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✍️ Editing: cursor.py

            File: /opt/hc_python/lib/python3.12/site-packages/sqlalchemy/engine/cursor.py

            Size: 74.52 KB | 
            Last Modified: 2025-04-04 15:08:36        

# engine/cursor.py
# Copyright (C) 2005-2025 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: https://www.opensource.org/licenses/mit-license.php
# mypy: allow-untyped-defs, allow-untyped-calls

"""Define cursor-specific result set constructs including
:class:`.CursorResult`."""

from __future__ import annotations

import collections
import functools
import operator
import typing
from typing import Any
from typing import cast
from typing import ClassVar
from typing import Dict
from typing import Iterator
from typing import List
from typing import Mapping
from typing import NoReturn
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import TYPE_CHECKING
from typing import TypeVar
from typing import Union

from .result import IteratorResult
from .result import MergedResult
from .result import Result
from .result import ResultMetaData
from .result import SimpleResultMetaData
from .result import tuplegetter
from .row import Row
from .. import exc
from .. import util
from ..sql import elements
from ..sql import sqltypes
from ..sql import util as sql_util
from ..sql.base import _generative
from ..sql.compiler import ResultColumnsEntry
from ..sql.compiler import RM_NAME
from ..sql.compiler import RM_OBJECTS
from ..sql.compiler import RM_RENDERED_NAME
from ..sql.compiler import RM_TYPE
from ..sql.type_api import TypeEngine
from ..util import compat
from ..util.typing import Literal
from ..util.typing import Self

if typing.TYPE_CHECKING:
    from .base import Connection
    from .default import DefaultExecutionContext
    from .interfaces import _DBAPICursorDescription
    from .interfaces import DBAPICursor
    from .interfaces import Dialect
    from .interfaces import ExecutionContext
    from .result import _KeyIndexType
    from .result import _KeyMapRecType
    from .result import _KeyMapType
    from .result import _KeyType
    from .result import _ProcessorsType
    from .result import _TupleGetterType
    from ..sql.type_api import _ResultProcessorType

_T = TypeVar("_T", bound=Any)

# metadata entry tuple indexes.
# using raw tuple is faster than namedtuple.
# these match up to the positions in
# _CursorKeyMapRecType
MD_INDEX: Literal[0] = 0
"""integer index in cursor.description

"""

MD_RESULT_MAP_INDEX: Literal[1] = 1
"""integer index in compiled._result_columns"""

MD_OBJECTS: Literal[2] = 2
"""other string keys and ColumnElement obj that can match.

This comes from compiler.RM_OBJECTS / compiler.ResultColumnsEntry.objects

"""

MD_LOOKUP_KEY: Literal[3] = 3
"""string key we usually expect for key-based lookup

this comes from compiler.RM_NAME / compiler.ResultColumnsEntry.name
"""

MD_RENDERED_NAME: Literal[4] = 4
"""name that is usually in cursor.description

this comes from compiler.RENDERED_NAME / compiler.ResultColumnsEntry.keyname
"""

MD_PROCESSOR: Literal[5] = 5
"""callable to process a result value into a row"""

MD_UNTRANSLATED: Literal[6] = 6
"""raw name from cursor.description"""

_CursorKeyMapRecType = Tuple[
    Optional[int],  # MD_INDEX, None means the record is ambiguously named
    int,  # MD_RESULT_MAP_INDEX
    List[Any],  # MD_OBJECTS
    str,  # MD_LOOKUP_KEY
    str,  # MD_RENDERED_NAME
    Optional["_ResultProcessorType[Any]"],  # MD_PROCESSOR
    Optional[str],  # MD_UNTRANSLATED
]

_CursorKeyMapType = Mapping["_KeyType", _CursorKeyMapRecType]

# same as _CursorKeyMapRecType except the MD_INDEX value is definitely
# not None
_NonAmbigCursorKeyMapRecType = Tuple[
    int,
    int,
    List[Any],
    str,
    str,
    Optional["_ResultProcessorType[Any]"],
    str,
]

class CursorResultMetaData(ResultMetaData):
    """Result metadata for DBAPI cursors."""

__slots__ = (
        "_keymap",
        "_processors",
        "_keys",
        "_keymap_by_result_column_idx",
        "_tuplefilter",
        "_translated_indexes",
        "_safe_for_cache",
        "_unpickled",
        "_key_to_index",
        # don't need _unique_filters support here for now.  Can be added
        # if a need arises.
    )

_keymap: _CursorKeyMapType
    _processors: _ProcessorsType
    _keymap_by_result_column_idx: Optional[Dict[int, _KeyMapRecType]]
    _unpickled: bool
    _safe_for_cache: bool
    _translated_indexes: Optional[List[int]]

returns_rows: ClassVar[bool] = True

def _has_key(self, key: Any) -> bool:
        return key in self._keymap

def _for_freeze(self) -> ResultMetaData:
        return SimpleResultMetaData(
            self._keys,
            extra=[self._keymap[key][MD_OBJECTS] for key in self._keys],
        )

def _make_new_metadata(
        self,
        *,
        unpickled: bool,
        processors: _ProcessorsType,
        keys: Sequence[str],
        keymap: _KeyMapType,
        tuplefilter: Optional[_TupleGetterType],
        translated_indexes: Optional[List[int]],
        safe_for_cache: bool,
        keymap_by_result_column_idx: Any,
    ) -> CursorResultMetaData:
        new_obj = self.__class__.__new__(self.__class__)
        new_obj._unpickled = unpickled
        new_obj._processors = processors
        new_obj._keys = keys
        new_obj._keymap = keymap
        new_obj._tuplefilter = tuplefilter
        new_obj._translated_indexes = translated_indexes
        new_obj._safe_for_cache = safe_for_cache
        new_obj._keymap_by_result_column_idx = keymap_by_result_column_idx
        new_obj._key_to_index = self._make_key_to_index(keymap, MD_INDEX)
        return new_obj

def _remove_processors(self) -> CursorResultMetaData:
        assert not self._tuplefilter
        return self._make_new_metadata(
            unpickled=self._unpickled,
            processors=[None] * len(self._processors),
            tuplefilter=None,
            translated_indexes=None,
            keymap={
                key: value[0:5] + (None,) + value[6:]
                for key, value in self._keymap.items()
            },
            keys=self._keys,
            safe_for_cache=self._safe_for_cache,
            keymap_by_result_column_idx=self._keymap_by_result_column_idx,
        )

def _splice_horizontally(
        self, other: CursorResultMetaData
    ) -> CursorResultMetaData:
        assert not self._tuplefilter

keymap = dict(self._keymap)
        offset = len(self._keys)
        keymap.update(
            {
                key: (
                    # int index should be None for ambiguous key
                    (
                        value[0] + offset
                        if value[0] is not None and key not in keymap
                        else None
                    ),
                    value[1] + offset,
                    *value[2:],
                )
                for key, value in other._keymap.items()
            }
        )
        return self._make_new_metadata(
            unpickled=self._unpickled,
            processors=self._processors + other._processors,  # type: ignore
            tuplefilter=None,
            translated_indexes=None,
            keys=self._keys + other._keys,  # type: ignore
            keymap=keymap,
            safe_for_cache=self._safe_for_cache,
            keymap_by_result_column_idx={
                metadata_entry[MD_RESULT_MAP_INDEX]: metadata_entry
                for metadata_entry in keymap.values()
            },
        )

def _reduce(self, keys: Sequence[_KeyIndexType]) -> ResultMetaData:
        recs = list(self._metadata_for_keys(keys))

indexes = [rec[MD_INDEX] for rec in recs]
        new_keys: List[str] = [rec[MD_LOOKUP_KEY] for rec in recs]

if self._translated_indexes:
            indexes = [self._translated_indexes[idx] for idx in indexes]
        tup = tuplegetter(*indexes)
        new_recs = [(index,) + rec[1:] for index, rec in enumerate(recs)]

keymap = {rec[MD_LOOKUP_KEY]: rec for rec in new_recs}
        # TODO: need unit test for:
        # result = connection.execute("raw sql, no columns").scalars()
        # without the "or ()" it's failing because MD_OBJECTS is None
        keymap.update(
            (e, new_rec)
            for new_rec in new_recs
            for e in new_rec[MD_OBJECTS] or ()
        )

return self._make_new_metadata(
            unpickled=self._unpickled,
            processors=self._processors,
            keys=new_keys,
            tuplefilter=tup,
            translated_indexes=indexes,
            keymap=keymap,  # type: ignore[arg-type]
            safe_for_cache=self._safe_for_cache,
            keymap_by_result_column_idx=self._keymap_by_result_column_idx,
        )

def _adapt_to_context(self, context: ExecutionContext) -> ResultMetaData:
        """When using a cached Compiled construct that has a _result_map,
        for a new statement that used the cached Compiled, we need to ensure
        the keymap has the Column objects from our new statement as keys.
        So here we rewrite keymap with new entries for the new columns
        as matched to those of the cached statement.

