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sqlalchemy/lib/sqlalchemy/engine/result.py
T
Federico Caselli a9b068ae56 Remove code deprecated before version 1.1 
- Remove deprecated method ``get_primary_keys` in the :class:`.Dialect` and
  :class:`.Inspector` classes.
- Remove deprecated event ``dbapi_error`` and the method ``ConnectionEvents.dbapi_error`.
- Remove support for deprecated engine URLs of the form ``postgres://``.
- Remove deprecated dialect ``mysql+gaerdbms``.
- Remove deprecated parameter ``quoting`` from :class:`.mysql.ENUM`
  and :class:`.mysql.SET` in the ``mysql`` dialect.
- Remove deprecated function ``comparable_property``. and function
  ``comparable_using`` in the declarative extension.
- Remove deprecated function ``compile_mappers``.
- Remove deprecated method ``collection.linker``.
- Remove deprecated method ``Session.prune`` and parameter ``Session.weak_identity_map``.
  This change also removes the class ``StrongInstanceDict``.
- Remove deprecated parameter ``mapper.order_by``.
- Remove deprecated parameter ``Session._enable_transaction_accounting`.
- Remove deprecated parameter ``Session.is_modified.passive``.
- Remove deprecated class ``Binary``. Please use :class:`.LargeBinary`.
- Remove deprecated methods ``Compiled.compile``, ``ClauseElement.__and__`` and
  ``ClauseElement.__or__`` and attribute ``Over.func``.
- Remove deprecated ``FromClause.count`` method.
- Remove deprecated parameter ``Table.useexisting``.
- Remove deprecated parameters ``text.bindparams`` and ``text.typemap``.

- Remove boolean support for the ``passive`` parameter in ``get_history``.
- Remove deprecated ``adapt_operator`` in ``UserDefinedType.Comparator``.

Fixes: #4643
Change-Id: Idcd390c77bf7b0e9957907716993bdaa3f1a1763
2020-04-09 00:33:22 +02:00

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# engine/result.py
# Copyright (C) 2005-2020 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
"""Define result set constructs including :class:`.Result`"""
import collections
import functools
import operator
from .row import _baserow_usecext
from .row import BaseRow # noqa
from .row import LegacyRow # noqa
from .row import Row # noqa
from .row import RowMapping # noqa
from .row import RowProxy # noqa
from .row import rowproxy_reconstructor # noqa
from .. import exc
from .. import util
from ..sql import expression
from ..sql import sqltypes
from ..sql import util as sql_util
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
if _baserow_usecext:
from sqlalchemy.cresultproxy import tuplegetter as _tuplegetter
_UNPICKLED = util.symbol("unpickled")
# cyclical import for sqlalchemy.future
_future_Result = None
# metadata entry tuple indexes.
# using raw tuple is faster than namedtuple.
MD_INDEX = 0 # integer index in cursor.description
MD_OBJECTS = 1 # other string keys and ColumnElement obj that can match
MD_LOOKUP_KEY = 2 # string key we usually expect for key-based lookup
MD_RENDERED_NAME = 3 # name that is usually in cursor.description
MD_PROCESSOR = 4 # callable to process a result value into a row
MD_UNTRANSLATED = 5 # raw name from cursor.description
class ResultMetaData(object):
__slots__ = ()
def _has_key(self, key):
return key in self._keymap
def _key_fallback(self, key, err):
if isinstance(key, int):
util.raise_(IndexError(key), replace_context=err)
else:
util.raise_(KeyError(key), replace_context=err)
class SimpleResultMetaData(ResultMetaData):
__slots__ = "keys", "_keymap", "_processors"
def __init__(self, keys, extra=None):
self.keys = list(keys)
len_keys = len(keys)
self._keymap = {
name: (index, name) for index, name in enumerate(self.keys)
}
if not _baserow_usecext:
self._keymap.update(
{
index: (index, None, self.keys[index])
for index in range(len_keys)
}
)
if extra:
for key, ex in zip(keys, extra):
rec = self._keymap[key]
self._keymap.update({e: rec for e in ex})
self._processors = [None] * len(keys)
def __getstate__(self):
return {"keys": self.keys}
def __setstate__(self, state):
self.__init__(state["keys"])
def _has_key(self, key):
return key in self._keymap
def _contains(self, value, row):
return value in row._data
def result_tuple(fields, extra=None):
parent = SimpleResultMetaData(fields, extra)
return functools.partial(Row, parent, parent._processors, parent._keymap)
class CursorResultMetaData(ResultMetaData):
"""Handle cursor.description, applying additional info from an execution
context."""
