def test_bool_xdr(self):
values = tf.cast(tf.constant(self.data["values"]), dtype=tf.bool)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.check_values(values, table, "bool", tf.bool)
def test_read_row_set(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table("fake_project", "fake_instance",
"test-table", ["fam1", "fam2"])
values = [[f"[{i,j}]" for j in range(2)] for i in range(20)]
ten = tf.constant(values)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.emulator.write_tensor(
"fake_project",
"fake_instance",
"test-table",
ten,
["row" + str(i).rjust(3, "0") for i in range(20)],
["fam1:col1", "fam2:col2"],
)
row_s = row_set.from_rows_or_ranges(
row_range.closed_range("row000", "row009"))
read_rows = [
r
for r in table.read_rows(["fam1:col1", "fam2:col2"], row_set=row_s)
]
self.assertEqual(len(read_rows), 10)
def test_double_xdr(self):
values = tf.constant(self.data["values"], dtype=tf.float64)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.check_values(values, table, "double", tf.float64)
def test_read(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table("fake_project", "fake_instance",
"test-table", ["fam1", "fam2"])
values = [[f"[{i,j}]" for j in range(2)] for i in range(20)]
ten = tf.constant(values)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.emulator.write_tensor(
"fake_project",
"fake_instance",
"test-table",
ten,
["row" + str(i).rjust(3, "0") for i in range(20)],
["fam1:col1", "fam2:col2"],
)
for i, r in enumerate(
table.read_rows(
["fam1:col1", "fam2:col2"],
row_set=row_set.from_rows_or_ranges(row_range.empty()),
)):
for j, c in enumerate(r):
self.assertEqual(values[i][j], c.numpy().decode())
def test_not_parallel_read(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table(
"fake_project",
"fake_instance",
"test-table",
["fam1", "fam2"],
splits=["row005", "row010", "row015"],
)
values = [[f"[{i,j}]" for j in range(2)] for i in range(20)]
ten = tf.constant(values)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.emulator.write_tensor(
"fake_project",
"fake_instance",
"test-table",
ten,
["row" + str(i).rjust(3, "0") for i in range(20)],
["fam1:col1", "fam2:col2"],
)
dataset = table.parallel_read_rows(
["fam1:col1", "fam2:col2"],
row_set=row_set.from_rows_or_ranges(row_range.infinite()),
num_parallel_calls=2,
)
results = [[v.numpy().decode() for v in row] for row in dataset]
self.assertEqual(repr(sorted(values)), repr(sorted(results)))
def test_parallel_read(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table(
"fake_project",
"fake_instance",
"test-table",
["fam1", "fam2"],
splits=["row005", "row010", "row015"],
)
values = [[f"[{i,j}]" for j in range(2)] for i in range(20)]
flat_values = [value for row in values for value in row]
ten = tf.constant(values)
client = BigtableClient("fake_project", "fake_instance")
table = client.get_table("test-table")
self.emulator.write_tensor(
"fake_project",
"fake_instance",
"test-table",
ten,
["row" + str(i).rjust(3, "0") for i in range(20)],
["fam1:col1", "fam2:col2"],
)
for r in table.parallel_read_rows(
["fam1:col1", "fam2:col2"],
row_set=row_set.from_rows_or_ranges(row_range.infinite()),
):
for c in r:
self.assertTrue(c.numpy().decode() in flat_values)
def test_split_empty(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table(
"fake_project",
"fake_instance",
"test-table",
["fam1", "fam2"],
)
client = BigtableClient("fake_project", "fake_instance")
rs = row_set.from_rows_or_ranges(row_range.empty())
num_parallel_calls = 2
self.assertRaises(
tf.errors.FailedPreconditionError,
lambda: core_ops.bigtable_split_row_set_evenly(
client._client_resource,
rs._impl,
"test-table",
num_parallel_calls,
),
)
def test_split_row_set(self):
os.environ["BIGTABLE_EMULATOR_HOST"] = self.emulator.get_addr()
self.emulator.create_table(
"fake_project",
"fake_instance",
"test-table",
["fam1", "fam2"],
splits=[
"row005", "row010", "row015", "row020", "row025", "row030"
],
)
values = [[f"[{i,j}]" for j in range(2)] for i in range(40)]
ten = tf.constant(values)
client = BigtableClient("fake_project", "fake_instance")
self.emulator.write_tensor(
"fake_project",
"fake_instance",
"test-table",
ten,
["row" + str(i).rjust(3, "0") for i in range(40)],
["fam1:col1", "fam2:col2"],
)
rs = row_set.from_rows_or_ranges(row_range.infinite())
num_parallel_calls = 2
samples = [
s for s in core_ops.bigtable_split_row_set_evenly(
client._client_resource,
rs._impl,
"test-table",
num_parallel_calls,
)
]
self.assertEqual(len(samples), num_parallel_calls)
num_parallel_calls = 6
samples = [
s for s in core_ops.bigtable_split_row_set_evenly(
client._client_resource,
rs._impl,
"test-table",
num_parallel_calls,
)
]
# The emulator may return different samples each time, so we can't
# expect an exact number, but it must be no more than num_parallel_calls
self.assertLessEqual(len(samples), num_parallel_calls)
num_parallel_calls = 1
samples = [
s for s in core_ops.bigtable_split_row_set_evenly(
client._client_resource,
rs._impl,
"test-table",
num_parallel_calls,
)
]
self.assertEqual(len(samples), num_parallel_calls)