"""

if not context.compiled or not context.compiled._result_columns:
            return self

compiled_statement = context.compiled.statement
        invoked_statement = context.invoked_statement

if TYPE_CHECKING:
            assert isinstance(invoked_statement, elements.ClauseElement)

if compiled_statement is invoked_statement:
            return self

assert invoked_statement is not None

# this is the most common path for Core statements when
        # caching is used.  In ORM use, this codepath is not really used
        # as the _result_disable_adapt_to_context execution option is
        # set by the ORM.

# make a copy and add the columns from the invoked statement
        # to the result map.

keymap_by_position = self._keymap_by_result_column_idx

if keymap_by_position is None:
            # first retrival from cache, this map will not be set up yet,
            # initialize lazily
            keymap_by_position = self._keymap_by_result_column_idx = {
                metadata_entry[MD_RESULT_MAP_INDEX]: metadata_entry
                for metadata_entry in self._keymap.values()
            }

assert not self._tuplefilter
        return self._make_new_metadata(
            keymap=compat.dict_union(
                self._keymap,
                {
                    new: keymap_by_position[idx]
                    for idx, new in enumerate(
                        invoked_statement._all_selected_columns
                    )
                    if idx in keymap_by_position
                },
            ),
            unpickled=self._unpickled,
            processors=self._processors,
            tuplefilter=None,
            translated_indexes=None,
            keys=self._keys,
            safe_for_cache=self._safe_for_cache,
            keymap_by_result_column_idx=self._keymap_by_result_column_idx,
        )

def __init__(
        self,
        parent: CursorResult[Any],
        cursor_description: _DBAPICursorDescription,
    ):
        context = parent.context
        self._tuplefilter = None
        self._translated_indexes = None
        self._safe_for_cache = self._unpickled = False

if context.result_column_struct:
            (
                result_columns,
                cols_are_ordered,
                textual_ordered,
                ad_hoc_textual,
                loose_column_name_matching,
            ) = context.result_column_struct
            num_ctx_cols = len(result_columns)
        else:
            result_columns = cols_are_ordered = (  # type: ignore
                num_ctx_cols
            ) = ad_hoc_textual = loose_column_name_matching = (
                textual_ordered
            ) = False

# merge cursor.description with the column info
        # present in the compiled structure, if any
        raw = self._merge_cursor_description(
            context,
            cursor_description,
            result_columns,
            num_ctx_cols,
            cols_are_ordered,
            textual_ordered,
            ad_hoc_textual,
            loose_column_name_matching,
        )

# processors in key order which are used when building up
        # a row
        self._processors = [
            metadata_entry[MD_PROCESSOR] for metadata_entry in raw
        ]

# this is used when using this ResultMetaData in a Core-only cache
        # retrieval context.  it's initialized on first cache retrieval
        # when the _result_disable_adapt_to_context execution option
        # (which the ORM generally sets) is not set.
        self._keymap_by_result_column_idx = None

# for compiled SQL constructs, copy additional lookup keys into
        # the key lookup map, such as Column objects, labels,
        # column keys and other names
        if num_ctx_cols:
            # keymap by primary string...
            by_key = {
                metadata_entry[MD_LOOKUP_KEY]: metadata_entry
                for metadata_entry in raw
            }

if len(by_key) != num_ctx_cols:
                # if by-primary-string dictionary smaller than
                # number of columns, assume we have dupes; (this check
                # is also in place if string dictionary is bigger, as
                # can occur when '*' was used as one of the compiled columns,
                # which may or may not be suggestive of dupes), rewrite
                # dupe records with "None" for index which results in
                # ambiguous column exception when accessed.
                #
                # this is considered to be the less common case as it is not
                # common to have dupe column keys in a SELECT statement.
                #
                # new in 1.4: get the complete set of all possible keys,
                # strings, objects, whatever, that are dupes across two
                # different records, first.
                index_by_key: Dict[Any, Any] = {}
                dupes = set()
                for metadata_entry in raw:
                    for key in (metadata_entry[MD_RENDERED_NAME],) + (
                        metadata_entry[MD_OBJECTS] or ()
                    ):
                        idx = metadata_entry[MD_INDEX]
                        # if this key has been associated with more than one
                        # positional index, it's a dupe
                        if index_by_key.setdefault(key, idx) != idx:
                            dupes.add(key)

# then put everything we have into the keymap excluding only
                # those keys that are dupes.
                self._keymap = {
                    obj_elem: metadata_entry
                    for metadata_entry in raw
                    if metadata_entry[MD_OBJECTS]
                    for obj_elem in metadata_entry[MD_OBJECTS]
                    if obj_elem not in dupes
                }

# then for the dupe keys, put the "ambiguous column"
                # record into by_key.
                by_key.update(
                    {
                        key: (None, None, [], key, key, None, None)
                        for key in dupes
                    }
                )

else:
                # no dupes - copy secondary elements from compiled
                # columns into self._keymap.  this is the most common
                # codepath for Core / ORM statement executions before the
                # result metadata is cached
                self._keymap = {
                    obj_elem: metadata_entry
                    for metadata_entry in raw
                    if metadata_entry[MD_OBJECTS]
                    for obj_elem in metadata_entry[MD_OBJECTS]
                }
            # update keymap with primary string names taking
            # precedence
            self._keymap.update(by_key)
        else:
            # no compiled objects to map, just create keymap by primary string
            self._keymap = {
                metadata_entry[MD_LOOKUP_KEY]: metadata_entry
                for metadata_entry in raw
            }

# update keymap with "translated" names.  In SQLAlchemy this is a
        # sqlite only thing, and in fact impacting only extremely old SQLite
        # versions unlikely to be present in modern Python versions.
        # however, the pyhive third party dialect is
        # also using this hook, which means others still might use it as well.
        # I dislike having this awkward hook here but as long as we need
        # to use names in cursor.description in some cases we need to have
        # some hook to accomplish this.
        if not num_ctx_cols and context._translate_colname:
            self._keymap.update(
                {
                    metadata_entry[MD_UNTRANSLATED]: self._keymap[
                        metadata_entry[MD_LOOKUP_KEY]
                    ]
                    for metadata_entry in raw
                    if metadata_entry[MD_UNTRANSLATED]
                }
            )

self._key_to_index = self._make_key_to_index(self._keymap, MD_INDEX)

def _merge_cursor_description(
        self,
        context,
        cursor_description,
        result_columns,
        num_ctx_cols,
        cols_are_ordered,
        textual_ordered,
        ad_hoc_textual,
        loose_column_name_matching,
    ):
        """Merge a cursor.description with compiled result column information.

There are at least four separate strategies used here, selected
        depending on the type of SQL construct used to start with.

