def __new__(cls, name, bases, attrs):
field_dict = OrderedDict()
field_defs = [(k.decode('utf-8'), v) for k, v in attrs.items() if isinstance(v, columns.Column)]
field_defs = sorted(field_defs, key=lambda x: x[1].position)
def _transform_column(field_name, field_obj):
field_dict[field_name] = field_obj
field_obj.set_column_name(field_name)
attrs[field_name] = models.ColumnDescriptor(field_obj)
# transform field definitions
for k, v in field_defs:
# don't allow a field with the same name as a built-in attribute or method
if k in BaseUserType.__dict__:
raise UserTypeDefinitionException("field '{0}' conflicts with built-in attribute/method".format(k))
_transform_column(k, v)
# create db_name -> model name map for loading
db_map = {}
for field_name, field in field_dict.items():
db_map[field.db_field_name] = field_name
attrs['_fields'] = field_dict
attrs['_db_map'] = db_map
klass = super(UserTypeMetaClass, cls).__new__(cls, name, bases, attrs)
return klass
def __new__(cls, name, bases, attrs):
field_dict = OrderedDict()
field_defs = [(k, v) for k, v in attrs.items()
if isinstance(v, columns.Column)]
field_defs = sorted(field_defs, key=lambda x: x[1].position)
def _transform_column(field_name, field_obj):
field_dict[field_name] = field_obj
field_obj.set_column_name(field_name)
attrs[field_name] = models.ColumnDescriptor(field_obj)
# transform field definitions
for k, v in field_defs:
# don't allow a field with the same name as a built-in attribute or method
if k in BaseUserType.__dict__:
raise UserTypeDefinitionException(
"field '{0}' conflicts with built-in attribute/method".
format(k))
_transform_column(k, v)
# create db_name -> model name map for loading
db_map = {}
for field_name, field in field_dict.items():
db_map[field.db_field_name] = field_name
attrs['_fields'] = field_dict
attrs['_db_map'] = db_map
klass = super(UserTypeMetaClass, cls).__new__(cls, name, bases, attrs)
return klass
def __new__(cls, name, bases, attrs):
# move column definitions into columns dict
# and set default column names
column_dict = OrderedDict()
primary_keys = OrderedDict()
pk_name = None
# get inherited properties
inherited_columns = OrderedDict()
for base in bases:
for k, v in getattr(base, '_defined_columns', {}).items():
inherited_columns.setdefault(k, v)
# short circuit __abstract__ inheritance
is_abstract = attrs['__abstract__'] = attrs.get('__abstract__', False)
# short circuit __discriminator_value__ inheritance
attrs['__discriminator_value__'] = attrs.get('__discriminator_value__')
# TODO __default__ttl__ should be removed in the next major release
options = attrs.get('__options__') or {}
attrs['__default_ttl__'] = options.get('default_time_to_live')
column_definitions = [(k, v) for k, v in attrs.items() if isinstance(v, columns.Column)]
column_definitions = sorted(column_definitions, key=lambda x: x[1].position)
is_polymorphic_base = any([c[1].discriminator_column for c in column_definitions])
column_definitions = [x for x in inherited_columns.items()] + column_definitions
discriminator_columns = [c for c in column_definitions if c[1].discriminator_column]
is_polymorphic = len(discriminator_columns) > 0
if len(discriminator_columns) > 1:
raise ModelDefinitionException('only one discriminator_column can be defined in a model, {0} found'.format(len(discriminator_columns)))
if attrs['__discriminator_value__'] and not is_polymorphic:
raise ModelDefinitionException('__discriminator_value__ specified, but no base columns defined with discriminator_column=True')
discriminator_column_name, discriminator_column = discriminator_columns[0] if discriminator_columns else (None, None)
if isinstance(discriminator_column, (columns.BaseContainerColumn, columns.Counter)):
raise ModelDefinitionException('counter and container columns cannot be used as discriminator columns')
# find polymorphic base class
polymorphic_base = None
if is_polymorphic and not is_polymorphic_base:
def _get_polymorphic_base(bases):
for base in bases:
if getattr(base, '_is_polymorphic_base', False):
return base
klass = _get_polymorphic_base(base.__bases__)
if klass:
return klass
polymorphic_base = _get_polymorphic_base(bases)
defined_columns = OrderedDict(column_definitions)
# check for primary key
if not is_abstract and not any([v.primary_key for k, v in column_definitions]):
raise ModelDefinitionException("At least 1 primary key is required.")
