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Mappings based transparently on multiple BTrees; good for rotating caches and logs.

Project Description

BForest API

BForests 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)
42

Now 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)
True

Now 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)
True

Now 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)
True

The bforest offers itervalues, iterkeys, and iteritems that have the same extended arguments as BTrees’ values, keys, and items.

>>> 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))
True

It 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)
True

Now 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
True

Let’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
True

The 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
True

Finally, 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
False

CHANGES

1.2 (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 contents and order. 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 variant
  • added L-variants

1.0 (?)

Initial release

Release History

Release History

This version
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1.2

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1.1.1

History Node

1.1

History Node

1.0

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