Chapter 2. How Python Runs Programs
This chapter and the next give a quick look at program execution—how you launch code, and how Python runs it. In this chapter, we’ll study the Python interpreter. Chapter 3 will then show you how to get your own programs up and running.
Startup details are inherently platform-specific, and some of the material in this chapter may not apply to the platform you work on, so you should feel free to skip parts not relevant to your intended use. Likewise, more advanced readers who have used similar tools in the past and prefer to get to the meat of the language quickly may want to file some of this chapter away as “for future reference.” For the rest of you, let’s learn how to run some code.
Introducing the Python Interpreter
So far, I’ve mostly been talking about Python as a programming language. But, as currently implemented, it’s also a software package called an interpreter. An interpreter is a kind of program that executes other programs. When you write a Python program, the Python interpreter reads your program and carries out the instructions it contains. In effect, the interpreter is a layer of software logic between your code and the computer hardware on your machine.
When the Python package is installed on your machine, it generates a number of components—minimally, an interpreter and a support library. Depending on how you use it, the Python interpreter may take the form of an executable program, or a set of libraries linked into another program. Depending on which flavor of Python you run, the interpreter itself may be implemented as a C program, a set of Java classes, or something else. Whatever form it takes, the Python code you write must always be run by this interpreter. And, to enable that, you must install a Python interpreter on your computer.
Python installation details vary by platform, and are covered in more depth in Appendix A. In short:
Windows users fetch and run a self-installing executable file that puts Python on their machines. Simply double-click and say Yes or Next at all prompts.
On Windows Vista, you may need to take extra steps to use the Python 2.5 MSI installer file; see Appendix A for more details.
Linux and Mac OS X users probably already have a usable Python preinstalled on their computers—it’s a standard component on these platforms today.
Some Linux users (and most Unix users) typically either install Python from RPM files, or compile it from its full source code distribution package.
Other platforms have installation techniques relevant to those platforms. For instance, Python is also available on cell phones, game consoles, and iPods, but the installation details vary too widely to cover here.
Python itself may be fetched from the downloads page at Python’s web site. It may also be found through various other distribution channels. Keep in mind that you should always check to see whether Python is already present before installing it. If you’re working on Windows, you’ll usually find Python in the Start menu, as captured in Figure 2-1 (these menu options are discussed in the next chapter). On Unix and Linux, Python probably lives in your /usr directory tree.
Because installation details are so platform-specific, we’ll finesse the rest of this story here. For more details on the installation process, consult Appendix A. For the purposes of this chapter and the next, I’ll assume that you’ve got Python ready to go.
Program Execution
What it means to write and run a Python script depends on whether you look at these tasks as a programmer, or as a Python interpreter. Both views offer important perspectives on Python programming.
The Programmer’s View
In its simplest form, a Python program is just a text file containing Python statements. For example, the following file, named script1.py, is one of the simplest Python scripts we could dream up, but it passes for an official Python program:
print 'hello world' print 2 ** 100
This file contains two Python print statements, which simply print a string (the text in quotes) and a numeric expression result (2 to the power 100) to the output stream. Don’t worry about the syntax of this code yet—for this chapter, we’re interested only in getting it to run. I’ll explain the print statement, and why you can raise 2 to the power 100 in Python without overflowing, in later parts of this book.
You can create such a file of statements with any text editor you like. By convention, Python program files are given names that end in .py; technically, this naming scheme is required only for files that are “imported,” as shown later in this book, but most Python files have .py names for consistency.
After you’ve typed these statements into a text file, you must tell Python to execute the file—which simply means to run all the statements in the file from top to bottom, one after another. As you’ll see in the next chapter, you can launch Python program files by command lines, by clicking their icons, and with other standard techniques. If all goes well, when you execute the file, you’ll see the results of the two print statements show up somewhere on your computer—by default, usually in the same window you were in when you ran the program:
For example, here’s what happened when I ran this script from a DOS command line on a Windows laptop (typically called a Command Prompt window, found in the Accessories program menu), to make sure it didn’t have any silly typos:
python script1.pyhello world 1267650600228229401496703205376
We’ve just run a Python script that prints a string and a number. We probably won’t win any programming awards with this code, but it’s enough to capture the basics of program execution.