counter_columns = [c for c in defined_columns.values() if isinstance(c, columns.Counter)]
data_columns = [c for c in defined_columns.values() if not c.primary_key and not isinstance(c, columns.Counter)]
if counter_columns and data_columns:
raise ModelDefinitionException('counter models may not have data columns')
has_partition_keys = any(v.partition_key for (k, v) in column_definitions)
def _transform_column(col_name, col_obj):
column_dict[col_name] = col_obj
if col_obj.primary_key:
primary_keys[col_name] = col_obj
col_obj.set_column_name(col_name)
# set properties
attrs[col_name] = ColumnDescriptor(col_obj)
partition_key_index = 0
# transform column definitions
for k, v in column_definitions:
# don't allow a column with the same name as a built-in attribute or method
if k in BaseModel.__dict__:
raise ModelDefinitionException("column '{0}' conflicts with built-in attribute/method".format(k))
# counter column primary keys are not allowed
if (v.primary_key or v.partition_key) and isinstance(v, columns.Counter):
raise ModelDefinitionException('counter columns cannot be used as primary keys')
# this will mark the first primary key column as a partition
# key, if one hasn't been set already
if not has_partition_keys and v.primary_key:
v.partition_key = True
has_partition_keys = True
if v.partition_key:
v._partition_key_index = partition_key_index
partition_key_index += 1
overriding = column_dict.get(k)
if overriding:
v.position = overriding.position
v.partition_key = overriding.partition_key
v._partition_key_index = overriding._partition_key_index
_transform_column(k, v)
partition_keys = OrderedDict(k for k in primary_keys.items() if k[1].partition_key)
clustering_keys = OrderedDict(k for k in primary_keys.items() if not k[1].partition_key)
if attrs.get('__compute_routing_key__', True):
key_cols = [c for c in partition_keys.values()]
partition_key_index = dict((col.db_field_name, col._partition_key_index) for col in key_cols)
key_cql_types = [c.cql_type for c in key_cols]
key_serializer = staticmethod(lambda parts, proto_version: [t.to_binary(p, proto_version) for t, p in zip(key_cql_types, parts)])
else:
partition_key_index = {}
key_serializer = staticmethod(lambda parts, proto_version: None)
# setup partition key shortcut
if len(partition_keys) == 0:
if not is_abstract:
raise ModelException("at least one partition key must be defined")
if len(partition_keys) == 1:
pk_name = [x for x in partition_keys.keys()][0]
attrs['pk'] = attrs[pk_name]
else:
# composite partition key case, get/set a tuple of values
_get = lambda self: tuple(self._values[c].getval() for c in partition_keys.keys())
_set = lambda self, val: tuple(self._values[c].setval(v) for (c, v) in zip(partition_keys.keys(), val))
attrs['pk'] = property(_get, _set)
# some validation
col_names = set()
for v in column_dict.values():
# check for duplicate column names
if v.db_field_name in col_names:
raise ModelException("{0} defines the column '{1}' more than once".format(name, v.db_field_name))
if v.clustering_order and not (v.primary_key and not v.partition_key):
raise ModelException("clustering_order may be specified only for clustering primary keys")
if v.clustering_order and v.clustering_order.lower() not in ('asc', 'desc'):
raise ModelException("invalid clustering order '{0}' for column '{1}'".format(repr(v.clustering_order), v.db_field_name))
col_names.add(v.db_field_name)
# create db_name -> model name map for loading
db_map = {}
for col_name, field in column_dict.items():
db_field = field.db_field_name
if db_field != col_name:
db_map[db_field] = col_name
# add management members to the class
attrs['_columns'] = column_dict
attrs['_primary_keys'] = primary_keys
attrs['_defined_columns'] = defined_columns
# maps the database field to the models key
attrs['_db_map'] = db_map
attrs['_pk_name'] = pk_name
attrs['_dynamic_columns'] = {}
attrs['_partition_keys'] = partition_keys
attrs['_partition_key_index'] = partition_key_index
