Check out the previous post if you haven't already. In this section, we'll define the PopulateBatch function and explore the ndb models and Task Queue operations that make it work.
Imports
Before defining the models and helper functions in models.py, let's first review the imports:import json from google.appengine.api import channel from google.appengine.ext.deferred import defer from google.appengine.ext import ndbAgain, we import json and channel for serialization and message passing. We import the defer function from the deferred library to abstract away task creation and take advantage of the ability to "defer" a function call to another thread of execution. Finally, we import ndb as a means for interacting with the App Engine Datastore.
Method Wrapper Built for Tasks
As we saw in the BeginWork handler in part two, units of work are passed to PopulateBatch as 3-tuples containing a method, the positional arguments and the keyword arguments to that method.In order to keep our task from hanging indefinitely due to unseen errors and to implicitly include the work unit in the batch, we define a wrapper around these method calls:
def AlwaysComplete(task, method, *args, **kwargs):
try:
method(*args, **kwargs)
except: # TODO: Consider failing differently.
pass
finally:
defer(task.Complete)
As you can see, we catch any and all errors thrown by our method and don't retry the method if it fails. In our example, if the call method(*args, **kwargs) fails, the data won’t be sent through the channel and the given square will not show up in the quilt. However, since we catch these exceptions, the batch will complete and the spinner will disappear with this square still missing.This part is likely going to be customized to the specific work involved, but for our case, we didn't want individual failures to cause the whole batch to fail. In addition, we implicitly link the work unit with a special type of task object in the datastore.
In the finally section of the error catch, we defer the Complete method on the task corresponding to this work unit. We defer the call to this complete method in order to avoid any errors (possibly from a failed datastore action) that the method may cause. If it were to throw an error, since AlwaysComplete is called in a deferred task, the task would retry and our worker unit would execute (or fail) again, which is bad if our user interface is not idempotent.
Task Model
As we saw above, we need a datastore model to represent tasks within a batch. We start out initially with a model having only one attribute — a boolean representing whether or not the task has completed.class BatchTask(ndb.Model): # Very important that the default value True of `indexed` is used here # since we need to query on BatchTask.completed completed = ndb.BooleanProperty(default=False)As we know, we'll need to define a Complete method in order to use the task in AlwaysComplete, but before doing so, we'll define another method which will put the task object in the datastore and pass a unit of work to AlwaysComplete:
@ndb.transactional
def Populate(self, method, *args, **kwargs):
self.put()
kwargs['_transactional'] = True
defer(AlwaysComplete, self.key, method, *args, **kwargs)
In this Populate method, we first put the object in the datastore transactionally by using the ndb.transactional decorator. By adding the _transactional keyword to the keyword arguments, defer strips away the underscore and creates a transactional task. By doing this"the task is only enqueued — and guaranteed to be enqueued — if the transaction is committed successfully."We need this deferred task to be enqueued transactionally for consistency of the completed boolean attribute. The datastore put in Populate uses the default value of False, but after Complete is called we want to set this boolean to True. If this value was not consistent, we may have a race condition that resulted in a completed task in the datastore being marked as incomplete. As we'll see later, we rely on this consistency for a query that will help us determine if our batch is done.
To signal that a unit of work has completed, we define the Complete method on the task object:
@ndb.transactional
def Complete(self):
self.completed = True
self.put()
batcher_parent = self.key.parent().get()
defer(batcher_parent.CheckComplete, _transactional=True)
It performs two functions. First, it sets completed to True in a transaction. Second, it retrieves the parent entity of the task object and defers the CheckComplete method on this parent. As we will see in more depth in the PopulateBatch function, we use a special type of batch parent object to create an entity group containing all the worker tasks for the batch. We don't want to check if the batch has completed until the datastore put has succeeded, so we defer the call to call to CheckComplete transactionally, just as we did with AlwaysComplete in the Populate method.Note: It may seem that these get calls to retrieve the parent via self.key.parent().get() are using more bandwidth than necessary. However, we are relying here on the power of ndb. Using a combination of instance caching and memcache, most (if not all) of these gets will use the cache and will not incur the cost of a round-trip to the datastore.
