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OpenHome/venv/Lib/site-packages/sqlalchemy/engine/result.py

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2021-07-21 21:33:05 +02:00
# engine/result.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: http://www.opensource.org/licenses/mit-license.php
"""Define generic result set constructs."""
import functools
import itertools
import operator
from .row import _baserow_usecext
from .row import Row
from .. import exc
from .. import util
from ..sql.base import _generative
from ..sql.base import HasMemoized
from ..sql.base import InPlaceGenerative
from ..util import collections_abc
from ..util import py2k
if _baserow_usecext:
from sqlalchemy.cresultproxy import tuplegetter
_row_as_tuple = tuplegetter
else:
def tuplegetter(*indexes):
it = operator.itemgetter(*indexes)
if len(indexes) > 1:
return it
else:
return lambda row: (it(row),)
def _row_as_tuple(*indexes):
# circumvent LegacyRow.__getitem__ pointing to
# _get_by_key_impl_mapping for now. otherwise we could
# use itemgetter
getters = [
operator.methodcaller("_get_by_int_impl", index)
for index in indexes
]
return lambda rec: tuple([getter(rec) for getter in getters])
class ResultMetaData(object):
"""Base for metadata about result rows."""
__slots__ = ()
_tuplefilter = None
_translated_indexes = None
_unique_filters = None
@property
def keys(self):
return RMKeyView(self)
def _has_key(self, key):
raise NotImplementedError()
def _for_freeze(self):
raise NotImplementedError()
def _key_fallback(self, key, err, raiseerr=True):
assert raiseerr
util.raise_(KeyError(key), replace_context=err)
def _warn_for_nonint(self, key):
util.warn_deprecated_20(
"Retrieving row members using strings or other non-integers is "
"deprecated; use row._mapping for a dictionary interface "
"to the row"
)
def _raise_for_nonint(self, key):
raise TypeError(
"TypeError: tuple indices must be integers or slices, not %s"
% type(key).__name__
)
def _index_for_key(self, keys, raiseerr):
raise NotImplementedError()
def _metadata_for_keys(self, key):
raise NotImplementedError()
def _reduce(self, keys):
raise NotImplementedError()
def _getter(self, key, raiseerr=True):
index = self._index_for_key(key, raiseerr)
if index is not None:
return operator.itemgetter(index)
else:
return None
def _row_as_tuple_getter(self, keys):
indexes = self._indexes_for_keys(keys)
return _row_as_tuple(*indexes)
class RMKeyView(collections_abc.KeysView):
__slots__ = ("_parent", "_keys")
def __init__(self, parent):
self._parent = parent
self._keys = [k for k in parent._keys if k is not None]
def __len__(self):
return len(self._keys)
def __repr__(self):
return "{0.__class__.__name__}({0._keys!r})".format(self)
def __iter__(self):
return iter(self._keys)
def __contains__(self, item):
if not _baserow_usecext and isinstance(item, int):
return False
# note this also includes special key fallback behaviors
# which also don't seem to be tested in test_resultset right now
return self._parent._has_key(item)
def __eq__(self, other):
return list(other) == list(self)
def __ne__(self, other):
return list(other) != list(self)
class SimpleResultMetaData(ResultMetaData):
"""result metadata for in-memory collections."""
__slots__ = (
"_keys",
"_keymap",
"_processors",
"_tuplefilter",
"_translated_indexes",
"_unique_filters",
)
def __init__(
self,
keys,
extra=None,
_processors=None,
_tuplefilter=None,
_translated_indexes=None,
_unique_filters=None,
):
self._keys = list(keys)
self._tuplefilter = _tuplefilter
self._translated_indexes = _translated_indexes
self._unique_filters = _unique_filters
if extra:
recs_names = [
(
(name,) + extras,
(index, name, extras),
)
for index, (name, extras) in enumerate(zip(self._keys, extra))
]
else:
recs_names = [
((name,), (index, name, ()))
for index, name in enumerate(self._keys)
]
self._keymap = {key: rec for keys, rec in recs_names for key in keys}
self._processors = _processors
def _has_key(self, key):
return key in self._keymap
def _for_freeze(self):
unique_filters = self._unique_filters
if unique_filters and self._tuplefilter:
unique_filters = self._tuplefilter(unique_filters)