The most common case is that of the compiled SQL expression construct,
        which generated the column names present in the raw SQL string and
        which has the identical number of columns as were reported by
        cursor.description.  In this case, we assume a 1-1 positional mapping
        between the entries in cursor.description and the compiled object.
        This is also the most performant case as we disregard extracting /
        decoding the column names present in cursor.description since we
        already have the desired name we generated in the compiled SQL
        construct.

The next common case is that of the completely raw string SQL,
        such as passed to connection.execute().  In this case we have no
        compiled construct to work with, so we extract and decode the
        names from cursor.description and index those as the primary
        result row target keys.

The remaining fairly common case is that of the textual SQL
        that includes at least partial column information; this is when
        we use a :class:`_expression.TextualSelect` construct.
        This construct may have
        unordered or ordered column information.  In the ordered case, we
        merge the cursor.description and the compiled construct's information
        positionally, and warn if there are additional description names
        present, however we still decode the names in cursor.description
        as we don't have a guarantee that the names in the columns match
        on these.   In the unordered case, we match names in cursor.description
        to that of the compiled construct based on name matching.
        In both of these cases, the cursor.description names and the column
        expression objects and names are indexed as result row target keys.

The final case is much less common, where we have a compiled
        non-textual SQL expression construct, but the number of columns
        in cursor.description doesn't match what's in the compiled
        construct.  We make the guess here that there might be textual
        column expressions in the compiled construct that themselves include
        a comma in them causing them to split.  We do the same name-matching
        as with textual non-ordered columns.

The name-matched system of merging is the same as that used by
        SQLAlchemy for all cases up through the 0.9 series.   Positional
        matching for compiled SQL expressions was introduced in 1.0 as a
        major performance feature, and positional matching for textual
        :class:`_expression.TextualSelect` objects in 1.1.
        As name matching is no longer
        a common case, it was acceptable to factor it into smaller generator-
        oriented methods that are easier to understand, but incur slightly
        more performance overhead.

"""

if (
            num_ctx_cols
            and cols_are_ordered
            and not textual_ordered
            and num_ctx_cols == len(cursor_description)
        ):
            self._keys = [elem[0] for elem in result_columns]
            # pure positional 1-1 case; doesn't need to read
            # the names from cursor.description

# most common case for Core and ORM

# this metadata is safe to cache because we are guaranteed
            # to have the columns in the same order for new executions
            self._safe_for_cache = True
            return [
                (
                    idx,
                    idx,
                    rmap_entry[RM_OBJECTS],
                    rmap_entry[RM_NAME],
                    rmap_entry[RM_RENDERED_NAME],
                    context.get_result_processor(
                        rmap_entry[RM_TYPE],
                        rmap_entry[RM_RENDERED_NAME],
                        cursor_description[idx][1],
                    ),
                    None,
                )
                for idx, rmap_entry in enumerate(result_columns)
            ]
        else:
            # name-based or text-positional cases, where we need
            # to read cursor.description names

if textual_ordered or (
                ad_hoc_textual and len(cursor_description) == num_ctx_cols
            ):
                self._safe_for_cache = True
                # textual positional case
                raw_iterator = self._merge_textual_cols_by_position(
                    context, cursor_description, result_columns
                )
            elif num_ctx_cols:
                # compiled SQL with a mismatch of description cols
                # vs. compiled cols, or textual w/ unordered columns
                # the order of columns can change if the query is
                # against a "select *", so not safe to cache
                self._safe_for_cache = False
                raw_iterator = self._merge_cols_by_name(
                    context,
                    cursor_description,
                    result_columns,
                    loose_column_name_matching,
                )
            else:
                # no compiled SQL, just a raw string, order of columns
                # can change for "select *"
                self._safe_for_cache = False
                raw_iterator = self._merge_cols_by_none(
                    context, cursor_description
                )

return [
                (
                    idx,
                    ridx,
                    obj,
                    cursor_colname,
                    cursor_colname,
                    context.get_result_processor(
                        mapped_type, cursor_colname, coltype
                    ),
                    untranslated,
                )
                for (
                    idx,
                    ridx,
                    cursor_colname,
                    mapped_type,
                    coltype,
                    obj,
                    untranslated,
                ) in raw_iterator
            ]

def _colnames_from_description(self, context, cursor_description):
        """Extract column names and data types from a cursor.description.

Applies unicode decoding, column translation, "normalization",
        and case sensitivity rules to the names based on the dialect.

"""

dialect = context.dialect
        translate_colname = context._translate_colname
        normalize_name = (
            dialect.normalize_name if dialect.requires_name_normalize else None
        )
        untranslated = None

self._keys = []

for idx, rec in enumerate(cursor_description):
            colname = rec[0]
            coltype = rec[1]

if translate_colname:
                colname, untranslated = translate_colname(colname)

if normalize_name:
                colname = normalize_name(colname)

self._keys.append(colname)

yield idx, colname, untranslated, coltype

def _merge_textual_cols_by_position(
        self, context, cursor_description, result_columns
    ):
        num_ctx_cols = len(result_columns)

if num_ctx_cols > len(cursor_description):
            util.warn(
                "Number of columns in textual SQL (%d) is "
                "smaller than number of columns requested (%d)"
                % (num_ctx_cols, len(cursor_description))
            )
        seen = set()

for (
            idx,
            colname,
            untranslated,
            coltype,
        ) in self._colnames_from_description(context, cursor_description):
            if idx < num_ctx_cols:
                ctx_rec = result_columns[idx]
                obj = ctx_rec[RM_OBJECTS]
                ridx = idx
                mapped_type = ctx_rec[RM_TYPE]
                if obj[0] in seen:
                    raise exc.InvalidRequestError(
                        "Duplicate column expression requested "
                        "in textual SQL: %r" % obj[0]
                    )
                seen.add(obj[0])
            else:
                mapped_type = sqltypes.NULLTYPE
                obj = None
                ridx = None
            yield idx, ridx, colname, mapped_type, coltype, obj, untranslated

def _merge_cols_by_name(
        self,
        context,
        cursor_description,
        result_columns,
        loose_column_name_matching,
    ):
        match_map = self._create_description_match_map(
            result_columns, loose_column_name_matching
        )
        mapped_type: TypeEngine[Any]

for (
            idx,
            colname,
            untranslated,
            coltype,
        ) in self._colnames_from_description(context, cursor_description):
            try:
                ctx_rec = match_map[colname]
            except KeyError:
                mapped_type = sqltypes.NULLTYPE
                obj = None
                result_columns_idx = None
            else:
                obj = ctx_rec[1]
                mapped_type = ctx_rec[2]
                result_columns_idx = ctx_rec[3]
            yield (
                idx,
                result_columns_idx,
                colname,
                mapped_type,
                coltype,
                obj,
                untranslated,
            )

@classmethod
    def _create_description_match_map(
        cls,
        result_columns: List[ResultColumnsEntry],
        loose_column_name_matching: bool = False,
    ) -> Dict[
        Union[str, object], Tuple[str, Tuple[Any, ...], TypeEngine[Any], int]
    ]:
        """when matching cursor.description to a set of names that are present
        in a Compiled object, as is the case with TextualSelect, get all the
        names we expect might match those in cursor.description.
        """

d: Dict[
            Union[str, object],
            Tuple[str, Tuple[Any, ...], TypeEngine[Any], int],
        ] = {}
        for ridx, elem in enumerate(result_columns):
            key = elem[RM_RENDERED_NAME]
            if key in d:
                # conflicting keyname - just add the column-linked objects
                # to the existing record.  if there is a duplicate column
                # name in the cursor description, this will allow all of those
                # objects to raise an ambiguous column error
                e_name, e_obj, e_type, e_ridx = d[key]
                d[key] = e_name, e_obj + elem[RM_OBJECTS], e_type, ridx
            else:
                d[key] = (elem[RM_NAME], elem[RM_OBJECTS], elem[RM_TYPE], ridx)

