- merge of ticket_3499 indexed access branch

- The "hashable" flag on special datatypes such as :class:`.postgresql.ARRAY`,
:class:`.postgresql.JSON` and :class:`.postgresql.HSTORE` is now
set to False, which allows these types to be fetchable in ORM
queries that include entities within the row.  fixes #3499
- The Postgresql :class:`.postgresql.ARRAY` type now supports multidimensional
indexed access, e.g. expressions such as ``somecol[5][6]`` without
any need for explicit casts or type coercions, provided
that the :paramref:`.postgresql.ARRAY.dimensions` parameter is set to the
desired number of dimensions. fixes #3487
- The return type for the :class:`.postgresql.JSON` and :class:`.postgresql.JSONB`
when using indexed access has been fixed to work like Postgresql itself,
and returns an expression that itself is of type :class:`.postgresql.JSON`
or :class:`.postgresql.JSONB`.  Previously, the accessor would return
:class:`.NullType` which disallowed subsequent JSON-like operators to be
used. part of fixes #3503
- The :class:`.postgresql.JSON`, :class:`.postgresql.JSONB` and
:class:`.postgresql.HSTORE` datatypes now allow full control over the
return type from an indexed textual access operation, either ``column[someindex].astext``
for a JSON type or ``column[someindex]`` for an HSTORE type,
via the :paramref:`.postgresql.JSON.astext_type` and
:paramref:`.postgresql.HSTORE.text_type` parameters. also part of fixes #3503
- The :attr:`.postgresql.JSON.Comparator.astext` modifier no longer
calls upon :meth:`.ColumnElement.cast` implicitly, as PG's JSON/JSONB
types allow cross-casting between each other as well.  Code that
makes use of :meth:`.ColumnElement.cast` on JSON indexed access,
e.g. ``col[someindex].cast(Integer)``, will need to be changed
to call :attr:`.postgresql.JSON.Comparator.astext` explicitly.  This is
part of the refactor in references #3503 for consistency in operator
use.

1 files changed, 34 insertions, 415 deletions

diff --git a/lib/sqlalchemy/dialects/postgresql/base.py b/lib/sqlalchemy/dialects/postgresql/base.py
index 64d19eda1..d175819c8 100644
--- a/lib/sqlalchemy/dialects/postgresql/base.py
+++ b/lib/sqlalchemy/dialects/postgresql/base.py
@@ -508,6 +508,36 @@ dialect in conjunction with the :class:`.Table` construct:
     `Postgresql CREATE TABLE options
     <http://www.postgresql.org/docs/9.3/static/sql-createtable.html>`_
 
+ARRAY Types
+-----------
+
+The Postgresql dialect supports arrays, both as multidimensional column types
+as well as array literals:
+
+* :class:`.postgresql.ARRAY` - ARRAY datatype
+
+* :class:`.postgresql.array` - array literal
+
+JSON Types
+----------
+
+The Postgresql dialect supports both JSON and JSONB datatypes, including
+psycopg2's native support and support for all of Postgresql's special
+operators:
+
+* :class:`.postgresql.JSON`
+
+* :class:`.postgresql.JSONB`
+
+HSTORE Type
+-----------
+
+The Postgresql HSTORE type as well as hstore literals are supported:
+
+* :class:`.postgresql.HSTORE` - HSTORE datatype
+
+* :class:`.postgresql.hstore` - hstore literal
+
 ENUM Types
 ----------
 
@@ -530,7 +560,7 @@ import re
 
 from ... import sql, schema, exc, util
 from ...engine import default, reflection
-from ...sql import compiler, expression, operators, default_comparator
+from ...sql import compiler, expression
 from ... import types as sqltypes
 
 try:
@@ -722,417 +752,6 @@ class TSVECTOR(sqltypes.TypeEngine):
     __visit_name__ = 'TSVECTOR'
 
