def test_extended_string_to_long(self, spark_session, input_value,
expected_value):
input_df = self.create_input_df(input_value, spark_session)
output_df = Mapper(
mapping=[("output_key", "input_key",
"extended_string_to_long")]).transform(input_df)
assert output_df.first().output_key == expected_value
assert isinstance(output_df.schema["output_key"].dataType, T.LongType)
def test_extended_string_unix_timestamp_ms_to_date_spark2(
self, spark_session, input_value, expected_value):
input_df = self.create_input_df(input_value, spark_session)
output_df = Mapper(mapping=[(
"output_key", "input_key",
"extended_string_unix_timestamp_ms_to_date")]).transform(input_df)
actual_value = output_df.first().output_key
assert actual_value == expected_value
assert isinstance(output_df.schema["output_key"].dataType, T.DateType)
def test_extended_string_to_float(self, spark_session, input_value,
expected_value):
input_df = self.create_input_df(input_value, spark_session)
output_df = Mapper(
mapping=[("output_key", "input_key",
"extended_string_to_float")]).transform(input_df)
actual_value = output_df.first().output_key
if actual_value is not None:
assert pytest.approx(actual_value) == expected_value
else:
assert actual_value == expected_value
assert isinstance(output_df.schema["output_key"].dataType, T.FloatType)
def test_spark_sql_object(self, spark_session, input_value_1,
input_value_2, mapper_function, expected_value):
input_df = self.create_input_df(input_value_1, input_value_2,
spark_session)
output_df = Mapper(mapping=[("output_key", mapper_function,
"as_is")]).transform(input_df)
actual = output_df.first().output_key
if isinstance(expected_value, datetime.datetime):
assert (expected_value - datetime.timedelta(seconds=30)
) < actual < datetime.datetime.now()
else:
assert actual == expected_value
def test_extended_string_to_date_spark2(self, spark_session, input_value,
expected_value):
input_df = self.create_input_df(input_value, spark_session)
output_df = Mapper(
mapping=[("output_key", "input_key",
"extended_string_to_date")]).transform(input_df)
try:
actual_value = output_df.first().output_key
except ValueError:
# If input is in milliseconds it will still be stored in the DF but cannot be collected in Python
actual_value = "out_of_range_for_python"
assert actual_value == expected_value
assert isinstance(output_df.schema["output_key"].dataType, T.DateType)
def test_generate_select_expression_for_unix_timestamp_ms_to_spark_timestamp(
self, input_value, spark_session):
input_df = spark_session.createDataFrame(
[Row(input_column=input_value)],
schema=T.StructType(
[T.StructField("input_column", T.LongType(), True)]))
output_df = Mapper(mapping=[(
"output_column", "input_column",
"unix_timestamp_ms_to_spark_timestamp")]).transform(input_df)
expected_value = datetime.datetime.fromtimestamp(input_value / 1000.0)
assert output_df.first(
).output_column == expected_value, "Processing of column value"
assert output_df.schema.fieldNames() == ["output_column"
], "Renaming of column"
assert output_df.schema["output_column"].dataType.typeName(
) == "timestamp", "Casting of column"
def test_generate_select_expression_for_meters_to_cm(
self, input_value, expected_value, spark_session):
input_df = spark_session.createDataFrame(
data=[Row(input_key=input_value)],
schema=T.StructType([
T.StructField("input_key", get_spark_data_type(input_value),
True)
]))
output_df = Mapper(mapping=[("output_column", "input_key",
"meters_to_cm")]).transform(input_df)
assert output_df.first(
).output_column == expected_value, "Processing of column value"
assert output_df.schema.fieldNames() == ["output_column"
], "Renaming of column"
assert output_df.schema["output_column"].dataType.typeName(
) == "integer", "Casting of column"