Current File : //opt/alt/python37/lib64/python3.7/site-packages/sqlalchemy/dialects/postgresql/array.py
# postgresql/array.py
# Copyright (C) 2005-2019 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
from .base import colspecs
from .base import ischema_names
from ... import types as sqltypes
from ...sql import expression
from ...sql import operators
try:
from uuid import UUID as _python_UUID # noqa
except ImportError:
_python_UUID = None
def Any(other, arrexpr, operator=operators.eq):
"""A synonym for the :meth:`.ARRAY.Comparator.any` method.
This method is legacy and is here for backwards-compatibility.
.. seealso::
:func:`.expression.any_`
"""
return arrexpr.any(other, operator)
def All(other, arrexpr, operator=operators.eq):
"""A synonym for the :meth:`.ARRAY.Comparator.all` method.
This method is legacy and is here for backwards-compatibility.
.. seealso::
:func:`.expression.all_`
"""
return arrexpr.all(other, operator)
class array(expression.Tuple):
"""A PostgreSQL ARRAY literal.
This is used to produce ARRAY literals in SQL expressions, e.g.::
from sqlalchemy.dialects.postgresql import array
from sqlalchemy.dialects import postgresql
from sqlalchemy import select, func
stmt = select([
array([1,2]) + array([3,4,5])
])
print(stmt.compile(dialect=postgresql.dialect()))
Produces the SQL::
SELECT ARRAY[%(param_1)s, %(param_2)s] ||
ARRAY[%(param_3)s, %(param_4)s, %(param_5)s]) AS anon_1
An instance of :class:`.array` will always have the datatype
:class:`.ARRAY`. The "inner" type of the array is inferred from
the values present, unless the ``type_`` keyword argument is passed::
array(['foo', 'bar'], type_=CHAR)
Multidimensional arrays are produced by nesting :class:`.array` constructs.
The dimensionality of the final :class:`.ARRAY` type is calculated by
recursively adding the dimensions of the inner :class:`.ARRAY` type::
stmt = select([
array([
array([1, 2]), array([3, 4]), array([column('q'), column('x')])
])
])
print(stmt.compile(dialect=postgresql.dialect()))
Produces::
SELECT ARRAY[ARRAY[%(param_1)s, %(param_2)s],
ARRAY[%(param_3)s, %(param_4)s], ARRAY[q, x]] AS anon_1
.. versionadded:: 1.3.6 added support for multidimensional array literals
.. seealso::
:class:`.postgresql.ARRAY`
"""
__visit_name__ = "array"
def __init__(self, clauses, **kw):
super(array, self).__init__(*clauses, **kw)
if isinstance(self.type, ARRAY):
self.type = ARRAY(
self.type.item_type,
dimensions=self.type.dimensions + 1
if self.type.dimensions is not None
else 2,
)
else:
self.type = ARRAY(self.type)
def _bind_param(self, operator, obj, _assume_scalar=False, type_=None):
if _assume_scalar or operator is operators.getitem:
# if getitem->slice were called, Indexable produces
# a Slice object from that
assert isinstance(obj, int)
return expression.BindParameter(
None,
obj,
_compared_to_operator=operator,
type_=type_,
_compared_to_type=self.type,
unique=True,
)
else:
return array(
[
self._bind_param(
operator, o, _assume_scalar=True, type_=type_
)
for o in obj
]
)
def self_group(self, against=None):
if against in (operators.any_op, operators.all_op, operators.getitem):
return expression.Grouping(self)
else:
return self
CONTAINS = operators.custom_op("@>", precedence=5)
CONTAINED_BY = operators.custom_op("<@", precedence=5)
OVERLAP = operators.custom_op("&&", precedence=5)
class ARRAY(sqltypes.ARRAY):
"""PostgreSQL ARRAY type.
.. versionchanged:: 1.1 The :class:`.postgresql.ARRAY` type is now
a subclass of the core :class:`.types.ARRAY` type.
The :class:`.postgresql.ARRAY` type is constructed in the same way
as the core :class:`.types.ARRAY` type; a member type is required, and a
number of dimensions is recommended if the type is to be used for more
than one dimension::
from sqlalchemy.dialects import postgresql
mytable = Table("mytable", metadata,
Column("data", postgresql.ARRAY(Integer, dimensions=2))
)
The :class:`.postgresql.ARRAY` type provides all operations defined on the
core :class:`.types.ARRAY` type, including support for "dimensions",
indexed access, and simple matching such as
:meth:`.types.ARRAY.Comparator.any` and
:meth:`.types.ARRAY.Comparator.all`. :class:`.postgresql.ARRAY` class also
provides PostgreSQL-specific methods for containment operations, including
:meth:`.postgresql.ARRAY.Comparator.contains`
:meth:`.postgresql.ARRAY.Comparator.contained_by`, and
:meth:`.postgresql.ARRAY.Comparator.overlap`, e.g.::
mytable.c.data.contains([1, 2])
The :class:`.postgresql.ARRAY` type may not be supported on all
PostgreSQL DBAPIs; it is currently known to work on psycopg2 only.
