Package: zope.bforest
BForest APIBForests are dictionary-like objects that use multiple BTrees for a backend and
support rotation of the composite trees.  This supports various implementations
of timed member expirations, enabling caches and semi-persistent storage.  A
useful and simple subclass would be to promote a key-value pair to the
first (newest) bucket whenever the key is accessed, for instance.  It also is
useful with disabling the rotation capability.Like btrees, bforests come in four flavors: Integer-Integer (IIBForest),
Integer-Object (IOBForest), Object-Integer (OIBForest), and Object-Object
(OOBForest).  The examples here will deal with them in the abstract: we will
create classes from the imaginary and representative BForest class, and
generate keys from KeyGenerator and values from ValueGenerator.  From the
examples you should be able to extrapolate usage of all four types.First let’s instantiate a bforest and look at an empty example.  By default,
a new bforest creates two composite btree buckets.>>> d = BForest()
>>> list(d.keys())
[]
>>> list(d.values())
[]
>>> len(d.buckets)
2
>>> dummy_key = KeyGenerator()
>>> d.get(dummy_key)
>>> d.get(dummy_key, 42)
42Now we’ll populate it.  We’ll first create a BTree we’ll use to compare.>>> original = BForest._treemodule.BTree()
>>> for i in range(10):
...     original[KeyGenerator()] = ValueGenerator()
...
>>> d.update(original)
>>> d == original
True
>>> list(d) == list(original)
True
>>> list(d.keys()) == list(original.keys())
True
>>> list(d.values()) == list(original.values())
True
>>> list(d.items()) == list(original.items())
True
>>> original_min = original.minKey()
>>> d.popitem() == (original_min, original.pop(original_min))
True
>>> original_min = original.minKey()
>>> d.pop(original_min) == original.pop(original_min)
True
>>> len(d) == len(original)
TrueNow let’s rotate the buckets.>>> d.rotateBucket()…and we’ll do the exact same test as above, first.>>> d == original
True
>>> list(d) == list(original)
True
>>> list(d.keys()) == list(original.keys())
True
>>> list(d.values()) == list(original.values())
True
>>> list(d.items()) == list(original.items())
True
>>> original_min = original.minKey()
>>> d.popitem() == (original_min, original.pop(original_min))
True
>>> original_min = original.minKey()
>>> d.pop(original_min) == original.pop(original_min)
True
>>> len(d) == len(original)
TrueNow we’ll make a new dictionary to represent changes made after the bucket
rotation.>>> second = BForest._treemodule.BTree()
>>> for i in range(10):
...     key = KeyGenerator()
...     value = ValueGenerator()
...     second[key] = value
...     d[key] = value
...
>>> original.update(second)…and we’ll do the exact same test as above, first.>>> d == original
True
>>> list(d) == list(original)
True
>>> list(d.keys()) == list(original.keys())
True
>>> list(d.values()) == list(original.values())
True
>>> list(d.items()) == list(original.items())
True
>>> original_min = original.minKey()
>>> d.popitem() == (original_min, original.pop(original_min))
True
>>> if original_min in second:
...     _ = second.pop(original_min)
>>> original_min = original.minKey()
>>> d.pop(original_min) == original.pop(original_min)
True
>>> if original_min in second:
...     _ = second.pop(original_min)
>>> len(d) == len(original)
TrueThe bforest offersitervalues,iterkeys, anditeritemsthat have
the same extended arguments as BTrees’values,keys, anditems.>>> list(d.itervalues()) == list(original.values())
True
>>> list(d.iteritems()) == list(original.items())
True
>>> list(d.iterkeys()) == list(original.keys())
True>>> keys = list(original)
>>> mid = keys[len(keys)//2]
>>> list(d.itervalues(min=mid)) == list(original.itervalues(min=mid))
True
>>> list(d.itervalues(max=mid)) == list(original.itervalues(max=mid))
True
>>> list(d.itervalues(min=mid, excludemin=True)) == list(