if loose_column_name_matching:
                # when using a textual statement with an unordered set
                # of columns that line up, we are expecting the user
                # to be using label names in the SQL that match to the column
                # expressions.  Enable more liberal matching for this case;
                # duplicate keys that are ambiguous will be fixed later.
                for r_key in elem[RM_OBJECTS]:
                    d.setdefault(
                        r_key,
                        (elem[RM_NAME], elem[RM_OBJECTS], elem[RM_TYPE], ridx),
                    )
        return d

def _merge_cols_by_none(self, context, cursor_description):
        for (
            idx,
            colname,
            untranslated,
            coltype,
        ) in self._colnames_from_description(context, cursor_description):
            yield (
                idx,
                None,
                colname,
                sqltypes.NULLTYPE,
                coltype,
                None,
                untranslated,
            )

if not TYPE_CHECKING:

def _key_fallback(
            self, key: Any, err: Optional[Exception], raiseerr: bool = True
        ) -> Optional[NoReturn]:
            if raiseerr:
                if self._unpickled and isinstance(key, elements.ColumnElement):
                    raise exc.NoSuchColumnError(
                        "Row was unpickled; lookup by ColumnElement "
                        "is unsupported"
                    ) from err
                else:
                    raise exc.NoSuchColumnError(
                        "Could not locate column in row for column '%s'"
                        % util.string_or_unprintable(key)
                    ) from err
            else:
                return None

def _raise_for_ambiguous_column_name(self, rec):
        raise exc.InvalidRequestError(
            "Ambiguous column name '%s' in "
            "result set column descriptions" % rec[MD_LOOKUP_KEY]
        )

def _index_for_key(self, key: Any, raiseerr: bool = True) -> Optional[int]:
        # TODO: can consider pre-loading ints and negative ints
        # into _keymap - also no coverage here
        if isinstance(key, int):
            key = self._keys[key]

try:
            rec = self._keymap[key]
        except KeyError as ke:
            x = self._key_fallback(key, ke, raiseerr)
            assert x is None
            return None

index = rec[0]

if index is None:
            self._raise_for_ambiguous_column_name(rec)
        return index

def _indexes_for_keys(self, keys):
        try:
            return [self._keymap[key][0] for key in keys]
        except KeyError as ke:
            # ensure it raises
            CursorResultMetaData._key_fallback(self, ke.args[0], ke)

def _metadata_for_keys(
        self, keys: Sequence[Any]
    ) -> Iterator[_NonAmbigCursorKeyMapRecType]:
        for key in keys:
            if int in key.__class__.__mro__:
                key = self._keys[key]

try:
                rec = self._keymap[key]
            except KeyError as ke:
                # ensure it raises
                CursorResultMetaData._key_fallback(self, ke.args[0], ke)

index = rec[MD_INDEX]

if index is None:
                self._raise_for_ambiguous_column_name(rec)

yield cast(_NonAmbigCursorKeyMapRecType, rec)

def __getstate__(self):
        # TODO: consider serializing this as SimpleResultMetaData
        return {
            "_keymap": {
                key: (
                    rec[MD_INDEX],
                    rec[MD_RESULT_MAP_INDEX],
                    [],
                    key,
                    rec[MD_RENDERED_NAME],
                    None,
                    None,
                )
                for key, rec in self._keymap.items()
                if isinstance(key, (str, int))
            },
            "_keys": self._keys,
            "_translated_indexes": self._translated_indexes,
        }

def __setstate__(self, state):
        self._processors = [None for _ in range(len(state["_keys"]))]
        self._keymap = state["_keymap"]
        self._keymap_by_result_column_idx = None
        self._key_to_index = self._make_key_to_index(self._keymap, MD_INDEX)
        self._keys = state["_keys"]
        self._unpickled = True
        if state["_translated_indexes"]:
            self._translated_indexes = cast(
                "List[int]", state["_translated_indexes"]
            )
            self._tuplefilter = tuplegetter(*self._translated_indexes)
        else:
            self._translated_indexes = self._tuplefilter = None

class ResultFetchStrategy:
    """Define a fetching strategy for a result object.

.. versionadded:: 1.4

"""

__slots__ = ()

alternate_cursor_description: Optional[_DBAPICursorDescription] = None

def soft_close(
        self, result: CursorResult[Any], dbapi_cursor: Optional[DBAPICursor]
    ) -> None:
        raise NotImplementedError()

def hard_close(
        self, result: CursorResult[Any], dbapi_cursor: Optional[DBAPICursor]
    ) -> None:
        raise NotImplementedError()

def yield_per(
        self,
        result: CursorResult[Any],
        dbapi_cursor: Optional[DBAPICursor],
        num: int,
    ) -> None:
        return

def fetchone(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
        hard_close: bool = False,
    ) -> Any:
        raise NotImplementedError()

def fetchmany(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
        size: Optional[int] = None,
    ) -> Any:
        raise NotImplementedError()

def fetchall(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
    ) -> Any:
        raise NotImplementedError()

def handle_exception(
        self,
        result: CursorResult[Any],
        dbapi_cursor: Optional[DBAPICursor],
        err: BaseException,
    ) -> NoReturn:
        raise err

class NoCursorFetchStrategy(ResultFetchStrategy):
    """Cursor strategy for a result that has no open cursor.

There are two varieties of this strategy, one for DQL and one for
    DML (and also DDL), each of which represent a result that had a cursor
    but no longer has one.

"""

__slots__ = ()

def soft_close(self, result, dbapi_cursor):
        pass

def hard_close(self, result, dbapi_cursor):
        pass

def fetchone(self, result, dbapi_cursor, hard_close=False):
        return self._non_result(result, None)

def fetchmany(self, result, dbapi_cursor, size=None):
        return self._non_result(result, [])

def fetchall(self, result, dbapi_cursor):
        return self._non_result(result, [])

def _non_result(self, result, default, err=None):
        raise NotImplementedError()

class NoCursorDQLFetchStrategy(NoCursorFetchStrategy):
    """Cursor strategy for a DQL result that has no open cursor.

This is a result set that can return rows, i.e. for a SELECT, or for an
    INSERT, UPDATE, DELETE that includes RETURNING. However it is in the state
    where the cursor is closed and no rows remain available.  The owning result
    object may or may not be "hard closed", which determines if the fetch
    methods send empty results or raise for closed result.

"""

__slots__ = ()

def _non_result(self, result, default, err=None):
        if result.closed:
            raise exc.ResourceClosedError(
                "This result object is closed."
            ) from err
        else:
            return default

_NO_CURSOR_DQL = NoCursorDQLFetchStrategy()

class NoCursorDMLFetchStrategy(NoCursorFetchStrategy):
    """Cursor strategy for a DML result that has no open cursor.

This is a result set that does not return rows, i.e. for an INSERT,
    UPDATE, DELETE that does not include RETURNING.

"""

__slots__ = ()

def _non_result(self, result, default, err=None):
        # we only expect to have a _NoResultMetaData() here right now.
        assert not result._metadata.returns_rows
        result._metadata._we_dont_return_rows(err)

_NO_CURSOR_DML = NoCursorDMLFetchStrategy()

class CursorFetchStrategy(ResultFetchStrategy):
    """Call fetch methods from a DBAPI cursor.

Alternate versions of this class may instead buffer the rows from
    cursors or not use cursors at all.

