Python Success Stories
To be the industry leader in Managed Hosting, you have to be fast and flexible. By using Python to implement our enterprise data systems, Rackspace can quickly and effectively change its internal systems to keep up with shifts in the industry and in our own business processes. We do this through a central customer information system called "CORE," which is used both for Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP). Python and CORE are key factors that enable Rackspace to provide our Fanatical Support(tm) and faster customer service.
Rackspace's central customer database started as a simple ERP system to provision and track managed servers. It began humbly, as a small collection of PHP pages that did the job nicely for the few hundred servers that was the beginning of Rackspace's customer base.
As Rackspace grew, that small PHP system became the center of business at Rackspace. Every time an opportunity to automate a process presented itself, it was rolled into that system.
After a couple of years, the result was a big, un-maintainable mess of thousands of PHP pages and modules that had been written and maintained primarily by one person. The limits of PHP (then version 3) had been stretched thin, the system was too much for one person to maintain, and it was difficult to bring in new people to help with it.
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Our first attempt to update the system came when PHP version 4 was released. This release promised better object oriented capabilities, and the time was right for Rackspace to dedicate more people to the project.
The system was totally redesigned from the ground up, including new database schemas and application design strategies. At this time we re-dubbed the project "CORE," an acronym for Core Objects Reused Everywhere, in order to reflect the overall design goal for CORE: modularity and reusability across all systems in the company. With that goal in mind, our team went to work using the object oriented features of PHP.
While we were able to re-fit the application and add increased functionality, the project ultimately failed due in large part to the problems encountered while using the object framework provided by PHP.
Memory leaks, inconsistent interfaces, inconsistent internal data model, randomly freed objects, multiple object copies despite explicit use of references, internal PHP errors, and untraceable code failures all but made the task impossible to accomplish in PHP.
Even after we achieved a relatively stable code base, we were nowhere near our goal of Core Objects Reused Everywhere because we had to depart from pure object-oriented methods just to work around the problems inherent in PHP. It became clear that PHP was unsuitable for our large scale, mission critical projects. A new solution had to be found.
Python in CORE
We had always considered Python to be an excellent candidate for implementing our enterprise system, but it was initially passed over in favor of building upon the existing (vast) code base we already had in PHP. At that time, we felt that PHP could be used successfully in CORE by introducing a better structured system design.
Unfortunately, that wasn't enough to overcome our other problems with PHP, so we re-evaluated our situation. The first alpha version of Python 2.2 had recently been released, and we decided to begin work on a new CORE framework using the new features that were available in that version.
The Power of Introspection
One of the first tasks in writing the new framework was to build its database interface.
Python's introspection model had been significantly enhanced with the release of Python 2.2. We decided to use it to build a generic database interface class based on a DBI 2.0 compliant database connector. In this approach, rather than writing queries or table-specific wrappers by hand, a meta-class abstracts all database queries into a single clean API.
We create descendents of this meta-class to make an API for each table. Each table's class contains a few class constants that describe the columns in the database. This way we can add new tables to the overall API quickly and simply without having to worry about implementation details for any specific table.
The API also uses meta-data to automatically validate and convert values passed to the database. This is done by a "normalizer" function that converts the Python data types being passed through the API into valid SQL values. The function also verifies types and formats that are not necessarily checked by the database or by Python, such as phone numbers and ZIP codes.
Reusing Objects Everywhere
Once the database API was complete, we created a second layer of classes on top of it. This higher level API implements the business logic for specific applications, such as contact management or trouble ticket handling. It also prevents users from performing operations that are inconsistent with Rackspace's business practices, or assigning data that would result in other types of high-level corruption of the data in the database.
With the creation of this second layer, we achieved our original goal of Core Objects Reused Everywhere. Programmers throughout the company began to use this API to implement interfaces to application functionality. This required little interaction with our API development team, and it could be done without fear of misusing the API.
While we designed the API primarily for CORE, the central enterprise application, it is reused in a number of other systems at Rackspace. For example, one group built a SOAP server on top of the API, in order to access it from their PHP applications. Other applications use the API directly, and it has been extremely gratifying to see our work reused and integrated so easily with other systems.
Integrating Python with Apache
With the API in place, our next task in developing CORE was to find a useful templating module to integrate our Python code with HTML pages running on the Apache web server.
After looking at a number of available Python-based templating modules, we opted to create a simple parser of our own. Our approach was to convert server-side template pages into Python servlets whose output is sent by the HTTP server to the user's browser.
Although this was a fairly simple exercise, we did run into some problems stemming from our design of the CORE database meta-class. We found that altering classes and modules at runtime, as is done by the meta-class, violates guidelines imposed by Python's optional restricted execution environment. Since we felt that restricted execution was a necessary component in supporting a persistent web module, we opted to deploy CORE using CGIs rather than mod_python or similar persistent solutions.
Since fast hardware and multiple servers are readily available, and since and our template parser pre-compiles and caches the Python servlet code that it produces, the CGI solution is sufficient for our needs. It also allows us to resolve issues such as database connection pooling and restricting the execution environment outside of Python.
Thanks to the unit testing module that comes with Python, our projects are reaching production with far fewer bugs than we had ever thought possible when we were using PHP. During maintenance with PHP, there was always a question of whether a change in one place would break something else in another part of the application.
We now write unit tests for each and every API as the API is being designed. This means that we can verify the changes in one module as well as its effects on all the others simply by running the unit tests for the entire API.
Since introducing Python and unit testing, the nature of the bugs that we see in deployed applications has shifted to include primarily those in the user interface, such as layout problems or faulty event handling.
These days, very few bugs come from the API itself, and even those are generally the result of poor revision management or DBA coordination during application deployment. Python can't solve _all_ problems during development, but it certainly has reduced the number of critical system defects for us.
Lack of documentation has been a major problem with our previous development efforts. We tried several tools and policies to document our PHP efforts, but in the end these failed. Code changed too quickly, and the code-level documentation tools available for PHP at the time were too finicky to justify the amount of effort required to get the documentation to parse correctly. Additionally, despite careful planning and coding strategies, the mixture of PHP and HTML made deciphering and understanding the code more difficult.
Fortunately, Python was designed with documentation in mind, with the use of "doc strings" for modules, classes and methods. Since documentation is actually a part of the language itself, and pydoc is a standard module in the Python distribution, it was easy to extract API documentation to HTML and other formats.
Over time, we have found that the syntactic structure of Python makes for extremely readable code, and that in itself helps in the overall task of documenting and maintaining code.
Python has dramatically improved development processes for the CORE project, and it has led to the faster development times and more rapid releases that allow us to keep up with Rackspace's ever-changing business processes.
Python enabled us to create a sophisticated dynamic data model that is flexible and easy to use for abstracting database operations. With it, we realized our goal of Core Objects Reused Everywhere.
Python's integrated unit testing and documentation tools greatly enhance our ability to deploy and maintain a more stable, error-free product.
The result is a successful enterprise application that is instrumental in the delivery of Rackspace Managed Hosting's promise of Fanatical Support, Unmatched Speed, and Unlimited Flexibility in the managed hosting industry.