attrs['_key_serializer'] = key_serializer
attrs['_clustering_keys'] = clustering_keys
attrs['_has_counter'] = len(counter_columns) > 0
# add polymorphic management attributes
attrs['_is_polymorphic_base'] = is_polymorphic_base
attrs['_is_polymorphic'] = is_polymorphic
attrs['_polymorphic_base'] = polymorphic_base
attrs['_discriminator_column'] = discriminator_column
attrs['_discriminator_column_name'] = discriminator_column_name
attrs['_discriminator_map'] = {} if is_polymorphic_base else None
# setup class exceptions
DoesNotExistBase = None
for base in bases:
DoesNotExistBase = getattr(base, 'DoesNotExist', None)
if DoesNotExistBase is not None:
break
DoesNotExistBase = DoesNotExistBase or attrs.pop('DoesNotExist', BaseModel.DoesNotExist)
attrs['DoesNotExist'] = type('DoesNotExist', (DoesNotExistBase,), {})
MultipleObjectsReturnedBase = None
for base in bases:
MultipleObjectsReturnedBase = getattr(base, 'MultipleObjectsReturned', None)
if MultipleObjectsReturnedBase is not None:
break
MultipleObjectsReturnedBase = MultipleObjectsReturnedBase or attrs.pop('MultipleObjectsReturned', BaseModel.MultipleObjectsReturned)
attrs['MultipleObjectsReturned'] = type('MultipleObjectsReturned', (MultipleObjectsReturnedBase,), {})
# create the class and add a QuerySet to it
klass = super(ModelMetaClass, cls).__new__(cls, name, bases, attrs)
udts = []
for col in column_dict.values():
columns.resolve_udts(col, udts)
for user_type in set(udts):
user_type.register_for_keyspace(klass._get_keyspace())
return klass
def __new__(cls, name, bases, attrs):
# move column definitions into columns dict
# and set default column names
column_dict = OrderedDict()
primary_keys = OrderedDict()
pk_name = None
# get inherited properties
inherited_columns = OrderedDict()
for base in bases:
for k, v in getattr(base, '_defined_columns', {}).items():
inherited_columns.setdefault(k, v)
# short circuit __abstract__ inheritance
is_abstract = attrs['__abstract__'] = attrs.get('__abstract__', False)
# short circuit __discriminator_value__ inheritance
attrs['__discriminator_value__'] = attrs.get('__discriminator_value__')
# TODO __default__ttl__ should be removed in the next major release
options = attrs.get('__options__') or {}
attrs['__default_ttl__'] = options.get('default_time_to_live')
column_definitions = [(k, v) for k, v in attrs.items() if isinstance(v, columns.Column)]
column_definitions = sorted(column_definitions, key=lambda x: x[1].position)
is_polymorphic_base = any([c[1].discriminator_column for c in column_definitions])
column_definitions = [x for x in inherited_columns.items()] + column_definitions
discriminator_columns = [c for c in column_definitions if c[1].discriminator_column]
is_polymorphic = len(discriminator_columns) > 0
if len(discriminator_columns) > 1:
raise ModelDefinitionException('only one discriminator_column can be defined in a model, {0} found'.format(len(discriminator_columns)))
if attrs['__discriminator_value__'] and not is_polymorphic:
raise ModelDefinitionException('__discriminator_value__ specified, but no base columns defined with discriminator_column=True')
discriminator_column_name, discriminator_column = discriminator_columns[0] if discriminator_columns else (None, None)
if isinstance(discriminator_column, (columns.BaseContainerColumn, columns.Counter)):
raise ModelDefinitionException('counter and container columns cannot be used as discriminator columns')
# find polymorphic base class
polymorphic_base = None
if is_polymorphic and not is_polymorphic_base:
def _get_polymorphic_base(bases):
for base in bases:
if getattr(base, '_is_polymorphic_base', False):
return base
klass = _get_polymorphic_base(base.__bases__)
if klass:
return klass
polymorphic_base = _get_polymorphic_base(bases)
defined_columns = OrderedDict(column_definitions)
# check for primary key
if not is_abstract and not any([v.primary_key for k, v in column_definitions]):
raise ModelDefinitionException("At least 1 primary key is required.")
