Package: zc.dict
An efficient, persistent and subclassable dictPersistentDict is very inefficient if it contains more than a couple
of values, and BTrees are not recommended to inherit from.This class is a simple wrapper over a BTree.  It retains the
efficiency of BTrees and is safe to use as a base class.  Also, it
implements the full Python dict interface.>>> from zc.dict import Dict
>>> d = Dict()
>>> d
<zc.dict.dict.Dict object at ...>>>> d['foo'] = 'bar'
>>> len(d)
1>>> d['bar'] = 'baz'
>>> len(d)
2Note that an important difference between the Python dict and this Dict is
that the Python dict uses hashes, and this uses BTree comparisons.
Practically, this means that your keys should be of homogenous types.
We use strings in these examples.Length is maintained separately, because len on a BTree is inefficient,
as it has to wake up all buckets in the tree from the database.>>> d._len
<BTrees.Length.Length object at ...>
>>> d._len()
2In order to keep updates efficient for small changes, we unroll them
as a series of setitems.>>> d.update({'bar': 'moo', 'ding': 'dong', 'beep': 'beep'})
>>> len(d)
4The Dict supports the fullupdateinterface.>>> d.update([['sha', 'zam'], ['ka', 'pow']])
>>> len(d)
6
>>> d['ka']
'pow'
>>> d.update(left='hook', right='jab')
>>> len(d)
8
>>> d['left']
'hook'popneeds to update the length.>>> d.pop('sha')
'zam'
>>> d.pop('ka')
'pow'
>>> d.pop('left')
'hook'
>>> d.pop('right')
'jab'
>>> len(d)
4…except when it doesn’t.>>> d.pop('nonexistent')
Traceback (most recent call last):
...
KeyError: 'nonexistent'
>>> d.pop('nonexistent', 42)
42
>>> len(d)
4setdefaultalso sometimes needs to update the length.>>> len(d)
4
>>> d.setdefault('newly created', 'value')
'value'
>>> d['newly created']
'value'
>>> len(d)
5>>> d.setdefault('newly created', 'other')
'value'
>>> d['newly created']
'value'
>>> len(d)
5>>> del d['newly created'] # set things back to the way they were...keys,valuesanditemsreturn normal Python lists.  Because of
the underlying BTree, these are always in sort order of the keys.>>> d.keys()
['bar', 'beep', 'ding', 'foo']>>> d.values()
['moo', 'beep', 'dong', 'bar']>>> d.items()
[('bar', 'moo'), ('beep', 'beep'), ('ding', 'dong'), ('foo', 'bar')]However, efficient BTree iterators are available via the iter methods:>>> iter(d)
<OO-iterator object at ...>
>>> d.iterkeys()
<OO-iterator object at ...>>>> d.iteritems()
<OO-iterator object at ...>>>> d.itervalues()
<OO-iterator object at ...>popitem removes from the dict and returns a key-value pair:>>> len(d)
4>>> d.popitem()
('bar', 'moo')>>> len(d)
3The copy method creates a copy of a Dict:>>> c = d.copy()
>>> c.items() == d.items()
TrueHowever we don’t support comparison, except for identity, because of
cowardice:>>> c == d
False
>>> Dict() == {}
False
>>> d == d
Trueclear removes all the keys from the dict:>>> d.clear()
>>> d.keys()
[]
>>> len(d)
0The rest of the dict methods are delegated to the underlying BTree:>>> c.has_key('beep')
True
>>> 'BEEP' in c
False
>>> c.get('nonexistent', 'default')
'default'SubclassingFor easy subclassing, the dict is intended to have three important
characteristics:All addition is done with __setitem__ so overriding it will control
addition.All removal is done with eitherpoporclearso overriding these
methods will control removal.Calling __init__ without passing an argument will not try to access theupdatemethod.Let’s demonstrate these with a quick subclass.>>> class Demo(Dict):
...     def __setitem__(self, key, value):
...         print '__setitem__', key, value
...         super(Demo, self).__setitem__(key, value)
...     def pop(self, key, *args):
...         print 'pop', key, args and arg[0] or '---'
...         return super(Demo, self).pop(key, *args)
...     def update(self, *args, **kwargs):
...         print 'update'
...         super(Demo, self).update(*args, **kwargs)
...     def clear(self):
...         print 'clear'
...         super(Demo, self).clear()
...>>> demo1 = Demo()
>>> demo2 = Demo([['foo', 'bar'], ['bing', 'baz']], sha='zam')
update
__setitem__ foo bar
__setitem__ bing baz
__setitem__ sha zam
>>> demo2.setdefault('babble')
__setitem__ babble None
>>> del demo2['bing']
pop bing ---
>>> demo2.popitem()
pop babble ---
('babble', None)
>>> demo2.clear()
clearRegression testsWhen setting an item that’s already in the dict, the length is not
increased:>>> d.clear()
>>> d['foo'] = 'bar'
>>> d['foo'] = 'baz'
>>> len(d)
1Ordered Dict: An persistent container that maintains orderAn OrderedDict provides most of the functionality of a Dict, with the
additional feature that it remembers the order in which items were added.
