates_struggling(
struggling_model):
bingo('struggling_gained_proficiency_post_struggling')
user_exercise.set_proficient(user_data)
user_data.reassess_if_necessary()
just_earned_proficiency = True
problem_log.earned_proficiency = True
badges.util_badges.update_with_user_exercise(
user_data,
user_exercise,
include_other_badges=True,
action_cache=badges.last_action_cache.LastActionCache.
get_cache_and_push_problem_log(user_data, problem_log))
# Update phantom user notifications
phantom_users.util_notify.update(user_data, user_exercise)
bingo([
'hints_problems_done',
'struggling_problems_done',
])
else:
# Only count wrong answer at most once per problem
if first_response:
user_exercise.update_proficiency_model(correct=False)
bingo([
'hints_wrong_problems',
'struggling_problems_wrong',
])
if user_exercise.is_struggling(struggling_model):
bingo('struggling_struggled_binary')
# If this is the first attempt, update review schedule appropriately
if attempt_number == 1:
user_exercise.schedule_review(completed)
user_exercise_graph = exercise_models.UserExerciseGraph.get_and_update(
user_data, user_exercise)
goals_updated = GoalList.update_goals(
user_data, lambda goal: goal.just_did_exercise(
user_data, user_exercise, just_earned_proficiency))
# Bulk put
db.put([user_data, user_exercise, user_exercise_graph.cache])
if async_problem_log_put:
# Defer the put of ProblemLog for now, as we think it might be causing hot tablets
# and want to shift it off to an automatically-retrying task queue.
# http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/
deferred.defer(exercise_models.commit_problem_log,
problem_log,
_queue="problem-log-queue",
_url="/_ah/queue/deferred_problemlog")
else:
exercise_models.commit_problem_log(problem_log)
if user_data is not None and user_data.coaches:
# Making a separate queue for the log summaries so we can clearly see how much they are getting used
deferred.defer(video_models.commit_log_summary_coaches,
problem_log,
user_data.coaches,
_queue="log-summary-queue",
_url="/_ah/queue/deferred_log_summary")
return user_exercise, user_exercise_graph, goals_updated
def attempt_problem(user_data, user_exercise, problem_number, attempt_number,
attempt_content, sha1, seed, completed, count_hints, time_taken,
review_mode, topic_mode, problem_type, ip_address, card, stack_uid,
topic_id, cards_done, cards_left, async_problem_log_put=True,
async_stack_log_put=True):
if user_exercise and user_exercise.belongs_to(user_data):
dt_now = datetime.datetime.now()
exercise = user_exercise.exercise_model
user_exercise.last_done = dt_now
user_exercise.seconds_per_fast_problem = exercise.seconds_per_fast_problem
user_data.record_activity(user_exercise.last_done)
# If somebody tries to answer a problem out-of-order, we need to raise
# an exception.
if problem_number != user_exercise.total_done + 1:
# If we hit this error, make absolutely sure the UserExerciseCache
# is up-to-date with this exercise's latest UserExercise state.
# If they are out of sync, the user may repeatedly hit this crash.
user_exercise_cache = exercise_models.UserExerciseCache.get(user_data)
user_exercise_cache.update(user_exercise)
user_exercise_cache.put()
# If the client thinks it is ahead of the server, then we have an
# issue worth paying attention to.
error_class = Exception
# If the client is behind the server, keep the error log quiet
# because this is so easily caused by having two exercise tabs open
# and letting one tab's client fall behind the server's state.
if problem_number < user_exercise.total_done + 1:
error_class = custom_exceptions.QuietException
# If the client isn't aware that the user has recently logged out,
# keep the error log quiet because this is so easily caused by
# logging out of one tab and continuing work in another.
if user_exercise.total_done == 0 and user_data.is_phantom:
error_class = custom_exceptions.QuietException
msg = "Problem out of order (%s, %s) for uid:%s, content:%s, seed:%s"
raise error_class(msg % (problem_number,
user_exercise.total_done + 1, user_data.user_id,
attempt_content, seed))
if len(sha1) <= 0:
raise Exception("Missing sha1 hash of problem content.")
if len(seed) <= 0:
raise Exception("Missing seed for problem content.")
if len(attempt_content) > 500:
raise Exception("Attempt content exceeded maximum length.")
