def test_unique_filtered_all(self):
result = self._fixture()
def uniq(row):
return row[0]
eq_(result.unique(uniq).all(), [(1, 1, 1), (2, 1, 2), (4, 1, 2)])
def test_one_unique_tricky_one(self):
# one() needs to keep consuming rows in order to find a non-unique
# one. unique() really slows things down
result = self._fixture(data=[(1, 1, 1), (1, 1, 1), (1, 1, 1), (2, 1,
1)])
assert_raises(exc.MultipleResultsFound, result.unique().one)
def test_one_unique(self):
# assert that one() counts rows after uniqueness has been applied.
# this would raise if we didnt have unique
result = self._fixture(data=[(1, 1, 1), (1, 1, 1)])
row = result.unique().one()
eq_(row, (1, 1, 1))
def test_unique_mappings_all(self):
result = self._fixture()
def uniq(row):
return row[0]
eq_(
result.unique(uniq).mappings().all(),
[
{
"a": 1,
"b": 1,
"c": 1
},
{
"a": 2,
"b": 1,
"c": 2
},
{
"a": 4,
"b": 1,
"c": 2
},
],
)
def test_unique_scalars_many(self):
result = self._fixture()
result = result.unique().scalars(1)
eq_(result.fetchmany(2), [1, 3])
eq_(result.fetchmany(2), [])
def test_unique_default_filters_rearrange_order(self):
result = self._fixture(
default_filters=[lambda x: x < 4, lambda x: x, lambda x: True])
eq_(
result.unique().columns("b", "a", "c").all(),
[(1, 1, 1), (3, 1, 2), (1, 4, 2)],
)
def test_partition_unique_yield_per(self):
result = self._fixture()
r = []
for partition in result.unique().yield_per(2).partitions():
r.append(list(partition))
eq_(r, [[(1, 1, 1), (2, 1, 2)], [(1, 3, 2), (4, 1, 2)]])
eq_(result.all(), [])
def test_unique_filtered_iterate(self):
result = self._fixture()
def uniq(row):
return row[0]
result = result.unique(uniq)
eq_(list(result), [(1, 1, 1), (2, 1, 2), (4, 1, 2)])
def test_unique_scalars_many_none(self):
result = self._fixture()
result = result.unique().scalars(1)
# this assumes the default fetchmany behavior of all() for
# the ListFetchStrategy
eq_(result.fetchmany(None), [1, 3])
eq_(result.fetchmany(None), [])
def test_merge_unique(self, dupe_fixture):
r1, r2 = dupe_fixture
r1.scalars("y")
r2.scalars("y")
result = r1.merge(r2)
# uniqued 2, 2, 1, 3
eq_(result.unique().all(), [2, 1, 3])
def test_unique_filtered_many(self):
result = self._fixture()
def uniq(row):
return row[0]
result = result.unique(uniq)
eq_(result.fetchmany(2), [(1, 1, 1), (2, 1, 2)])
eq_(result.fetchmany(2), [(4, 1, 2)])
eq_(result.fetchmany(2), [])
def test_unique_default_filters_rearrange_twice(self):
# test that the default uniqueness filter is reconfigured
# each time columns() is called
result = self._fixture(
default_filters=[lambda x: x < 4, lambda x: x, lambda x: True])
result = result.unique()
# 1, 1, 1 -> True, 1, True
eq_(result.fetchone(), (1, 1, 1))
# now rearrange for b, a, c
# 1, 2, 2 -> 1, True, True
# 3, 1, 2 -> 3, True, True
result = result.columns("b", "a", "c")
eq_(result.fetchone(), (3, 1, 2))
# now rearrange for c, a
# 2, 4 -> True, False
result = result.columns("c", "a")
eq_(result.fetchall(), [(2, 4)])
def go():
result = s.execute(stmt)
for partition in result.unique().partitions(3):
pass
def test_unique_scalars_iterate(self):
result = self._fixture()
result = result.unique().scalars(1)
eq_(list(result), [1, 3])
def test_scalars_one_w_unique(self):
result = self._fixture(data=[(1, None, 2)])
result = result.unique()
eq_(result.scalars().one(), 1)
def test_unique_scalars_all(self):
result = self._fixture()
eq_(result.unique().scalars(1).all(), [1, 3])
def test_unique_default_filters_rearrange_scalar(self):
result = self._fixture(
default_filters=[lambda x: x < 4, lambda x: x, lambda x: True]
)
eq_(result.unique().scalars(1).all(), [1, 3])
def test_unique_default_filters(self):
result = self._fixture(
default_filters=[lambda x: x < 4, lambda x: x, lambda x: True]
)
eq_(result.unique().all(), [(1, 1, 1), (1, 3, 2), (4, 1, 2)])
