JezK
Edit File: cursor.py
# engine/cursor.py # Copyright (C) 2005-2023 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 """Define cursor-specific result set constructs including :class:`.BaseCursorResult`, :class:`.CursorResult`.""" import collections import functools from .result import Result from .result import ResultMetaData from .result import SimpleResultMetaData from .result import tuplegetter from .row import LegacyRow from .. import exc from .. import util from ..sql import expression from ..sql import sqltypes from ..sql import util as sql_util from ..sql.base import _generative 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 _UNPICKLED = util.symbol("unpickled") # metadata entry tuple indexes. # using raw tuple is faster than namedtuple. MD_INDEX = 0 # integer index in cursor.description MD_RESULT_MAP_INDEX = 1 # integer index in compiled._result_columns MD_OBJECTS = 2 # other string keys and ColumnElement obj that can match MD_LOOKUP_KEY = 3 # string key we usually expect for key-based lookup MD_RENDERED_NAME = 4 # name that is usually in cursor.description MD_PROCESSOR = 5 # callable to process a result value into a row MD_UNTRANSLATED = 6 # raw name from cursor.description class CursorResultMetaData(ResultMetaData): """Result metadata for DBAPI cursors.""" __slots__ = ( "_keymap", "case_sensitive", "_processors", "_keys", "_keymap_by_result_column_idx", "_tuplefilter", "_translated_indexes", "_safe_for_cache" # don't need _unique_filters support here for now. Can be added # if a need arises. ) returns_rows = True def _has_key(self, key): return key in self._keymap def _for_freeze(self): return SimpleResultMetaData( self._keys, extra=[self._keymap[key][MD_OBJECTS] for key in self._keys], ) def _reduce(self, keys): recs = list(self._metadata_for_keys(keys)) indexes = [rec[MD_INDEX] for rec in recs] new_keys = [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_metadata = self.__class__.__new__(self.__class__) new_metadata.case_sensitive = self.case_sensitive new_metadata._processors = self._processors new_metadata._keys = new_keys new_metadata._tuplefilter = tup new_metadata._translated_indexes = indexes new_recs = [ (index,) + rec[1:] for index, rec in enumerate(self._metadata_for_keys(keys)) ] new_metadata._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 new_metadata._keymap.update( { e: new_rec for new_rec in new_recs for e in new_rec[MD_OBJECTS] or () } ) return new_metadata def _adapt_to_context(self, context): """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._result_columns: return self compiled_statement = context.compiled.statement invoked_statement = context.invoked_statement if compiled_statement is invoked_statement: return self # make a copy and add the columns from the invoked statement # to the result map. md = self.__class__.__new__(self.__class__) md._keymap = dict(self._keymap) keymap_by_position = self._keymap_by_result_column_idx for idx, new in enumerate(invoked_statement._all_selected_columns): try: rec = keymap_by_position[idx] except KeyError: # this can happen when there are bogus column entries # in a TextualSelect pass else: md._keymap[new] = rec md.case_sensitive = self.case_sensitive md._processors = self._processors assert not self._tuplefilter md._tuplefilter = None md._translated_indexes = None md._keys = self._keys md._keymap_by_result_column_idx = self._keymap_by_result_column_idx md._safe_for_cache = self._safe_for_cache return md def __init__(self, parent, cursor_description): context = parent.context dialect = context.dialect self._tuplefilter = None self._translated_indexes = None self.case_sensitive = dialect.case_sensitive self._safe_for_cache = 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 ) = ( 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, ) self._keymap = {} # processors in key order for certain per-row # views like __iter__ and slices self._processors = [ metadata_entry[MD_PROCESSOR] for metadata_entry in raw ] if context.compiled: self._keymap_by_result_column_idx = { metadata_entry[MD_RESULT_MAP_INDEX]: metadata_entry for metadata_entry in raw } # keymap by primary string... by_key = dict( [ (metadata_entry[MD_LOOKUP_KEY], metadata_entry) for metadata_entry in raw ] ) # 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: 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 = {} dupes = set() for metadata_entry in raw: for key in (metadata_entry[MD_RENDERED_NAME],) + ( metadata_entry[MD_OBJECTS] or () ): if not self.case_sensitive and isinstance( key, util.string_types ): key = key.lower() 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.update( [ (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) for key in dupes}) else: # no dupes - copy secondary elements from compiled # columns