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This post is another entry in our ongoing series of guest posts contributed by App Engine developers. Today we partner with Jason Collins and Shawn Rusaw of VendAsta Technologies who discuss their simplistic workflow management system for Python App Engine called Fantasm. Fantasm is very simple to start using but incredibly powerful. So powerful in fact, that they couldn't fit it into one blog post. So for more detail, see the Implementing Workflows on App Engine with Fantasm article in our documentation.
Most software systems of reasonable size need to implement workflows: a series of processing steps and decision points over an artifact, typically a document. You are likely using workflows even if you don't realize it; often processing is built up iteratively over time until you're left with a complex system with lots of subtleties. These systems can mysterious and difficult to manage or extend.
A formal workflow engine can offer help in these instances. It allows developers to break the steps and decision points into manageable and testable chunks, so that processing is predictable and measurable. Workflow engines can offer visibility into system operations and metrics around execution times and failure points. They provide retry mechanisms and allow tasks to be distributed among multiple computers.
We have developed Fantasm to be just such a workflow engine. Fantasm is a Python library that allows you to specify your tasks and steps between those tasks in a YAML configuration file. It uses a finite state machine model where the tasks are states and the steps are transitions. As a developer, you implement the code that executes inside of a state and are only responsible to return an event that is used to identify the appropriate transition to the next state. It is similar to the App Engine Pipeline API but is more simplistic and serves as a user-friendly introduction to the concept of workflows in App Engine apps.
Fantasm has become the central processing tool within our organization and we hope it can be of help to yours. For a more in-depth look at Fantasm (including examples!), please see the full Implementing Workflows on App Engine with Fantasm article in the App Engine documentation.
Posted by Wesley Chun, App Engine team
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I'm Reza and work in London, UK for a startup called TweetDeck. Our vision is to develop the best tools to manage and filter real time information streams like Twitter, Facebook, LinkedIn and MySpace. We offer products like our TweetDeck desktop client built on Adobe AIR, and our iPhone application. We are happy to say that we use App Engine as key part in our backend.

We're a small startup, so early on we started to look for tools that would give us the biggest bang for our buck. Combined with the fact we love Python, we thought it might be worth taking a look at what Google App Engine had to offer. The first feature we started playing with was the mail sending facility.

It was easy! Sending mail was a single call-- no messing around. Combined with the fact that it was sent via tried-and-tested Google Mail servers, this meant that we had a simple mailing solution where we didn't have to deal with spam blacklists, mail retries or SPF records. We could really see App Engine being our sole email provider for transactional emails (new users and forgotten passwords), but also for newsletter-type mailing.

When we got started, our existing backend was hosted on Amazon EC2 and SimpleDB, and we knew that we needed a way for the two systems to communicate with each other. App Engine provides all the basic tools to define any sort of resources you want--but more importantly, it has the Python standard library. We implemented a small mail API, with authentication provided by an HMAC-SHA1 of the request information and a shared secret key. The API has been made extremely general: its JSON input format contains fields to send messages to blocks of email addresses that are either defined in the request itself, or which exist as a template in App Engine (templates are defined as strings in a Python module).

The whole setup currently works quite well. We're already extending our mailing system to use App Engine's task queues-- exposing a number of queues to break large mailing jobs into a series of subtasks, thus spreading mailing sending over a large period of time. We have plans to make an even tighter bridge between our EC2 systems and App Engine, which involves keeping our subscriber list entirely in App Engine, and adding and removing from that list as appropriate.

We also use App Engine for various other prototypes and smaller applications that are part of our product. We use it to serve our "TweetDeck Recommends" feed, and we've even developed small tools to apply TweetDeck fan badges on Twitter homepage backgrounds using the Imaging API! The lesson from us, of course, is that using something like App Engine doesn't have to mean everything runs on it. It's an extremely good platform for creating APIs or smaller parts of your application that do specific tasks, and do them well. Think of it as the UNIX metaphor applied to the Cloud.

We love that we've been able to grow the functionality that Tweetdeck provides by progressively using more of the cloud. App Engine provides the perfect platform to compose new services quickly, iterate on them in production, and scale with demand as Tweetdeck's install base grows. Thanks to App Engine and the cloud, there's nothing holding us back from tackling the needs of our user base.

Here is a video interview of Reza and the Tweetdeck team.


Guest Post by Reza Lotun @rlotun, TweetDeck
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