__slots__ = (
"_keymap",
"case_sensitive",
"matched_on_name",
"_processors",
"keys",
)
def _adapt_to_context(self, context):
"""When using a cached result metadata against a new context,
we need to rewrite the _keymap so that it has the specific
Column objects in the new context inside of it. this accommodates
for select() constructs that contain anonymized columns and
are cached.
"""
if not context.compiled._result_columns:
return self
compiled_statement = context.compiled.statement
invoked_statement = context.invoked_statement
# same statement was invoked as the one we cached against,
# return self
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 = self._keymap.copy()
# match up new columns positionally to the result columns
for existing, new in zip(
context.compiled._result_columns,
invoked_statement._exported_columns_iterator(),
):
md._keymap[new] = md._keymap[existing[RM_NAME]]
md.case_sensitive = self.case_sensitive
md.matched_on_name = self.matched_on_name
md._processors = self._processors
md.keys = self.keys
return md
def __init__(self, parent, cursor_description):
context = parent.context
dialect = context.dialect
self.case_sensitive = dialect.case_sensitive
self.matched_on_name = 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 = {}
if not _baserow_usecext:
# keymap indexes by integer index: this is only used
# in the pure Python BaseRow.__getitem__
# implementation to avoid an expensive
# isinstance(key, util.int_types) in the most common
# case path
len_raw = len(raw)
self._keymap.update(
[
(metadata_entry[MD_INDEX], metadata_entry)
for metadata_entry in raw
]
+ [
(metadata_entry[MD_INDEX] - len_raw, metadata_entry)
for metadata_entry in raw
]
)
# processors in key order for certain per-row
# views like __iter__ and slices
self._processors = [
metadata_entry[MD_PROCESSOR] 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 ()
):
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, (), 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:`.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:`.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
return [
(
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:
# 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
raw_iterator = self._merge_cols_by_name(
context,
cursor_description,
result_columns,
loose_column_name_matching,
)
else:
# no compiled SQL, just a raw string
raw_iterator = self._merge_cols_by_none(
context, cursor_description
)
return [
(
idx,
obj,
cursor_colname,
cursor_colname,
context.get_result_processor(
mapped_type, cursor_colname, coltype
),
untranslated,
)
for (
idx,
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]
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
yield idx, 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
)
self.matched_on_name = True
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
else:
obj = ctx_rec[1]
mapped_type = ctx_rec[2]
yield 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 elem in 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 = d[key]
d[key] = e_name, e_obj + elem[RM_OBJECTS], e_type
else:
d[key] = (elem[RM_NAME], elem[RM_OBJECTS], elem[RM_TYPE])
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])
)
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, 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 _warn_for_nonint(self, key):
raise TypeError(
"TypeError: tuple indices must be integers or slices, not %s"
% type(key).__name__
)
def _getter(self, key, raiseerr=True):
try:
rec = self._keymap[key]
except KeyError as ke:
rec = self._key_fallback(key, ke, raiseerr)
if rec is None:
return None
index, obj = rec[0:2]
if index is None:
self._raise_for_ambiguous_column_name(rec)
return operator.methodcaller("_get_by_key_impl_mapping", index)
def _tuple_getter(self, keys, raiseerr=True):
"""Given a list of keys, return a callable that will deliver a tuple.
This is strictly used by the ORM and the keys are Column objects.
However, this might be some nice-ish feature if we could find a very
clean way of presenting it.
note that in the new world of "row._mapping", this is a mapping-getter.
maybe the name should indicate that somehow.