# TODO: are we freezing the result with or without uniqueness
# applied?
return SimpleResultMetaData(
self._keys,
extra=[self._keymap[key][2] for key in self._keys],
_unique_filters=unique_filters,
)
def __getstate__(self):
return {
"_keys": self._keys,
"_translated_indexes": self._translated_indexes,
}
def __setstate__(self, state):
if state["_translated_indexes"]:
_translated_indexes = state["_translated_indexes"]
_tuplefilter = tuplegetter(*_translated_indexes)
else:
_translated_indexes = _tuplefilter = None
self.__init__(
state["_keys"],
_translated_indexes=_translated_indexes,
_tuplefilter=_tuplefilter,
)
def _contains(self, value, row):
return value in row._data
def _index_for_key(self, key, raiseerr=True):
if int in key.__class__.__mro__:
key = self._keys[key]
try:
rec = self._keymap[key]
except KeyError as ke:
rec = self._key_fallback(key, ke, raiseerr)
return rec[0]
def _indexes_for_keys(self, keys):
return [self._keymap[key][0] for key in keys]
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:
rec = self._key_fallback(key, ke, True)
yield rec
def _reduce(self, keys):
try:
metadata_for_keys = [
self._keymap[
self._keys[key] if int in key.__class__.__mro__ else key
]
for key in keys
]
except KeyError as ke:
self._key_fallback(ke.args[0], ke, True)
indexes, new_keys, extra = zip(*metadata_for_keys)
if self._translated_indexes:
indexes = [self._translated_indexes[idx] for idx in indexes]
tup = tuplegetter(*indexes)
new_metadata = SimpleResultMetaData(
new_keys,
extra=extra,
_tuplefilter=tup,
_translated_indexes=indexes,
_processors=self._processors,
_unique_filters=self._unique_filters,
)
return new_metadata
def result_tuple(fields, extra=None):
parent = SimpleResultMetaData(fields, extra)
return functools.partial(
Row, parent, parent._processors, parent._keymap, Row._default_key_style
)
# a symbol that indicates to internal Result methods that
# "no row is returned". We can't use None for those cases where a scalar
# filter is applied to rows.
_NO_ROW = util.symbol("NO_ROW")
class ResultInternal(InPlaceGenerative):
_real_result = None
_generate_rows = True
_unique_filter_state = None
_post_creational_filter = None
@HasMemoized.memoized_attribute
def _row_getter(self):
real_result = self._real_result if self._real_result else self
if real_result._source_supports_scalars:
if not self._generate_rows:
return None
else:
_proc = real_result._process_row
def process_row(
metadata, processors, keymap, key_style, scalar_obj
):
return _proc(
metadata, processors, keymap, key_style, (scalar_obj,)
)
else:
process_row = real_result._process_row
key_style = real_result._process_row._default_key_style
metadata = self._metadata
keymap = metadata._keymap
processors = metadata._processors
tf = metadata._tuplefilter
if tf and not real_result._source_supports_scalars:
if processors:
processors = tf(processors)
_make_row_orig = functools.partial(
process_row, metadata, processors, keymap, key_style
)
def make_row(row):
return _make_row_orig(tf(row))
else:
make_row = functools.partial(
process_row, metadata, processors, keymap, key_style
)
fns = ()
if real_result._row_logging_fn:
fns = (real_result._row_logging_fn,)
else:
fns = ()
if fns:
_make_row = make_row
def make_row(row):
row = _make_row(row)
for fn in fns:
row = fn(row)
return row
return make_row
@HasMemoized.memoized_attribute
def _iterator_getter(self):
make_row = self._row_getter
post_creational_filter = self._post_creational_filter
if self._unique_filter_state:
uniques, strategy = self._unique_strategy
def iterrows(self):
for row in self._fetchiter_impl():
obj = make_row(row) if make_row else row
hashed = strategy(obj) if strategy else obj
if hashed in uniques:
continue
uniques.add(hashed)
if post_creational_filter:
obj = post_creational_filter(obj)
yield obj
else:
def iterrows(self):
for row in self._fetchiter_impl():
row = make_row(row) if make_row else row
if post_creational_filter:
row = post_creational_filter(row)
yield row
return iterrows
def _raw_all_rows(self):
make_row = self._row_getter
rows = self._fetchall_impl()
return [make_row(row) for row in rows]
def _allrows(self):
post_creational_filter = self._post_creational_filter
make_row = self._row_getter
rows = self._fetchall_impl()
if make_row:
made_rows = [make_row(row) for row in rows]
else:
made_rows = rows
if self._unique_filter_state:
uniques, strategy = self._unique_strategy
rows = [
made_row
for made_row, sig_row in [
(
made_row,
strategy(made_row) if strategy else made_row,
)
for made_row in made_rows
]
if sig_row not in uniques and not uniques.add(sig_row)
]
else:
rows = made_rows
if post_creational_filter:
rows = [post_creational_filter(row) for row in rows]
return rows
@HasMemoized.memoized_attribute
def _onerow_getter(self):
make_row = self._row_getter
post_creational_filter = self._post_creational_filter
if self._unique_filter_state:
uniques, strategy = self._unique_strategy
def onerow(self):
_onerow = self._fetchone_impl
while True:
row = _onerow()
if row is None:
return _NO_ROW
else:
obj = make_row(row) if make_row else row
hashed = strategy(obj) if strategy else obj
if hashed in uniques:
continue
else:
uniques.add(hashed)
if post_creational_filter:
obj = post_creational_filter(obj)
return obj
else:
def onerow(self):
row = self._fetchone_impl()
if row is None:
return _NO_ROW
else:
row = make_row(row) if make_row else row
if post_creational_filter:
row = post_creational_filter(row)
return row
return onerow
@HasMemoized.memoized_attribute
def _manyrow_getter(self):
make_row = self._row_getter
post_creational_filter = self._post_creational_filter
if self._unique_filter_state:
uniques, strategy = self._unique_strategy
def filterrows(make_row, rows, strategy, uniques):
if make_row:
rows = [make_row(row) for row in rows]
if strategy:
made_rows = (
(made_row, strategy(made_row)) for made_row in rows
)
else:
made_rows = ((made_row, made_row) for made_row in rows)
return [
made_row
for made_row, sig_row in made_rows
if sig_row not in uniques and not uniques.add(sig_row)
]
def manyrows(self, num):
collect = []
_manyrows = self._fetchmany_impl
if num is None:
# if None is passed, we don't know the default
# manyrows number, DBAPI has this as cursor.arraysize
# different DBAPIs / fetch strategies may be different.
# do a fetch to find what the number is. if there are
# only fewer rows left, then it doesn't matter.
real_result = (
self._real_result if self._real_result else self
)
if real_result._yield_per:
num_required = num = real_result._yield_per
else:
rows = _manyrows(num)
num = len(rows)
collect.extend(
filterrows(make_row, rows, strategy, uniques)
)
num_required = num - len(collect)
else:
num_required = num
while num_required:
rows = _manyrows(num_required)
if not rows:
break
collect.extend(
filterrows(make_row, rows, strategy, uniques)
)
num_required = num - len(collect)
if post_creational_filter:
collect = [post_creational_filter(row) for row in collect]
return collect
else:
def manyrows(self, num):
if num is None:
real_result = (
self._real_result if self._real_result else self
)
num = real_result._yield_per
rows = self._fetchmany_impl(num)
if make_row:
rows = [make_row(row) for row in rows]
if post_creational_filter:
rows = [post_creational_filter(row) for row in rows]
return rows
return manyrows
def _only_one_row(
self,
raise_for_second_row,
raise_for_none,
scalar,
):
onerow = self._fetchone_impl
row = onerow(hard_close=True)
if row is None:
if raise_for_none:
raise exc.NoResultFound(
"No row was found when one was required"
)
else:
return None
if scalar and self._source_supports_scalars:
self._generate_rows = False
make_row = None
else:
make_row = self._row_getter
try:
row = make_row(row) if make_row else row
except:
self._soft_close(hard=True)
raise
if raise_for_second_row:
if self._unique_filter_state:
# for no second row but uniqueness, need to essentially
# consume the entire result :(
uniques, strategy = self._unique_strategy
existing_row_hash = strategy(row) if strategy else row
while True:
next_row = onerow(hard_close=True)
if next_row is None:
next_row = _NO_ROW
break
try:
next_row = make_row(next_row) if make_row else next_row
if strategy:
if existing_row_hash == strategy(next_row):
continue
elif row == next_row:
continue
# here, we have a row and it's different
break
except:
self._soft_close(hard=True)
raise
else:
next_row = onerow(hard_close=True)
if next_row is None:
next_row = _NO_ROW
if next_row is not _NO_ROW:
self._soft_close(hard=True)
raise exc.MultipleResultsFound(
"Multiple rows were found when exactly one was required"
if raise_for_none
else "Multiple rows were found when one or none "
"was required"
)
else:
next_row = _NO_ROW
# if we checked for second row then that would have
# closed us :)
self._soft_close(hard=True)
if not scalar:
post_creational_filter = self._post_creational_filter
if post_creational_filter:
row = post_creational_filter(row)
if scalar and make_row:
return row[0]
else:
return row
def _iter_impl(self):
return self._iterator_getter(self)
def _next_impl(self):
row = self._onerow_getter(self)
if row is _NO_ROW:
raise StopIteration()
else:
return row
@_generative
def _column_slices(self, indexes):
real_result = self._real_result if self._real_result else self
if real_result._source_supports_scalars and len(indexes) == 1:
self._generate_rows = False
else:
self._generate_rows = True
self._metadata = self._metadata._reduce(indexes)
@HasMemoized.memoized_attribute
def _unique_strategy(self):
uniques, strategy = self._unique_filter_state
real_result = (
self._real_result if self._real_result is not None else self
)
if not strategy and self._metadata._unique_filters:
if (
real_result._source_supports_scalars
and not self._generate_rows
):
strategy = self._metadata._unique_filters[0]
else:
filters = self._metadata._unique_filters
if self._metadata._tuplefilter:
filters = self._metadata._tuplefilter(filters)
strategy = operator.methodcaller("_filter_on_values", filters)
return uniques, strategy
class _WithKeys(object):
# used mainly to share documentation on the keys method.
# py2k does not allow overriding the __doc__ attribute.
def keys(self):
"""Return an iterable view which yields the string keys that would
be represented by each :class:`.Row`.