Batch Parent Model
Given what we rely on in BatchTask, we need to define a special type of datastore object that will act as the parent entity for a batch. Since we are going to use it to check when a batch is complete, we define the boolean attribute all_tasks_loaded to signal whether or not all worker tasks from the batch have begun. We can use this as a short circuit in our CheckComplete method (or as a guard against premature completion).class TaskBatcher(ndb.Model): all_tasks_loaded = ndb.BooleanProperty(default=False, indexed=False)To check if a batch is complete, we first determine if all tasks have loaded. If that is the case, we perform an ancestor query that simply attempts to fetch the first worker task in the entity group which has not yet completed. If such a task does not exist, we know the batch has completed, and so start to clean up the task and batch parent objects from the datastore.
def CheckComplete(self):
# Does not need to be transactional since it doesn't change data
session_id = self.key.id()
if self.all_tasks_loaded:
incomplete = BatchTask.query(BatchTask.completed == False,
ancestor=self.key).fetch(1)
if len(incomplete) == 0:
channel.send_message(session_id, json.dumps({'status': 'complete'}))
self.CleanUp()
return
channel.send_message(session_id, json.dumps({'status': 'incomplete'}))
We again do the utmost at this step to ensure consistency by using an ancestor query:"There are scenarios in which any pending modifications are guaranteed to be completely applied...any ancestor queries in the High Replication datastore. In both cases, query results will always be current and consistent."After checking if a batch is complete, we need to communicate the status back to the client. We'll rely on PopulateBatch to create instances of TaskBatcher with the ID of the session corresponding to the batch as the datastore key. We send a status complete or incomplete message to the client using the session ID for the channel. In order to correctly handle these messages on the client, we'll need to update the onmessage handler (defined in part two) to account for status updates:
socket.onmessage = function(msg) { var response = JSON.parse(msg.data); if (response.status !== undefined) { setStatus(response.status); } else { var squareIndex = 8*response.row + response.column; var squareId = '#square' + squareIndex.toString(); $(squareId).css('background-color', response.color); } }Just as the setStatus method revealed the progress spinner when work began, it will remove the spinner when the status is complete.
We'll next define the CleanUp method that is called when the batch is complete:
def CleanUp(self):
children = BatchTask.query(ancestor=self.key).iter(keys_only=True)
ndb.delete_multi(children)
self.key.delete()
This method uses the key from the batch parent to perform another ancestor query and creates an object which can iterate over all the keys of the tasks in the batch. By using the delete_multi function provided by ndb, we are able to delete these in parallel rather than waiting for each to complete. After deleting all the tasks, the batcher deletes itself and clean up is done. Since the TaskBatcher.CheckComplete spawns CleanUp in a deferred task, if the deletes time out, the task will try again until all tasks in the batch are deleted.As a final method on TaskBatcher, we define something similar to BatchTask.Populate that is triggered after all workers in the batch have been added:
@ndb.transactional
def Ready(self):
self.all_tasks_loaded = True
self.put()
self.CheckComplete()
This simply signals that all tasks from the batch have loaded by setting all_tasks_loaded to True and calls CheckComplete in case all the tasks in the batch have already completed. This check is necessary because if all worker tasks complete before all_tasks_loaded is True, then none of the checks initiated by those tasks would signal completion. We use a transaction to avoid a race condition with the initial datastore put — a put which is a signal that all tasks have not loaded.Populating a Batch
With our two models in hand, we are finally ready to define the PopulateBatch function used (in part two) by the BeginWork handler. We want users of this function to be able to call it directly, but don't want it to block the process they call it in, so we wrap the real function in a function that will simply defer the work:def PopulateBatch(session_id, work): defer(_PopulateBatch, session_id, work)In the actual function, we first create a TaskBatcher object using the session ID as the key and put it into the datastore using the default value of False for all_tasks_loaded. Since this is a single synchronous put, it blocks the thread of execution and we can be sure our parent is in the datastore before members of the entity group (the task objects) are created.
def _PopulateBatch(session_id, work): batcher_key = ndb.Key(TaskBatcher, session_id) batcher = TaskBatcher(key=batcher_key) batcher.put()After doing this, we loop through all the 3-tuples in the passed in batch of work. For each unit of work, we create a task using the batcher as parent and then call the Populate method on the task using the method, positional arguments and keyword arguments provided in the unit of work.
for method, args, kwargs in work:
task = BatchTask(parent=batcher_key)
task.Populate(method, *args, **kwargs)
Finally, to signal that all tasks in the batch have been added, we call the Ready method on the batch parent:batcher.Ready()Note: This approach can cause performance issues as the number of tasks grows, since contentious puts within the entity group can cause task completions to stall or retry. I (or my colleagues) will be following up with two posts on the following topics:
- using task tagging and pull queues to achieve a similar result, but reducing contention
- exploring ways to extend this model to a hierarchical model where tasks may have subtasks
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