"""
indexes = []
for key in keys:
try:
rec = self._keymap[key]
except KeyError as ke:
rec = self._key_fallback(key, ke, raiseerr)
if rec is None:
return None
index, obj = rec[0:2]
if index is None:
self._raise_for_ambiguous_column_name(obj)
indexes.append(index)
if _baserow_usecext:
return _tuplegetter(*indexes)
else:
return self._pure_py_tuplegetter(*indexes)
def _pure_py_tuplegetter(self, *indexes):
getters = [
operator.methodcaller("_get_by_key_impl_mapping", index)
for index in indexes
]
return lambda rec: tuple(getter(rec) for getter in getters)
def __getstate__(self):
return {
"_keymap": {
key: (rec[MD_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,
"matched_on_name": self.matched_on_name,
}
def __setstate__(self, state):
self._processors = [None for _ in range(len(state["keys"]))]
self._keymap = state["_keymap"]
self.keys = state["keys"]
self.case_sensitive = state["case_sensitive"]
self.matched_on_name = state["matched_on_name"]
class LegacyCursorResultMetaData(CursorResultMetaData):
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._label
and (key._label if self.case_sensitive else key._label.lower())
in map_
):
result = map_[
key._label if self.case_sensitive else key._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(
"Retreiving 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(
"Retreiving 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 sqlalchemy.future 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 CursorFetchStrategy(object):
"""Define a cursor strategy for a result object.
Subclasses define different ways of fetching rows, typically but
not necessarily using a DBAPI cursor object.
.. versionadded:: 1.4
"""
__slots__ = ("dbapi_cursor", "cursor_description")
def __init__(self, dbapi_cursor, cursor_description):
self.dbapi_cursor = dbapi_cursor
self.cursor_description = cursor_description
@classmethod
def create(cls, result):
raise NotImplementedError()
def soft_close(self, result):
raise NotImplementedError()
def hard_close(self, result):
raise NotImplementedError()
def fetchone(self):
raise NotImplementedError()
def fetchmany(self, size=None):
raise NotImplementedError()
def fetchall(self):
raise NotImplementedError()
class NoCursorDQLFetchStrategy(CursorFetchStrategy):
"""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__ = ("closed",)
def __init__(self, closed):
self.closed = closed
self.cursor_description = None
def soft_close(self, result):
pass
def hard_close(self, result):
self.closed = True
def fetchone(self):
return self._non_result(None)
def fetchmany(self, size=None):
return self._non_result([])
def fetchall(self):
return self._non_result([])
def _non_result(self, default, err=None):
if self.closed:
util.raise_(
exc.ResourceClosedError("This result object is closed."),
replace_context=err,
)
else:
return default
class NoCursorDMLFetchStrategy(CursorFetchStrategy):
"""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__ = ("closed",)
def __init__(self, closed):
self.closed = closed
self.cursor_description = None
def soft_close(self, result):
pass
def hard_close(self, result):
self.closed = True
def fetchone(self):
return self._non_result(None)
def fetchmany(self, size=None):
return self._non_result([])
def fetchall(self):
return self._non_result([])
def _non_result(self, default, err=None):
util.raise_(
exc.ResourceClosedError(
"This result object does not return rows. "
"It has been closed automatically."
),
replace_context=err,
)
class DefaultCursorFetchStrategy(CursorFetchStrategy):
"""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.