Additionally, the :class:`.postgresql.ARRAY` type does not work directly in
conjunction with the :class:`.ENUM` type. For a workaround, see the
special type at :ref:`postgresql_array_of_enum`.
.. seealso::
:class:`.types.ARRAY` - base array type
:class:`.postgresql.array` - produces a literal array value.
"""
class Comparator(sqltypes.ARRAY.Comparator):
"""Define comparison operations for :class:`.ARRAY`.
Note that these operations are in addition to those provided
by the base :class:`.types.ARRAY.Comparator` class, including
:meth:`.types.ARRAY.Comparator.any` and
:meth:`.types.ARRAY.Comparator.all`.
"""
def contains(self, other, **kwargs):
"""Boolean expression. Test if elements are a superset of the
elements of the argument array expression.
"""
return self.operate(CONTAINS, other, result_type=sqltypes.Boolean)
def contained_by(self, other):
"""Boolean expression. Test if elements are a proper subset of the
elements of the argument array expression.
"""
return self.operate(
CONTAINED_BY, other, result_type=sqltypes.Boolean
)
def overlap(self, other):
"""Boolean expression. Test if array has elements in common with
an argument array expression.
"""
return self.operate(OVERLAP, other, result_type=sqltypes.Boolean)
comparator_factory = Comparator
def __init__(
self, item_type, as_tuple=False, dimensions=None, zero_indexes=False
):
"""Construct an ARRAY.
E.g.::
Column('myarray', ARRAY(Integer))
Arguments are:
:param item_type: The data type of items of this array. Note that
dimensionality is irrelevant here, so multi-dimensional arrays like
``INTEGER[][]``, are constructed as ``ARRAY(Integer)``, not as
``ARRAY(ARRAY(Integer))`` or such.
:param as_tuple=False: Specify whether return results
should be converted to tuples from lists. DBAPIs such
as psycopg2 return lists by default. When tuples are
returned, the results are hashable.
:param dimensions: if non-None, the ARRAY will assume a fixed
number of dimensions. This will cause the DDL emitted for this
ARRAY to include the exact number of bracket clauses ``[]``,
and will also optimize the performance of the type overall.
Note that PG arrays are always implicitly "non-dimensioned",
meaning they can store any number of dimensions no matter how
they were declared.
:param zero_indexes=False: when True, index values will be converted
between Python zero-based and PostgreSQL one-based indexes, e.g.
a value of one will be added to all index values before passing
to the database.
.. versionadded:: 0.9.5
"""
if isinstance(item_type, ARRAY):
raise ValueError(
"Do not nest ARRAY types; ARRAY(basetype) "
"handles multi-dimensional arrays of basetype"
)
if isinstance(item_type, type):
item_type = item_type()
self.item_type = item_type
self.as_tuple = as_tuple
self.dimensions = dimensions
self.zero_indexes = zero_indexes
@property
def hashable(self):
return self.as_tuple
@property
def python_type(self):
return list
def compare_values(self, x, y):
return x == y
def _proc_array(self, arr, itemproc, dim, collection):
if dim is None:
arr = list(arr)
if (
dim == 1
or dim is None
and (
# this has to be (list, tuple), or at least
# not hasattr('__iter__'), since Py3K strings
# etc. have __iter__
not arr
or not isinstance(arr[0], (list, tuple))
)
):
if itemproc:
return collection(itemproc(x) for x in arr)
else:
return collection(arr)
else:
return collection(
self._proc_array(
x,
itemproc,
dim - 1 if dim is not None else None,
collection,
)
for x in arr
)
def bind_processor(self, dialect):
item_proc = self.item_type.dialect_impl(dialect).bind_processor(
dialect
)
def process(value):
if value is None:
return value
else:
return self._proc_array(
value, item_proc, self.dimensions, list
)
return process
def result_processor(self, dialect, coltype):
item_proc = self.item_type.dialect_impl(dialect).result_processor(
dialect, coltype
)
def process(value):
if value is None:
return value
else:
return self._proc_array(
value,
item_proc,
self.dimensions,
tuple if self.as_tuple else list,
)
return process
colspecs[sqltypes.ARRAY] = ARRAY
ischema_names["_array"] = ARRAY
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