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bazarr/libs/sqlalchemy/engine/cursor.py

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# engine/cursor.py
# Copyright (C) 2005-2024 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)
buf = list(self._rowbuffer)
lb = len(buf)
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:
result._soft_close()
else:
buf.extend(new)
result = buf[0:size]
self._rowbuffer = collections.deque(buf[size:])
return result
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)
buf = list(self._rowbuffer)
rows = buf[0:size]
self._rowbuffer = collections.deque(buf[size:])
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`
"""
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.
"""
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.prefetch_cols
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 'rowcount' reports the number of rows *matched*
by the WHERE criterion of an UPDATE or DELETE statement.
.. 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* - an UPDATE statement, for example,
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 by default to return the match
count in all cases.
* :attr:`_engine.CursorResult.rowcount`
is *only* useful in conjunction
with an UPDATE or DELETE statement. Contrary to what the Python
DBAPI says, it does *not* reliably return the
number of rows available from the results of a SELECT statement
as DBAPIs cannot support this functionality when rows are
unbuffered.
* :attr:`_engine.CursorResult.rowcount`
may not be fully implemented by
all dialects. In particular, most DBAPIs do not support an
aggregate rowcount result from an executemany call.
The :meth:`_engine.CursorResult.supports_sane_rowcount` and
:meth:`_engine.CursorResult.supports_sane_multi_rowcount` methods
will report from the dialect if each usage is known to be
supported.
* Statements that use RETURNING may not return a correct
rowcount.
.. seealso::
:ref:`tutorial_update_delete_rowcount` - in the :ref:`unified_tutorial`
""" # 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)
setup_rowcounts = self.context._has_rowcount
if setup_rowcounts:
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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