"""
@classmethod
def create(cls, result):
dbapi_cursor = result.cursor
description = dbapi_cursor.description
if description is None:
return NoCursorDMLFetchStrategy(False)
else:
return cls(dbapi_cursor, description)
def soft_close(self, result):
result.cursor_strategy = NoCursorDQLFetchStrategy(False)
def hard_close(self, result):
result.cursor_strategy = NoCursorDQLFetchStrategy(True)
def fetchone(self):
return self.dbapi_cursor.fetchone()
def fetchmany(self, size=None):
if size is None:
return self.dbapi_cursor.fetchmany()
else:
return self.dbapi_cursor.fetchmany(size)
def fetchall(self):
return self.dbapi_cursor.fetchall()
class BufferedRowCursorFetchStrategy(DefaultCursorFetchStrategy):
"""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")
def __init__(
self, max_row_buffer, dbapi_cursor, description, initial_buffer
):
super(BufferedRowCursorFetchStrategy, self).__init__(
dbapi_cursor, description
)
self._max_row_buffer = max_row_buffer
self._growth_factor = 5
self._rowbuffer = initial_buffer
self._bufsize = min(self._max_row_buffer, self._growth_factor)
@classmethod
def create(cls, result):
"""Buffered row strategy has to buffer the first rows *before*
cursor.description is fetched so that it works with named cursors
correctly
"""
dbapi_cursor = result.cursor
initial_buffer = collections.deque(dbapi_cursor.fetchmany(1))
description = dbapi_cursor.description
if description is None:
return NoCursorDMLFetchStrategy(False)
else:
max_row_buffer = result.context.execution_options.get(
"max_row_buffer", 1000
)
return cls(
max_row_buffer, dbapi_cursor, description, initial_buffer
)
def __buffer_rows(self):
size = self._bufsize
self._rowbuffer = collections.deque(self.dbapi_cursor.fetchmany(size))
if size < self._max_row_buffer:
self._bufsize = min(
self._max_row_buffer, size * self._growth_factor
)
def soft_close(self, result):
self._rowbuffer.clear()
super(BufferedRowCursorFetchStrategy, self).soft_close(result)
def hard_close(self, result):
self._rowbuffer.clear()
super(BufferedRowCursorFetchStrategy, self).hard_close(result)
def fetchone(self):
if not self._rowbuffer:
self.__buffer_rows()
if not self._rowbuffer:
return None
return self._rowbuffer.popleft()
def fetchmany(self, size=None):
if size is None:
return self.fetchall()
result = []
for x in range(0, size):
row = self.fetchone()
if row is None:
break
result.append(row)
return result
def fetchall(self):
self._rowbuffer.extend(self.dbapi_cursor.fetchall())
ret = self._rowbuffer
self._rowbuffer = collections.deque()
return ret
class FullyBufferedCursorFetchStrategy(DefaultCursorFetchStrategy):
"""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",)
def __init__(self, dbapi_cursor, description, initial_buffer=None):
super(FullyBufferedCursorFetchStrategy, self).__init__(
dbapi_cursor, description
)
if initial_buffer is not None:
self._rowbuffer = collections.deque(initial_buffer)
else:
self._rowbuffer = self._buffer_rows()
@classmethod
def create_from_buffer(cls, dbapi_cursor, description, buffer):
return cls(dbapi_cursor, description, buffer)
def _buffer_rows(self):
return collections.deque(self.dbapi_cursor.fetchall())
def soft_close(self, result):
self._rowbuffer.clear()
super(FullyBufferedCursorFetchStrategy, self).soft_close(result)
def hard_close(self, result):
self._rowbuffer.clear()
super(FullyBufferedCursorFetchStrategy, self).hard_close(result)
def fetchone(self):
if self._rowbuffer:
return self._rowbuffer.popleft()
else:
return None
def fetchmany(self, size=None):
if size is None:
return self.fetchall()
result = []
for x in range(0, size):
row = self.fetchone()
if row is None:
break
result.append(row)
return result
def fetchall(self):
ret = self._rowbuffer
self._rowbuffer = collections.deque()
return ret
class BaseResult(object):
"""Base class for database result objects.
:class:`.BaseResult` is the base class for the 1.x style
:class:`.ResultProxy` class as well as the 2.x style
:class:`.future.Result` class.
