# PB Copyable: Passing Complex Types

## Overview

This chapter focuses on how to use PB to pass complex types (specifically class instances) to and from a remote process. The first section is on simply copying the contents of an object to a remote process (`pb.Copyable`). The second covers how to copy those contents once, then update them later when they change (`Cacheable`).

## Motivation

From the previous chapter, you've seen how to pass basic types to a remote process, by using them in the arguments or return values of a `callRemote` function. However, if you've experimented with it, you may have discovered problems when trying to pass anything more complicated than a primitive int/list/dict/string type, or another `pb.Referenceable` object. At some point you want to pass entire objects between processes, instead of having to reduce them down to dictionaries on one end and then re-instantiating them on the other.

## Passing Objects

The most obvious and straightforward way to send an object to a remote process is with something like the following code. It also happens that this code doesn't work, as will be explained below.

    class LilyPond:
      def __init__(self, frogs):
        self.frogs = frogs

    pond = LilyPond(12)
    ref.callRemote("sendPond", pond)

If you try to run this, you might hope that a suitable remote end which implements the `remote_sendPond` method would see that method get invoked with an instance from the `LilyPond` class. But instead, you'll encounter the dreaded `InsecureJelly` exception. This is Twisted's way of telling you that you've violated a security restriction, and that the receiving end refuses to accept your object.

### Security Options

What's the big deal? What's wrong with just copying a class into another process' namespace?

Reversing the question might make it easier to see the issue: what is the problem with accepting a stranger's request to create an arbitrary object in your local namespace? The real question is how much power you are granting them: what actions can they convince you to take on the basis of the bytes they are sending you over that remote connection.

Objects generally represent more power than basic types like strings and dictionaries because they also contain (or reference) code, which can modify other data structures when executed. Once previously-trusted data is subverted, the rest of the program is compromised.

The built-in Python batteries included classes are relatively tame, but you still wouldn't want to let a foreign program use them to create arbitrary objects in your namespace or on your computer. Imagine a protocol that involved sending a file-like object with a `read()` method that was supposed to used later to retrieve a document. Then imagine what if that object were created with `os.fdopen("~/.gnupg/secring.gpg")`. Or an instance of `telnetlib.Telnet("localhost", "chargen")`.

Classes you've written for your own program are likely to have far more power. They may run code during `__init__`, or even have special meaning simply because of their existence. A program might have `User` objects to represent user accounts, and have a rule that says all `User` objects in the system are referenced when authorizing a login session. (In this system, `User.__init__` would probably add the object to a global list of known users). The simple act of creating an object would give access to somebody. If you could be tricked into creating a bad object, an unauthorized user would get access.

So object creation needs to be part of a system's security design. The dotted line between trusted inside and untrusted outside needs to describe what may be done in response to outside events. One of those events is the receipt of an object through a PB remote procedure call, which is a request to create an object in your inside namespace. The question is what to do in response to it. For this reason, you must explicitly specific what remote classes will be accepted, and how their local representatives are to be created.

### What class to use?

Another basic question to answer before we can do anything useful with an incoming serialized object is: what class should we create? The simplistic answer is to create the same kind that was serialized on the sender's end of the wire, but this is not as easy or as straightforward as you might think. Remember that the request is coming from a different program, using a potentially different set of class libraries. In fact, since PB has also been implemented in Java, Emacs-Lisp, and other languages, there's no guarantee that the sender is even running Python! All we know on the receiving end is a list of two things which describe the instance they are trying to send us: the name of the class, and a representation of the contents of the object.

PB lets you specify the mapping from remote class names to local classes with the `setUnjellyableForClass` function

Note that, in this context, unjelly is a verb with the opposite meaning of jelly. The verb to jelly means to serialize an object or data structure into a sequence of bytes (or other primitive transmittable/storable representation), while to unjelly means to unserialize the bytestream into a live object in the receiver's memory space. Unjellyable is a noun, (not an adjective), referring to the the class that serves as a destination or recipient of the unjellying process. A is unjellyable into B means that a serialized representation A (of some remote object) can be unserialized into a local object of type B. It is these objects B that are the Unjellyable second argument of the `setUnjellyableForClass` function.

In particular, unjellyable does not mean cannot be jellied. `Unpersistable` means not persistable, but unjelly, unserialize, and unpickle mean to reverse the operations of jellying, serializing, and pickling.

. This function takes a remote/sender class reference (either the fully-qualified name as used by the sending end, or a class object from which the name can be extracted), and a local/recipient class (used to create the local representation for incoming serialized objects). Whenever the remote end sends an object, the class name that they transmit is looked up in the table controlled by this function. If a matching class is found, it is used to create the local object. If not, you get the `InsecureJelly` exception.