Python’s View
The brief description in the prior section is fairly standard for scripting languages, and it’s usually all that most Python programmers need to know. You type code into text files, and you run those files through the interpreter. Under the hood, though, a bit more happens when you tell Python to “go.” Although knowledge of Python internals is not strictly required for Python programming, a basic understanding of the runtime structure of Python can help you grasp the bigger picture of program execution.
When you instruct Python to run your script, there are a few steps that Python carries out before your code actually starts crunching away. Specifically, it’s first compiled to something called “byte code” and then routed to something called a “virtual machine.”
Byte code compilation
Internally, and almost completely hidden from you, when you execute a program, Python first compiles your source code (the statements in your file) into a format known as byte code. Compilation is simply a translation step, and byte code is a lower-level, platform-independent representation of your source code. Roughly, Python translates each of your source statements into a group of byte code instructions by decomposing them into individual steps. This byte code translation is performed to speed execution—byte code can be run much more quickly than the original source code statements in your text file.
You’ll notice that the prior paragraph said that this is almost completely hidden from you. If the Python process has write access on your machine, it will store the byte code of your program in files that end with a .pyc extension (“.pyc” means compiled “.py” source). You will see these files show up on your computer after you’ve run a few programs alongside the corresponding source code files (that is, in the same directories).
Python saves byte code like this as a startup speed optimization. The next time you run your program, Python will load the .pyc and skip the compilation step, as long as you haven’t changed your source code since the byte code was last saved. Python automatically checks the timestamps of source and byte code files to know when it must recompile—if you resave your source code, byte code is automatically re-created the next time your program is run.
If Python cannot write the byte code files to your machine, your program still works—the byte code is generated in memory and simply discarded on program exit.[3] However, because .pyc files speed startup time, you’ll want to make sure they are written for larger programs. Byte code files are also one way to ship Python programs—Python is happy to run a program if all it can find are .pyc files, even if the original .py source files are absent. (See "Frozen Binaries" later in this chapter for another shipping option.)
The Python Virtual Machine (PVM)
Once your program has been compiled to byte code (or the byte code has been loaded from existing .pyc files), it is shipped off for execution to something generally known as the Python Virtual Machine (PVM, for the more acronym-inclined among you). The PVM sounds more impressive than it is; really, it’s not a separate program, and it need not be installed by itself. In fact, the PVM is just a big loop that iterates through your byte code instructions, one by one, to carry out their operations. The PVM is the runtime engine of Python; it’s always present as part of the Python system, and it’s the component that truly runs your scripts. Technically, it’s just the last step of what is called the “Python interpreter.”
Figure 2-2 illustrates the runtime structure described here. Keep in mind that all of this complexity is deliberately hidden from Python programmers. Byte code compilation is automatic, and the PVM is just part of the Python system that you have installed on your machine. Again, programmers simply code and run files of statements.
Performance implications
Readers with a background in fully compiled languages such as C and C++ might notice a few differences in the Python model. For one thing, there is usually no build or “make” step in Python work: code runs immediately after it is written. For another, Python byte code is not binary machine code (e.g., instructions for an Intel chip). Byte code is a Python-specific representation.
This is why some Python code may not run as fast as C or C++ code, as described in Chapter 1—the PVM loop, not the CPU chip, still must interpret the byte code, and byte code instructions require more work than CPU instructions. On the other hand, unlike in classic interpreters, there is still an internal compile step—Python does not need to reanalyze and reparse each source statement repeatedly. The net effect is that pure Python code runs at speeds somewhere between those of a traditional compiled language and a traditional interpreted language. See Chapter 1 for more on Python performance implications.
