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The Trellis is built on a simple Python STM (Software Transactional Memory) and "Observer Pattern" implementation. This document specifies how that implementation works, and tests it.
You should read this document if you plan to implement your own Trellis cell types or other custom data structures, or if you just want to know how things work "under the hood".
An STM's job is to manage "atomic" changes to objects: i.e., multiple changes that must happen as a unit, or else be rolled back.
To do this, the STM must have a history: a record of the actions taken within a given "atomic" change set. The PEAK implementation of an STM history is found in the peak.events.stm module:
>>> from peak.events.stm import STMHistory >>> hist = STMHistory()
A history object's atomically() method invokes a function as an atomic operation, and its active attribute indicates whether it is currently performing an atomic operation:
>>> def is_active(): ... print "Active?", hist.active >>> is_active() Active? False >>> hist.atomically(is_active) Active? True >>> is_active() Active? False
Nested calls to atomically() simply execute the function, without affecting the history:
>>> def nested_operation(): ... hist.atomically(is_active) ... is_active() >>> hist.atomically(nested_operation) Active? True Active? True
In order to get notification of commits or aborts, you can register Python "context managers" with the history, using the manage() method. The manage() method takes a context manager and calls its __enter__() method immediately, then calls its __exit__() method when the current atomic operation is completed. This allows things like locks or other resources to be acquired as the operation progresses, and then get automatically released when the operation is completed:
>>> class DemoManager(object): ... def __init__(self, num): ... self.num = num ... def __enter__(self): ... print "Manager", self.num, "entering" ... def __exit__(self, typ, val, tb): ... print "Manager", self.num, "exiting", typ, val, tb >>> hist.manage(DemoManager(1)) Traceback (most recent call last): ... AssertionError: Can't manage without active history >>> hist.atomically(hist.manage, DemoManager(2)) Manager 2 entering Manager 2 exiting None None None
The same context manager can be passed to manage repeatedly, but its enter/exit methods will only be called once:
>>> def multi_manage(): ... mgr = DemoManager(3) ... hist.manage(mgr) ... hist.manage(mgr) >>> hist.atomically(multi_manage) Manager 3 entering Manager 3 exiting None None None
And if multiple context managers are registered, their __exit__ methods are called in the opposite order from their __enter__ methods:
>>> def multi_manage(): ... hist.manage(DemoManager(4)) ... hist.manage(DemoManager(5)) >>> hist.atomically(multi_manage) Manager 4 entering Manager 5 entering Manager 5 exiting None None None Manager 4 exiting None None None
The __exit__() method is normally called with three None values, unless an exception occurs during the operation. In that case, the sys.exc_info() of the exception is passed in to the manager(s), before the exception is re-raised:
>>> def do_error(): ... hist.manage(DemoManager(6)) ... raise TypeError("Testing!") >>> try: ... hist.atomically(do_error) ... except TypeError: ... print "caught exception" Manager 6 entering Manager 6 exiting ...TypeError... Testing! <traceback object...> caught exception
The __exit__() method should not raise an error. Also note that, unlike normal Python context managers, the return value of __exit__() is ignored. (In other words, STM context managers cannot cause an exception to be ignored.)
If an __exit__() method does raise an error, however, subsequent context managers will be passed the type, value, and traceback of the failing manager, and the exception will be reraised:
>>> class ErrorManager(DemoManager): ... def __exit__(self, typ, val, tb): ... super(ErrorManager, self).__exit__(typ, val, tb) ... raise RuntimeError("Haha!") >>> def manage_with_error(): ... hist.manage(DemoManager(7)) ... hist.manage(ErrorManager("error")) ... hist.manage(DemoManager(8)) >>> try: ... hist.atomically(manage_with_error) ... except RuntimeError: ... print "caught exception" Manager 7 entering Manager error entering Manager 8 entering Manager 8 exiting None None None Manager error exiting None None None Manager 7 exiting ...RuntimeError... Haha! <traceback object...> caught exception
In other words, all context managers are guaranteed to have their __exit__ methods called, even if one fails. The exception that comes out of the atomically() call will be the most recently-raised exception.
Last, but not least, history objects have a in_cleanup attribute that indicates whether they are currently in the process of comitting or aborting an operation. This can be useful if context managers might call code that needs to behave differently during a commit/abort than during an atomic operation:
>>> hist.in_cleanup False >>> class CleanupManager: ... def __enter__(self): ... print "on entry:", hist.in_cleanup ... def __exit__(self, typ, val, tb): ... print "on exit:", hist.in_cleanup >>> hist.atomically(hist.manage, CleanupManager()) on entry: False on exit: True
While you can use context managers to implement some forms of commit/rollback, it's easier for most things to use a history object's "undo" log. The log records "undo actions": functions (and optional positional arguments) that can be used to undo whatever operations have been done so far. For example:
>>> def undoing(msg): ... print "undoing", msg >>> def with_undo(): ... hist.on_undo(undoing, "op 1") ... hist.on_undo(undoing, "op 2") >>> hist.atomically(with_undo) # success, nothing gets undone
Nothing happened here, because the operation completed successfully. But if an error occurs, the undo functions are called in reverse order:
>>> def with_undo(): ... hist.on_undo(undoing, "op 1") ... hist.on_undo(undoing, "op 2") ... raise TypeError("foo") >>> try: ... hist.atomically(with_undo) ... except TypeError: ... print "caught exception" undoing op 2 undoing op 1 caught exception
But this does NOT happen if the error occurs in a manager's __exit__() method:
>>> def with_undo(): ... hist.manage(ErrorManager("error")) ... hist.on_undo(undoing, "op 1") ... hist.on_undo(undoing, "op 2") >>> try: ... hist.atomically(with_undo) ... except RuntimeError: ... print "caught exception" Manager error entering Manager error exiting None None None caught exception
(That's because managers may be used to control locking or other context setups that need to still be in place for the undo's to be safe.)
