Python InstructorTask.create_output_for_success方法代码示例

# 需要导入模块: from lms.djangoapps.instructor_task.models import InstructorTask [as 别名]
# 或者: from lms.djangoapps.instructor_task.models.InstructorTask import create_output_for_success [as 别名]
def initialize_subtask_info(entry, action_name, total_num, subtask_id_list):
    """
    Store initial subtask information to InstructorTask object.

    The InstructorTask's "task_output" field is initialized.  This is a JSON-serialized dict.
    Counters for 'attempted', 'succeeded', 'failed', 'skipped' keys are initialized to zero,
    as is the 'duration_ms' value.  A 'start_time' is stored for later duration calculations,
    and the total number of "things to do" is set, so the user can be told how much needs to be
    done overall.  The `action_name` is also stored, to help with constructing more readable
    task_progress messages.

    The InstructorTask's "subtasks" field is also initialized.  This is also a JSON-serialized dict.
    Keys include 'total', 'succeeded', 'retried', 'failed', which are counters for the number of
    subtasks.  'Total' is set here to the total number, while the other three are initialized to zero.
    Once the counters for 'succeeded' and 'failed' match the 'total', the subtasks are done and
    the InstructorTask's "status" will be changed to SUCCESS.

    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 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
    is done creating subtasks.  Doing so also simplifies the save() here, as it avoids the need
    for locking.

    Monitoring code should assume that if an InstructorTask has subtask information, that it should
    rely on the status stored in the InstructorTask object, rather than status stored in the
    corresponding AsyncResult.
    """
    task_progress = {
        'action_name': action_name,
        'attempted': 0,
        'failed': 0,
        'skipped': 0,
        'succeeded': 0,
        'total': total_num,
        'duration_ms': int(0),
        'start_time': time()
    }
    entry.task_output = InstructorTask.create_output_for_success(task_progress)
    entry.task_state = PROGRESS

    # 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!
    # 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,
        'failed': 0,
        'status': subtask_status
    }
    entry.subtasks = json.dumps(subtask_dict)

    # and save the entry immediately, before any subtasks actually start work:
    entry.save_now()
    return task_progress

# 需要导入模块: from lms.djangoapps.instructor_task.models import InstructorTask [as 别名]
# 或者: from lms.djangoapps.instructor_task.models.InstructorTask import create_output_for_success [as 别名]
    def on_success(self, task_progress, task_id, args, kwargs):
        """
        Update InstructorTask object corresponding to this task with info about success.

        Updates task_output and task_state.  But it shouldn't actually do anything
        if the task is only creating subtasks to actually do the work.

        Assumes `task_progress` is a dict containing the task's result, with the following keys:

          'attempted': number of attempts made
          'succeeded': number of attempts that "succeeded"
          'skipped': number of attempts that "skipped"
          'failed': number of attempts that "failed"
          'total': number of possible subtasks to attempt
          'action_name': user-visible verb to use in status messages.  Should be past-tense.
              Pass-through of input `action_name`.
          'duration_ms': how long the task has (or had) been running.

        This is JSON-serialized and stored in the task_output column of the InstructorTask entry.

        """
        TASK_LOG.debug(u'Task %s: success returned with progress: %s', task_id, task_progress)
        # We should be able to find the InstructorTask object to update
        # based on the task_id here, without having to dig into the
        # original args to the task.  On the other hand, the entry_id
        # is the first value passed to all such args, so we'll use that.
        # And we assume that it exists, else we would already have had a failure.
        entry_id = args[0]
        entry = InstructorTask.objects.get(pk=entry_id)
        # Check to see if any subtasks had been defined as part of this task.
        # If not, then we know that we're done.  (If so, let the subtasks
        # handle updating task_state themselves.)
        if len(entry.subtasks) == 0:
            entry.task_output = InstructorTask.create_output_for_success(task_progress)
            entry.task_state = SUCCESS
            entry.save_now()

# 需要导入模块: from lms.djangoapps.instructor_task.models import InstructorTask [as 别名]
# 或者: from lms.djangoapps.instructor_task.models.InstructorTask import create_output_for_success [as 别名]
def _update_instructor_task(instructor_task, task_result):
    """
    Updates and possibly saves a InstructorTask entry based on a task Result.

    Used when updated status is requested.

    The `instructor_task` that is passed in is updated in-place, but
    is usually not saved.  In general, tasks that have finished (either with
    success or failure) should have their entries updated by the task itself,
    so are not updated here.  Tasks that are still running are not updated
    and saved while they run.  The one exception to the no-save rule are tasks that
    are in a "revoked" state.  This may mean that the task never had the
    opportunity to update the InstructorTask entry.

    Tasks that are in progress and have subtasks doing the processing do not look
    to the task's AsyncResult object.  When subtasks are running, the
    InstructorTask object itself is updated with the subtasks' progress,
    not any AsyncResult object.  In this case, the InstructorTask is
    not updated at all.

    Calculates json to store in "task_output" field of the `instructor_task`,
    as well as updating the task_state.

