/usr/lib/python2.7/dist-packages/pycassa/columnfamilymap.py is in python-pycassa 1.11.2.1-1.
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Provides a way to map an existing class of objects to a column family.
This can help to cut down boilerplate code related to converting
objects to a row format and back again. ColumnFamilyMap is primarily
useful when you have one "object" per row.
.. seealso:: :mod:`pycassa.types` for selecting data types for object
attributes and infomation about creating custom data
types.
"""
from pycassa.types import CassandraType
from pycassa.columnfamily import ColumnFamily
import pycassa.util as util
import inspect
__all__ = ['ColumnFamilyMap']
def create_instance(cls, **kwargs):
instance = cls()
map(lambda (k,v): setattr(instance, k, v), kwargs.iteritems())
return instance
class ColumnFamilyMap(ColumnFamily):
"""
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.
"""
def __init__(self, cls, pool, column_family, raw_columns=False, **kwargs):
"""
Instances of `cls` are returned from :meth:`get()`, :meth:`multiget()`,
:meth:`get_range()` and :meth:`get_indexed_slices()`.
`pool` is a :class:`~pycassa.pool.ConnectionPool` that will be used
in the same way a :class:`~.ColumnFamily` uses one.
`column_family` is the name of a column family to tie to `cls`.
If `raw_columns` is ``True``, all columns will be fetched into the
`raw_columns` field in requests.
"""
ColumnFamily.__init__(self, pool, column_family, **kwargs)
self.cls = cls
self.autopack_names = False
self.raw_columns = raw_columns
self.dict_class = util.OrderedDict
self.defaults = {}
self.fields = []
for name, val_type in inspect.getmembers(self.cls):
if name != 'key' and isinstance(val_type, CassandraType):
self.fields.append(name)
self.column_validators[name] = val_type
self.defaults[name] = val_type.default
if hasattr(self.cls, 'key') and isinstance(self.cls.key, CassandraType):
self.key_validation_class = self.cls.key
def combine_columns(self, columns):
combined_columns = columns
if self.raw_columns:
combined_columns['raw_columns'] = columns
for column, default in self.defaults.items():
combined_columns.setdefault(column, default)
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.
All other parameters behave the same as in :meth:`.ColumnFamily.get()`.
"""
if 'columns' not in kwargs and not self.super and not self.raw_columns:
kwargs['columns'] = self.fields
columns = ColumnFamily.get(self, key, *args, **kwargs)
if self.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.super and not self.raw_columns:
kwargs['columns'] = self.fields
kcmap = ColumnFamily.multiget(self, *args, **kwargs)
ret = self.dict_class()
for key, columns in kcmap.iteritems():
if self.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_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:`.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.super and not self.raw_columns:
kwargs['columns'] = self.fields
for key, columns in ColumnFamily.get_range(self, *args, **kwargs):
if self.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, *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.
See :meth:`.ColumnFamily.get_indexed_slices()` for
an explanation of the parameters.
"""
assert not self.super, "get_indexed_slices() is not " \
"supported by super column families"
if 'columns' not in kwargs and not self.raw_columns:
kwargs['columns'] = self.fields
for key, columns in ColumnFamily.get_indexed_slices(self, *args, **kwargs):
combined = self.combine_columns(columns)
yield create_instance(self.cls, key=key, **combined)
def _get_instance_as_dict(self, instance, columns=None):
fields = columns or self.fields
instance_dict = {}
for field in fields:
val = getattr(instance, field, None)
if val is not None and not isinstance(val, CassandraType):
instance_dict[field] = val
if self.super:
instance_dict = {instance.super_column: instance_dict}
return instance_dict
def insert(self, instance, columns=None, timestamp=None, ttl=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.
"""
if columns is None:
fields = self.fields
else:
fields = columns
insert_dict = self._get_instance_as_dict(instance, columns=fields)
return ColumnFamily.insert(self, instance.key, insert_dict,
timestamp=timestamp, ttl=ttl,
write_consistency_level=write_consistency_level)
def batch_insert(self, instances, timestamp=None, ttl=None,
write_consistency_level=None):
"""
Insert or update stored instances.
`instances` should be a list containing instances of `cls` to store.
"""
insert_dict = dict(
[(instance.key, self._get_instance_as_dict(instance))
for instance in instances]
)
return ColumnFamily.batch_insert(self, insert_dict,
timestamp=timestamp, ttl=ttl,
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.super:
return ColumnFamily.remove(self, instance.key,
super_column=instance.super_column,
columns=columns,
write_consistency_level=write_consistency_level)
else:
return ColumnFamily.remove(self, instance.key, columns,
write_consistency_level=write_consistency_level)
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