counter_columns = [c for c in defined_columns.values() if isinstance(c, columns.Counter)]
data_columns = [c for c in defined_columns.values() if not c.primary_key and not isinstance(c, columns.Counter)]
if counter_columns and data_columns:
raise ModelDefinitionException('counter models may not have data columns')
has_partition_keys = any(v.partition_key for (k, v) in column_definitions)
def _transform_column(col_name, col_obj):
column_dict[col_name] = col_obj
if col_obj.primary_key:
primary_keys[col_name] = col_obj
col_obj.set_column_name(col_name)
# set properties
attrs[col_name] = ColumnDescriptor(col_obj)
partition_key_index = 0
# transform column definitions
for k, v in column_definitions:
# don't allow a column with the same name as a built-in attribute or method
if k in BaseModel.__dict__:
raise ModelDefinitionException("column '{0}' conflicts with built-in attribute/method".format(k))
# counter column primary keys are not allowed
if (v.primary_key or v.partition_key) and isinstance(v, columns.Counter):
raise ModelDefinitionException('counter columns cannot be used as primary keys')
# this will mark the first primary key column as a partition
# key, if one hasn't been set already
if not has_partition_keys and v.primary_key:
v.partition_key = True
has_partition_keys = True
if v.partition_key:
v._partition_key_index = partition_key_index
partition_key_index += 1
overriding = column_dict.get(k)
if overriding:
v.position = overriding.position
v.partition_key = overriding.partition_key
v._partition_key_index = overriding._partition_key_index
_transform_column(k, v)
partition_keys = OrderedDict(k for k in primary_keys.items() if k[1].partition_key)
clustering_keys = OrderedDict(k for k in primary_keys.items() if not k[1].partition_key)
if attrs.get('__compute_routing_key__', True):
key_cols = [c for c in partition_keys.values()]
partition_key_index = dict((col.db_field_name, col._partition_key_index) for col in key_cols)
key_cql_types = [c.cql_type for c in key_cols]
key_serializer = staticmethod(lambda parts, proto_version: [t.to_binary(p, proto_version) for t, p in zip(key_cql_types, parts)])
else:
partition_key_index = {}
key_serializer = staticmethod(lambda parts, proto_version: None)
# setup partition key shortcut
if len(partition_keys) == 0:
if not is_abstract:
raise ModelException("at least one partition key must be defined")
if len(partition_keys) == 1:
pk_name = [x for x in partition_keys.keys()][0]
attrs['pk'] = attrs[pk_name]
else:
# composite partition key case, get/set a tuple of values
_get = lambda self: tuple(self._values[c].getval() for c in partition_keys.keys())
_set = lambda self, val: tuple(self._values[c].setval(v) for (c, v) in zip(partition_keys.keys(), val))
attrs['pk'] = property(_get, _set)
# some validation
col_names = set()
for v in column_dict.values():
# check for duplicate column names
if v.db_field_name in col_names:
raise ModelException("{0} defines the column '{1}' more than once".format(name, v.db_field_name))
if v.clustering_order and not (v.primary_key and not v.partition_key):
raise ModelException("clustering_order may be specified only for clustering primary keys")
if v.clustering_order and v.clustering_order.lower() not in ('asc', 'desc'):
raise ModelException("invalid clustering order '{0}' for column '{1}'".format(repr(v.clustering_order), v.db_field_name))
col_names.add(v.db_field_name)
# create db_name -> model name map for loading
db_map = {}
for col_name, field in column_dict.items():
db_field = field.db_field_name
if db_field != col_name:
db_map[db_field] = col_name
# add management members to the class
attrs['_columns'] = column_dict
attrs['_primary_keys'] = primary_keys
attrs['_defined_columns'] = defined_columns
# maps the database field to the models key
attrs['_db_map'] = db_map
attrs['_pk_name'] = pk_name
attrs['_dynamic_columns'] = {}