...     original.itervalues(min=mid, excludemin=True))
True
>>> list(d.itervalues(max=mid, excludemax=True)) == list(
...     original.itervalues(max=mid, excludemax=True))
True>>> list(d.iterkeys(min=mid)) == list(original.iterkeys(min=mid))
True
>>> list(d.iterkeys(max=mid)) == list(original.iterkeys(max=mid))
True
>>> list(d.iterkeys(min=mid, excludemin=True)) == list(
...     original.iterkeys(min=mid, excludemin=True))
True
>>> list(d.iterkeys(max=mid, excludemax=True)) == list(
...     original.iterkeys(max=mid, excludemax=True))
True>>> list(d.iteritems(min=mid)) == list(original.iteritems(min=mid))
True
>>> list(d.iteritems(max=mid)) == list(original.iteritems(max=mid))
True
>>> list(d.iteritems(min=mid, excludemin=True)) == list(
...     original.iteritems(min=mid, excludemin=True))
True
>>> list(d.iteritems(max=mid, excludemax=True)) == list(
...     original.iteritems(max=mid, excludemax=True))
TrueIt also offers maxKey and minKey, like BTrees.>>> d.maxKey() == original.maxKey()
True
>>> d.minKey() == original.minKey()
True
>>> d.maxKey(mid) == original.maxKey(mid)
True
>>> d.minKey(mid) == original.minKey(mid)
TrueNow if we rotate the buckets, the first set of items will be gone, but the
second will remain.>>> d.rotateBucket()
>>> d == original
False
>>> d == second
TrueLet’s set a value, check the copy behavior,  and then rotate it one more time.>>> third = BForest._treemodule.BTree({KeyGenerator(): ValueGenerator()})
>>> d.update(third)
>>> copy = d.copy()
>>> copy == d
True
>>> copy != second # because second doesn't have the values of third
True
>>> list(copy.buckets[0].items()) == list(d.buckets[0].items())
True
>>> list(copy.buckets[1].items()) == list(d.buckets[1].items())
True
>>> copy[KeyGenerator()] = ValueGenerator()
>>> copy == d
False
>>> d.rotateBucket()
>>> d == third
True
>>> d.clear()
>>> d == BForest() == {}
True>>> d.update(second)We’ll make a value in one bucket that we’ll override in another.>>> d[third.keys()[0]] = ValueGenerator()
>>> d.rotateBucket()
>>> d.update(third)
>>> second.update(third)
>>> d == second
True
>>> second == d
TrueThe tree method converts the bforest to a btree efficiently for a common case
of more items in buckets than buckets.>>> tree = d.tree()
>>> d_items = list(d.items())
>>> d_items.sort()
>>> t_items = list(tree.items())
>>> t_items.sort()
>>> t_items == d_items
TrueFinally, comparisons work similarly to dicts but in a simpleminded
way–improvements welcome!  We’ve already looked at a lot of examples above,
but here are some additional cases>>> d == None
False
>>> d == [1, 2]
False
>>> d != None
True
>>> None == d
False
>>> d != None
True
>>> d >= second
True
>>> d >= dict(second)
True
>>> d <= second
True
>>> d <= dict(second)
True
>>> d > second
False
>>> d > dict(second)
False
>>> d < second
False
>>> d > dict(second)
False
>>> original_min = second.minKey()
>>> del second[original_min]
>>> original_min in d
True
>>> d > second
True
>>> d < second
False
>>> d >= second
True
>>> d <= second
False
>>> second < d
True
>>> second > d
False
>>> second <= d
True
>>> second >= d
FalseCHANGES1.2 (2008-05-09)Bugfixes:added omitted __ne__ implementation.Features:added minKey, maxKey, like BTrees.gave itervalues, iteritems, and iterkeys same extra arguments as BTrees’
values, items, and keys: min, max, excludemin, excludemax.changed implementation of iter[…] functions to try to only wake up buckets
as needed.Incompatible Changes:changed definition of __eq__: now compares contentsandorder.  Tries to
only wake up buckets as needed.1.1.1 (2008-04-09)Bugfix:periodic variant was pseudo-guaranteeing maximum period, not minimum
period, contradicting documentation.  Changed implementation and test to
match documentation (i.e., guarantees minimum period; maximum period is
a bit fuzzy, as described in docs).1.1 (2008-03-08)Features:added periodic variantadded L-variants1.0 (?)Initial release