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The most obvious use case is data import. A developer looking to import large amounts of data would take the following steps: Create a CSV file containing the data you want to import. The assumption here is that each line of data corresponds to a datastore entity you want to create Upload the CSV file to the blobstore. You’ll need billing to be enabled for this to work. Create your Mapper, push it live and run your job importing your data. This isn’t meant to be a replacement for the bulk uploader tool; merely an alternative. This method requires a good amount more programmatic changes for custom data transforms. The advantage of this method is that the work is done on the server side, whereas the bulk uploader makes use of the remote API to get work done. Let’s get started on each of the steps.

to build Mappers that map across some large, contiguous piece of data as opposed to Entities in the datastore

to write large batch processing jobs without having to think about the plumbing details.

it is a very easy way to perform some operation on every single Entity of a given Kind in your datastore in parallel

What would you have to build for yourself if Mapper weren’t available? Begin querying over every Entity in chained Task Queues Store beginning and end cursors (introduced in 1.3.5) Create tasks to work with chunks of your datastore Write the code to manipulate your data Build an interface to control your batch jobs Build a callback system for your multitudes of parallelized workers to call when the entire task has completed It’s certainly not a trivial amount of work

The mapper API isn't just limited to mapping over datastore entities, either. You can map over lines in a text file in the blobstore, or over the contents of a zip file in the blobstore. It's even possible to write your own data sources

Thanks to the Task Queue API released in SDK 1.2.3, it's easier than ever to do work 'offline', separate from user serving requests. In some cases, however, setting up a handler for each distinct task you want to run can be cumbersome, as can serializing and deserializing complex arguments for the task - particularly if you have many diverse but small tasks that you want to run on the queue. Fortunately, a new library in release 1.2.5 of the SDK makes these ad-hoc tasks much easier to write and execute. This library is found in google.appengine.ext.deferred, and from here on in we'll refer to it as the 'deferred' library. The deferred library lets you bypass all the work of setting up dedicated task handlers and serializing and deserializing your parameters by exposing a simple function, deferred.defer().

To demonstrate how powerful the deferred library can be, we're going to reprise an example from the remote_api article - the Mapper class. Like the example in the remote_api article, this class will make it easy to iterate over a large set of entities, making changes or calculating totals. Unlike the remote_api version, though, our version won't require an external computer to run it on, and it'll be more efficient to boot!

Task Queue items are limited to 10kb of associated data. This means that when the deferred library serializes the details of your call, it must amount to less than 10 kilobytes in order to fit on the Task Queue directly. No need to panic, though: If you try to enqueue a task that is too big to fit on the queue by itself, the deferred library will automatically create a new Entity in the datastore to hold information about the task, and will delete the entity once the task has been run. This means that in practice, your function call can be up to 1MB once serialized.