 
-class _Slice(expression.ColumnElement):
-    __visit_name__ = 'slice'
-    type = sqltypes.NULLTYPE
-
-    def __init__(self, slice_, source_comparator):
-        self.start = default_comparator._check_literal(
-            source_comparator.expr,
-            operators.getitem, slice_.start)
-        self.stop = default_comparator._check_literal(
-            source_comparator.expr,
-            operators.getitem, slice_.stop)
-
-
-class Any(expression.ColumnElement):
-
-    """Represent the clause ``left operator ANY (right)``.  ``right`` must be
-    an array expression.
-
-    .. seealso::
-
-        :class:`.postgresql.ARRAY`
-
-        :meth:`.postgresql.ARRAY.Comparator.any` - ARRAY-bound method
-
-    """
-    __visit_name__ = 'any'
-
-    def __init__(self, left, right, operator=operators.eq):
-        self.type = sqltypes.Boolean()
-        self.left = expression._literal_as_binds(left)
-        self.right = right
-        self.operator = operator
-
-
-class All(expression.ColumnElement):
-
-    """Represent the clause ``left operator ALL (right)``.  ``right`` must be
-    an array expression.
-
-    .. seealso::
-
-        :class:`.postgresql.ARRAY`
-
-        :meth:`.postgresql.ARRAY.Comparator.all` - ARRAY-bound method
-
-    """
-    __visit_name__ = 'all'
-
-    def __init__(self, left, right, operator=operators.eq):
-        self.type = sqltypes.Boolean()
-        self.left = expression._literal_as_binds(left)
-        self.right = right
-        self.operator = 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)
-
-    .. versionadded:: 0.8 Added the :class:`~.postgresql.array` literal type.
-
-    See also:
-
-    :class:`.postgresql.ARRAY`
-
-    """
-    __visit_name__ = 'array'
-
-    def __init__(self, clauses, **kw):
-        super(array, self).__init__(*clauses, **kw)
-        self.type = ARRAY(self.type)
-
-    def _bind_param(self, operator, obj):
-        return array([
-            expression.BindParameter(None, o, _compared_to_operator=operator,
-                                     _compared_to_type=self.type, unique=True)
-            for o in obj
-        ])
-
-    def self_group(self, against=None):
-        return self
-
-
-class ARRAY(sqltypes.Concatenable, sqltypes.TypeEngine):
-
-    """Postgresql ARRAY type.
-
-    Represents values as Python lists.
-
-    An :class:`.ARRAY` type is constructed given the "type"
-    of element::
-
-        mytable = Table("mytable", metadata,
-                Column("data", ARRAY(Integer))
-            )
-
-    The above type represents an N-dimensional array,
-    meaning Postgresql will interpret values with any number
-    of dimensions automatically.   To produce an INSERT
-    construct that passes in a 1-dimensional array of integers::
-
-        connection.execute(
-                mytable.insert(),
-                data=[1,2,3]
-        )
-
-    The :class:`.ARRAY` type can be constructed given a fixed number
-    of dimensions::
-
-        mytable = Table("mytable", metadata,
-                Column("data", ARRAY(Integer, dimensions=2))
-            )
-
-    This has the effect of the :class:`.ARRAY` type
-    specifying that number of bracketed blocks when a :class:`.Table`
-    is used in a CREATE TABLE statement, or when the type is used
-    within a :func:`.expression.cast` construct; it also causes
-    the bind parameter and result set processing of the type
-    to optimize itself to expect exactly that number of dimensions.
-    Note that Postgresql itself still allows N dimensions with such a type.
-
-    SQL expressions of type :class:`.ARRAY` have support for "index" and
-    "slice" behavior.  The Python ``[]`` operator works normally here, given
-    integer indexes or slices.  Note that Postgresql arrays default
-    to 1-based indexing.  The operator produces binary expression
-    constructs which will produce the appropriate SQL, both for
-    SELECT statements::
-
-        select([mytable.c.data[5], mytable.c.data[2:7]])
-
-    as well as UPDATE statements when the :meth:`.Update.values` method
-    is used::
-
-        mytable.update().values({
-            mytable.c.data[5]: 7,
-            mytable.c.data[2:7]: [1, 2, 3]
-        })
-
-    .. note::
-
-        Multi-dimensional support for the ``[]`` operator is not supported
-        in SQLAlchemy 1.0.  Please use the :func:`.type_coerce` function
-        to cast an intermediary expression to ARRAY again as a workaround::
-
-            expr = type_coerce(my_array_column[5], ARRAY(Integer))[6]
-
-        Multi-dimensional support will be provided in a future release.
-
-    :class:`.ARRAY` provides special methods for containment operations,
-    e.g.::
-
-        mytable.c.data.contains([1, 2])
-
-    For a full list of special methods see :class:`.ARRAY.Comparator`.
-
-    .. versionadded:: 0.8 Added support for index and slice operations
-       to the :class:`.ARRAY` type, including support for UPDATE
-       statements, and special array containment operations.
-
-    The :class:`.ARRAY` type may not be supported on all DBAPIs.
-    It is known to work on psycopg2 and not pg8000.