# Build up problem log for deferred put
problem_log = exercise_models.ProblemLog(
key_name=exercise_models.ProblemLog.key_for(user_data, user_exercise.exercise, problem_number),
user=user_data.user,
user_id=user_data.user_id,
exercise=user_exercise.exercise,
problem_number=problem_number,
time_taken=time_taken,
time_done=dt_now,
count_hints=count_hints,
hint_used=count_hints > 0,
correct=completed and not count_hints and (attempt_number == 1),
sha1=sha1,
seed=seed,
problem_type=problem_type,
count_attempts=attempt_number,
attempts=[attempt_content],
ip_address=ip_address,
review_mode=review_mode,
topic_mode=topic_mode,
)
first_response = (attempt_number == 1 and count_hints == 0) or (count_hints == 1 and attempt_number == 0)
just_earned_proficiency = False
# Users can only attempt problems for themselves, so the experiment
# bucket always corresponds to the one for this current user
struggling_model = StrugglingExperiment.get_alternative_for_user(
user_data, current_user=True) or StrugglingExperiment.DEFAULT
if completed:
user_exercise.total_done += 1
if problem_log.correct:
proficient = user_data.is_proficient_at(user_exercise.exercise)
explicitly_proficient = user_data.is_explicitly_proficient_at(user_exercise.exercise)
suggested = user_data.is_suggested(user_exercise.exercise)
problem_log.suggested = suggested
problem_log.points_earned = points.ExercisePointCalculator(user_exercise, topic_mode, suggested, proficient)
user_data.add_points(problem_log.points_earned)
# Streak only increments if problem was solved correctly (on first attempt)
user_exercise.total_correct += 1
user_exercise.streak += 1
user_exercise.longest_streak = max(user_exercise.longest_streak, user_exercise.streak)
user_exercise.update_proficiency_model(correct=True)
gae_bingo.gae_bingo.bingo([
'struggling_problems_correct',
'problem_correct_binary', # Core metric
'problem_correct_count', # Core metric
])
if user_exercise.progress >= 1.0 and not explicitly_proficient:
gae_bingo.gae_bingo.bingo([
'struggling_gained_proficiency_all',
])
if not user_exercise.has_been_proficient():
gae_bingo.gae_bingo.bingo([
'new_proficiency_binary', # Core metric
'new_proficiency_count', # Core metric
])
if user_exercise.history_indicates_struggling(struggling_model):
gae_bingo.gae_bingo.bingo(
'struggling_gained_proficiency_post_struggling')
user_exercise.set_proficient(user_data)
user_data.reassess_if_necessary()
just_earned_proficiency = True
problem_log.earned_proficiency = True
badges.util_badges.update_with_user_exercise(
user_data,
user_exercise,
include_other_badges=True,
action_cache=badges.last_action_cache.LastActionCache.get_cache_and_push_problem_log(user_data, problem_log))
# Update phantom user notifications
phantom_users.util_notify.update(user_data, user_exercise)
gae_bingo.gae_bingo.bingo([
'struggling_problems_done',
'problem_attempt_binary', # Core metric
'problem_attempt_count', # Core metric
])
else:
# Only count wrong answer at most once per problem
if first_response:
user_exercise.update_proficiency_model(correct=False)
gae_bingo.gae_bingo.bingo([
'struggling_problems_wrong',
'problem_incorrect_count', # Core metric
'problem_incorrect_binary', # Core metric
])
if user_exercise.is_struggling(struggling_model):
gae_bingo.gae_bingo.bingo('struggling_struggled_binary')
# If this is the first attempt, update review schedule appropriately
if attempt_number == 1:
user_exercise.schedule_review(completed)
user_exercise_graph = exercise_models.UserExerciseGraph.get_and_update(user_data, user_exercise)
goals_updated = GoalList.update_goals(user_data,
lambda goal: goal.just_did_exercise(user_data, user_exercise,
just_earned_proficiency))
# Bulk put
db.put([user_data, user_exercise, user_exercise_graph.cache])
if async_problem_log_put:
# Defer the put of ProblemLog for now, as we think it might be causing hot tablets
# and want to shift it off to an automatically-retrying task queue.
# http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/
deferred.defer(exercise_models.commit_problem_log, problem_log,
_queue="problem-log-queue",
_url="/_ah/queue/deferred_problemlog")
else:
exercise_models.commit_problem_log(problem_log)
if user_data is not None and user_data.coaches:
# Making a separate queue for the log summaries so we can clearly see how much they are getting used
deferred.defer(video_models.commit_log_summary_coaches, problem_log, user_data.coaches,
_queue="log-summary-queue",
_url="/_ah/queue/deferred_log_summary")
if user_data is not None and completed and stack_uid:
# Update the stack log iff the user just finished this card.
# If the request is missing the UID of the stack we're on, then
# this is an old stack and we just won't log it
stack_log_source = exercise_models.StackLog(
key_name=exercise_models.StackLog.key_for(user_data.user_id, stack_uid),
user_id=user_data.user_id,
time_last_done=datetime.datetime.now(),
review_mode=review_mode,
topic_mode=topic_mode,
exercise_id=user_exercise.exercise,
topic_id=topic_id,
cards_list=[],
extra_data={},
)
if topic_mode:
stack_log_source.extra_data['topic_mode'] = calc_topic_mode_log_stats(
user_exercise_graph, topic_id, just_earned_proficiency)
if async_stack_log_put:
deferred.defer(exercise_models.commit_stack_log,
stack_log_source, card, cards_done, cards_left,
problem_log.__class__.__name__,
str(problem_log.key()),
_queue="stack-log-queue",
_url="/_ah/queue/deferred_stacklog")
else:
exercise_models.commit_stack_log(
stack_log_source, card, cards_done, cards_left,
problem_log.__class__.__name__, str(problem_log.key()))
return user_exercise, user_exercise_graph, goals_updated
def attempt_problem(user_data,
user_exercise,
problem_number,
attempt_number,
attempt_content,
sha1,
seed,
completed,
count_hints,
time_taken,
review_mode,
topic_mode,
problem_type,
ip_address,
card,
stack_uid,
topic_id,
cards_done,
cards_left,
async_problem_log_put=True,
async_stack_log_put=True):
if user_exercise and user_exercise.belongs_to(user_data):
dt_now = datetime.datetime.now()
exercise = user_exercise.exercise_model
user_exercise.last_done = dt_now
user_exercise.seconds_per_fast_problem = exercise.seconds_per_fast_problem
user_data.record_activity(user_exercise.last_done)
# If somebody tries to answer a problem out-of-order, we need to raise
# an exception.
if problem_number != user_exercise.total_done + 1:
# If we hit this error, make absolutely sure the UserExerciseCache
# is up-to-date with this exercise's latest UserExercise state.
# If they are out of sync, the user may repeatedly hit this crash.
user_exercise_cache = exercise_models.UserExerciseCache.get(
user_data)
user_exercise_cache.update(user_exercise)
user_exercise_cache.put()
# If the client thinks it is ahead of the server, then we have an
# issue worth paying attention to.
error_class = Exception
# If the client is behind the server, keep the error log quiet
# because this is so easily caused by having two exercise tabs open
# and letting one tab's client fall behind the server's state.
if problem_number < user_exercise.total_done + 1:
error_class = custom_exceptions.QuietException
# If the client isn't aware that the user has recently logged out,
# keep the error log quiet because this is so easily caused by
# logging out of one tab and continuing work in another.
if user_exercise.total_done == 0 and user_data.is_phantom:
error_class = custom_exceptions.QuietException
msg = "Problem out of order (%s, %s) for uid:%s, content:%s, seed:%s"
raise error_class(msg %
(problem_number, user_exercise.total_done + 1,
user_data.user_id, attempt_content, seed))
if len(sha1) <= 0:
raise Exception("Missing sha1 hash of problem content.")
if len(seed) <= 0:
raise Exception("Missing seed for problem content.")
if len(attempt_content) > 500:
raise Exception("Attempt content exceeded maximum length.")
# Build up problem log for deferred put
problem_log = exercise_models.ProblemLog(
key_name=exercise_models.ProblemLog.key_for(
user_data, user_exercise.exercise, problem_number),
user=user_data.user,
user_id=user_data.user_id,
exercise=user_exercise.exercise,
problem_number=problem_number,
time_taken=time_taken,
time_done=dt_now,
count_hints=count_hints,
hint_used=count_hints > 0,
correct=completed and not count_hints and (attempt_number == 1),
sha1=sha1,
seed=seed,
problem_type=problem_type,
count_attempts=attempt_number,
attempts=[attempt_content],
ip_address=ip_address,
review_mode=review_mode,
topic_mode=topic_mode,
)
first_response = (attempt_number == 1
and count_hints == 0) or (count_hints == 1
and attempt_number == 0)
just_earned_proficiency = False
# Users can only attempt problems for themselves, so the experiment
# bucket always corresponds to the one for this current user
struggling_model = StrugglingExperiment.get_alternative_for_user(
user_data, current_user=True) or StrugglingExperiment.DEFAULT
if completed:
user_exercise.total_done += 1
if problem_log.correct:
proficient = user_data.is_proficient_at(user_exercise.exercise)
explicitly_proficient = user_data.is_explicitly_proficient_at(
user_exercise.exercise)
suggested = user_data.is_suggested(user_exercise.exercise)
problem_log.suggested = suggested
problem_log.points_earned = points.ExercisePointCalculator(
user_exercise, topic_mode, suggested, proficient)
user_data.add_points(problem_log.points_earned)
# Streak only increments if problem was solved correctly (on first attempt)
user_exercise.total_correct += 1
user_exercise.streak += 1
user_exercise.longest_streak = max(
user_exercise.longest_streak, user_exercise.streak)
user_exercise.update_proficiency_model(correct=True)
gae_bingo.gae_bingo.bingo([
'struggling_problems_correct',
'problem_correct_binary', # Core metric
'problem_correct_count', # Core metric
])
if user_exercise.progress >= 1.0 and not explicitly_proficient:
gae_bingo.gae_bingo.bingo([
'struggling_gained_proficiency_all',
])
if not user_exercise.has_been_proficient():
gae_bingo.gae_bingo.bingo([
'new_proficiency_binary', # Core metric
'new_proficiency_count', # Core metric
])
if user_exercise.history_indicates_struggling(
struggling_model):
gae_bingo.gae_bingo.bingo(
'struggling_gained_proficiency_post_struggling')
user_exercise.set_proficient(user_data)
user_data.reassess_if_necessary()
just_earned_proficiency = True
problem_log.earned_proficiency = True
badges.util_badges.update_with_user_exercise(
user_data,
user_exercise,
include_other_badges=True,
action_cache=badges.last_action_cache.LastActionCache.
