path: root/lib/sqlalchemy/engine/cursor.py

# engine/cursor.py
# Copyright (C) 2005-2021 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 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 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",
        "_processors",
        "_keys",
        "_keymap_by_result_column_idx",
        "_tuplefilter",
        "_translated_indexes",
        "_safe_for_cache",
        "_unpickled"
        # 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._unpickled = self._unpickled
        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._unpickled = self._unpickled
        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
        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,
                loose_column_name_matching,
            ) = context.result_column_struct
            num_ctx_cols = len(result_columns)
        else:
            result_columns = (
                cols_are_ordered
            ) = (
                num_ctx_cols
            ) = 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,
            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 by-primary-string dictionary smaller (or bigger?!) than
            # number of columns, assume we have dupes, rewrite
            # dupe records with "None" for index which results in
            # ambiguous column exception when accessed.
            if len(by_key) != num_ctx_cols:
                # 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 ()
                    ):
                        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,
        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.

        """

        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],
                    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:
                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 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,
    ):
        match_map = self._create_description_match_map(
            result_columns, 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,
        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 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:
            if self._unpickled and isinstance(key, elements.ColumnElement):
                util.raise_(
                    exc.NoSuchColumnError(
                        "Row was unpickled; lookup by ColumnElement "
                        "is unsupported"
                    ),
                    replace_context=err,
                )
            else:
                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, (str, int))
            },
            "_keys": self._keys,
            "_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._unpickled = True
        if state["_translated_indexes"]:
            self._translated_indexes = 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 = 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()


_NO_RESULT_METADATA = _NoResultMetaData()


class BaseCursorResult:
    """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``
       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:`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

    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)


ResultProxy = CursorResult


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(Row):
    """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