It also provides the API to reorder the items.Importantly, the OrderedDict currently uses a PersistentList to store
the order, which does not have good behavior for large collections,
because the entire collection of keys must be pickled every time any
key, or ordering, changes.  A BList would be a preferred data structure,
but that has not yet been released.>>> from zc.dict import OrderedDict
>>> d = OrderedDict()
>>> d
<zc.dict.dict.OrderedDict object at ...>>>> d['foo'] = 'bar'
>>> len(d)
1>>> d['bar'] = 'baz'
>>> len(d)
2>>> d['foo']
'bar'
>>> d['bar']
'baz'The keys are currently in the order added.>>> list(d)
['foo', 'bar']Note that an important difference between the Python dict and the
OrderedDict is that the Python dict uses hashes, and this uses BTree
comparisons. Practically, this means that your keys should be of
homogenous types. We use strings in these examples.Length is maintained separately, because len on a BTree is inefficient,
as it has to wake up all buckets in the tree from the database.>>> d._len
<BTrees.Length.Length object at ...>
>>> d._len()
2In order to keep updates efficient for small changes, we unroll them
as a series of setitems.>>> d.update({'bar': 'moo', 'ding': 'dong', 'beep': 'beep'})
>>> len(d)
4Note that the result of an update of multiple new items in a data
structure without order will add the new items to the end of the ordered
dict in an undefined order.  To set our order, we need to introduce a new
method:updateOrder.  This method is a heavy-handed approach to changing
the order: supply a new one.>>> list(d) == ['bar', 'beep', 'ding', 'foo']
False
>>> d.updateOrder(('bar', 'beep', 'ding', 'foo'))
>>> d.keys()
['bar', 'beep', 'ding', 'foo']updateOrderexpects the entire list of keys in the new order>>> d.updateOrder(['bar', 'beep', 'ding'])
Traceback (most recent call last):
...
ValueError: Incompatible key set.>>> d.updateOrder(['bar', 'beep', 'ding', 'sha', 'foo'])
Traceback (most recent call last):
...
ValueError: Incompatible key set.>>> d.updateOrder(['bar', 'beep', 'ding', 'sha'])
Traceback (most recent call last):
...
ValueError: Incompatible key set.>>> d.updateOrder(['bar', 'beep', 'ding', 'ding'])
Traceback (most recent call last):
...
ValueError: Duplicate keys in order.The Dict supports the fullupdateinterface.  If the input values are
ordered, the result will be as well.>>> d.update([['sha', 'zam'], ['ka', 'pow']])
>>> len(d)
6
>>> d['ka']
'pow'
>>> d.keys()
['bar', 'beep', 'ding', 'foo', 'sha', 'ka']If keyword arguments are used, no order to the new items is implied, but
it otherwise works as expected.>>> d.update(left='hook', right='jab')
>>> len(d)
8
>>> d['left']
'hook'popneeds to update the length, and maintain the order.>>> d.pop('sha')
'zam'
>>> d.pop('ka')
'pow'
>>> d.pop('left')
'hook'
>>> d.pop('right')
'jab'
>>> len(d)
4
>>> d.keys()
['bar', 'beep', 'ding', 'foo']…except when it doesn’t.>>> d.pop('nonexistent')
Traceback (most recent call last):
...