"""
out_parameters = None
_metadata = None
_soft_closed = False
closed = False
@classmethod
def _create_for_context(cls, context):
if context._is_future_result:
obj = object.__new__(_future_Result)
else:
obj = object.__new__(ResultProxy)
obj.__init__(context)
return obj
def __init__(self, context):
self.context = context
self.dialect = context.dialect
self.cursor = context.cursor
self.connection = context.root_connection
self._echo = (
self.connection._echo and context.engine._should_log_debug()
)
self._init_metadata()
def _init_metadata(self):
self.cursor_strategy = strat = self.context.get_result_cursor_strategy(
self
)
if strat.cursor_description is not None:
if self.context.compiled:
if self.context.compiled._cached_metadata:
cached_md = self.context.compiled._cached_metadata
self._metadata = cached_md._adapt_to_context(self.context)
else:
self._metadata = (
self.context.compiled._cached_metadata
) = self._cursor_metadata(self, strat.cursor_description)
else:
self._metadata = self._cursor_metadata(
self, strat.cursor_description
)
if self._echo:
self.context.engine.logger.debug(
"Col %r", tuple(x[0] for x in strat.cursor_description)
)
# leave cursor open so that execution context can continue
# setting up things like rowcount
def keys(self):
"""Return the list of string keys that would represented by each
:class:`.Row`."""
if self._metadata:
return self._metadata.keys
else:
return []
def _getter(self, key, raiseerr=True):
try:
getter = self._metadata._getter
except AttributeError as err:
return self.cursor_strategy._non_result(None, err)
else:
return getter(key, raiseerr)
def _tuple_getter(self, key, raiseerr=True):
try:
getter = self._metadata._tuple_getter
except AttributeError as err:
return self.cursor_strategy._non_result(None, err)
else:
return getter(key, raiseerr)
def _has_key(self, key):
try:
has_key = self._metadata._has_key
except AttributeError as err:
return self.cursor_strategy._non_result(None, err)
else:
return has_key(key)
def _soft_close(self, hard=False):
"""Soft close this :class:`.ResultProxy`.
This releases all DBAPI cursor resources, but leaves the
ResultProxy "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:`.ResultProxy.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)
else:
self.cursor_strategy.soft_close(self)
if not self._soft_closed:
cursor = self.cursor
self.cursor = None
self.connection._safe_close_cursor(cursor)
self._soft_closed = True
@util.memoized_property
def inserted_primary_key(self):
"""Return the primary key for the row just inserted.
The return value is a list of scalar values
corresponding to the list of primary key columns
in the target table.
This only applies to single row :func:`~.sql.expression.insert`
constructs which did not explicitly specify
:meth:`.Insert.returning`.
Note that primary key columns which specify a
server_default clause,
or otherwise do not qualify as "autoincrement"
columns (see the notes at :class:`.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 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
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(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`
"""
return self.context.returned_defaults
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:`.ResultProxy.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:`.ResultProxy.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:`.ResultProxy.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:`.ResultProxy.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:`.ResultProxy.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:`.ResultProxy.supports_sane_rowcount` and
:meth:`.ResultProxy.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.
"""
try:
return self.context.rowcount
except BaseException as e:
self.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
@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:`~ResultProxy.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.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
@property
def returns_rows(self):
"""True if this :class:`.ResultProxy` returns rows.
I.e. if it is legal to call the methods
:meth:`~.ResultProxy.fetchone`,
:meth:`~.ResultProxy.fetchmany`
:meth:`~.ResultProxy.fetchall`.
"""
return self._metadata is not None
@property
def is_insert(self):
"""True if this :class:`.ResultProxy` 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 ResultProxy(BaseResult):
"""A facade around a DBAPI cursor object.
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.
Within the scope of the 1.x series of SQLAlchemy, the :class:`.ResultProxy`
will in fact return instances of the :class:`.LegacyRow` class, 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:`.ResultProxy` and :class:`.Row` objects.
"""
_autoclose_connection = False
_process_row = LegacyRow
_cursor_metadata = LegacyCursorResultMetaData
_cursor_strategy_cls = DefaultCursorFetchStrategy
def __iter__(self):
"""Implement iteration protocol."""
while True:
row = self.fetchone()
if row is None:
return
else:
yield row
def close(self):
"""Close this ResultProxy.
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:`.ResultProxy`
exhausts all available rows. :meth:`.ResultProxy.close` is generally
an optional method except in the case when discarding a
:class:`.ResultProxy` that still has additional rows pending for fetch.