into self._keymap self._keymap.update( [ (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) # update keymap with "translated" names (sqlite-only thing) 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] ] ) 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 te 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. """ case_sensitive = context.dialect.case_sensitive 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 # 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].lower() if not case_sensitive else 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 case_sensitive = dialect.case_sensitive translate_colname = context._translate_colname description_decoder = ( dialect._description_decoder if dialect.description_encoding else None ) 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 description_decoder: colname = description_decoder(colname) if translate_colname: colname, untranslated = translate_colname(colname) if normalize_name: colname = normalize_name(colname) self._keys.append(colname) if not case_sensitive: colname = colname.lower() yield idx, colname, untranslated, coltype def _merge_textual_cols_by_position( self, context, cursor_description, result_columns ): num_ctx_cols = len(result_columns) if result_columns else None 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, ): dialect = context.dialect case_sensitive = dialect.case_sensitive match_map = self._create_description_match_map( result_columns, case_sensitive, loose_column_name_matching ) 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, case_sensitive=True, loose_column_name_matching=False, ): """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 = {} for ridx, elem in enumerate(result_columns): key = elem[RM_RENDERED_NAME] if not case_sensitive: key = key.lower() 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, ) def _key_fallback(self, key, err, raiseerr=True): if raiseerr: util.raise_( exc.NoSuchColumnError( "Could not locate column in row for column '%s'" % util.string_or_unprintable(key) ), replace_context=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, raiseerr=True): # 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: rec = self._key_fallback(key, ke, raiseerr) if rec 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): 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[0] if index is None: self._raise_for_ambiguous_column_name(rec) yield rec def __getstate__(self): return { "_keymap": { key: (rec[MD_INDEX], rec[MD_RESULT_MAP_INDEX], _UNPICKLED, key) for key, rec in self._keymap.items() if isinstance(key, util.string_types + util.int_types) }, "_keys": self._keys, "case_sensitive": self.case_sensitive, "_translated_indexes": self._translated_indexes, "_tuplefilter": self._tuplefilter, } def __setstate__(self, state): self._processors = [None for _ in range(len(state["_keys"]))] self._keymap = state["_keymap"] self._keymap_by_result_column_idx = { rec[MD_RESULT_MAP_INDEX]: rec for rec in self._keymap.values() } self._keys = state["_keys"] self.case_sensitive = state["case_sensitive"] if state["_translated_indexes"]: self._translated_indexes = state["_translated_indexes"] self._tuplefilter = tuplegetter(*self._translated_indexes) else: self._translated_indexes = self._tuplefilter = None class LegacyCursorResultMetaData(CursorResultMetaData): __slots__ = () def _contains(self, value, row): key = value if key in self._keymap: util.warn_deprecated_20( "Using the 'in' operator to test for string or column " "keys, or integer indexes, in a :class:`.Row` object is " "deprecated and will " "be removed in a future release. " "Use the `Row._fields` or `Row._mapping` attribute, i.e. " "'key in row._fields'", ) return True else: return self._key_fallback(key, None, False) is not None def _key_fallback(self, key, err, raiseerr=True): map_ = self._keymap result = None if isinstance(key, util.string_types): result = map_.get(key if self.case_sensitive else key.lower()) elif isinstance(key, expression.ColumnElement): if ( key._tq_label and ( key._tq_label if self.case_sensitive else key._tq_label.lower() ) in map_ ): result = map_[ key._tq_label if self.case_sensitive else key._tq_label.lower() ] elif ( hasattr(key, "name") and (key.name if self.case_sensitive else key.name.lower()) in map_ ): # match is only on name. result = map_[ key.name if self.case_sensitive else key.name.lower() ] # search extra hard to make sure this # isn't a column/label name overlap. # this check isn't currently available if the row # was unpickled. if result is not None and result[MD_OBJECTS] not in ( None, _UNPICKLED, ): for obj in result[MD_OBJECTS]: if key._compare_name_for_result(obj): break else: result = None if result is not None: if result[MD_OBJECTS] is _UNPICKLED: util.warn_deprecated( "Retrieving