Analysis: Google App Engine alluring, will be hard to escape

vices, Google is betting that developers and startups will opt to host their applications in Google's cloud, taking advantage of Google APIs, and leveraging the nearly unlimited processing po

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In comparison, Amazon's offering is broken out into functional services. You can use Amazon's S3 storage solution by itself, or in conjunction with their computing capacity services, or with their payment service, fulfillment service, or queuing service. While Amazon's services are broad, generalized, and language/platform agnostic, Google's wager is geared exclusively towards web applications and the services developers require to make their applications run.

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Google has built an infrastructure that allows developers to create web applications programmed in Python code and upload them to and run on Google servers. Developers are provided with a number of optional Google-related services such as an authentication service built on top of Google accounts, a fast datastore backend based on Google's BigTable project, and Google's infrastructure for delivering e-mail.

The Google App Engine currently only supports the Python runtime for running applications and supports any frameworks that speak CGI or WSGI (with a CGI adaptor). While individuals who are unfamiliar with Python may find this initial requirement a setback, there are a number of web frameworks compatible with the service to make their transition easier. These include Django, CherryPy, Pylons, and web.py. Although many of these work just fine, the App Engine does not support certain aspects of some of these. Most noticeably, Django's models are not supported due to the unique nature of Google's datastore. These have been supplanted with Google APIs for storage, authentication, and more. Google also provides some workarounds for applications that depend on Django database models to function, like a bridge for Django form validation.

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Most noticeably, Django's models are not supported due to the unique nature of Google's datastore. These have been supplanted with Google APIs for storage, authentication, and more.

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The downsides to this program are fairly apparent. First, developers must write all of their code in Python. While Python is a great dynamic language with a robust standard library, and over 15 years of active development, there will be some prospective users who would rather deploy their code in a different language. Google reiterates several times within the documentation that Python is only the first of many, but that they are still in the process of considering other languages for use in the project at this time.

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Once you've created an established application on top of Google's authentication service and stored all your data within the company's datastore, removing all this code and data and moving it to another location would appear to a be fairly onerous task.

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