A simple "untwisted" approach to event-driven programming

## Project description

(NOTE: As of 0.7a1, many new features have been added to the Trellis API, and some old ones have been deprecated. If you are upgrading from an older version, please see the porting guide for details.)

Whether it’s an application server or a desktop application, any sufficiently complex system is event-driven – and that usually means callbacks.

Unfortunately, explicit callback management is to event-driven programming what explicit memory management is to most other kinds of programming: a tedious hassle and a significant source of unnecessary bugs.

For example, even in a single-threaded program, callbacks can create race conditions, if the callbacks are fired in an unexpected order. If a piece of code can cause callbacks to be fired “in the middle of something”, both that code and the callbacks can get confused.

Of course, that’s why most GUI libraries and other large event-driven systems usually have some way for you to temporarily block callbacks from happening. This lets you fix or workaround your callback order dependency bugs… at the cost of adding even more tedious callback management. And it still doesn’t fix the problem of forgetting to cancel callbacks… or register needed ones in the first place!

The Trellis solves all of these problems by introducing automatic callback management, in much the same way that Python does automatic memory management. Instead of worrying about subscribing or “listening” to events and managing the order of callbacks, you just write rules to compute values. The Trellis “sees” what values your rules access, and thus knows what rules may need to be rerun when something changes – not unlike the operation of a spreadsheet.

But even more important, it also ensures that callbacks can’t happen while code is “in the middle of something”. Any action a rule takes that would cause a new event to fire is automatically deferred until all of the applicable rules have had a chance to respond to the event(s) in progress. And, if you try to access the value of a rule that hasn’t been updated yet, it’s automatically updated on-the-fly so that it reflects the current event in progress.

No stale data. No race conditions. No callback management. That’s what the Trellis gives you.

Here’s a super-trivial example:

>>> from peak.events import trellis

>>> class TempConverter(trellis.Component):
...     F = trellis.maintain(
...         lambda self: self.C * 1.8 + 32,
...         initially = 32
...     )
...     C = trellis.maintain(
...         lambda self: (self.F - 32)/1.8,
...         initially = 0
...     )
...     @trellis.perform
...     def show_values(self):
...         print "Celsius......", self.C
...         print "Fahrenheit...", self.F

>>> tc = TempConverter(C=100)
Celsius...... 100
Fahrenheit... 212.0

>>> tc.F = 32
Celsius...... 0.0
Fahrenheit... 32

>>> tc.C = -40
Celsius...... -40
Fahrenheit... -40.0

As you can see, each attribute is updated if the other one changes, and the show_values action is invoked any time the dependent values change… but not if they don’t:

>>> tc.C = -40

Since the value didn’t change, none of the rules based on it were recalculated.

Now, imagine all this, but scaled up to include rules that can depend on things like how long it’s been since something happened… whether a mouse button was clicked… whether a socket is readable… or whether a Twisted “deferred” object has fired. With automatic dependency tracking that spans function calls, so you don’t even need to know what values your rule depends on, let alone having to explicitly code any dependencies in!

Imagine painless MVC, where you simply write rules like the above to update GUI widgets with application values… and vice versa.

And then, you’ll have the tiny beginning of a mere glimpse… of what the Trellis can do for you.

Other Python libraries exist which attempt to do similar things, of course; PyCells and Cellulose are two. However, only the Trellis supports fully circular rules (like the temperature conversion example above), and intra-pulse write conflict detection. The Trellis also uses less memory for each cell (rule/value object), and offers many other features that either PyCells or Cellulose lack.

The Trellis package can can be downloaded from the Python Package Index or installed using Easy Install, and it has a fair amount of documentation, including the following manuals:

Release highlights for 0.7a2:

• Removed APIs that were deprecated in 0.7a1

• Rollback now occurs over an entire atomic operation, even if more than one recalc pass occurs within that atomic operation.

• Added collections.Hub type for publish/subscribe operations similar to PyDispatcher, but in a declarative, callback-free, and extensible manner.

• Various bugfixes

Questions, discussion, and bug reports for the Trellis should be directed to the PEAK mailing list.

## Project details

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