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"""
Provides a means for mapping an existing class to a column family.
.. seealso:: :mod:`pycassa.types`
In addition to the default :class:`~pycassa.types.Column` classes,
you may also define your own types for the mapper. For example, the
IntString may be defined as:
.. code-block:: python
>>> class IntString(pycassa.Column):
... def pack(self, val):
... return str(val)
... def unpack(self, val):
... return int(val)
...
"""
from pycassa.types import Column
from pycassa.cassandra.ttypes import IndexExpression, IndexClause
__all__ = ['ColumnFamilyMap']
def create_instance(cls, **kwargs):
instance = cls()
instance.__dict__.update(kwargs)
return instance
class ColumnFamilyMap(object):
""" Maps an existing class to a column family. """
def __init__(self, cls, column_family, columns=None, raw_columns=False):
"""
Maps an existing class to a column family. Class fields become columns,
and instances of that class can be represented as rows in standard column
families or super columns in super column families.
Instances of `cls` are returned from :meth:`get()`, :meth:`multiget()`,
:meth:`get_range()` and :meth:`get_indexed_slices()`.
`column_family` is a :class:`~pycassa.columnfamily.ColumnFamily` to
tie with `cls`. This :class:`ColumnFamily` should almost always have
`autopack_names` and `autopack_values` set to ``False``.
If `raw_columns` is ``True``, all columns will be fetched into the
`raw_columns` field in requests.
"""
self.cls = cls
self.column_family = column_family
self.raw_columns = raw_columns
self.dict_class = self.column_family.dict_class
self.columns = self.dict_class()
for name, column in self.cls.__dict__.iteritems():
if not isinstance(column, Column):
continue
self.columns[name] = column
def combine_columns(self, columns):
combined_columns = self.dict_class()
if self.raw_columns:
combined_columns['raw_columns'] = self.dict_class()
for column, type in self.columns.iteritems():
combined_columns[column] = type.default
for column, value in columns.iteritems():
col_cls = self.columns.get(column, None)
if col_cls is not None:
combined_columns[column] = col_cls.unpack(value)
if self.raw_columns:
combined_columns['raw_columns'][column] = value
return combined_columns
def get(self, key, *args, **kwargs):
"""
Creates one or more instances of `cls` from the row with key `key`.
The fields that are retreived may be specified using `columns`, which
should be a list of column names.
If the column family is a super column family, a list of `cls`
instances will be returned, one for each super column. If
the `super_column` parameter is not supplied, then `columns`
specifies which super columns will be used to create instances
of `cls`. If the `super_column` parameter *is* supplied, only
one instance of `cls` will be returned; if `columns` is specified
in this case, only those attributes listed in `columns` will be fetched.
"""
if 'columns' not in kwargs and not self.column_family.super and not self.raw_columns:
kwargs['columns'] = self.columns.keys()
columns = self.column_family.get(key, *args, **kwargs)
if self.column_family.super:
if 'super_column' not in kwargs:
vals = self.dict_class()
for super_column, subcols in columns.iteritems():
combined = self.combine_columns(subcols)
vals[super_column] = create_instance(self.cls, key=key,
super_column=super_column, **combined)
return vals
combined = self.combine_columns(columns)
return create_instance(self.cls, key=key,
super_column=kwargs['super_column'],
**combined)
combined = self.combine_columns(columns)
return create_instance(self.cls, key=key, **combined)
def multiget(self, *args, **kwargs):
"""
Like :meth:`get()`, but a list of keys may be specified.
The result of multiget will be a dictionary where the keys
are the keys from the `keys` argument, minus any missing rows.
The value for each key in the dictionary will be the same as
if :meth:`get()` were called on that individual key.
"""
if 'columns' not in kwargs and not self.column_family.super and not self.raw_columns:
kwargs['columns'] = self.columns.keys()
kcmap = self.column_family.multiget(*args, **kwargs)
ret = self.dict_class()
for key, columns in kcmap.iteritems():
if self.column_family.super:
if 'super_column' not in kwargs:
vals = self.dict_class()
for super_column, subcols in columns.iteritems():
combined = self.combine_columns(subcols)
vals[super_column] = create_instance(self.cls, key=key, super_column=super_column, **combined)
ret[key] = vals
else:
combined = self.combine_columns(columns)
ret[key] = create_instance(self.cls, key=key, super_column=kwargs['super_column'], **combined)
else:
combined = self.combine_columns(columns)
ret[key] = create_instance(self.cls, key=key, **combined)
return ret
def get_count(self, *args, **kwargs):
"""
Count the number of columns for a key.
.. deprecated:: 0.6.0
Use :meth:`pycassa.columnfamily.ColumnFamily.get()` instead.