In general you expect both ends to share the same codebase: either you control the program that is running on both ends of the wire, or both programs share some kind of common language that is implemented in code which exists on both ends. You wouldn't expect them to send you an object of the MyFooziWhatZit class unless you also had a definition for that class. So it is reasonable for the Jelly layer to reject all incoming classes except the ones that you have explicitly marked with `setUnjellyableForClass`. But keep in mind that the sender's idea of a `User` object might differ from the recipient's, either through namespace collisions between unrelated packages, version skew between nodes that haven't been updated at the same rate, or a malicious intruder trying to cause your code to fail in some interesting or potentially vulnerable way.

## pb.Copyable

Ok, enough of this theory. How do you send a fully-fledged object from one side to the other?

copy_sender.py copy_receiver.py

The sending side has a class called `LilyPond`. To make this eligble for transport through `callRemote` (either as an argument, a return value, or something referenced by either of those [like a dictionary value]), it must inherit from one of the four `Serializable` classes. In this section, we focus on `Copyable`. The copyable subclass of `LilyPond` is called `CopyPond`. We create an instance of it and send it through `callRemote` as an argument to the receiver's `remote_takePond` method. The Jelly layer will serialize (jelly) that object as an instance with a class name of copy_sender.CopyPond and some chunk of data that represents the object's state. `pond.__class__.__module__` and `pond.__class__.__name__` are used to derive the class name string. The object's `getStateToCopy` method is used to get the state: this is provided by `pb.Copyable`, and the default just retrieves `self.__dict__`. This works just like the optional `__getstate__` method used by `pickle`. The pair of name and state are sent over the wire to the receiver.

The receiving end defines a local class named `ReceiverPond` to represent incoming `LilyPond` instances. This class derives from the sender's `LilyPond` class (with a fully-qualified name of `copy_sender.LilyPond`), which specifies how we expect it to behave. We trust that this is the same `LilyPond` class as the sender used. (At the very least, we hope ours will be able to accept a state created by theirs). It also inherits from `pb.RemoteCopy`, which is a requirement for all classes that act in this local-representative role (those which are given to the second argument of `setUnjellyableForClass`). `RemoteCopy` provides the methods that tell the Jelly layer how to create the local object from the incoming serialized state.

Then `setUnjellyableForClass` is used to register the two classes. This has two effects: instances of the remote class (the first argument) will be allowed in through the security layer, and instances of the local class (the second argument) will be used to contain the state that is transmitted when the sender serializes the remote object.

When the receiver unserializes (unjellies) the object, it will create an instance of the local `ReceiverPond` class, and hand the transmitted state (usually in the form of a dictionary) to that object's `setCopyableState` method. This acts just like the `__setstate__` method that `pickle` uses when unserializing an object. `getStateToCopy`/`setCopyableState` are distinct from `__getstate__`/`__setstate__` to allow objects to be persisted (across time) differently than they are transmitted (across [memory]space).

When this is run, it produces the following output:

    [-] twisted.spread.pb.PBServerFactory starting on 8800
    [-] Starting factory <twisted.spread.pb.PBServerFactory instance at
    0x406159cc>
    [Broker,0,127.0.0.1]  got pond: <__builtin__.ReceiverPond instance at
    0x406ec5ec>
    [Broker,0,127.0.0.1] 7 frogs

    % ./copy_sender.py
    7 frogs
    copy_sender.CopyPond
    pond arrived safe and sound
    Main loop terminated.
    %

### Controlling the Copied State

By overriding `getStateToCopy` and `setCopyableState`, you can control how the object is transmitted over the wire. For example, you might want perform some data-reduction: pre-compute some results instead of sending all the raw data over the wire. Or you could replace references to a local object on the sender's side with markers before sending, then upon receipt replace those markers with references to a receiver-side proxy that could perform the same operations against a local cache of data.

Another good use for `getStateToCopy` is to implement local-only attributes: data that is only accessible by the local process, not to any remote users. For example, a `.password` attribute could be removed from the object state before sending to a remote system. Combined with the fact that `Copyable` objects return unchanged from a round trip, this could be used to build a challenge-response system (in fact PB does this with `pb.Referenceable` objects to implement authorization as described here).

Whatever `getStateToCopy` returns from the sending object will be serialized and sent over the wire; `setCopyableState` gets whatever comes over the wire and is responsible for setting up the state of the object it lives in.

copy2_classes.py copy2_sender.py copy2_receiver.py

In this example, the classes are defined in a separate source file, which also sets up the binding between them. The `SenderPond` and `ReceiverPond` are unrelated save for this binding: they happen to implement the same methods, but use different internal instance variables to accomplish them.

The recipient of the object doesn't even have to import the class definition into their namespace. It is sufficient that they import the class definition (and thus execute the `setUnjellyableForClass` statement). The Jelly layer remembers the class definition until a matching object is received. The sender of the object needs the definition, of course, to create the object in the first place.

