PayPal Engineering Blog: Python by the C side

Mahmoud’s note: This will be my last post on the PayPal Engineering blog. If you’ve enjoyed this sort of content subscribe to my blog/pythondoeswhat.com or follow me on Twitter. It’s been fun!

All the world is legacy code, and there is always another, lower layer to peel away. These realities cause developers around the world to go on regular pilgrimage, from the terra firma of Python to the coasts of C. From zlib to SQLite to OpenSSL, whether pursuing speed, efficiency, or features, the waters are powerful, and often choppy. The good news is, when you’re writing Python, C interactions can be a day at the beach.

A brief history

As the name suggests, CPython, the primary implementation of Python used by millions, is written in C. Python core developers embraced and exposed Python’s strong C roots, taking a traditional tack on portability, contrasting with the “write once, debug everywhere” approach popularized elsewhere. The community followed suit with the core developers, developing several methods for linking to C. Years of these interactions have made Python a wonderful environment for interfacing with operating systems, data processing libraries, and everything the C world has to offer.

This has given us a lot of choices, and we’ve tried all of the standouts:

There’s a lot of history and detail that doesn’t fit into a table, but every option falls into one of three categories:

1. Writing C
2. Writing code that translates to C
3. Writing code that calls into libraries that present a C interface

Each has its merits, so we’ll explore each category, then finish with a real, live, worked example.

Writing C

Python’s core developers did it and so can you. Writing C extensions to Python gives an interface that fits like a glove, but also requires knowing, writing, building, and debugging C. The bugs are much more severe, too, as a segmentation fault that kills the whole process is much worse than a Python exception, especially in an asynchronous environment with hundreds of requests being handled within the same process. Not to mention that the glove is also tailored to CPython, and won’t fit quite right, or at all, in other execution environments.

At PayPal, we’ve used C extensions to speed up our service serialization. And while we’ve solved the build and portability issue, we’ve lost track of our share of references and have moved on from writing straight C extensions for new code.

Translating to C

After years of writing C, certain developers decide that they can do better. Some of them are certainly onto something.

Going Cythonic

Cython is a superset of the Python programming language that has been turning type-annotated Python into C extensions for nearly a decade, longer if you count its predecessor, Pyrex. Apart from its maturity, the points that matters to us are:

• Every Python file is a valid Cython file, enabling incremental, iterative optimization
• The generated C is highly portable, building on Windows, Mac, and Linux
• It’s common practice to check in the generated C, meaning that builders don’t need to have Cython installed.

Not to mention that the generated C often makes use of performance tricks that are too tedious or arcane to write by hand, partially motivated by scientific computing’s constant push. And through all that, Cython code maintains a high level of integration with Python itself, right down to the stack trace and line numbers.

PayPal has certainly benefitted from their efforts through high-performance Cython users like gevent, lxml, and NumPy. While our first go with Cython didn’t stick in 2011, since 2015, all native extensions have been written and rewritten to use Cython. It wasn’t always this way however.

A sip, not a SWIG

An early contributor to Python at PayPal got us started using SWIG, the Simplified Wrapper and Interface Generator, to wrap PayPal C++ infrastructure. It served its purpose for a while, but every modification was a slog compared to more Pythonic techniques. It wasn’t long before we decided it wasn’t our cup of tea.

Long ago SWIG may have rivaled extension modules as Python programmers’ method of choice. These days it seems to suit the needs of C library developers looking for a fast and easy way to wrap their C bindings for multiple languages. It also says something that searching for SWIG usage in Python nets as much SWIG replacement libraries as SWIG usage itself.

Calling into C

So far all our examples have involved extra build steps, portability concerns, and quite a bit of writing languages other than Python. Now we’ll dig into some approaches that more closely match Python’s own dynamic nature: ctypes and cffi.

Both ctypes and cffi leverage C’s Foreign Function Interface (FFI), a sort of low-level API that declares callable entrypoints to compiled artifacts like shared objects (.so files) on Linux/FreeBSD/etc. and dynamic-link libraries (.dll files) on Windows. Shared objects take a bit more work to call, so ctypes and cffi both use libffi, a C library that enables dynamic calls into other C libraries.

Shared libraries in C have some gaps that libffi helps fill. A Linux .so, Windows .dll, or OS X .dylib is only going to provide symbols: a mapping from names to memory locations, usually function pointers. Dynamic linkers do not provide any information about how to use these memory locations. When dynamically linking shared libraries to C code, header files provide the function signatures; as long as the shared library and application are ABI compatible, everything works fine. The ABI is defined by the C compiler, and is usually carefully managed so as not to change too often.

However, Python is not a C compiler, so it has no way to properly call into C even with a known memory location and function signature. This is where libffi comes in. If symbols define where to call the API, and header files define what API to call, libffi translates these two pieces of information into how to call the API. Even so, we still need a layer above libffi that translates native Python types to C and vice versa, among other tasks.

ctypes

ctypes is an early and Pythonic approach to FFI interactions, most notable for its inclusion in the Python standard library.

ctypes works, it works well, and it works across CPython, PyPy, Jython, IronPython, and most any Python runtime worth its salt. Using ctypes, you can access C APIs from pure Python with no external dependencies. This makes it great for scratching that quick C itch, like a Windows API that hasn’t been exposed in the os module. If you have an otherwise small module that just needs to access one or two C functions, ctypes allows you to do so without adding a heavyweight dependency.

