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transparent remote/recursive evaluation between Python and other languages

Project description

The Python module bond supports transparent remote/recursive evaluation between Python and another interpreter through automatic call serialization.

In poorer words, a bond lets you call functions in other languages as they were normal Python functions. It also allows other languages to call Python functions as if they were native.

Remote output is also transparently redirected locally, and since the evaluation is performed through a persistent co-process, you can actually spawn interpreters on different hosts through “ssh” efficiently.

bond currently supports PHP, Perl, JavaScript (Node.js) and Python itself.

A simple example

>>> # Let's bond with a PHP interpreter
>>> from bond.PHP import PHP
>>> php = PHP()
>>> php.eval_block('echo "Hello world!\n";')
Hello world!

>>> # Make an expensive split function using PHP's explode
>>> split = php.callable('explode')
>>> split(' ', "Hello world splitted by PHP!")
[u'Hello', u'world', u'splitted', u'by', u'PHP!']

>>> # Call Python from PHP
>>> def call_me():
...     print("Hi, this is Python talking!")
>>> php.export(call_me)
>>> php.eval('call_me()')
Hi, this is Python talking!

>>> # Use some remote resources
>>> remote_php = PHP('ssh remote php')
>>> remote_php.eval_block('function call_me() { echo "Hi from " . system("hostname") . "!"; }')
>>> remote_php.eval('call_me()')
Hi from remote!

>>> # Bridge two worlds!
>>> from bond.Perl import Perl
>>> perl = Perl()
>>> php.proxy('explode', perl)
>>> # note: explode is now available to Perl
>>> perl.eval('explode("=", "Mind=blown!")')
[u'Mind', u'blown!']


I needed bond for refactoring a large PHP project, mostly. With bond you can rewrite your program incrementally, while still executing all your existing code unchanged. You can start by rewriting just a single function:

from bond.PHP import PHP
import sys

php = PHP()

def new_function(arg)
   # do something here

php.export(new_function, 'function_to_be_replaced')'main', sys.argv)

It turns out that the same approach can be useful to perform remote computation as well. The wire protocol is simple enough to be extended to any language supporting an interactive interpreter.



When constructing a bond, the following keyword arguments are always allowed in constructors:


Command used to execute the interactive interpreter.


Default arguments used to execute the interactive interpreter.


Additional arguments to pass to the interpreter.


Working directory for the interpreter (defaults to current working directory).


Environment for the interpreter (defaults to os.environ).


Defines the timeout for the underlying communication protocol. Note that bond cannot distinguish between a slow call or noise generated while the interpreter is set up. Defaults to 60 seconds.


Accepts a file handle which is used to log the entire communication with the underlying interpreter for debugging purposes.


Enables/disables “transparent exceptions”. If trans_except is enabled, exceptions will be forwarded across the bond using the original data-type. If trans_except is disabled (the default for all languages except Python), then local exceptions will always contain a string representation of the remote exception instead, which avoids serialization errors.


The bond class supports the following methods:


Evaluate and return the value of a single statement of code in the interpreter.


Execute a “code” block inside the interpreter. Any construct which is legal by the current interpreter is allowed. Nothing is returned.


Terminate the communication with the interpreter.

call(name, *args):

Call a function “name” in the interpreter using the supplied list of arguments *args (apply *args to a callable statement defined by “name”). The arguments are automatically converted to their other language’s counterpart. The return value is captured and converted back to Python as well.


Return a function that calls “name”:

explode = php.callable('explode')
# Now you can call explode as a normal, local function
explode(' ', 'Hello world')

export(func, name):

Export a local function “func” so that can be called on the remote language as “name”. If “name” is not specified, use the local function name directly. Note that “func” must be a function reference, not a function name.

proxy(name, other, remote):

Export a function “name” from the current bond to “other”, named as “remote”. If “remote” is not provided, the same value as “name” is used.


Start an interactive session with the underlying interpreter. By default, all input lines are executed with bond.eval_block(). If “!” is pre-pended, execute a single statement with bond.eval() and print it’s return value.

You can construct a bond by using the appropriate subclass:

from bond.<language> import <language>
interpreter = <language>().

Language support


Python, as the identity language, has no restriction on data types. Everything is pickled on both sides, including exceptions.



  • The PHP’s command line and the readline module needs to be installed for the interactive interpreter to work properly. On Debian/Ubuntu, you’ll need php5-cli and php5-readline.


  • Performed remotely using JSON. Implement the JsonSerializable interface to tweak which/how objects are encoded.


  • You cannot use “call” on a built-in function such as “echo” (use “eval” in that case). You have to use a real function instead, like “print”.
  • Unfortunately, you cannot catch “fatal errors” in PHP. If the evaluated code triggers a “fatal error” it will terminate the bond without appeal. A common example of “fatal error” in PHP is attempting to use an undefined variable or function (which could happen while prototyping).


Perl is a quirky language, due to its syntax. We assume here you’re an experienced Perl developer.


  • The JSON and Data::Dump modules are required (libjson-perl and libdata-dump-perl in Debian/Ubuntu).


  • Performed remotely using JSON. Implement the TO_JSON method on blessed references to tweak which/how objects are encoded.


  • By default, evaluation is forced in array context, as otherwise most of the built-ins working with arrays would return an useless scalar. Use the “scalar” keyword for the rare cases when you really need it to.

  • You can “call” any function-like statement, as long as the last argument is expected to be an argument list. This allows you to call builtins directly:'map { $_ + 1 }', [1, 2, 3])
  • You can of course “call” a statement that returns any CODE. Meaning that you can call references to functions as long as you dereference them first:'&$fun_ref', ...)'&{ $any->{expression} }', ...)

    Likewise you can “call” objects methods directly:'$object->method', ...)


JavaScript is supported through Node.js.


  • Only Node.js v0.10.29 has been tested. On Debian/Ubuntu, the required package is simply called nodejs.


  • Performed remotely using JSON. Implement the “toJSON” property to tweak which/how objects are encoded.


  • Currently, the code expects an unix-like environment with /dev/stdin to perform synchronous I/O.
  • Since there’s no distinction between “plain” objects (dictionaries) and any other object, almost everything will be silently serialized. Define a custom “toJSON” property on your objects to control this behavior.

Common traits/limitations

  • Except for Python, only basic types (booleans, numbers, strings, lists, arrays and maps/dictionaries) can be transferred between the interpreters.

    If an object that cannot be serialized reaches a “call”, “eval”, or even a non-local return such as an error or exception, it will generate a SerializationException on the Python side.

  • Serialization is performed locally using JSON. Implement a custom JSONEncoder to tweak which/how objects are encoded.

  • Strings are always UTF-8 encoded.

  • References are implicitly broken as objects are transferred by value. This is obvious, as you’re talking with a separate process, but it can easily be forgotten due to the blurring of the boundary.

  • Calling functions across the bridge is slow, also in Python, due to the serialization. But the execution speed of the functions themselves is not affected. This might be perfectly reasonable if there are only occasional calls between languages, and/or the calls themselves take a significant fraction of time.

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