The keys can represent the labels of the columns returned by a core
statement or the names of the orm classes returned by an orm
execution.
The view also can be tested for key containment using the Python
``in`` operator, which will test both for the string keys represented
in the view, as well as for alternate keys such as column objects.
.. versionchanged:: 1.4 a key view object is returned rather than a
plain list.
"""
return self._metadata.keys
class Result(_WithKeys, ResultInternal):
"""Represent a set of database results.
.. versionadded:: 1.4 The :class:`.Result` object provides a completely
updated usage model and calling facade for SQLAlchemy Core and
SQLAlchemy ORM. In Core, it forms the basis of the
:class:`.CursorResult` object which replaces the previous
:class:`.ResultProxy` interface. When using the ORM, a higher level
object called :class:`.ChunkedIteratorResult` is normally used.
.. seealso::
:ref:`tutorial_fetching_rows` - in the :doc:`/tutorial/index`
"""
_process_row = Row
_row_logging_fn = None
_source_supports_scalars = False
_yield_per = None
_attributes = util.immutabledict()
def __init__(self, cursor_metadata):
self._metadata = cursor_metadata
def _soft_close(self, hard=False):
raise NotImplementedError()
@_generative
def yield_per(self, num):
"""Configure the row-fetching strategy to fetch num rows at a time.
This impacts the underlying behavior of the result when iterating over
the result object, or otherwise making use of methods such as
:meth:`_engine.Result.fetchone` that return one row at a time. Data
from the underlying cursor or other data source will be buffered up to
this many rows in memory, and the buffered collection will then be
yielded out one row at at time or as many rows are requested. Each time
the buffer clears, it will be refreshed to this many rows or as many
rows remain if fewer remain.
The :meth:`_engine.Result.yield_per` method is generally used in
conjunction with the
:paramref:`_engine.Connection.execution_options.stream_results`
execution option, which will allow the database dialect in use to make
use of a server side cursor, if the DBAPI supports it.
Most DBAPIs do not use server side cursors by default, which means all
rows will be fetched upfront from the database regardless of the
:meth:`_engine.Result.yield_per` setting. However,
:meth:`_engine.Result.yield_per` may still be useful in that it batches
the SQLAlchemy-side processing of the raw data from the database, and
additionally when used for ORM scenarios will batch the conversion of
database rows into ORM entity rows.
.. versionadded:: 1.4
:param num: number of rows to fetch each time the buffer is refilled.
If set to a value below 1, fetches all rows for the next buffer.
"""
self._yield_per = num
@_generative
def unique(self, strategy=None):
"""Apply unique filtering to the objects returned by this
:class:`_engine.Result`.
When this filter is applied with no arguments, the rows or objects
returned will filtered such that each row is returned uniquely. The
algorithm used to determine this uniqueness is by default the Python
hashing identity of the whole tuple. In some cases a specialized
per-entity hashing scheme may be used, such as when using the ORM, a
scheme is applied which works against the primary key identity of
returned objects.
The unique filter is applied **after all other filters**, which means
if the columns returned have been refined using a method such as the
:meth:`_engine.Result.columns` or :meth:`_engine.Result.scalars`
method, the uniquing is applied to **only the column or columns
returned**. This occurs regardless of the order in which these
methods have been called upon the :class:`_engine.Result` object.
The unique filter also changes the calculus used for methods like
:meth:`_engine.Result.fetchmany` and :meth:`_engine.Result.partitions`.
When using :meth:`_engine.Result.unique`, these methods will continue
to yield the number of rows or objects requested, after uniquing
has been applied. However, this necessarily impacts the buffering
behavior of the underlying cursor or datasource, such that multiple
underlying calls to ``cursor.fetchmany()`` may be necessary in order
to accumulate enough objects in order to provide a unique collection
of the requested size.
:param strategy: a callable that will be applied to rows or objects
being iterated, which should return an object that represents the
unique value of the row. A Python ``set()`` is used to store
these identities. If not passed, a default uniqueness strategy
is used which may have been assembled by the source of this
:class:`_engine.Result` object.
"""
self._unique_filter_state = (set(), strategy)
def columns(self, *col_expressions):
r"""Establish the columns that should be returned in each row.
This method may be used to limit the columns returned as well
as to reorder them. The given list of expressions are normally
a series of integers or string key names. They may also be
appropriate :class:`.ColumnElement` objects which correspond to
a given statement construct.
E.g.::
statement = select(table.c.x, table.c.y, table.c.z)
result = connection.execute(statement)
for z, y in result.columns('z', 'y'):
# ...
Example of using the column objects from the statement itself::
for z, y in result.columns(
statement.selected_columns.c.z,
statement.selected_columns.c.y
):
# ...
.. versionadded:: 1.4
:param \*col_expressions: indicates columns to be returned. Elements
may be integer row indexes, string column names, or appropriate
:class:`.ColumnElement` objects corresponding to a select construct.