get_cache_and_push_problem_log(user_data, problem_log))
# Update phantom user notifications
phantom_users.util_notify.update(user_data, user_exercise)
gae_bingo.gae_bingo.bingo([
'struggling_problems_done',
'problem_attempt_binary', # Core metric
'problem_attempt_count', # Core metric
])
else:
# Only count wrong answer at most once per problem
if first_response:
user_exercise.update_proficiency_model(correct=False)
gae_bingo.gae_bingo.bingo([
'struggling_problems_wrong',
'problem_incorrect_count', # Core metric
'problem_incorrect_binary', # Core metric
])
if user_exercise.is_struggling(struggling_model):
gae_bingo.gae_bingo.bingo('struggling_struggled_binary')
# If this is the first attempt, update review schedule appropriately
if attempt_number == 1:
user_exercise.schedule_review(completed)
user_exercise_graph = exercise_models.UserExerciseGraph.get_and_update(
user_data, user_exercise)
goals_updated = GoalList.update_goals(
user_data, lambda goal: goal.just_did_exercise(
user_data, user_exercise, just_earned_proficiency))
# Bulk put
db.put([user_data, user_exercise, user_exercise_graph.cache])
if async_problem_log_put:
# Defer the put of ProblemLog for now, as we think it might be causing hot tablets
# and want to shift it off to an automatically-retrying task queue.
# http://ikaisays.com/2011/01/25/app-engine-datastore-tip-monotonically-increasing-values-are-bad/
deferred.defer(exercise_models.commit_problem_log,
problem_log,
_queue="problem-log-queue",
_url="/_ah/queue/deferred_problemlog")
else:
exercise_models.commit_problem_log(problem_log)
if user_data is not None and user_data.coaches:
# Making a separate queue for the log summaries so we can clearly see how much they are getting used
deferred.defer(video_models.commit_log_summary_coaches,
problem_log,
user_data.coaches,
_queue="log-summary-queue",
_url="/_ah/queue/deferred_log_summary")
if user_data is not None and completed and stack_uid:
# Update the stack log iff the user just finished this card.
# If the request is missing the UID of the stack we're on, then
# this is an old stack and we just won't log it
stack_log_source = exercise_models.StackLog(
key_name=exercise_models.StackLog.key_for(
user_data.user_id, stack_uid),
user_id=user_data.user_id,
time_last_done=datetime.datetime.now(),
review_mode=review_mode,
topic_mode=topic_mode,
exercise_id=user_exercise.exercise,
topic_id=topic_id,
cards_list=[],
extra_data={},
)
if topic_mode:
stack_log_source.extra_data[
'topic_mode'] = calc_topic_mode_log_stats(
user_exercise_graph, topic_id, just_earned_proficiency)
if async_stack_log_put:
deferred.defer(exercise_models.commit_stack_log,
stack_log_source,
card,
cards_done,
cards_left,
problem_log.__class__.__name__,
str(problem_log.key()),
_queue="stack-log-queue",
_url="/_ah/queue/deferred_stacklog")
else:
exercise_models.commit_stack_log(
stack_log_source, card, cards_done, cards_left,
problem_log.__class__.__name__, str(problem_log.key()))
return user_exercise, user_exercise_graph, goals_updated