When run, the `copy2` example emits the following:

    % twistd -n -y copy2_receiver.py
    [-] twisted.spread.pb.PBServerFactory starting on 8800
    [-] Starting factory <twisted.spread.pb.PBServerFactory instance at
    0x40604b4c>
    [Broker,0,127.0.0.1]  got pond: <copy2_classes.ReceiverPond instance at
    0x406eb2ac>
    [Broker,0,127.0.0.1]  count 7

    % ./copy2_sender.py
    count 7
    pond arrived safe and sound
    Main loop terminated.
    %

### Things To Watch Out For

- The first argument to `setUnjellyableForClass` must refer to the class as known by the sender. The sender has no way of knowing about how your local `import` statements are set up, and Python's flexible namespace semantics allow you to access the same class through a variety of different names. You must match whatever the sender does. Having both ends import the class from a separate file, using a canonical module name (no sibiling imports), is a good way to get this right, especially when both the sending and the receiving classes are defined together, with the `setUnjellyableForClass` immediately following them. (XXX: this works, but does this really get the right names into the table? Or does it only work because both are defined in the same (wrong) place?)
- The class that is sent must inherit from `pb.Copyable`. The class that is registered to receive it must inherit from `pb.RemoteCopy` `pb.RemoteCopy` is actually defined as `flavors.RemoteCopy`, but `pb.RemoteCopy` is the preferred way to access it.
- The same class can be used to send and receive. Just have it inherit from both `pb.Copyable` and `pb.RemoteCopy`. This will also make it possible to send the same class symmetrically back and forth over the wire. But don't get confused about when it is coming (and using `setCopyableState`) versus when it is going (using `getStateToCopy`).
- `InsecureJelly` exceptions are raised by the receiving end. They will be delivered asynchronously to an `errback` handler. If you do not add one to the `Deferred` returned by `callRemote`, then you will never receive notification of the problem.
- The class that is derived from `pb.RemoteCopy` will be created using a constructor `__init__` method that takes no arguments. All setup must be performed in the `setCopyableState` method. As the docstring on `RemoteCopy` says, don't implement a constructor that requires arguments in a subclass of `RemoteCopy`. XXX: check this, the code around jelly._Unjellier.unjelly:489 tries to avoid calling `__init__` just in case the constructor requires args.

### More Information

- `pb.Copyable` is mostly implemented in `twisted.spread.flavors`, and the docstrings there are the best source of additional information.
- `Copyable` is also used in `twisted.web.distrib` to deliver HTTP requests to other programs for rendering, allowing subtrees of URL space to be delegated to multiple programs (on multiple machines).
- `twisted.manhole.explorer` also uses `Copyable` to distribute debugging information from the program under test to the debugging tool.

## pb.Cacheable

Sometimes the object you want to send to the remote process is big and slow. big means it takes a lot of data (storage, network bandwidth, processing) to represent its state. slow means that state doesn't change very frequently. It may be more efficient to send the full state only once, the first time it is needed, then afterwards only send the differences or changes in state whenever it is modified. The `pb.Cacheable` class provides a framework to implement this.

`pb.Cacheable` is derived from `pb.Copyable`, so it is based upon the idea of an object's state being captured on the sending side, and then turned into a new object on the receiving side. This is extended to have an object publishing on the sending side (derived from `pb.Cacheable`), matched with one observing on the receiving side (derived from `pb.RemoteCache`).

To effectively use `pb.Cacheable`, you need to isolate changes to your object into accessor functions (specifically setter functions). Your object needs to get control every single time some attribute is changedof course you could be clever and add a hook to `__setattr__`, along with magical change-announcing subclasses of the usual builtin types, to detect changes that result from normal = set operations. The result might be hard to maintain or extend, though..

You derive your sender-side class from `pb.Cacheable`, and you add two methods: `getStateToCacheAndObserveFor` and `stoppedObserving`. The first is called when a remote caching reference is first created, and retrieves the data with which the cache is first filled. It also provides an object called the observer this is actually a `RemoteCacheObserver`, but it isn't very useful to subclass or modify, so simply treat it as a little demon that sits in your `pb.Cacheable` class and helps you distribute change notifications. The only useful thing to do with it is to run its `callRemote` method, which acts just like a normal `pb.Referenceable`'s method of the same name. that points at that receiver-side cache. Every time the state of the object is changed, you give a message to the observer, informing them of the change. The other method, `stoppedObserving`, is called when the remote cache goes away, so that you can stop sending updates.