:return: this :class:`_engine.Result` object with the modifications
given.
"""
return self._column_slices(col_expressions)
def scalars(self, index=0):
"""Return a :class:`_result.ScalarResult` filtering object which
will return single elements rather than :class:`_row.Row` objects.
E.g.::
>>> result = conn.execute(text("select int_id from table"))
>>> result.scalars().all()
[1, 2, 3]
When results are fetched from the :class:`_result.ScalarResult`
filtering object, the single column-row that would be returned by the
:class:`_result.Result` is instead returned as the column's value.
.. versionadded:: 1.4
:param index: integer or row key indicating the column to be fetched
from each row, defaults to ``0`` indicating the first column.
:return: a new :class:`_result.ScalarResult` filtering object referring
to this :class:`_result.Result` object.
"""
return ScalarResult(self, index)
def _getter(self, key, raiseerr=True):
"""return a callable that will retrieve the given key from a
:class:`.Row`.
"""
if self._source_supports_scalars:
raise NotImplementedError(
"can't use this function in 'only scalars' mode"
)
return self._metadata._getter(key, raiseerr)
def _tuple_getter(self, keys):
"""return a callable that will retrieve the given keys from a
:class:`.Row`.
"""
if self._source_supports_scalars:
raise NotImplementedError(
"can't use this function in 'only scalars' mode"
)
return self._metadata._row_as_tuple_getter(keys)
def mappings(self):
"""Apply a mappings filter to returned rows, returning an instance of
:class:`_result.MappingResult`.
When this filter is applied, fetching rows will return
:class:`.RowMapping` objects instead of :class:`.Row` objects.
.. versionadded:: 1.4
:return: a new :class:`_result.MappingResult` filtering object
referring to this :class:`_result.Result` object.
"""
return MappingResult(self)
def _raw_row_iterator(self):
"""Return a safe iterator that yields raw row data.
This is used by the :meth:`._engine.Result.merge` method
to merge multiple compatible results together.
"""
raise NotImplementedError()
def _fetchiter_impl(self):
raise NotImplementedError()
def _fetchone_impl(self, hard_close=False):
raise NotImplementedError()
def _fetchall_impl(self):
raise NotImplementedError()
def _fetchmany_impl(self, size=None):
raise NotImplementedError()
def __iter__(self):
return self._iter_impl()
def __next__(self):
return self._next_impl()
if py2k:
def next(self): # noqa
return self._next_impl()
def partitions(self, size=None):
"""Iterate through sub-lists of rows of the size given.
Each list will be of the size given, excluding the last list to
be yielded, which may have a small number of rows. No empty
lists will be yielded.
The result object is automatically closed when the iterator
is fully consumed.
Note that the backend driver will usually buffer the entire result
ahead of time unless the
:paramref:`.Connection.execution_options.stream_results` execution
option is used indicating that the driver should not pre-buffer
results, if possible. Not all drivers support this option and
the option is silently ignored for those who do not.
.. versionadded:: 1.4
:param size: indicate the maximum number of rows to be present
in each list yielded. If None, makes use of the value set by
:meth:`_engine.Result.yield_per`, if present, otherwise uses the
:meth:`_engine.Result.fetchmany` default which may be backend
specific.
:return: iterator of lists
"""
getter = self._manyrow_getter
while True:
partition = getter(self, size)
if partition:
yield partition
else:
break
def fetchall(self):
"""A synonym for the :meth:`_engine.Result.all` method."""
return self._allrows()
def fetchone(self):
"""Fetch one row.
When all rows are exhausted, returns None.
This method is provided for backwards compatibility with
SQLAlchemy 1.x.x.
To fetch the first row of a result only, use the
:meth:`_engine.Result.first` method. To iterate through all
rows, iterate the :class:`_engine.Result` object directly.
:return: a :class:`.Row` object if no filters are applied, or None
if no rows remain.
"""
row = self._onerow_getter(self)
if row is _NO_ROW:
return None
else:
return row
def fetchmany(self, size=None):
"""Fetch many rows.
When all rows are exhausted, returns an empty list.
This method is provided for backwards compatibility with
SQLAlchemy 1.x.x.
To fetch rows in groups, use the :meth:`._result.Result.partitions`
method.
:return: a list of :class:`.Row` objects.
"""
return self._manyrow_getter(self, size)
def all(self):
"""Return all rows in a list.
Closes the result set after invocation. Subsequent invocations
will return an empty list.
.. versionadded:: 1.4
:return: a list of :class:`.Row` objects.
"""
return self._allrows()
def first(self):
"""Fetch the first row or None if no row is present.
Closes the result set and discards remaining rows.
.. note:: This method returns one **row**, e.g. tuple, by default.