In the case of a result that is the product of
:ref:`connectionless execution <dbengine_implicit>`,
the underlying :class:`.Connection` object is also closed, which
:term:`releases` DBAPI connection resources.
.. deprecated:: 2.0 "connectionless" execution is deprecated and will
be removed in version 2.0. Version 2.0 will feature the
:class:`.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`
"""
self._soft_close(hard=True)
def _soft_close(self, hard=False):
soft_closed = self._soft_closed
super(ResultProxy, self)._soft_close(hard=hard)
if (
not soft_closed
and self._soft_closed
and self._autoclose_connection
):
self.connection.close()
def __next__(self):
"""Implement the Python next() protocol.
This method, mirrored as both ``.next()`` and ``.__next__()``, is part
of Python's API for producing iterator-like behavior.
.. versionadded:: 1.2
"""
row = self.fetchone()
if row is None:
raise StopIteration()
else:
return row
next = __next__
def process_rows(self, rows):
process_row = self._process_row
metadata = self._metadata
keymap = metadata._keymap
processors = metadata._processors
if self._echo:
log = self.context.engine.logger.debug
l = []
for row in rows:
log("Row %r", sql_util._repr_row(row))
l.append(process_row(metadata, processors, keymap, row))
return l
else:
return [
process_row(metadata, processors, keymap, row) for row in rows
]
def fetchall(self):
"""Fetch all rows, just like DB-API ``cursor.fetchall()``.
After all rows have been exhausted, the underlying DBAPI
cursor resource is released, and the object may be safely
discarded.
Subsequent calls to :meth:`.ResultProxy.fetchall` will return
an empty list. After the :meth:`.ResultProxy.close` method is
called, the method will raise :class:`.ResourceClosedError`.
:return: a list of :class:`.Row` objects
"""
try:
l = self.process_rows(self.cursor_strategy.fetchall())
self._soft_close()
return l
except BaseException as e:
self.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
def fetchmany(self, size=None):
"""Fetch many rows, just like DB-API
``cursor.fetchmany(size=cursor.arraysize)``.
After all rows have been exhausted, the underlying DBAPI
cursor resource is released, and the object may be safely
discarded.
Calls to :meth:`.ResultProxy.fetchmany` after all rows have been
exhausted will return
an empty list. After the :meth:`.ResultProxy.close` method is
called, the method will raise :class:`.ResourceClosedError`.
:return: a list of :class:`.Row` objects
"""
try:
l = self.process_rows(self.cursor_strategy.fetchmany(size))
if len(l) == 0:
self._soft_close()
return l
except BaseException as e:
self.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
def _onerow(self):
return self.fetchone()
def fetchone(self):
"""Fetch one row, just like DB-API ``cursor.fetchone()``.
After all rows have been exhausted, the underlying DBAPI
cursor resource is released, and the object may be safely
discarded.
Calls to :meth:`.ResultProxy.fetchone` after all rows have
been exhausted will return ``None``.
After the :meth:`.ResultProxy.close` method is
called, the method will raise :class:`.ResourceClosedError`.
:return: a :class:`.Row` object, or None if no rows remain
"""
try:
row = self.cursor_strategy.fetchone()
if row is not None:
return self.process_rows([row])[0]
else:
self._soft_close()
return None
except BaseException as e:
self.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
def first(self):
"""Fetch the first row and then close the result set unconditionally.
After calling this method, the object is fully closed,
e.g. the :meth:`.ResultProxy.close` method will have been called.
:return: a :class:`.Row` object, or None if no rows remain
"""
try:
row = self.cursor_strategy.fetchone()
except BaseException as e:
self.connection._handle_dbapi_exception(
e, None, None, self.cursor, self.context
)
try:
if row is not None:
return self.process_rows([row])[0]
else:
return None
finally:
self.close()
def scalar(self):
"""Fetch the first column of the first row, and close the result set.
After calling this method, the object is fully closed,
e.g. the :meth:`.ResultProxy.close` method will have been called.
:return: a Python scalar value , or None if no rows remain
"""
row = self.first()
if row is not None:
return row[0]
else:
return None
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
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