"""
return self.column_family.get_count(*args, **kwargs)
def get_range(self, *args, **kwargs):
"""
Get an iterator over instances in a specified key range.
Like :meth:`multiget()`, whether a single instance or multiple
instances are returned per-row when the column family is a super
column family depends on what parameters are passed.
For an explanation of how :meth:`get_range` works and a description
of the parameters, see :meth:`pycassa.columnfamily.ColumnFamily.get_range()`.
Example usage with a standard column family:
.. code-block:: python
>>> pool = pycassa.ConnectionPool('Keyspace1')
>>> usercf = pycassa.ColumnFamily(pool, 'Users')
>>> cfmap = pycassa.ColumnFamilyMap(MyClass, usercf)
>>> users = cfmap.get_range(row_count=2, columns=['name', 'age'])
>>> for key, user in users:
... print user.name, user.age
Miles Davis 84
Winston Smith 42
"""
if 'columns' not in kwargs and not self.column_family.super and not self.raw_columns:
kwargs['columns'] = self.columns.keys()
for key, columns in self.column_family.get_range(*args, **kwargs):
if self.column_family.super:
if 'super_column' not in kwargs:
vals = self.dict_class()
for super_column, subcols in columns.iteritems():
combined = self.combine_columns(subcols)
vals[super_column] = create_instance(self.cls, key=key, super_column=super_column, **combined)
yield vals
else:
combined = self.combine_columns(columns)
yield create_instance(self.cls, key=key, super_column=kwargs['super_column'], **combined)
else:
combined = self.combine_columns(columns)
yield create_instance(self.cls, key=key, **combined)
def get_indexed_slices(self, instance=None, *args, **kwargs):
"""
Fetches a list of instances that satisfy an index clause. Similar
to :meth:`get_range()`, but uses an index clause instead of a key range.
If `instance` is supplied, its values will be used for each
:class:`IndexExpression` where the name matches one of the instance's
attribute names. This makes packing the values in the :class:`IndexExpresssion`
simpler when possible.
See :meth:`pycassa.columnfamily.ColumnFamily.get_indexed_slices()` for
an explanation of the parameters.
"""
if 'columns' not in kwargs and not self.column_family.super and not self.raw_columns:
kwargs['columns'] = self.columns.keys()
# Autopack the index clause's values
if instance is not None:
new_exprs = []
for expr in kwargs['index_clause'].expressions:
new_expr = IndexExpression(expr.column_name, expr.op,
value=self.columns[expr.column_name].pack(instance.__dict__[expr.column_name]))
new_exprs.append(new_expr)
old_clause = kwargs['index_clause']
new_clause = IndexClause(new_exprs, old_clause.start_key, old_clause.count)
kwargs['index_clause'] = new_clause
keyslice_map = self.column_family.get_indexed_slices(*args, **kwargs)
ret = self.dict_class()
for key, columns in keyslice_map:
if self.column_family.super:
if 'super_column' not in kwargs:
vals = self.dict_class()
for super_column, subcols in columns.iteritems():
combined = self.combine_columns(subcols)
vals[super_column] = create_instance(self.cls, key=key, super_column=super_column, **combined)
ret[key] = vals
else:
combined = self.combine_columns(columns)
ret[key] = create_instance(self.cls, key=key, super_column=kwargs['super_column'], **combined)
else:
combined = self.combine_columns(columns)
ret[key] = create_instance(self.cls, key=key, **combined)
return ret
def insert(self, instance, columns=None, write_consistency_level=None):
"""
Insert or update stored instances.
`instance` should be an instance of `cls` to store.
The `columns` parameter allows to you specify which attributes of
`instance` should be inserted or updated. If left as ``None``, all
attributes will be inserted.
"""
insert_dict = {}
if columns is None:
columns = self.columns.keys()
for column in columns:
if instance.__dict__.has_key(column) and instance.__dict__[column] is not None:
insert_dict[column] = self.columns[column].pack(instance.__dict__[column])
if self.column_family.super:
insert_dict = {instance.super_column: insert_dict}
return self.column_family.insert(instance.key, insert_dict,
write_consistency_level=write_consistency_level)
def remove(self, instance, columns=None, write_consistency_level=None):
"""
Removes a stored instance.
The `columns` parameter is a list of columns that should be removed.
If this is left as the default value of ``None``, the entire stored
instance will be removed.
"""
if self.column_family.super:
return self.column_family.remove(instance.key,
super_column=instance.super_column,
columns=columns,
write_consistency_level=write_consistency_level)
else:
return self.column_family.remove(instance.key, columns,
write_consistency_level=write_consistency_level)
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