Development implications
Another ramification of Python’s execution model is that there is really no distinction between the development and execution environments. That is, the systems that compile and execute your source code are really one and the same. This similarity may have a bit more significance to readers with a background in traditional compiled languages, but in Python, the compiler is always present at runtime, and is part of the system that runs programs.
This makes for a much more rapid development cycle. There is no need to precompile and link before execution may begin; simply type and run the code. This also adds a much more dynamic flavor to the language—it is possible, and often very convenient, for Python programs to construct and execute other Python programs at runtime. The eval and exec built-ins, for instance, accept and run strings containing Python program code. This structure is also why Python lends itself to product customization—because Python code can be changed on the fly, users can modify the Python parts of a system onsite without needing to have or compile the entire system’s code.
At a more fundamental level, keep in mind that all we really have in Python is runtime—there is no initial compile-time phase at all, and everything happens as the program is running. This even includes operations such as the creation of functions and classes and the linkage of modules. Such events occur before execution in more static languages, but happen as programs execute in Python. As we’ll see, the net effect makes for a much more dynamic programming experience than that to which some readers may be accustomed.
Execution Model Variations
Before moving on, I should point out that the internal execution flow described in the prior section reflects the standard implementation of Python today, and is not really a requirement of the Python language itself. Because of that, the execution model is prone to changing with time. In fact, there are already a few systems that modify the picture in Figure 2-2 somewhat. Let’s take a few moments to explore the most prominent of these variations.
Python Implementation Alternatives
Really, as this book is being written, there are three primary implementations of the Python language—CPython, Jython, and IronPython—along with a handful of secondary implementations such as Stackless Python. In brief, CPython is the standard implementation; all the others have very specific purposes and roles. All implement the same Python language, but execute programs in different ways.
CPython
The original, and standard, implementation of Python is usually called CPython, when you want to contrast it with the other two. Its name comes from the fact that it is coded in portable ANSI C language code. This is the Python that you fetch from http://www.python.org, get with the ActivePython distribution, and have automatically on most Linux and Mac OS X machines. If you’ve found a preinstalled version of Python on your machine, it’s probably CPython, unless your company is using Python in very specialized ways.
Unless you want to script Java or .NET applications with Python, you probably want to use the standard CPython system. Because it is the reference implementation of the language, it tends to run the fastest, be the most complete, and be more robust than the alternative systems. Figure 2-2 reflects CPython’s runtime architecture.
Jython
The Jython system (originally known as JPython) is an alternative implementation of the Python language, targeted for integration with the Java programming language. Jython consists of Java classes that compile Python source code to Java byte code and then route the resulting byte code to the Java Virtual Machine (JVM). Programmers still code Python statements in .py text files as usual; the Jython system essentially just replaces the rightmost two bubbles in Figure 2-2 with Java-based equivalents.
Jython’s goal is to allow Python code to script Java applications, much as CPython allows Python to script C and C++ components. Its integration with Java is remarkably seamless. Because Python code is translated to Java byte code, it looks and feels like a true Java program at runtime. Jython scripts can serve as web applets and servlets, build Java-based GUIs, and so on. Moreover, Jython includes integration support that allows Python code to import and use Java classes as though they were coded in Python. Because Jython is slower and less robust than CPython, though, it is usually seen as a tool of interest primarily to Java developers looking for a scripting language to be a frontend to Java code.
IronPython
A third (and, at this writing, still somewhat new) implementation of Python, IronPython is designed to allow Python programs to integrate with applications coded to work with Microsoft’s .NET Framework for Windows, as well as the Mono open source equivalent for Linux. .NET and its C# programming language runtime system are designed to be a language-neutral object communication layer, in the spirit of Microsoft’s earlier COM model. IronPython allows Python programs to act as both client and server components, accessible from other .NET languages.