For a while, PayPal Python code used ctypes after moving off of SWIG. We found it easier to call into vanilla shared objects built from C++ with an extern C rather than deal with the SWIG toolchain. ctypes is still used incidentally throughout the code for exactly this: unobtrusively calling into certain shared objects that are widely deployed. A great open-source example of this use case is oscrypto, which does exactly this for secure networking. That said, ctypes is not ideal for huge libraries or libraries that change often. Porting signatures from headers to Python code is tedious and error-prone.

cffi

cffi, our most modern approach to C integration, comes out of the PyPy project. They were seeking an approach that would lend itself to the optimization potential of PyPy, and they ended up creating a library that fixes many of the pains of ctypes. Rather than handcrafting Python representations of the function signatures, you simply load or paste them in from C header files.

For all its convenience, cffi’s approach has its limits. C is really almost two languages, taking into account preprocessor macros. A macro performs string replacement, which opens a Fun World of Possibilities, as straightforward or as complicated as you can imagine. cffi’s approach is limited around these macros, so applicability will depend on the library with which you are integrating.

On the plus side, cffi does achieve its stated goal of outperforming ctypes under PyPy, while remaining comparable to ctypes under CPython. The project is still quite young, and we are excited to see where it goes next.

A Tale of 3 Integrations: PKCS11

We promised an example, and we almost made it three.

PKCS11 is a cryptography standard for interacting with many hardware and software security systems. The 200-plus-page core specification includes many things, including the official client interface: A large set of C header-style information. There are a variety of pre-existing bindings, but each device has its own vendor-specific quirks, so what are we waiting for?

Metaprogramming

As stated earlier, ctypes is not great for sprawling interfaces. The drudgery of converting function signatures invites transcription bugs. We somewhat automated it, but the approach was far from perfect.

Our second approach, using cffi, worked well for our first version’s supported feature subset, but unfortunately PKCS11 uses its own CK_DECLARE_FUNCTION macro instead of regular C syntax for defining functions. Therefore, cffi’s approach of skipping #define macros will result in syntactically invalid C code that cannot be parsed. On the other hand, there are other macro symbols which are compiler or operating system intrinsics (e.g. __cplusplus, _WIN32, __linux__). So even if cffi attempted to evaluate every macro, we would immediately runs into problems.

So in short, we’re faced with a hard problem. The PKCS11 standard is a gnarly piece of C. In particular:

1. Many hundreds of important constant values are created with #define
2. Macros are defined, then re-defined to something different later on in the same file
3. pkcs11f.h is included multiple times, even once as the body of a struct

In the end, the solution that worked best was to write up a rigorous parser for the particular conventions used by the slow-moving standard, generate Cython, which generates C, which finally gives us access to the complete client, with the added performance bonus in certain cases. Biting this bullet took all of a day and a half, we’ve been very satisfied with the result, and it’s all thanks to a special trick up our sleeves.

Parsing Expression Grammars

Parsing expression grammars (PEGs) combine the power of a true parser generating an abstract syntax tree, not unlike the one used for Python itself, all with the convenience of regular expressions. One might think of PEGs as recursive regular expressions. There are several good libraries for Python, including parsimonious and parsley. We went with the former for its simplicity.

For this application, we defined a two grammars, one for pkcs11f.h and one for pkcs11t.h:

PKCS11FGRAMMARfile=(comment/func/"")*func=func_hdrfunc_argsfunc_hdr="CK_PKCS11_FUNCTION_INFO("name")"func_args=arg_hdr" ("arg*" ); #endif"arg_hdr=" #ifdef CK_NEED_ARG_LIST"(""comment)?arg=""type""name","?""commentname=identifiertype=identifieridentifier=~"[A-Z_][A-Z0-9_]*"icomment=~"(/\*.*?\*/)"msPKCS11TGRAMMARfile=(comment/define/typedef/struct_typedef/func_typedef/struct_alias_typedef/ignore)*typedef=" typedef"typeidentifier";"struct_typedef=" typedef struct"identifier""?"{"(comment/member)*" }"identifier";"struct_alias_typedef=" typedef struct"identifier" CK_PTR"?identifier";"func_typedef=" typedef CK_CALLBACK_FUNCTION(CK_RV,"identifier")("(identifieridentifier","?comment?)*" );"member=identifieridentifierarray_size?";"comment?array_size="["~"[0-9]"+"]"define="#define"identifier(hexval/decval/" (~0UL)"/identifier/~" \([A-Z_]*\|0x[0-9]{8}\)")hexval=~" 0x[A-F0-9]{8}"idecval=~" [0-9]+"type=" unsigned char"/" unsigned long int"/" long int"/(identifier" CK_PTR")/identifieridentifier=""?~"[A-Z_][A-Z0-9_]*"icomment=""?~"(/\*.*?\*/)"msignore=(" #ifndef"identifier)/" #endif"/""

Short, but dense, in true grammatical style. Looking at the whole program, it’s a straightforward process:

1. Apply the grammars to the header files to get our abstract syntax tree.
2. Walks the AST and sift out the semantically important pieces, function signatures in our case.
3. Generate code from the function signature data structures.

Using only 200 lines of code to bring such a massive standard to bear, along with the portability and performance of Cython, through the power of PEGs ranks as one of the high points of Python in practice at PayPal.

Wrapping up

It’s been a long journey, but we stayed afloat and we’re happy to have made it. To recap:

• Python and C are hand-in-glove made for one another.
• Different C integration techniques have their applications, our stances are:
  • ctypes for dynamic calls to small, stable interfaces
  • cffi for dynamic calls to larger interfaces, especially when targeting PyPy
  • Old-fashioned C extensions if you’re already good at them
  • Cython-based C extensions for the rest
  • SWIG pretty much never
• Parsing Expression Grammars are great!

All of this encapsulates perfectly why we love Python so much. Python is a great starter language, but it also has serious chops as a systems language and ecosystem. That bottom-to-top, rags-to-riches, books-to-bits story is what makes it the ineffable, incomparable language that it is.

C you around!

Kurt and Mahmoud