row values using Column objects from a " "row that was unpickled is deprecated; adequate " "state cannot be pickled for this to be efficient. " "This usage will raise KeyError in a future release.", version="1.4", ) else: util.warn_deprecated( "Retrieving row values using Column objects with only " "matching names as keys is deprecated, and will raise " "KeyError in a future release; only Column " "objects that are explicitly part of the statement " "object should be used.", version="1.4", ) if result is None: if raiseerr: util.raise_( exc.NoSuchColumnError( "Could not locate column in row for column '%s'" % util.string_or_unprintable(key) ), replace_context=err, ) else: return None else: map_[key] = result return result def _warn_for_nonint(self, key): util.warn_deprecated_20( "Using non-integer/slice indices on Row is deprecated and will " "be removed in version 2.0; please use row._mapping[<key>], or " "the mappings() accessor on the Result object.", stacklevel=4, ) def _has_key(self, key): if key in self._keymap: return True else: return self._key_fallback(key, None, False) is not None class ResultFetchStrategy(object): """Define a fetching strategy for a result object. .. versionadded:: 1.4 """ __slots__ = () alternate_cursor_description = None def soft_close(self, result, dbapi_cursor): raise NotImplementedError() def hard_close(self, result, dbapi_cursor): raise NotImplementedError() def yield_per(self, result, dbapi_cursor, num): return def fetchone(self, result, dbapi_cursor, hard_close=False): raise NotImplementedError() def fetchmany(self, result, dbapi_cursor, size=None): raise NotImplementedError() def fetchall(self, result): raise NotImplementedError() def handle_exception(self, result, dbapi_cursor, err): 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: util.raise_( exc.ResourceClosedError("This result object is closed."), replace_context=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, dbapi_cursor): result.cursor_strategy = _NO_CURSOR_DQL def hard_close(self, result, dbapi_cursor): result.cursor_strategy = _NO_CURSOR_DQL def handle_exception(self, result, dbapi_cursor, err): result.connection._handle_dbapi_exception( err, None, None, dbapi_cursor, result.context ) def yield_per(self, result, dbapi_cursor, num): result.cursor_strategy = BufferedRowCursorFetchStrategy( dbapi_cursor, {"max_row_buffer": num}, initial_buffer=collections.deque(), growth_factor=0, ) def fetchone(self, result, dbapi_cursor, hard_close=False): 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, dbapi_cursor, size=None): 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, dbapi_cursor): 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(BufferedRowCursorFetchStrategy, self).soft_close( result, dbapi_cursor ) def hard_close(self, result, dbapi_cursor): self._rowbuffer.clear() super(BufferedRowCursorFetchStrategy, self).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(FullyBufferedCursorFetchStrategy, self).soft_close( result, dbapi_cursor ) def hard_close(self, result, dbapi_cursor): self._rowbuffer.clear() super(FullyBufferedCursorFetchStrategy, self).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): util.raise_( exc.ResourceClosedError( "This result object does not return rows. " "It has been closed automatically." ), replace_context=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 keys(self): self._we_dont_return_rows() class _LegacyNoResultMetaData(_NoResultMetaData): @property def keys(self): util.warn_deprecated_20( "Calling the .keys() method on a result set that does not return " "rows is deprecated and will raise ResourceClosedError in " "SQLAlchemy 2.0.", ) return [] _NO_RESULT_METADATA = _NoResultMetaData() _LEGACY_NO_RESULT_METADATA = _LegacyNoResultMetaData() class BaseCursorResult(object): """Base class for database result objects.""" out_parameters = None _metadata = None _soft_closed = False closed = False def __init__(self, context, cursor_strategy, cursor_description): 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 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 else: log_row = None metadata = self._init_metadata(context, cursor_description) keymap = metadata._keymap processors = metadata._processors process_row = self._process_row key_style = process_row._default_key_style _make_row = functools.partial( process_row, metadata, processors, keymap, key_style ) if log_row: def make_row(row): made_row = _make_row(row) log_row(made_row) return made_row else: make_row = _make_row self._set_memoized_attribute("_row_getter", make_row) else: self._metadata = self._no_result_metadata def _init_metadata(self, context, cursor_description): if context.compiled: if context.compiled._cached_metadata: metadata = self.context.compiled._cached_metadata else: metadata = self._cursor_metadata(self, cursor_description) if metadata._safe_for_cache: context.