"""

__slots__ = ()

def soft_close(
        self, result: CursorResult[Any], dbapi_cursor: Optional[DBAPICursor]
    ) -> None:
        result.cursor_strategy = _NO_CURSOR_DQL

def hard_close(
        self, result: CursorResult[Any], dbapi_cursor: Optional[DBAPICursor]
    ) -> None:
        result.cursor_strategy = _NO_CURSOR_DQL

def handle_exception(
        self,
        result: CursorResult[Any],
        dbapi_cursor: Optional[DBAPICursor],
        err: BaseException,
    ) -> NoReturn:
        result.connection._handle_dbapi_exception(
            err, None, None, dbapi_cursor, result.context
        )

def yield_per(
        self,
        result: CursorResult[Any],
        dbapi_cursor: Optional[DBAPICursor],
        num: int,
    ) -> None:
        result.cursor_strategy = BufferedRowCursorFetchStrategy(
            dbapi_cursor,
            {"max_row_buffer": num},
            initial_buffer=collections.deque(),
            growth_factor=0,
        )

def fetchone(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
        hard_close: bool = False,
    ) -> Any:
        try:
            row = dbapi_cursor.fetchone()
            if row is None:
                result._soft_close(hard=hard_close)
            return row
        except BaseException as e:
            self.handle_exception(result, dbapi_cursor, e)

def fetchmany(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
        size: Optional[int] = None,
    ) -> Any:
        try:
            if size is None:
                l = dbapi_cursor.fetchmany()
            else:
                l = dbapi_cursor.fetchmany(size)

if not l:
                result._soft_close()
            return l
        except BaseException as e:
            self.handle_exception(result, dbapi_cursor, e)

def fetchall(
        self,
        result: CursorResult[Any],
        dbapi_cursor: DBAPICursor,
    ) -> Any:
        try:
            rows = dbapi_cursor.fetchall()
            result._soft_close()
            return rows
        except BaseException as e:
            self.handle_exception(result, dbapi_cursor, e)

_DEFAULT_FETCH = CursorFetchStrategy()

class BufferedRowCursorFetchStrategy(CursorFetchStrategy):
    """A cursor fetch strategy with row buffering behavior.

This strategy buffers the contents of a selection of rows
    before ``fetchone()`` is called.  This is to allow the results of
    ``cursor.description`` to be available immediately, when
    interfacing with a DB-API that requires rows to be consumed before
    this information is available (currently psycopg2, when used with
    server-side cursors).

The pre-fetching behavior fetches only one row initially, and then
    grows its buffer size by a fixed amount with each successive need
    for additional rows up the ``max_row_buffer`` size, which defaults
    to 1000::

with psycopg2_engine.connect() as conn:

result = conn.execution_options(
                stream_results=True, max_row_buffer=50
            ).execute(text("select * from table"))

.. versionadded:: 1.4 ``max_row_buffer`` may now exceed 1000 rows.

.. seealso::

:ref:`psycopg2_execution_options`
    """

__slots__ = ("_max_row_buffer", "_rowbuffer", "_bufsize", "_growth_factor")

def __init__(
        self,
        dbapi_cursor,
        execution_options,
        growth_factor=5,
        initial_buffer=None,
    ):
        self._max_row_buffer = execution_options.get("max_row_buffer", 1000)

if initial_buffer is not None:
            self._rowbuffer = initial_buffer
        else:
            self._rowbuffer = collections.deque(dbapi_cursor.fetchmany(1))
        self._growth_factor = growth_factor

if growth_factor:
            self._bufsize = min(self._max_row_buffer, self._growth_factor)
        else:
            self._bufsize = self._max_row_buffer

@classmethod
    def create(cls, result):
        return BufferedRowCursorFetchStrategy(
            result.cursor,
            result.context.execution_options,
        )

def _buffer_rows(self, result, dbapi_cursor):
        """this is currently used only by fetchone()."""

size = self._bufsize
        try:
            if size < 1:
                new_rows = dbapi_cursor.fetchall()
            else:
                new_rows = dbapi_cursor.fetchmany(size)
        except BaseException as e:
            self.handle_exception(result, dbapi_cursor, e)

if not new_rows:
            return
        self._rowbuffer = collections.deque(new_rows)
        if self._growth_factor and size < self._max_row_buffer:
            self._bufsize = min(
                self._max_row_buffer, size * self._growth_factor
            )

def yield_per(self, result, dbapi_cursor, num):
        self._growth_factor = 0
        self._max_row_buffer = self._bufsize = num

def soft_close(self, result, dbapi_cursor):
        self._rowbuffer.clear()
        super().soft_close(result, dbapi_cursor)

def hard_close(self, result, dbapi_cursor):
        self._rowbuffer.clear()
        super().hard_close(result, dbapi_cursor)

def fetchone(self, result, dbapi_cursor, hard_close=False):
        if not self._rowbuffer:
            self._buffer_rows(result, dbapi_cursor)
            if not self._rowbuffer:
                try:
                    result._soft_close(hard=hard_close)
                except BaseException as e:
                    self.handle_exception(result, dbapi_cursor, e)
                return None
        return self._rowbuffer.popleft()

def fetchmany(self, result, dbapi_cursor, size=None):
        if size is None:
            return self.fetchall(result, dbapi_cursor)

rb = self._rowbuffer
        lb = len(rb)
        close = False
        if size > lb:
            try:
                new = dbapi_cursor.fetchmany(size - lb)
            except BaseException as e:
                self.handle_exception(result, dbapi_cursor, e)
            else:
                if not new:
                    # defer closing since it may clear the row buffer
                    close = True
                else:
                    rb.extend(new)

res = [rb.popleft() for _ in range(min(size, len(rb)))]
        if close:
            result._soft_close()
        return res

def fetchall(self, result, dbapi_cursor):
        try:
            ret = list(self._rowbuffer) + list(dbapi_cursor.fetchall())
            self._rowbuffer.clear()
            result._soft_close()
            return ret
        except BaseException as e:
            self.handle_exception(result, dbapi_cursor, e)

class FullyBufferedCursorFetchStrategy(CursorFetchStrategy):
    """A cursor strategy that buffers rows fully upon creation.

Used for operations where a result is to be delivered
    after the database conversation can not be continued,
    such as MSSQL INSERT...OUTPUT after an autocommit.

"""

__slots__ = ("_rowbuffer", "alternate_cursor_description")

def __init__(
        self, dbapi_cursor, alternate_description=None, initial_buffer=None
    ):
        self.alternate_cursor_description = alternate_description
        if initial_buffer is not None:
            self._rowbuffer = collections.deque(initial_buffer)
        else:
            self._rowbuffer = collections.deque(dbapi_cursor.fetchall())

def yield_per(self, result, dbapi_cursor, num):
        pass

def soft_close(self, result, dbapi_cursor):
        self._rowbuffer.clear()
        super().soft_close(result, dbapi_cursor)

def hard_close(self, result, dbapi_cursor):
        self._rowbuffer.clear()
        super().hard_close(result, dbapi_cursor)

def fetchone(self, result, dbapi_cursor, hard_close=False):
        if self._rowbuffer:
            return self._rowbuffer.popleft()
        else:
            result._soft_close(hard=hard_close)
            return None

def fetchmany(self, result, dbapi_cursor, size=None):
        if size is None:
            return self.fetchall(result, dbapi_cursor)

rb = self._rowbuffer
        rows = [rb.popleft() for _ in range(min(size, len(rb)))]
        if not rows:
            result._soft_close()
        return rows

def fetchall(self, result, dbapi_cursor):
        ret = self._rowbuffer
        self._rowbuffer = collections.deque()
        result._soft_close()
        return ret

class _NoResultMetaData(ResultMetaData):
    __slots__ = ()

returns_rows = False

def _we_dont_return_rows(self, err=None):
        raise exc.ResourceClosedError(
            "This result object does not return rows. "
            "It has been closed automatically."
        ) from err

def _index_for_key(self, keys, raiseerr):
        self._we_dont_return_rows()

def _metadata_for_keys(self, key):
        self._we_dont_return_rows()

def _reduce(self, keys):
        self._we_dont_return_rows()

@property
    def _keymap(self):
        self._we_dont_return_rows()

@property
    def _key_to_index(self):
        self._we_dont_return_rows()

@property
    def _processors(self):
        self._we_dont_return_rows()

@property
    def keys(self):
        self._we_dont_return_rows()

_NO_RESULT_METADATA = _NoResultMetaData()

def null_dml_result() -> IteratorResult[Any]:
    it: IteratorResult[Any] = IteratorResult(_NoResultMetaData(), iter([]))
    it._soft_close()
    return it

class CursorResult(Result[_T]):
    """A Result that is representing state from a DBAPI cursor.

