Glue It All Together With Python

Glue It All Together With Python

Guido van Rossum
CNRI
1895 Preston White Drive
Reston, VA 20191
Email: guido@cnri.reston.va.us, guido@python.org

Position paper for the OMG-DARPA-MCC Workshop on Compositional Software Architecture in Monterey, California, January 6-8, 1998.

Introduction

Python is an advanced scripting language that is being used successfully to glue together large software components. It spans multiple platforms, middleware products, and application domains. Python is an object-oriented language with high-level data structures, dynamic typing, and dynamic binding. Python has been around since 1991, and has a very active user community. For more information, see the Python website http://www.python.org.

Like Tcl, Python is easily extensible with C/C++/Java code, and easily embeddable in applications. Python even uses Tk, the Tcl GUI toolkit, for a de-facto standard portable GUI toolkit. Unlike Tcl, however, Python supports object-oriented programming. Python programmers can create classes, use multiple inheritance, define methods, overload operators, and so on.

Python's Strengths

Syntactically, Python code looks like executable pseudo code. Program development using Python is 5-10 times faster than using C/C++, and 3-5 times faster than using Java. In many cases, a prototype of an application can be written in Python without writing any C/C++/Java code. Often, the prototype is sufficiently functional and performs well enough to be delivered as the final product, saving considerable development time. Other times, the prototype can be translated in part or in whole to C++ or Java -- Python's object-oriented nature makes the translation a straightforward process.

The best approach is often to write only the performance-critical parts of the application in C++ or Java, and use Python for all higher-level control and customization. There are several anecdotes about applications that started out as pure C++ cpde to which Python was added as an extension language, where in each new version the percentage of the application written in Python increased, while also increasing the overall performance, functionality and reliability of the application. (E.g. Case Study: Python in a Commercial Environment, by Greg Stein, Microsoft, in Proceedings of the 6th International Python Conference, and the Alice VR project at UvA and CMU.)

Python has a strong presence on the web. It is suitable for CGI programming (on all platforms: Unix, Windows and Mac); there are interfaces to all major commercial databases. Python has a library that interfaces to the main Internet and web protocols, and has HTML parsing and generation toolkits. Python was a major implementation language for Infoseek when they were smaller. At least one company (Digital Creations) is selling a suite of server side tools using Python. And finally, Python has been used to implement a web browser (Grail).

Python is also well represented in the distributed systems world. It is one of the main languages supported by Xerox PARC's ILU (Inter-Language Unification; a CORBA compatible distributed object system), and many distributed applications have been built in Python using ILU. Python is also used by the Hector project at the University of Queensland, Australia.

Finally, Python is well integrated with the Windows platforms. Python programs can interact with COM and DCOM services, and can even implement new COM and DCOM services (which is not possible using Visual Basic!). Python can also be used as a scripting engine in Microsoft's Active Scripting architecture.

Using Python as an Integration Language

Relevant to the topic of this workshop, Python is in use at many places as an integration language, used to glue together ("steer") existing components. The strategy here is to create Python extension modules (written in C/C++) that make the functionality of large components written in C/C++ available to the Python programmer. The extension ("glue") modules are required because Python cannot call C/C++ functions directly; the glue extensions handle conversion between Python data types and C/C++ data types and error checking, translation error return values into Python exception.

Creation of glue extensions is simplified by the existence of SWIG, which reads header files containing function and method prototypes and automatically generates the necessary type conversion and error checking code. In situations where the underlying code (usually C code) doesn't use an object-oriented model, the glue extension can in turn be wrapped in a Python module that defines a proper class hierarchy, while delegating the performance critical operations to the C code.

Using Python, better applications can be developed because different kinds of programmers can work together on a project. For example, when building a scientific application, C/C++ programmers can implement efficient numerical algorithms, while scientists on the same project can write Python programs that test and use those algorithms. The scientist doesn't have to learn a low-level programming language, and the C/C++ programmer doesn't need to understand the science involved.

Without Python, large amounts of C/C++ code often have to be written just to provide a flexible enough input mechanism so that scientists can feed the program its data, in all the variantions that are required for reasons of experimental setup (for instance). With Python, Python can be used to wrote a much more flexible input mechanism in a much shorter time, or Python itself can be the ultimate flexible input mechanism. As an extreme example, Lawrence Livermore National Laboratories is using Python to eventually replace a scripting language (BASIS) that was developed in house for the same purpose; BASIS started out as a simple input mechanism for Fortran programs, and gradually acquired many features of scripting languages (variables, conditionals, loops, procedures and so on) with increasing awkwardness.

Because Python has existing interfaces to so many different components in very different application domains, Python is ideal for oddball integration tasks. It can link a commercial database to number-crunching code; it can add a graphical user interface to a network management tool; it can send email from a virtual reality application.

Conclusion

Python can fulfill an important integration role in the design of large applications with a long life expectancy. It allows a fast response to changes in user requirements that require adapting the higher-level application logic without changing the fundamental underlying components. It also allows quick adaptation of the application to changes in the underlying components.


Epilogue: Python and Java Integration

A new Python implementation written in 100% Pure Java, dubbed JPython, is currently under development; alpha releases are available for evaluation. JPython offers seamless scripting for Java. It is a full implementation of the Python language and standard library, adding direct access to the universe of Java classes. Java code can also use Python classes -- this is important for callbacks, for instance.

The main thrust for JPython is that it does for Java what Python already does for C and C++: to present programmers with more options in the trade-off between development time and execution time, by providing a more dynamic, more expressive alternative. JPython's integration with Java is superior to Python's integration with C/C++: due to Java's Reflection API, JPython can use arbitrary Java classes without the help of a wrapper generator such as SWIG. (C/C++ code must first be made available to Java through the Java native code interface; once it is callable from Java it is callable from JPython.)

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