whoami7 - Manager
:
/
proc
/
self
/
root
/
opt
/
hc_python
/
lib64
/
python3.8
/
site-packages
/
sqlalchemy
/
engine
/
Upload File:
files >> //proc/self/root/opt/hc_python/lib64/python3.8/site-packages/sqlalchemy/engine/cursor.py
# 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) 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` """ 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 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
Copyright ©2021 || Defacer Indonesia