.. versionchanged:: 1.4  The :class:`.CursorResult``
       class replaces the previous :class:`.ResultProxy` interface.
       This classes are based on the :class:`.Result` calling API
       which provides an updated usage model and calling facade for
       SQLAlchemy Core and SQLAlchemy ORM.

Returns database rows via the :class:`.Row` class, which provides
    additional API features and behaviors on top of the raw data returned by
    the DBAPI.   Through the use of filters such as the :meth:`.Result.scalars`
    method, other kinds of objects may also be returned.

.. seealso::

:ref:`tutorial_selecting_data` - introductory material for accessing
        :class:`_engine.CursorResult` and :class:`.Row` objects.

"""

__slots__ = (
        "context",
        "dialect",
        "cursor",
        "cursor_strategy",
        "_echo",
        "connection",
    )

_metadata: Union[CursorResultMetaData, _NoResultMetaData]
    _no_result_metadata = _NO_RESULT_METADATA
    _soft_closed: bool = False
    closed: bool = False
    _is_cursor = True

context: DefaultExecutionContext
    dialect: Dialect
    cursor_strategy: ResultFetchStrategy
    connection: Connection

def __init__(
        self,
        context: DefaultExecutionContext,
        cursor_strategy: ResultFetchStrategy,
        cursor_description: Optional[_DBAPICursorDescription],
    ):
        self.context = context
        self.dialect = context.dialect
        self.cursor = context.cursor
        self.cursor_strategy = cursor_strategy
        self.connection = context.root_connection
        self._echo = echo = (
            self.connection._echo and context.engine._should_log_debug()
        )

if cursor_description is not None:
            # inline of Result._row_getter(), set up an initial row
            # getter assuming no transformations will be called as this
            # is the most common case

metadata = self._init_metadata(context, cursor_description)

_make_row: Any
            _make_row = functools.partial(
                Row,
                metadata,
                metadata._effective_processors,
                metadata._key_to_index,
            )

if context._num_sentinel_cols:
                sentinel_filter = operator.itemgetter(
                    slice(-context._num_sentinel_cols)
                )

def _sliced_row(raw_data):
                    return _make_row(sentinel_filter(raw_data))

sliced_row = _sliced_row
            else:
                sliced_row = _make_row

if echo:
                log = self.context.connection._log_debug

def _log_row(row):
                    log("Row %r", sql_util._repr_row(row))
                    return row

self._row_logging_fn = _log_row

def _make_row_2(row):
                    return _log_row(sliced_row(row))

make_row = _make_row_2
            else:
                make_row = sliced_row
            self._set_memoized_attribute("_row_getter", make_row)

else:
            assert context._num_sentinel_cols == 0
            self._metadata = self._no_result_metadata

def _init_metadata(self, context, cursor_description):
        if context.compiled:
            compiled = context.compiled

if compiled._cached_metadata:
                metadata = compiled._cached_metadata
            else:
                metadata = CursorResultMetaData(self, cursor_description)
                if metadata._safe_for_cache:
                    compiled._cached_metadata = metadata

# result rewrite/ adapt step.  this is to suit the case
            # when we are invoked against a cached Compiled object, we want
            # to rewrite the ResultMetaData to reflect the Column objects
            # that are in our current SQL statement object, not the one
            # that is associated with the cached Compiled object.
            # the Compiled object may also tell us to not
            # actually do this step; this is to support the ORM where
            # it is to produce a new Result object in any case, and will
            # be using the cached Column objects against this database result
            # so we don't want to rewrite them.
            #
            # Basically this step suits the use case where the end user
            # is using Core SQL expressions and is accessing columns in the
            # result row using row._mapping[table.c.column].
            if (
                not context.execution_options.get(
                    "_result_disable_adapt_to_context", False
                )
                and compiled._result_columns
                and context.cache_hit is context.dialect.CACHE_HIT
                and compiled.statement is not context.invoked_statement
            ):
                metadata = metadata._adapt_to_context(context)

self._metadata = metadata

else:
            self._metadata = metadata = CursorResultMetaData(
                self, cursor_description
            )
        if self._echo:
            context.connection._log_debug(
                "Col %r", tuple(x[0] for x in cursor_description)
            )
        return metadata

def _soft_close(self, hard=False):
        """Soft close this :class:`_engine.CursorResult`.

This releases all DBAPI cursor resources, but leaves the
        CursorResult "open" from a semantic perspective, meaning the
        fetchXXX() methods will continue to return empty results.

This method is called automatically when:

* all result rows are exhausted using the fetchXXX() methods.
        * cursor.description is None.

This method is **not public**, but is documented in order to clarify
        the "autoclose" process used.

.. seealso::

:meth:`_engine.CursorResult.close`

"""

if (not hard and self._soft_closed) or (hard and self.closed):
            return

if hard:
            self.closed = True
            self.cursor_strategy.hard_close(self, self.cursor)
        else:
            self.cursor_strategy.soft_close(self, self.cursor)

if not self._soft_closed:
            cursor = self.cursor
            self.cursor = None  # type: ignore
            self.connection._safe_close_cursor(cursor)
            self._soft_closed = True

@property
    def inserted_primary_key_rows(self):
        """Return the value of
        :attr:`_engine.CursorResult.inserted_primary_key`
        as a row contained within a list; some dialects may support a
        multiple row form as well.

.. note:: As indicated below, in current SQLAlchemy versions this
           accessor is only useful beyond what's already supplied by
           :attr:`_engine.CursorResult.inserted_primary_key` when using the
           :ref:`postgresql_psycopg2` dialect.   Future versions hope to
           generalize this feature to more dialects.

This accessor is added to support dialects that offer the feature
        that is currently implemented by the :ref:`psycopg2_executemany_mode`
        feature, currently **only the psycopg2 dialect**, which provides
        for many rows to be INSERTed at once while still retaining the
        behavior of being able to return server-generated primary key values.

* **When using the psycopg2 dialect, or other dialects that may support
          "fast executemany" style inserts in upcoming releases** : When
          invoking an INSERT statement while passing a list of rows as the
          second argument to :meth:`_engine.Connection.execute`, this accessor
          will then provide a list of rows, where each row contains the primary
          key value for each row that was INSERTed.

* **When using all other dialects / backends that don't yet support
          this feature**: This accessor is only useful for **single row INSERT
          statements**, and returns the same information as that of the
          :attr:`_engine.CursorResult.inserted_primary_key` within a
          single-element list. When an INSERT statement is executed in
          conjunction with a list of rows to be INSERTed, the list will contain
          one row per row inserted in the statement, however it will contain
          ``None`` for any server-generated values.

Future releases of SQLAlchemy will further generalize the
        "fast execution helper" feature of psycopg2 to suit other dialects,
        thus allowing this accessor to be of more general use.

.. versionadded:: 1.4

.. seealso::

:attr:`_engine.CursorResult.inserted_primary_key`

"""
        if not self.context.compiled:
            raise exc.InvalidRequestError(
                "Statement is not a compiled expression construct."
            )
        elif not self.context.isinsert:
            raise exc.InvalidRequestError(
                "Statement is not an insert() expression construct."
            )
        elif self.context._is_explicit_returning:
            raise exc.InvalidRequestError(
                "Can't call inserted_primary_key "
                "when returning() "
                "is used."
            )
        return self.context.inserted_primary_key_rows

@property
    def inserted_primary_key(self):
        """Return the primary key for the row just inserted.

The return value is a :class:`_result.Row` object representing
        a named tuple of primary key values in the order in which the
        primary key columns are configured in the source
        :class:`_schema.Table`.