On the receiver end, you make your cache class inherit from `pb.RemoteCache`, and implement the `setCopyableState` as you would for a `pb.RemoteCopy` object. In addition, you must implement methods to receive the updates sent to the observer by the `pb.Cacheable`: these methods should have names that start with `observe_`, and match the `callRemote` invocations from the sender side just as the usual `remote_*` and `perspective_*` methods match normal `callRemote` calls.

The first time a reference to the `pb.Cacheable` object is sent to any particular recipient, a sender-side Observer will be created for it, and the `getStateToCacheAndObserveFor` method will be called to get the current state and register the Observer. The state which that returns is sent to the remote end and turned into a local representation using `setCopyableState` just like `pb.RemoteCopy`, described above (in fact it inherits from that class).

After that, your setter functions on the sender side should call `callRemote` on the Observer, which causes `observe_*` methods to run on the receiver, which are then supposed to update the receiver-local (cached) state.

When the receiver stops following the cached object and the last reference goes away, the `pb.RemoteCache` object can be freed. Just before it dies, it tells the sender side it no longer cares about the original object. When that reference count goes to zero, the Observer goes away and the `pb.Cacheable` object can stop announcing every change that takes place. The `stoppedObserving` method is used to tell the `pb.Cacheable` that the Observer has gone away.

With the `pb.Cacheable` and `pb.RemoteCache` classes in place, bound together by a call to `pb.setUnjellyableForClass`, all that remains is to pass a reference to your `pb.Cacheable` over the wire to the remote end. The corresponding `pb.RemoteCache` object will automatically be created, and the matching methods will be used to keep the receiver-side slave object in sync with the sender-side master object.

### Example

Here is a complete example, in which the `MasterDuckPond` is controlled by the sending side, and the `SlaveDuckPond` is a cache that tracks changes to the master:

cache_classes.py cache_sender.py cache_receiver.py

When run, this example emits the following:

    % twistd -n -y cache_receiver.py
    [-] twisted.spread.pb.PBServerFactory starting on 8800
    [-] Starting factory <twisted.spread.pb.PBServerFactory instance at
    0x40615acc>
    [Broker,0,127.0.0.1]  cache - sitting, er, setting ducks
    [Broker,0,127.0.0.1] got pond: <cache_classes.SlaveDuckPond instance at
    0x406eb5ec>
    [Broker,0,127.0.0.1] [2] ducks:  ['one duck', 'two duck']
    [Broker,0,127.0.0.1]  cache - addDuck
    [Broker,0,127.0.0.1] [3] ducks:  ['one duck', 'two duck', 'ugly duckling']
    [Broker,0,127.0.0.1]  cache - removeDuck
    [Broker,0,127.0.0.1] [2] ducks:  ['two duck', 'ugly duckling']
    [Broker,0,127.0.0.1] dropping pond
    %

    % ./cache_sender.py
    I have [2] ducks
    I have [3] ducks
    I have [2] ducks
    Main loop terminated.
    %

Points to notice:

- There is one `Observer` for each remote program that holds an active reference. Multiple references inside the same program don't matter: the serialization layer notices the duplicates and does the appropriate reference countingthis applies to multiple references through the same `Broker`. If you've managed to make multiple TCP connections to the same program, you deserve whatever you get..
- Multiple Observers need to be kept in a list, and all of them need to be updated when something changes. By sending the initial state at the same time as you add the observer to the list, in a single atomic action that cannot be interrupted by a state change, you insure that you can send the same status update to all the observers.
- The `observer.callRemote` calls can still fail. If the remote side has disconnected very recently and `stoppedObserving` has not yet been called, you may get a `DeadReferenceError`. It is a good idea to add an errback to those `callRemote`s to throw away such an error. This is a useful idiom:

      observer.callRemote('foo', arg).addErrback(lambda f: None)

  (XXX: verify that this is actually a concern)
- `getStateToCacheAndObserverFor` must return some object that represents the current state of the object. This may simply be the object's `__dict__` attribute. It is a good idea to remove the `pb.Cacheable`-specific members of it before sending it to the remote end. The list of Observers, in particular, should be left out, to avoid dizzying recursive Cacheable references. The mind boggles as to the potential consequences of leaving in such an item.
- A `perspective` argument is available to `getStateToCacheAndObserveFor`, as well as `stoppedObserving`. I think the purpose of this is to allow viewer-specific changes to the way the cache is updated. If all remote viewers are supposed to see the same data, it can be ignored.

XXX: understand, then explain use of varying cached state depending upon perspective.

### More Information

- The best source for information comes from the docstrings in `twisted.spread.flavors`, where `pb.Cacheable` is implemented.
- `twisted.manhole.explorer` uses `Cacheable`, and does some fairly interesting things with it. (XXX: I've heard explorer is currently broken, it might not be a good example to recommend)
- The `spread.publish` module also uses `Cacheable`, and might be a source of further information.