-
-    See also:
-
-    :class:`.postgresql.array` - produce a literal array value.
-
-    """
-    __visit_name__ = 'ARRAY'
-
-    class Comparator(sqltypes.Concatenable.Comparator):
-
-        """Define comparison operations for :class:`.ARRAY`."""
-
-        def __getitem__(self, index):
-            shift_indexes = 1 if self.expr.type.zero_indexes else 0
-            if isinstance(index, slice):
-                if shift_indexes:
-                    index = slice(
-                        index.start + shift_indexes,
-                        index.stop + shift_indexes,
-                        index.step
-                    )
-                index = _Slice(index, self)
-                return_type = self.type
-            else:
-                index += shift_indexes
-                return_type = self.type.item_type
-
-            return default_comparator._binary_operate(
-                self.expr, operators.getitem, index,
-                result_type=return_type)
-
-        def any(self, other, operator=operators.eq):
-            """Return ``other operator ANY (array)`` clause.
-
-            Argument places are switched, because ANY requires array
-            expression to be on the right hand-side.
-
-            E.g.::
-
-                from sqlalchemy.sql import operators
-
-                conn.execute(
-                    select([table.c.data]).where(
-                            table.c.data.any(7, operator=operators.lt)
-                        )
-                )
-
-            :param other: expression to be compared
-            :param operator: an operator object from the
-             :mod:`sqlalchemy.sql.operators`
-             package, defaults to :func:`.operators.eq`.
-
-            .. seealso::
-
-                :class:`.postgresql.Any`
-
-                :meth:`.postgresql.ARRAY.Comparator.all`
-
-            """
-            return Any(other, self.expr, operator=operator)
-
-        def all(self, other, operator=operators.eq):
-            """Return ``other operator ALL (array)`` clause.
-
-            Argument places are switched, because ALL requires array
-            expression to be on the right hand-side.
-
-            E.g.::
-
-                from sqlalchemy.sql import operators
-
-                conn.execute(
-                    select([table.c.data]).where(
-                            table.c.data.all(7, operator=operators.lt)
-                        )
-                )
-
-            :param other: expression to be compared
-            :param operator: an operator object from the
-             :mod:`sqlalchemy.sql.operators`
-             package, defaults to :func:`.operators.eq`.
-
-            .. seealso::
-
-                :class:`.postgresql.All`
-
-                :meth:`.postgresql.ARRAY.Comparator.any`
-
-            """
-            return All(other, self.expr, operator=operator)
-
-        def contains(self, other, **kwargs):
-            """Boolean expression.  Test if elements are a superset of the
-            elements of the argument array expression.
-            """
-            return self.expr.op('@>')(other)
-
-        def contained_by(self, other):
-            """Boolean expression.  Test if elements are a proper subset of the
-            elements of the argument array expression.
-            """
-            return self.expr.op('<@')(other)
-
-        def overlap(self, other):
-            """Boolean expression.  Test if array has elements in common with
-            an argument array expression.
-            """
-            return self.expr.op('&&')(other)
-
-        def _adapt_expression(self, op, other_comparator):
-            if isinstance(op, operators.custom_op):
-                if op.opstring in ['@>', '<@', '&&']:
-                    return op, sqltypes.Boolean
-            return sqltypes.Concatenable.Comparator.\
-                _adapt_expression(self, op, other_comparator)
-
-    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 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
-
-PGArray = ARRAY
-
-
 class ENUM(sqltypes.Enum):
 
     """Postgresql ENUM type.
@@ -1547,8 +1166,8 @@ class PGDDLCompiler(compiler.DDLCompiler):
             else:
                 colspec += " SERIAL"
         else:
-            colspec += " " + self.dialect.type_compiler.process(column.type,
-                                                    type_expression=column)
+            colspec += " " + self.dialect.type_compiler.process(
+                column.type, type_expression=column)
             default = self.get_column_default_string(column)
             if default is not None:
                 colspec += " DEFAULT " + default
@@ -2448,7 +2067,7 @@ class PGDialect(default.DefaultDialect):
         if coltype:
             coltype = coltype(*args, **kwargs)
             if is_array:
-                coltype = ARRAY(coltype)
+                coltype = self.ischema_names['_array'](coltype)
         else:
             util.warn("Did not recognize type '%s' of column '%s'" %
                       (attype, name))