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]. compiled = context.compiled if ( compiled and compiled._result_columns and context.cache_hit is context.dialect.CACHE_HIT and not context.execution_options.get( "_result_disable_adapt_to_context", False ) and compiled.statement is not context.invoked_statement ): metadata = metadata._adapt_to_context(context) self._metadata = metadata else: self._metadata = metadata = self._cursor_metadata( 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. .. versionadded:: 1.0.0 .. 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 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 @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. .. versionadded:: 0.9.0 .. 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): """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* 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) @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 class CursorResult(BaseCursorResult, Result): """A Result that is representing state from a DBAPI cursor. .. versionchanged:: 1.4 The :class:`.CursorResult` and :class:`.LegacyCursorResult` classes replace the previous :class:`.ResultProxy` interface. These 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. Within the scope of the 1.x series of SQLAlchemy, Core SQL results in version 1.4 return an instance of :class:`._engine.LegacyCursorResult` which takes the place of the ``CursorResult`` class used for the 1.3 series and previously. This object returns rows as :class:`.LegacyRow` objects, which maintains Python mapping (i.e. dictionary) like behaviors upon the object itself. Going forward, the :attr:`.Row._mapping` attribute should be used for dictionary behaviors. .. seealso:: :ref:`coretutorial_selecting` - introductory material for accessing :class:`_engine.CursorResult` and :class:`.Row` objects. """ _cursor_metadata = CursorResultMetaData _cursor_strategy_cls = CursorFetchStrategy _no_result_metadata = _NO_RESULT_METADATA _is_cursor = True 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): merged_result = super(CursorResult, self).merge(*others) setup_rowcounts = not self._metadata.returns_rows if setup_rowcounts: merged_result.rowcount = sum( result.rowcount for result in (self,) + others ) return merged_result def close(self): """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): self._yield_per = num self.cursor_strategy.yield_per(self, self.cursor, num) class LegacyCursorResult(CursorResult): """Legacy version of :class:`.CursorResult`. This class includes connection "connection autoclose" behavior for use with "connectionless" execution, as well as delivers rows using the :class:`.LegacyRow` row implementation. .. versionadded:: 1.4 """ _autoclose_connection = False _process_row = LegacyRow _cursor_metadata = LegacyCursorResultMetaData _cursor_strategy_cls = CursorFetchStrategy _no_result_metadata = _LEGACY_NO_RESULT_METADATA def close(self): """Close this :class:`_engine.LegacyCursorResult`. This method has the same behavior as that of :meth:`._engine.CursorResult`, but it also may close the underlying :class:`.Connection` for the case of "connectionless" execution. .. deprecated:: 2.0 "connectionless" execution is deprecated and will be removed in version 2.0. Version 2.0 will feature the :class:`_future.Result` object that will no longer affect the status of the originating connection in any case. 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` :ref:`dbengine_implicit` """ self._soft_close(hard=True) def _soft_close(self, hard=False): soft_closed = self._soft_closed super(LegacyCursorResult, self)._soft_close(hard=hard) if ( not soft_closed and self._soft_closed and self._autoclose_connection ): self.connection.close() ResultProxy = LegacyCursorResult class BufferedRowResultProxy(ResultProxy): """A ResultProxy with row buffering behavior. .. deprecated:: 1.4 this class is now supplied using a strategy object. See :class:`.BufferedRowCursorFetchStrategy`. """ _cursor_strategy_cls = BufferedRowCursorFetchStrategy class FullyBufferedResultProxy(ResultProxy): """A result proxy that buffers rows fully upon creation. .. deprecated:: 1.4 this class is now supplied using a strategy object. See :class:`.FullyBufferedCursorFetchStrategy`. """ _cursor_strategy_cls = FullyBufferedCursorFetchStrategy class BufferedColumnRow(LegacyRow): """Row is now BufferedColumn in all cases""" class BufferedColumnResultProxy(ResultProxy): """A ResultProxy with column buffering behavior. .. versionchanged:: 1.4 This is now the default behavior of the Row and this class does not change behavior in any way. """ _process_row = BufferedColumnRow