To return exactly one single scalar value, that is, the first
column of the first row, use the :meth:`.Result.scalar` method,
or combine :meth:`.Result.scalars` and :meth:`.Result.first`.
:return: a :class:`.Row` object, or None
if no rows remain.
.. seealso::
:meth:`_result.Result.scalar`
:meth:`_result.Result.one`
"""
return self._only_one_row(
raise_for_second_row=False, raise_for_none=False, scalar=False
)
def one_or_none(self):
"""Return at most one result or raise an exception.
Returns ``None`` if the result has no rows.
Raises :class:`.MultipleResultsFound`
if multiple rows are returned.
.. versionadded:: 1.4
:return: The first :class:`.Row` or None if no row is available.
:raises: :class:`.MultipleResultsFound`
.. seealso::
:meth:`_result.Result.first`
:meth:`_result.Result.one`
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=False, scalar=False
)
def scalar_one(self):
"""Return exactly one scalar result or raise an exception.
This is equivalent to calling :meth:`.Result.scalars` and then
:meth:`.Result.one`.
.. seealso::
:meth:`.Result.one`
:meth:`.Result.scalars`
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=True, scalar=True
)
def scalar_one_or_none(self):
"""Return exactly one or no scalar result.
This is equivalent to calling :meth:`.Result.scalars` and then
:meth:`.Result.one_or_none`.
.. seealso::
:meth:`.Result.one_or_none`
:meth:`.Result.scalars`
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=False, scalar=True
)
def one(self):
"""Return exactly one row or raise an exception.
Raises :class:`.NoResultFound` if the result returns no
rows, or :class:`.MultipleResultsFound` if multiple rows
would be returned.
.. note:: This method returns one **row**, e.g. tuple, by default.
To return exactly one single scalar value, that is, the first
column of the first row, use the :meth:`.Result.scalar_one` method,
or combine :meth:`.Result.scalars` and :meth:`.Result.one`.
.. versionadded:: 1.4
:return: The first :class:`.Row`.
:raises: :class:`.MultipleResultsFound`, :class:`.NoResultFound`
.. seealso::
:meth:`_result.Result.first`
:meth:`_result.Result.one_or_none`
:meth:`_result.Result.scalar_one`
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=True, scalar=False
)
def scalar(self):
"""Fetch the first column of the first row, and close the result set.
Returns None if there are no rows to fetch.
No validation is performed to test if additional rows remain.
After calling this method, the object is fully closed,
e.g. the :meth:`_engine.CursorResult.close`
method will have been called.
:return: a Python scalar value , or None if no rows remain.
"""
return self._only_one_row(
raise_for_second_row=False, raise_for_none=False, scalar=True
)
def freeze(self):
"""Return a callable object that will produce copies of this
:class:`.Result` when invoked.
The callable object returned is an instance of
:class:`_engine.FrozenResult`.
This is used for result set caching. The method must be called
on the result when it has been unconsumed, and calling the method
will consume the result fully. When the :class:`_engine.FrozenResult`
is retrieved from a cache, it can be called any number of times where
it will produce a new :class:`_engine.Result` object each time
against its stored set of rows.
.. seealso::
:ref:`do_orm_execute_re_executing` - example usage within the
ORM to implement a result-set cache.
"""
return FrozenResult(self)
def merge(self, *others):
"""Merge this :class:`.Result` with other compatible result
objects.
The object returned is an instance of :class:`_engine.MergedResult`,
which will be composed of iterators from the given result
objects.
The new result will use the metadata from this result object.
The subsequent result objects must be against an identical
set of result / cursor metadata, otherwise the behavior is
undefined.
"""
return MergedResult(self._metadata, (self,) + others)
class FilterResult(ResultInternal):
"""A wrapper for a :class:`_engine.Result` that returns objects other than
:class:`_result.Row` objects, such as dictionaries or scalar objects.
"""
_post_creational_filter = None
def _soft_close(self, hard=False):
self._real_result._soft_close(hard=hard)
@property
def _attributes(self):
return self._real_result._attributes
def _fetchiter_impl(self):
return self._real_result._fetchiter_impl()
def _fetchone_impl(self, hard_close=False):
return self._real_result._fetchone_impl(hard_close=hard_close)
def _fetchall_impl(self):
return self._real_result._fetchall_impl()
def _fetchmany_impl(self, size=None):
return self._real_result._fetchmany_impl(size=size)
class ScalarResult(FilterResult):
"""A wrapper for a :class:`_result.Result` that returns scalar values
rather than :class:`_row.Row` values.
The :class:`_result.ScalarResult` object is acquired by calling the
:meth:`_result.Result.scalars` method.
A special limitation of :class:`_result.ScalarResult` is that it has
no ``fetchone()`` method; since the semantics of ``fetchone()`` are that
the ``None`` value indicates no more results, this is not compatible
with :class:`_result.ScalarResult` since there is no way to distinguish
between ``None`` as a row value versus ``None`` as an indicator. Use
``next(result)`` to receive values individually.