KeyError: 'nonexistent'
>>> d.pop('nonexistent', 42)
42
>>> len(d)
4setdefaultalso sometimes needs to update the length.>>> len(d)
4
>>> d.setdefault('newly created', 'value')
'value'
>>> d['newly created']
'value'
>>> len(d)
5
>>> d.keys()
['bar', 'beep', 'ding', 'foo', 'newly created']>>> d.setdefault('newly created', 'other')
'value'
>>> d['newly created']
'value'
>>> len(d)
5>>> del d['newly created'] # set things back to the way they were...keys,valuesanditemsreturn normal Python lists.  Because of
the underlying BTree, these are always in sort order of the keys.>>> d.keys()
['bar', 'beep', 'ding', 'foo']>>> d.values()
['moo', 'beep', 'dong', 'bar']>>> d.items()
[('bar', 'moo'), ('beep', 'beep'), ('ding', 'dong'), ('foo', 'bar')]However, efficient iterators are available via the iter methods:>>> iter(d)
<iterator object at ...>
>>> d.iterkeys()
<iterator object at ...>>>> d.iteritems()
<generator object at ...>>>> d.itervalues()
<generator object at ...>popitem removes an item from the dict and returns a key-value pair:>>> len(d)
4>>> d.popitem()
('bar', 'moo')>>> len(d)
3The copy method creates a copy of a Dict:>>> c = d.copy()
>>> c.items() == d.items()
TrueHowever we don’t support comparison, except for identity, because of
cowardice:>>> c == d
False
>>> OrderedDict() == {}
False
>>> d == d
Trueclear removes all the keys from the dict:>>> d.clear()
>>> d.keys()
[]
>>> len(d)
0The rest of the dict methods are delegated to the underlying BTree:>>> c.has_key('beep')
True
>>> 'BEEP' in c
False
>>> c.get('nonexistent', 'default')
'default'SubclassingFor easy subclassing, the ordered dict is intended to have three important
characteristics:All addition is done with __setitem__ so overriding it will control
addition.All removal is done with eitherpoporclearso overriding these
methods will control removal.Calling __init__ without passing an argument will not try to access theupdatemethod.Let’s demonstrate these with a quick subclass.>>> class Demo(OrderedDict):
...     def __setitem__(self, key, value):
...         print '__setitem__', key, value
...         super(Demo, self).__setitem__(key, value)
...     def pop(self, key, *args):
...         print 'pop', key, args and arg[0] or '---'
...         return super(Demo, self).pop(key, *args)
...     def update(self, *args, **kwargs):
...         print 'update'
...         super(Demo, self).update(*args, **kwargs)
...     def clear(self):
...         print 'clear'
...         super(Demo, self).clear()
...>>> demo1 = Demo()
>>> demo2 = Demo([['foo', 'bar'], ['bing', 'baz']], sha='zam')
update
__setitem__ foo bar
__setitem__ bing baz
__setitem__ sha zam
>>> demo2.setdefault('babble')
__setitem__ babble None
>>> del demo2['bing']
pop bing ---
>>> demo2.popitem()
pop babble ---
('babble', None)
>>> demo2.clear()
clearRegression testsWhen setting an item that’s already in the dict, the length is not
increased:>>> d.clear()
>>> d['foo'] = 'bar'
>>> d['foo'] = 'baz'
>>> len(d)
1Legacy testsOld databases may need to find something importable from zc.dict.ordered.>>> from zc.dict.ordered import OrderedDict as Olde
>>> Olde is OrderedDict
True