    For a successful task, the json contains the output of the task result.
    For a failed task, the json contains "exception", "message", and "traceback"
    keys.   A revoked task just has a "message" stating it was revoked.
    """
    # Pull values out of the result object as close to each other as possible.
    # If we wait and check the values later, the values for the state and result
    # are more likely to have changed.  Pull the state out first, and
    # then code assuming that the result may not exactly match the state.
    task_id = task_result.task_id
    result_state = task_result.state
    returned_result = task_result.result
    result_traceback = task_result.traceback

    # Assume we don't always save the InstructorTask entry if we don't have to,
    # but that in most cases we will update the InstructorTask in-place with its
    # current progress.
    entry_needs_updating = True
    entry_needs_saving = False
    task_output = None

    if instructor_task.task_state == PROGRESS and len(instructor_task.subtasks) > 0:
        # This happens when running subtasks:  the result object is marked with SUCCESS,
        # meaning that the subtasks have successfully been defined.  However, the InstructorTask
        # will be marked as in PROGRESS, until the last subtask completes and marks it as SUCCESS.
        # We want to ignore the parent SUCCESS if subtasks are still running, and just trust the
        # contents of the InstructorTask.
        entry_needs_updating = False
    elif result_state in [PROGRESS, SUCCESS]:
        # construct a status message directly from the task result's result:
        # it needs to go back with the entry passed in.
        log.info("background task (%s), state %s:  result: %s", task_id, result_state, returned_result)
        task_output = InstructorTask.create_output_for_success(returned_result)
    elif result_state == FAILURE:
        # on failure, the result's result contains the exception that caused the failure
        exception = returned_result
        traceback = result_traceback if result_traceback is not None else ''
        log.warning("background task (%s) failed: %s %s", task_id, returned_result, traceback)
        task_output = InstructorTask.create_output_for_failure(exception, result_traceback)
    elif result_state == REVOKED:
        # on revocation, the result's result doesn't contain anything
        # but we cannot rely on the worker thread to set this status,
        # so we set it here.
        entry_needs_saving = True
        log.warning("background task (%s) revoked.", task_id)
        task_output = InstructorTask.create_output_for_revoked()

    # save progress and state into the entry, even if it's not being saved:
    # when celery is run in "ALWAYS_EAGER" mode, progress needs to go back
    # with the entry passed in.
    if entry_needs_updating:
        instructor_task.task_state = result_state
        if task_output is not None:
            instructor_task.task_output = task_output

        if entry_needs_saving:
            instructor_task.save()

# 需要导入模块: from lms.djangoapps.instructor_task.models import InstructorTask [as 别名]
# 或者: from lms.djangoapps.instructor_task.models.InstructorTask import create_output_for_success [as 别名]
def _update_subtask_status(entry_id, current_task_id, new_subtask_status):
    """
    Update the status of the subtask in the parent InstructorTask object tracking its progress.

    Uses select_for_update to lock the InstructorTask object while it is being updated.
    The operation is surrounded by a try/except/else that permit the manual transaction to be
    committed on completion, or rolled back on error.

    The InstructorTask's "task_output" field is updated.  This is a JSON-serialized dict.
    Accumulates values for 'attempted', 'succeeded', 'failed', 'skipped' from `new_subtask_status`
    into the corresponding values in the InstructorTask's task_output.  Also updates the 'duration_ms'
    value with the current interval since the original InstructorTask started.  Note that this
    value is only approximate, since the subtask may be running on a different server than the
    original task, so is subject to clock skew.

    The InstructorTask's "subtasks" field is also updated.  This is also a JSON-serialized dict.
    Keys include 'total', 'succeeded', 'retried', 'failed', which are counters for the number of
    subtasks.  'Total' is expected to have been set at the time the subtasks were created.
    The other three counters are incremented depending on the value of `status`.  Once the counters
    for 'succeeded' and 'failed' match the 'total', the subtasks are done and the InstructorTask's
    "status" is changed to SUCCESS.

    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 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 subtask %s for instructor task %d with status %s",
                  current_task_id, entry_id, new_subtask_status)

    try:
        entry = InstructorTask.objects.select_for_update().get(pk=entry_id)
        subtask_dict = json.loads(entry.subtasks)
        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 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.to_dict()

        # Update the parent task progress.
        # Set the estimate of duration, but only if it
        # increases.  Clock skew between time() returned by different machines
        # may result in non-monotonic values for duration.
        task_progress = json.loads(entry.task_output)
        start_time = task_progress['start_time']
        prev_duration = task_progress['duration_ms']
        new_duration = int((time() - start_time) * 1000)
        task_progress['duration_ms'] = max(prev_duration, new_duration)

        # Update counts only when subtask is done.
        # 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
        if new_subtask_status is not None and new_state in READY_STATES:
            for statname in ['attempted', 'succeeded', 'failed', 'skipped']:
                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
        # entire new_subtask_status dict.
        if new_state == SUCCESS:
            subtask_dict['succeeded'] += 1
        elif new_state in READY_STATES:
            subtask_dict['failed'] += 1
        num_remaining = subtask_dict['total'] - subtask_dict['succeeded'] - subtask_dict['failed']

        # If we're done with the last task, update the parent status to indicate that.
        # At present, we mark the task as having succeeded.  In future, we should see
        # if there was a catastrophic failure that occurred, and figure out how to
        # report that here.
        if num_remaining <= 0:
            entry.task_state = SUCCESS
        entry.subtasks = json.dumps(subtask_dict)
        entry.task_output = InstructorTask.create_output_for_success(task_progress)

        TASK_LOG.debug("about to save....")
        entry.save()
        TASK_LOG.info("Task output updated to %s for subtask %s of instructor task %d",
                      entry.task_output, current_task_id, entry_id)
    except Exception:
        TASK_LOG.exception("Unexpected error while updating InstructorTask.")
        dog_stats_api.increment('instructor_task.subtask.update_exception')
        raise