.. versionchanged:: 1.4.8 - the
           :attr:`_engine.CursorResult.inserted_primary_key`
           value is now a named tuple via the :class:`_result.Row` class,
           rather than a plain tuple.

This accessor only applies to single row :func:`_expression.insert`
        constructs which did not explicitly specify
        :meth:`_expression.Insert.returning`.    Support for multirow inserts,
        while not yet available for most backends, would be accessed using
        the :attr:`_engine.CursorResult.inserted_primary_key_rows` accessor.

Note that primary key columns which specify a server_default clause, or
        otherwise do not qualify as "autoincrement" columns (see the notes at
        :class:`_schema.Column`), and were generated using the database-side
        default, will appear in this list as ``None`` unless the backend
        supports "returning" and the insert statement executed with the
        "implicit returning" enabled.

Raises :class:`~sqlalchemy.exc.InvalidRequestError` if the executed
        statement is not a compiled expression construct
        or is not an insert() construct.

"""

if self.context.executemany:
            raise exc.InvalidRequestError(
                "This statement was an executemany call; if primary key "
                "returning is supported, please "
                "use .inserted_primary_key_rows."
            )

ikp = self.inserted_primary_key_rows
        if ikp:
            return ikp[0]
        else:
            return None

def last_updated_params(self):
        """Return the collection of updated parameters from this
        execution.

Raises :class:`~sqlalchemy.exc.InvalidRequestError` if the executed
        statement is not a compiled expression construct
        or is not an update() construct.

"""
        if not self.context.compiled:
            raise exc.InvalidRequestError(
                "Statement is not a compiled expression construct."
            )
        elif not self.context.isupdate:
            raise exc.InvalidRequestError(
                "Statement is not an update() expression construct."
            )
        elif self.context.executemany:
            return self.context.compiled_parameters
        else:
            return self.context.compiled_parameters[0]

def last_inserted_params(self):
        """Return the collection of inserted parameters from this
        execution.

Raises :class:`~sqlalchemy.exc.InvalidRequestError` if the executed
        statement is not a compiled expression construct
        or is not an insert() construct.

"""
        if not self.context.compiled:
            raise exc.InvalidRequestError(
                "Statement is not a compiled expression construct."
            )
        elif not self.context.isinsert:
            raise exc.InvalidRequestError(
                "Statement is not an insert() expression construct."
            )
        elif self.context.executemany:
            return self.context.compiled_parameters
        else:
            return self.context.compiled_parameters[0]

@property
    def returned_defaults_rows(self):
        """Return a list of rows each containing the values of default
        columns that were fetched using
        the :meth:`.ValuesBase.return_defaults` feature.

The return value is a list of :class:`.Row` objects.

.. versionadded:: 1.4

"""
        return self.context.returned_default_rows

def splice_horizontally(self, other):
        """Return a new :class:`.CursorResult` that "horizontally splices"
        together the rows of this :class:`.CursorResult` with that of another
        :class:`.CursorResult`.

.. tip::  This method is for the benefit of the SQLAlchemy ORM and is
           not intended for general use.

"horizontally splices" means that for each row in the first and second
        result sets, a new row that concatenates the two rows together is
        produced, which then becomes the new row.  The incoming
        :class:`.CursorResult` must have the identical number of rows.  It is
        typically expected that the two result sets come from the same sort
        order as well, as the result rows are spliced together based on their
        position in the result.

The expected use case here is so that multiple INSERT..RETURNING
        statements (which definitely need to be sorted) against different
        tables can produce a single result that looks like a JOIN of those two
        tables.

E.g.::

r1 = connection.execute(
                users.insert().returning(
                    users.c.user_name, users.c.user_id, sort_by_parameter_order=True
                ),
                user_values,
            )

r2 = connection.execute(
                addresses.insert().returning(
                    addresses.c.address_id,
                    addresses.c.address,
                    addresses.c.user_id,
                    sort_by_parameter_order=True,
                ),
                address_values,
            )

rows = r1.splice_horizontally(r2).all()
            assert rows == [
                ("john", 1, 1, "foo@bar.com", 1),
                ("jack", 2, 2, "bar@bat.com", 2),
            ]

.. versionadded:: 2.0

.. seealso::

:meth:`.CursorResult.splice_vertically`

"""  # noqa: E501

clone = self._generate()
        total_rows = [
            tuple(r1) + tuple(r2)
            for r1, r2 in zip(
                list(self._raw_row_iterator()),
                list(other._raw_row_iterator()),
            )
        ]

clone._metadata = clone._metadata._splice_horizontally(other._metadata)

clone.cursor_strategy = FullyBufferedCursorFetchStrategy(
            None,
            initial_buffer=total_rows,
        )
        clone._reset_memoizations()
        return clone

def splice_vertically(self, other):
        """Return a new :class:`.CursorResult` that "vertically splices",
        i.e. "extends", the rows of this :class:`.CursorResult` with that of
        another :class:`.CursorResult`.

.. tip::  This method is for the benefit of the SQLAlchemy ORM and is
           not intended for general use.

"vertically splices" means the rows of the given result are appended to
        the rows of this cursor result. The incoming :class:`.CursorResult`
        must have rows that represent the identical list of columns in the
        identical order as they are in this :class:`.CursorResult`.

.. versionadded:: 2.0

.. seealso::

:meth:`.CursorResult.splice_horizontally`

"""
        clone = self._generate()
        total_rows = list(self._raw_row_iterator()) + list(
            other._raw_row_iterator()
        )

clone.cursor_strategy = FullyBufferedCursorFetchStrategy(
            None,
            initial_buffer=total_rows,
        )
        clone._reset_memoizations()
        return clone

def _rewind(self, rows):
        """rewind this result back to the given rowset.

this is used internally for the case where an :class:`.Insert`
        construct combines the use of
        :meth:`.Insert.return_defaults` along with the
        "supplemental columns" feature.

"""

if self._echo:
            self.context.connection._log_debug(
                "CursorResult rewound %d row(s)", len(rows)
            )

# the rows given are expected to be Row objects, so we
        # have to clear out processors which have already run on these
        # rows
        self._metadata = cast(
            CursorResultMetaData, self._metadata
        )._remove_processors()

self.cursor_strategy = FullyBufferedCursorFetchStrategy(
            None,
            # TODO: if these are Row objects, can we save on not having to
            # re-make new Row objects out of them a second time?  is that
            # what's actually happening right now?  maybe look into this
            initial_buffer=rows,
        )
        self._reset_memoizations()
        return self

@property
    def returned_defaults(self):
        """Return the values of default columns that were fetched using
        the :meth:`.ValuesBase.return_defaults` feature.

The value is an instance of :class:`.Row`, or ``None``
        if :meth:`.ValuesBase.return_defaults` was not used or if the
        backend does not support RETURNING.

.. seealso::

:meth:`.ValuesBase.return_defaults`

"""

if self.context.executemany:
            raise exc.InvalidRequestError(
                "This statement was an executemany call; if return defaults "
                "is supported, please use .returned_defaults_rows."
            )

rows = self.context.returned_default_rows
        if rows:
            return rows[0]
        else:
            return None

def lastrow_has_defaults(self):
        """Return ``lastrow_has_defaults()`` from the underlying
        :class:`.ExecutionContext`.

See :class:`.ExecutionContext` for details.

"""

return self.context.lastrow_has_defaults()

def postfetch_cols(self):
        """Return ``postfetch_cols()`` from the underlying
        :class:`.ExecutionContext`.

See :class:`.ExecutionContext` for details.

Raises :class:`~sqlalchemy.exc.InvalidRequestError` if the executed
        statement is not a compiled expression construct
        or is not an insert() or update() construct.

"""

if not self.context.compiled:
            raise exc.InvalidRequestError(
                "Statement is not a compiled expression construct."
            )
        elif not self.context.isinsert and not self.context.isupdate:
            raise exc.InvalidRequestError(
                "Statement is not an insert() or update() "
                "expression construct."
            )
        return self.context.postfetch_cols

def prefetch_cols(self):
        """Return ``prefetch_cols()`` from the underlying
        :class:`.ExecutionContext`.