Note, by the way, that undo functions must NEVER raise errors, under any circumstances. If they do, any undo functions that have not been called yet, will never be called.
In addition to the automatic rollback on error, you can also record savepoints within an atomic operation, and then rollback to that savepoint at any time later:
>>> def with_savepoint(): ... hist.on_undo(undoing, "op 1") ... sp = hist.savepoint() ... hist.on_undo(undoing, "op 2") ... hist.on_undo(undoing, "op 3") ... hist.rollback_to(sp) >>> hist.atomically(with_savepoint) undoing op 3 undoing op 2
If you want to defer an action until the overall operation is ready to commit, you can use the on_commit(func, *args) method:
>>> def do_something(msg): ... print "committing", msg >>> def do_with_commit(): ... hist.on_commit(do_something, "hello!") ... print "registered commit operation" >>> hist.atomically(do_with_commit) registered commit operation committing hello!
Commit actions are called in the order they are registered (before any managers) and their registrations are subject to undo:
>>> def do_with_commit_and_undo(): ... hist.manage(DemoManager('test')) ... hist.on_commit(do_something, 1) ... sp = hist.savepoint() ... hist.on_commit(do_something, 2) ... hist.rollback_to(sp) ... hist.on_commit(do_something, 3) >>> hist.atomically(do_with_commit_and_undo) Manager test entering committing 1 committing 3 Manager test exiting None None None
Commit actions are also allowed to record undo actions, in case there is an error later in the commit process:
>>> def do_with_commit_and_undo(): ... def f1(): ... print "f1 running" ... hist.on_undo(f3) ... def f2(): ... print "f2 running" ... raise AssertionError ... def f3(): ... print "f3 running" ... hist.on_commit(f1) ... hist.on_commit(f2) >>> try: ... hist.atomically(do_with_commit_and_undo) ... except AssertionError: ... print "caught exception" f1 running f2 running f3 running caught exception
And of course, commit actions are not run if an error occurs before they have a chance to run:
>>> def do_no_commit(): ... hist.on_commit(do_something, "should not happen") ... raise AssertionError >>> try: ... hist.atomically(do_no_commit) ... except AssertionError: ... print "caught exception" caught exception
As a convenience, you can use the change_attr() method of history objects to automatically log an undo function to restore the old value of the attribute being set:
>>> class SomeObject: ... pass >>> s1 = SomeObject() >>> s1.foo = 'bar' >>> def setattr_normally(): ... hist.change_attr(s1, 'foo', "baz") >>> hist.atomically(setattr_normally) >>> s1.foo 'baz' >>> def setattr_rollback(): ... hist.change_attr(s1, 'foo', "spam") ... raise TypeError >>> hist.atomically(setattr_rollback) Traceback (most recent call last): ... TypeError >>> s1.foo # result is unchanged after rollback 'baz'
As you can see, this saves you the work of recording the undo operation manually.
Above and beyond the bare STM system, the Trellis also needs a strong "observer" system that manages subjects, listeners, and the links between them:
>>> from peak.events import stm
In addition, the observer framework supports calculating and caching the "safe recalculation order" of a network of subjects and listeners, so that listeners that modify other subjects are (generally) run before the listeners that depend on those subjects. In the event that a listener modifies a subject that has already been read during a given recalculation, the recalculation order is adjusted, and the atomic operation's history is undone to the point where the modified subject was first read. And, if two listeners interact in such a way that each is modifying something the other has read, then a stm.CircularityError is raised, detailing what listeners were involved in the loop.
A Controller is a more sophisticated version of an STMHistory, that also schedules and tracks the execution of listeners (rules) and what subjects they've read or changed. The Trellis uses a per-thread Controller instance to implement cell recalculation. Controller objects have the following additional methods and attributes, many of which must be invoked by subjects or listeners in order to record their activity effectively:
Explicitly invoke a listener.run() in a controlled manner. This is normally used only when a listener must be run for the first time, and its output must be immediately available. (Otherwise, it would be enough to schedule() it for later.) Cells use this method when their value is read and they have not been initialized.
During the run, the Controller pretends that the listener was run in a previous recalculation, for purposes of determining which rules were run (and therefore may need undoing in the event of an order change) in the current calculation. The how's and why's of this are fairly tedious: just remember that if you are trying to have your listener run for the first time so its dependencies can be initialized, you should use this method to do it. (Since simply calling listener.run() won't set the current_listener or track dependencies correctly.)
Note that most of these methods and attributes are only usable while an atomic action is in effect. (That is, when the .active attribute is true.) The only exceptions are schedule() and cancel().
During normal Trellis operation, there is only one Controller instance, which is available as trellis.ctrl. It is actually an instance of a LocalController subclass, which is a threading.local. That is, even though it is a single object, it has thread-specific state, allowing it to be used as a singleton even by multithreaded programs.
As a shortcut, the trellis module exports the following methods to its main namespace, so that you can use them without the ctrl. prefix:
Thus, trellis.on_commit() is a convenience shorthand for trellis.ctrl.on_commit(). (You must still refer to the ctrl object for access to attributes such as ctrl.current_listener.)
If you wish to replace the standard LocalController (e.g. for testing or a specialized application), you must use the trellis.install_controller() function, which replaces the shortcuts with the methods of the provided object, and sets trellis.ctrl to the replacement object.
(Of course, this will only be useful if you do it before any modules import their own copy of trellis.ctrl or any of the shortcuts, or if they always prefix their uses with trellis.. It's thus best if you install your custom controller very early in the life of your program, if possible.)
TODO: write up AbstractCell, ConstantMixin, value/get_value/set_value, etc.