"""
_generate_rows = False
def __init__(self, real_result, index):
self._real_result = real_result
if real_result._source_supports_scalars:
self._metadata = real_result._metadata
self._post_creational_filter = None
else:
self._metadata = real_result._metadata._reduce([index])
self._post_creational_filter = operator.itemgetter(0)
self._unique_filter_state = real_result._unique_filter_state
def unique(self, strategy=None):
"""Apply unique filtering to the objects returned by this
:class:`_engine.ScalarResult`.
See :meth:`_engine.Result.unique` for usage details.
"""
self._unique_filter_state = (set(), strategy)
return self
def partitions(self, size=None):
"""Iterate through sub-lists of elements of the size given.
Equivalent to :meth:`_result.Result.partitions` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
getter = self._manyrow_getter
while True:
partition = getter(self, size)
if partition:
yield partition
else:
break
def fetchall(self):
"""A synonym for the :meth:`_engine.ScalarResult.all` method."""
return self._allrows()
def fetchmany(self, size=None):
"""Fetch many objects.
Equivalent to :meth:`_result.Result.fetchmany` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._manyrow_getter(self, size)
def all(self):
"""Return all scalar values in a list.
Equivalent to :meth:`_result.Result.all` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._allrows()
def __iter__(self):
return self._iter_impl()
def __next__(self):
return self._next_impl()
if py2k:
def next(self): # noqa
return self._next_impl()
def first(self):
"""Fetch the first object or None if no object is present.
Equivalent to :meth:`_result.Result.first` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=False, raise_for_none=False, scalar=False
)
def one_or_none(self):
"""Return at most one object or raise an exception.
Equivalent to :meth:`_result.Result.one_or_none` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=False, scalar=False
)
def one(self):
"""Return exactly one object or raise an exception.
Equivalent to :meth:`_result.Result.one` except that
scalar values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=True, scalar=False
)
class MappingResult(_WithKeys, FilterResult):
"""A wrapper for a :class:`_engine.Result` that returns dictionary values
rather than :class:`_engine.Row` values.
The :class:`_engine.MappingResult` object is acquired by calling the
:meth:`_engine.Result.mappings` method.
"""
_generate_rows = True
_post_creational_filter = operator.attrgetter("_mapping")
def __init__(self, result):
self._real_result = result
self._unique_filter_state = result._unique_filter_state
self._metadata = result._metadata
if result._source_supports_scalars:
self._metadata = self._metadata._reduce([0])
def unique(self, strategy=None):
"""Apply unique filtering to the objects returned by this
:class:`_engine.MappingResult`.
See :meth:`_engine.Result.unique` for usage details.
"""
self._unique_filter_state = (set(), strategy)
return self
def columns(self, *col_expressions):
r"""Establish the columns that should be returned in each row."""
return self._column_slices(col_expressions)
def partitions(self, size=None):
"""Iterate through sub-lists of elements of the size given.
Equivalent to :meth:`_result.Result.partitions` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
getter = self._manyrow_getter
while True:
partition = getter(self, size)
if partition:
yield partition
else:
break
def fetchall(self):
"""A synonym for the :meth:`_engine.MappingResult.all` method."""
return self._allrows()
def fetchone(self):
"""Fetch one object.
Equivalent to :meth:`_result.Result.fetchone` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
row = self._onerow_getter(self)
if row is _NO_ROW:
return None
else:
return row
def fetchmany(self, size=None):
"""Fetch many objects.
Equivalent to :meth:`_result.Result.fetchmany` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._manyrow_getter(self, size)
def all(self):
"""Return all scalar values in a list.
Equivalent to :meth:`_result.Result.all` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._allrows()
def __iter__(self):
return self._iter_impl()
def __next__(self):
return self._next_impl()
if py2k:
def next(self): # noqa
return self._next_impl()
def first(self):
"""Fetch the first object or None if no object is present.
Equivalent to :meth:`_result.Result.first` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=False, raise_for_none=False, scalar=False
)
def one_or_none(self):
"""Return at most one object or raise an exception.
Equivalent to :meth:`_result.Result.one_or_none` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=False, scalar=False
)
def one(self):
"""Return exactly one object or raise an exception.
Equivalent to :meth:`_result.Result.one` except that
mapping values, rather than :class:`_result.Row` objects,
are returned.
"""
return self._only_one_row(
raise_for_second_row=True, raise_for_none=True, scalar=False
)
class FrozenResult(object):
"""Represents a :class:`.Result` object in a "frozen" state suitable
for caching.
The :class:`_engine.FrozenResult` object is returned from the
:meth:`_engine.Result.freeze` method of any :class:`_engine.Result`
object.