By implementation, IronPython is very much like Jython (and, in fact, is being developed by the same creator)—it replaces the last two bubbles in Figure 2-2 with equivalents for execution in the .NET environment. Also, like Jython, IronPython has a special focus—it is primarily of interest to developers integrating Python with .NET components. Because it is being developed by Microsoft, though, IronPython might also be able to leverage some important optimization tools for better performance. IronPython’s scope is still evolving as I write this; for more details, consult the Python online resources, or search the Web.[4]
Execution Optimization Tools
CPython, Jython, and IronPython all implement the Python language in similar ways: by compiling source code to byte code, and executing the byte code on an appropriate virtual machine. Still other systems, including the Psyco just-in-time compiler, and the Shedskin C++ translator, instead attempt to optimize the basic execution model. These systems are not required knowledge at this point in your Python career, but a quick look at their place in the execution model might help demystify the model in general.
The Psyco just-in-time compiler
The Psyco system is not another Python implementation, but rather, a component that extends the byte code execution model to make programs run faster. In terms of Figure 2-2, Psyco is an enhancement to the PVM that collects and uses type information while the program runs to translate portions of the program’s byte code all the way down to real binary machine code for faster execution. Psyco accomplishes this translation without requiring changes to the code or a separate compilation step during development.
Roughly, while your program runs, Psyco collects information about the kinds of objects being passed around; that information can be used to generate highly efficient machine code tailored for those object types. Once generated, the machine code then replaces the corresponding part of the original byte code to speed your program’s overall execution. The net effect is that, with Psyco, your program becomes much quicker over time, and as it is running. In ideal cases, some Python code may become as fast as compiled C code under Psyco.
Because this translation from byte code happens at program runtime, Psyco is generally known as a just-in-time (JIT) compiler. Psyco is actually a bit different from the JIT compilers some readers may have seen for the Java language, though. Really, Psyco is a specializing JIT compiler—it generates machine code tailored to the data types that your program actually uses. For example, if a part of your program uses different data types at different times, Psyco may generate a different version of machine code to support each different type combination.
Psyco has been shown to speed Python code dramatically. According to its web page, Psyco provides “2x to 100x speed-ups, typically 4x, with an unmodified Python interpreter and unmodified source code, just a dynamically loadable C extension module.” Of equal significance, the largest speedups are realized for algorithmic code written in pure Python—exactly the sort of code you might normally migrate to C to optimize. With Psyco, such migrations become even less important.
Psyco is not yet a standard part of Python; you will have to fetch and install it separately. It is also still something of a research project, so you’ll have to track its evolution online. In fact, at this writing, although Psyco can still be fetched and installed by itself, it appears that much of the system may eventually be absorbed into the newer “PyPy” project—an attempt to reimplement Python’s PVM in Python code, to better support optimizations like Psyco.
Perhaps the largest downside of Psyco is that it currently only generates machine code for Intel x86 architecture chips, though this includes Windows, Linux, and recent Macs. For more details on the Psyco extension, and other JIT efforts that may arise, consult http://www.python.org; you can also check out Psyco’s home page, which currently resides at http://psyco.sourceforge.net.
The Shedskin C++ translator
Shedskin is an emerging system that takes a different approach to Python program execution—it attempts to translate Python source code to C++ code, which your computer’s C++ compiler then compiles to machine code. As such, it represents a platform-neutral approach to running Python code. Shedskin is still somewhat experimental as I write these words, and it limits Python programs to an implicit statically typed constraint that is technically not normal Python, so we won’t go into further detail here. Initial results, though, show that it has the potential to outperform both standard Python and the Psyco extension in terms of execution speed, and it is a promising project. Search the Web for details on the project’s current status.
Frozen Binaries
Sometimes when people ask for a “real” Python compiler, what they’re really seeking is simply a way to generate standalone binary executables from their Python programs. This is more a packaging and shipping idea than an execution-flow concept, but it’s somewhat related. With the help of third-party tools that you can fetch off the Web, it is possible to turn your Python programs into true executables—known as frozen binaries in the Python world.