See :class:`.ExecutionContext` for details.

Raises :class:`~sqlalchemy.exc.InvalidRequestError` if the executed
        statement is not a compiled expression construct
        or is not an insert() or update() construct.

"""

def supports_sane_rowcount(self):
        """Return ``supports_sane_rowcount`` from the dialect.

See :attr:`_engine.CursorResult.rowcount` for background.

"""

return self.dialect.supports_sane_rowcount

def supports_sane_multi_rowcount(self):
        """Return ``supports_sane_multi_rowcount`` from the dialect.

See :attr:`_engine.CursorResult.rowcount` for background.

"""

return self.dialect.supports_sane_multi_rowcount

@util.memoized_property
    def rowcount(self) -> int:
        """Return the 'rowcount' for this result.

The primary purpose of 'rowcount' is to report the number of rows
        matched by the WHERE criterion of an UPDATE or DELETE statement
        executed once (i.e. for a single parameter set), which may then be
        compared to the number of rows expected to be updated or deleted as a
        means of asserting data integrity.

This attribute is transferred from the ``cursor.rowcount`` attribute
        of the DBAPI before the cursor is closed, to support DBAPIs that
        don't make this value available after cursor close.   Some DBAPIs may
        offer meaningful values for other kinds of statements, such as INSERT
        and SELECT statements as well.  In order to retrieve ``cursor.rowcount``
        for these statements, set the
        :paramref:`.Connection.execution_options.preserve_rowcount`
        execution option to True, which will cause the ``cursor.rowcount``
        value to be unconditionally memoized before any results are returned
        or the cursor is closed, regardless of statement type.

For cases where the DBAPI does not support rowcount for a particular
        kind of statement and/or execution, the returned value will be ``-1``,
        which is delivered directly from the DBAPI and is part of :pep:`249`.
        All DBAPIs should support rowcount for single-parameter-set
        UPDATE and DELETE statements, however.

.. note::

Notes regarding :attr:`_engine.CursorResult.rowcount`:

* This attribute returns the number of rows *matched*,
             which is not necessarily the same as the number of rows
             that were actually *modified*. For example, an UPDATE statement
             may have no net change on a given row if the SET values
             given are the same as those present in the row already.
             Such a row would be matched but not modified.
             On backends that feature both styles, such as MySQL,
             rowcount is configured to return the match
             count in all cases.

* :attr:`_engine.CursorResult.rowcount` in the default case is
             *only* useful in conjunction with an UPDATE or DELETE statement,
             and only with a single set of parameters. For other kinds of
             statements, SQLAlchemy will not attempt to pre-memoize the value
             unless the
             :paramref:`.Connection.execution_options.preserve_rowcount`
             execution option is used.  Note that contrary to :pep:`249`, many
             DBAPIs do not support rowcount values for statements that are not
             UPDATE or DELETE, particularly when rows are being returned which
             are not fully pre-buffered.   DBAPIs that dont support rowcount
             for a particular kind of statement should return the value ``-1``
             for such statements.

* :attr:`_engine.CursorResult.rowcount` may not be meaningful
             when executing a single statement with multiple parameter sets
             (i.e. an :term:`executemany`). Most DBAPIs do not sum "rowcount"
             values across multiple parameter sets and will return ``-1``
             when accessed.

* SQLAlchemy's :ref:`engine_insertmanyvalues` feature does support
             a correct population of :attr:`_engine.CursorResult.rowcount`
             when the :paramref:`.Connection.execution_options.preserve_rowcount`
             execution option is set to True.

* Statements that use RETURNING may not support rowcount, returning
             a ``-1`` value instead.

.. seealso::

:ref:`tutorial_update_delete_rowcount` - in the :ref:`unified_tutorial`

:paramref:`.Connection.execution_options.preserve_rowcount`

"""  # noqa: E501
        try:
            return self.context.rowcount
        except BaseException as e:
            self.cursor_strategy.handle_exception(self, self.cursor, e)
            raise  # not called

@property
    def lastrowid(self):
        """Return the 'lastrowid' accessor on the DBAPI cursor.

This is a DBAPI specific method and is only functional
        for those backends which support it, for statements
        where it is appropriate.  It's behavior is not
        consistent across backends.

Usage of this method is normally unnecessary when
        using insert() expression constructs; the
        :attr:`~CursorResult.inserted_primary_key` attribute provides a
        tuple of primary key values for a newly inserted row,
        regardless of database backend.

"""
        try:
            return self.context.get_lastrowid()
        except BaseException as e:
            self.cursor_strategy.handle_exception(self, self.cursor, e)

@property
    def returns_rows(self):
        """True if this :class:`_engine.CursorResult` returns zero or more
        rows.

I.e. if it is legal to call the methods
        :meth:`_engine.CursorResult.fetchone`,
        :meth:`_engine.CursorResult.fetchmany`
        :meth:`_engine.CursorResult.fetchall`.

Overall, the value of :attr:`_engine.CursorResult.returns_rows` should
        always be synonymous with whether or not the DBAPI cursor had a
        ``.description`` attribute, indicating the presence of result columns,
        noting that a cursor that returns zero rows still has a
        ``.description`` if a row-returning statement was emitted.

This attribute should be True for all results that are against
        SELECT statements, as well as for DML statements INSERT/UPDATE/DELETE
        that use RETURNING.   For INSERT/UPDATE/DELETE statements that were
        not using RETURNING, the value will usually be False, however
        there are some dialect-specific exceptions to this, such as when
        using the MSSQL / pyodbc dialect a SELECT is emitted inline in
        order to retrieve an inserted primary key value.

"""
        return self._metadata.returns_rows

@property
    def is_insert(self):
        """True if this :class:`_engine.CursorResult` is the result
        of a executing an expression language compiled
        :func:`_expression.insert` construct.

When True, this implies that the
        :attr:`inserted_primary_key` attribute is accessible,
        assuming the statement did not include
        a user defined "returning" construct.

"""
        return self.context.isinsert

def _fetchiter_impl(self):
        fetchone = self.cursor_strategy.fetchone

while True:
            row = fetchone(self, self.cursor)
            if row is None:
                break
            yield row

def _fetchone_impl(self, hard_close=False):
        return self.cursor_strategy.fetchone(self, self.cursor, hard_close)

def _fetchall_impl(self):
        return self.cursor_strategy.fetchall(self, self.cursor)

def _fetchmany_impl(self, size=None):
        return self.cursor_strategy.fetchmany(self, self.cursor, size)

def _raw_row_iterator(self):
        return self._fetchiter_impl()

def merge(self, *others: Result[Any]) -> MergedResult[Any]:
        merged_result = super().merge(*others)
        if self.context._has_rowcount:
            merged_result.rowcount = sum(
                cast("CursorResult[Any]", result).rowcount
                for result in (self,) + others
            )
        return merged_result

def close(self) -> Any:
        """Close this :class:`_engine.CursorResult`.

This closes out the underlying DBAPI cursor corresponding to the
        statement execution, if one is still present.  Note that the DBAPI
        cursor is automatically released when the :class:`_engine.CursorResult`
        exhausts all available rows.  :meth:`_engine.CursorResult.close` is
        generally an optional method except in the case when discarding a
        :class:`_engine.CursorResult` that still has additional rows pending
        for fetch.

After this method is called, it is no longer valid to call upon
        the fetch methods, which will raise a :class:`.ResourceClosedError`
        on subsequent use.

.. seealso::

:ref:`connections_toplevel`

"""
        self._soft_close(hard=True)

@_generative
    def yield_per(self, num: int) -> Self:
        self._yield_per = num
        self.cursor_strategy.yield_per(self, self.cursor, num)
        return self

ResultProxy = CursorResult

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