attrs['_partition_keys'] = partition_keys
attrs['_partition_key_index'] = partition_key_index
attrs['_key_serializer'] = key_serializer
attrs['_clustering_keys'] = clustering_keys
attrs['_has_counter'] = len(counter_columns) > 0
# add polymorphic management attributes
attrs['_is_polymorphic_base'] = is_polymorphic_base
attrs['_is_polymorphic'] = is_polymorphic
attrs['_polymorphic_base'] = polymorphic_base
attrs['_discriminator_column'] = discriminator_column
attrs['_discriminator_column_name'] = discriminator_column_name
attrs['_discriminator_map'] = {} if is_polymorphic_base else None
# setup class exceptions
DoesNotExistBase = None
for base in bases:
DoesNotExistBase = getattr(base, 'DoesNotExist', None)
if DoesNotExistBase is not None:
break
DoesNotExistBase = DoesNotExistBase or attrs.pop('DoesNotExist', BaseModel.DoesNotExist)
attrs['DoesNotExist'] = type('DoesNotExist', (DoesNotExistBase,), {})
MultipleObjectsReturnedBase = None
for base in bases:
MultipleObjectsReturnedBase = getattr(base, 'MultipleObjectsReturned', None)
if MultipleObjectsReturnedBase is not None:
break
MultipleObjectsReturnedBase = MultipleObjectsReturnedBase or attrs.pop('MultipleObjectsReturned', BaseModel.MultipleObjectsReturned)
attrs['MultipleObjectsReturned'] = type('MultipleObjectsReturned', (MultipleObjectsReturnedBase,), {})
# create the class and add a QuerySet to it
klass = super(ModelMetaClass, cls).__new__(cls, name, bases, attrs)
udts = []
for col in column_dict.values():
columns.resolve_udts(col, udts)
for user_type in set(udts):
user_type.register_for_keyspace(klass._get_keyspace())
return klass
def select_from_table(self, table_name, limit=10000, **kwargs):
result = []
schema = self.schema_for_table(table_name)
partition_names = []
for column in schema.partition_key:
partition_names.append(column.name)
# Sort kwargs so they are in the same order as the columns.
ordered_kwargs = OrderedDict()
for name in self._models[table_name]._columns.keys():
for key, value in kwargs.items():
if key.split('__')[0] == name and value is not None:
ordered_kwargs[key] = value
# Convert arguments to filters
partitions = [[]]
filters = []
for key, value in ordered_kwargs.items():
key_value = key.split('__')[0]
operator = None if len(
key.split('__')) == 1 else key.split('__')[1]
if key_value in partition_names:
if operator == 'in':
original_partitions = list(partitions)
partitions = []
for _ in range(len(value)):
for element in original_partitions:
partitions.append(list(element))
for multiplier in range(len(value)):
for index in range(len(original_partitions)):
partitions[multiplier * len(original_partitions) +
index].append(value[multiplier])
else:
for partition in partitions:
partition.append(value)
filters.append(self.filter_for_argument(key, value))
if len(partitions[0]) == 0:
partitions = self.cluster.database[
self.keyspace][table_name].keys()
candidate_elements = []
for partition in partitions:
for key, row in self.cluster.database[self.keyspace][table_name][
tuple(partition)].items():
does_match = True
for filter in filters:
if not filter(row[1]):
does_match = False
break
if not does_match:
continue
candidate_elements.append((partition, key))
# To reverse, they all need to agree. This might not work with compound clustering keys, but we really
# shouldn't use those anyways
is_reversed = True
for column in schema.primary_key:
if column in schema.partition_key:
continue
if not column.is_reversed:
is_reversed = False
candidate_elements.sort(reverse=is_reversed)
for candidate in candidate_elements:
element = self.cluster.database[self.keyspace][table_name][tuple(
candidate[0])][candidate[1]]
deadline = element[0]
if deadline is None or deadline > time.time():
result.append(element[1])
limit -= 1
if limit <= 0:
return result
else:
del self.cluster.database[self.keyspace][table_name][tuple(
candidate[0])][candidate[1]]
return result