Frozen binaries bundle together the byte code of your program files, along with the PVM (interpreter), and any Python support files your program needs, into a single package. There are some variations on this theme, but the end result can be a single binary executable program (e.g., an .exe file on Windows) that can easily be shipped to customers. In Figure 2-2, it is as though the byte code and PVM are merged into a single component—a frozen binary file.
Today, three primary systems are capable of generating frozen binaries: py2exe (for Windows), PyInstaller (which is similar to py2exe, but works on Linux and Unix, too, and is also capable of generating self-installing binaries), and freeze (the original). You may have to fetch these tools separately from Python itself, but they are available free of charge. They are also constantly evolving, so see http://www.python.org and the Vaults of Parnassus web site (http://www.vex.net/parnassus/) for more on these tools. To give you an idea of the scope of these systems, py2exe can freeze standalone programs that use the Tkinter, Pmw, wxPython, and PyGTK GUI libraries; programs that use the pygame game programming toolkit; win32com client programs; and more.
Frozen binaries are not the same as the output of a true compiler—they run byte code through a virtual machine. Hence, apart from a possible startup improvement, frozen binaries run at the same speed as the original source files. Frozen binaries are not small (they contain a PVM), but by current standards, they are not unusually large either. Because Python is embedded in the frozen binary, it does not have to be installed on the receiving end to run your program. Moreover, because your code is embedded in the frozen binary, it is effectively hidden from recipients.
This single file-packaging scheme is especially appealing to developers of commercial software. For instance, a Python-coded user interface program based on the Tkinter toolkit can be frozen into an executable file and shipped as a self-contained program on a CD or on the Web. End users do not need to install (or even have to know about) Python to run the shipped program.
Future Possibilities?
Finally, note that the runtime execution model sketched here is really an artifact of the current implementation of Python, and not the language itself. For instance, it’s not impossible that a full, traditional compiler for translating Python source code to machine code may appear during the shelf life of this book (although one has not in over a decade). New byte code formats and implementation variants may also be adopted in the future. For instance:
The emerging Parrot project aims to provide a common byte code format, virtual machine, and optimization techniques for a variety of programming languages (see http://www.python.org).
The Stackless Python system is a standard CPython implementation variant that does not save state on the C language call stack. This makes Python more easily ported to small stack architectures, and opens up novel programming possibilities, such as coroutines.
The new PyPy project is an attempt to reimplement the PVM in Python itself to enable new implementation techniques.
Although such future implementation schemes may alter the runtime structure of Python somewhat, it seems likely that the byte code compiler will still be the standard for some time to come. The portability and runtime flexibility of byte code are important features of many Python systems. Moreover, adding type constraint declarations to support static compilation would break the flexibility, conciseness, simplicity, and overall spirit of Python coding. Due to Python’s highly dynamic nature, any future implementation will likely retain many artifacts of the current PVM.
Chapter Summary
This chapter introduced the execution model of Python (how Python runs your programs) and explored some common variations on that model (just-in-time compilers and the like). Although you don’t really need to come to grips with Python internals to write Python scripts, a passing acquaintance with this chapter’s topics will help you truly understand how your programs run once you start coding them. In the next chapter, you’ll start actually running some code of your own. First, though, here’s the usual chapter quiz.
BRAIN BUILDER
[3] * And, strictly speaking, byte code is saved only for files that are imported, not for the top-level file of a program. We’ll explore imports in Chapter 3, and again in Part V. Byte code is also never saved for code typed at the interactive prompt, which is described in Chapter 3.
[4] * Jython and IronPython are completely independent implementations of Python that compile Python source for different runtime architectures. It is also possible to access Java and .NET software from standard CPython programs: the JPype and Python for .NET systems, for example, allow CPython code to call out to Java and .NET components.