# Transparent Proxies (DEPRECATED)

Warning

This is a feature that was tried experimentally long ago, and we found no really good use cases. The basic functionality is still there, but we don't recommend using it. Some of the examples below might not work any more (e.g. you can't tproxy a list object any more). The rest can be done by hacking in standard Python. If anyone is interested in working on tproxy again, he is welcome, but we don't regard this as an interesting extension.

PyPy's Transparent Proxies allow routing of operations on objects to a callable. Application-level code can customize objects without interfering with the type system - `type(proxied_list) is list` holds true when `proxied_list` is a proxied built-in `list` - while giving you full control on all operations that are performed on the `proxied_list`.

See [D12.1] for more context, motivation and usage of transparent proxies.

## Example of the core mechanism

The following example proxies a list and will return `42` on any addition operations:

    $ py.py --objspace-std-withtproxy
    >>>> from __pypy__ import tproxy
    >>>> def f(operation, *args, **kwargs):
    >>>>    if operation == '__add__':
    >>>>         return 42
    >>>>    raise AttributeError
    >>>>
    >>>> i = tproxy(list, f)
    >>>> type(i)
    list
    >>>> i + 3
    42

## Example of recording all operations on builtins

Suppose we want to have a list which stores all operations performed on it for later analysis. We can use the lib_pypy/tputil.py module to help with transparently proxying builtin instances:

    from tputil import make_proxy

    history = []
    def recorder(operation):
        history.append(operation)
        return operation.delegate()

    >>>> l = make_proxy(recorder, obj=[])
    >>>> type(l)
    list
    >>>> l.append(3)
    >>>> len(l)
    1
    >>>> len(history)
    2

`make_proxy(recorder, obj=[])` creates a transparent list proxy that allows us to delegate operations to the `recorder()` function. Calling `type(l)` does not lead to any operation being executed at all.

Note that `append()` shows up as `__getattribute__()` and that `type(l)` does not show up at all - the type is the only aspect of the instance which the proxy controller cannot change.

## Transparent Proxy PyPy builtins and support

If you are using the --objspace-std-withtproxy option the __pypy__ module provides the following builtins:

tproxy(type, controller)

  Returns a proxy object representing the given type and forwarding all operations on this type to the controller. On each operation, `controller(opname, *args, **kwargs)` will be called.

get_tproxy_controller(obj)

  Returns the responsible controller for a given object. For non-proxied objects `None` is returned.

## tputil helper module

The lib_pypy/tputil.py module provides:

make_proxy(controller, type, obj)

  Creates a transparent proxy controlled by the given `controller` callable. The proxy will appear as a completely regular instance of the given type, but all operations on it are sent to the specified controller - which receives a `ProxyOperation` instance on each operation. If `type` is not specified, it defaults to `type(obj)` if `obj` is specified.

  ProxyOperation instances have the following attributes:

  proxyobj

    The transparent proxy object of this operation.

  opname

    The name of this operation.

  args

    Any positional arguments for this operation.

  kwargs

    Any keyword arguments for this operation.

  obj

    (Only if provided to `make_proxy()`)

    A concrete object.

  delegate()

    If a concrete object instance `obj` was specified in the call to `make_proxy()`, then `proxyoperation.delegate()` can be called to delegate the operation to the object instance.

## Further points of interest

A lot of tasks could be performed using transparent proxies, including, but not limited to:

- Remote versions of objects, on which we can directly perform operations (think about transparent distribution)

- Access to persistent storage such as a database (imagine an SQL object mapper which looks like any other object).

- Access to external data structures, such as other languages, as normal objects (of course some operations could raise exceptions, but since operations are executed at the application level, that is not a major problem)

## Implementation Notes

PyPy's standard object space allows us to internally have multiple implementations of a type and change the implementation at run-time, while application-level code consistently sees the exact same type and object. Multiple performance optimizations using these features have already been implemented: alternative object implementations. Transparent Proxies use this architecture to provide control back to application-level code.

Transparent proxies are implemented on top of the standard object space, in pypy/objspace/std/proxyobject.py, pypy/objspace/std/proxyobject.py and pypy/objspace/std/transparent.py. To use them you will need to pass a --objspace-std-withtproxy option to `pypy` or `translate.py`. This registers implementations named `W_TransparentXxx` - which usually correspond to an appropriate `W_XxxObject` - and includes some interpreter hacks for objects that are too close to the interpreter to be implemented in the standard object space. The types of objects that can be proxied this way are user created classes & functions, lists, dicts, exceptions, tracebacks and frames.

D12.1

  High-Level Backends and Interpreter Feature Prototypes, PyPy EU-Report, 2007, https://bitbucket.org/pypy/extradoc/raw/tip/eu-report/D12.1_H-L-Backends_and_Feature_Prototypes-2007-03-22.pdf