A new iterable :class:`.Result` object is generated from a fixed
set of data each time the :class:`.FrozenResult` is invoked as
a callable::
result = connection.execute(query)
frozen = result.freeze()
unfrozen_result_one = frozen()
for row in unfrozen_result_one:
print(row)
unfrozen_result_two = frozen()
rows = unfrozen_result_two.all()
# ... etc
.. versionadded:: 1.4
.. seealso::
:ref:`do_orm_execute_re_executing` - example usage within the
ORM to implement a result-set cache.
:func:`_orm.loading.merge_frozen_result` - ORM function to merge
a frozen result back into a :class:`_orm.Session`.
"""
def __init__(self, result):
self.metadata = result._metadata._for_freeze()
self._source_supports_scalars = result._source_supports_scalars
self._attributes = result._attributes
if self._source_supports_scalars:
self.data = list(result._raw_row_iterator())
else:
self.data = result.fetchall()
def rewrite_rows(self):
if self._source_supports_scalars:
return [[elem] for elem in self.data]
else:
return [list(row) for row in self.data]
def with_new_rows(self, tuple_data):
fr = FrozenResult.__new__(FrozenResult)
fr.metadata = self.metadata
fr._attributes = self._attributes
fr._source_supports_scalars = self._source_supports_scalars
if self._source_supports_scalars:
fr.data = [d[0] for d in tuple_data]
else:
fr.data = tuple_data
return fr
def __call__(self):
result = IteratorResult(self.metadata, iter(self.data))
result._attributes = self._attributes
result._source_supports_scalars = self._source_supports_scalars
return result
class IteratorResult(Result):
"""A :class:`.Result` that gets data from a Python iterator of
:class:`.Row` objects.
.. versionadded:: 1.4
"""
def __init__(
self,
cursor_metadata,
iterator,
raw=None,
_source_supports_scalars=False,
):
self._metadata = cursor_metadata
self.iterator = iterator
self.raw = raw
self._source_supports_scalars = _source_supports_scalars
def _soft_close(self, **kw):
self.iterator = iter([])
def _raw_row_iterator(self):
return self.iterator
def _fetchiter_impl(self):
return self.iterator
def _fetchone_impl(self, hard_close=False):
row = next(self.iterator, _NO_ROW)
if row is _NO_ROW:
self._soft_close(hard=hard_close)
return None
else:
return row
def _fetchall_impl(self):
try:
return list(self.iterator)
finally:
self._soft_close()
def _fetchmany_impl(self, size=None):
return list(itertools.islice(self.iterator, 0, size))
def null_result():
return IteratorResult(SimpleResultMetaData([]), iter([]))
class ChunkedIteratorResult(IteratorResult):
"""An :class:`.IteratorResult` that works from an iterator-producing callable.
The given ``chunks`` argument is a function that is given a number of rows
to return in each chunk, or ``None`` for all rows. The function should
then return an un-consumed iterator of lists, each list of the requested
size.
The function can be called at any time again, in which case it should
continue from the same result set but adjust the chunk size as given.
.. versionadded:: 1.4
"""
def __init__(
self,
cursor_metadata,
chunks,
source_supports_scalars=False,
raw=None,
dynamic_yield_per=False,
):
self._metadata = cursor_metadata
self.chunks = chunks
self._source_supports_scalars = source_supports_scalars
self.raw = raw
self.iterator = itertools.chain.from_iterable(self.chunks(None))
self.dynamic_yield_per = dynamic_yield_per
@_generative
def yield_per(self, num):
# TODO: this throws away the iterator which may be holding
# onto a chunk. the yield_per cannot be changed once any
# rows have been fetched. either find a way to enforce this,
# or we can't use itertools.chain and will instead have to
# keep track.
self._yield_per = num
self.iterator = itertools.chain.from_iterable(self.chunks(num))
def _fetchmany_impl(self, size=None):
if self.dynamic_yield_per:
self.iterator = itertools.chain.from_iterable(self.chunks(size))
return super(ChunkedIteratorResult, self)._fetchmany_impl(size=size)
class MergedResult(IteratorResult):
"""A :class:`_engine.Result` that is merged from any number of
:class:`_engine.Result` objects.
Returned by the :meth:`_engine.Result.merge` method.
.. versionadded:: 1.4
"""
closed = False
def __init__(self, cursor_metadata, results):
self._results = results
super(MergedResult, self).__init__(
cursor_metadata,
itertools.chain.from_iterable(
r._raw_row_iterator() for r in results
),
)
self._unique_filter_state = results[0]._unique_filter_state
self._yield_per = results[0]._yield_per
# going to try something w/ this in next rev
self._source_supports_scalars = results[0]._source_supports_scalars
self._attributes = self._attributes.merge_with(
*[r._attributes for r in results]
)
def close(self):
self._soft_close(hard=True)
def _soft_close(self, hard=False):
for r in self._results:
r._soft_close(hard=hard)
if hard:
self.closed = True
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