In programming, there are some problems with recursive algorithms. One of them is the stack: each recursion adds a new frame to the stack. This can be mitigated using tail recursion, but tail recursion isn’t always possible. A second problem is loops: consider loading a series of files which contain an “include” instruction, where one of the files contains an “include” for itself or for another file which contains an “include” for it.
One good solution for these problems is to recast the recursive algorithm as an iterative one. A WorkQueue is a tool for doing exactly this; it is an iterator for work items, where work items may be added to the queue during iteration. A WorkQueue also has support for tracking items that have been added to the queue before, eliminating the loop problem by simply ignoring duplicates.
The basic usage of a WorkQueue is very simple–one simply initializes it with a sequence of work items, then iterates over the items, adding new items as necessary:
wq = workq.WorkQueue([item]) for item in wq: # Do work # Add one item to the queue wq.add(new_item) # Alternatively, add a sequence of items to the queue wq.extend(new_items)
Each item added to the queue is tracked, and attempts to add an item that has already been added will simply be ignored. This can be disabled by passing unique=False to the WorkQueue constructor. If the work items are not hashable, or must be kept unique based on some property of the item (e.g., an instance attribute), then pass a key callable to the WorkQueue constructor; this callable will be called with one argument–the work item–and must return the key corresponding to that item.
The length of a WorkQueue object is the number of work items still in the queue. A WorkQueue also keeps track of the total number of work items that have been added to it; this count can be accessed through the count property. Finally, the number of items that have been worked can be accessed through the worked property. As an example:
>>> wq = workq.WorkQueue(['a', 'b']) >>> next(wq) 'a' >>> wq.add('c') >>> len(wq) 2 >>> wq.count 3 >>> wq.worked 1
A WorkQueue object uses a Python set to track the items that have previously been added, to prevent duplications. This isn’t required for every application, and some applications may have a large number of work items, or even a never-ending stream of them. To accommodate this, the unique keyword argument may be passed to the WorkQueue constructor with a value of False; this will disable using the set and keep memory usage under control.
Another problem with using set is that some work items may not be hashable, or the objects may be distinct when the work that they represent is not. To accommodate this, use the key keyword argument to the WorkQueue constructor; this identifies a callable, similar to the key parameters of sort() and sorted(), which is passed the work item and returns a key to use with the set. This can also be used to keep memory usage under control, by allowing the set to store, say, a short string, rather than a large object.
TODO: Figure out how to actually get changelog content.
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