def __init__(self,
name,
source=None,
destination=None,
custom_workflow_param=None):
self.custom_workflow_param = custom_workflow_param
Workflow.__init__(self, name, source, destination)
def __init__(
self,
name,
source=None,
destination=None,
interval="day",
threshold=2.5,
window=7,
raw_data_col_name="_raw",
):
self.interval = interval
self._threshold = threshold
self._window = window
self._snp = SplunkNotableParser()
self._raw_data_col_name = raw_data_col_name
Workflow.__init__(self, name, source, destination)
def test_benchmark_decorator(tmpdir, mock_env_home, set_workflow_config):
# Dummy function
def func(self):
return DataFrame()
benchmarked_func = Workflow.benchmark(func)
source = set_workflow_config[1]
destination = set_workflow_config[2]
test_dir = tmpdir.mkdir("tmp_test_workflow")
input_path = str(test_dir.join("person.csv"))
input_df.to_csv(input_path, index=False)
output_path = str(test_dir.join("output_benchmark.csv"))
source["input_path"] = input_path
destination["output_path"] = output_path
# Create new workflow with source and destination configurations
tb = spy(
TestWorkflowImpl(source=source,
destination=destination,
name="test-workflow"))
benchmarked_func(tb.run_workflow)
# Verify that run_workflow was not called, instead expect that benchmark wrapper function will be called
verify(tb, times=0).run_workflow(...)
def test_benchmark_decorator(
mock_env_home, set_workflow_config, input_path, output_path
):
# Dummy function
def func(self):
return DataFrame()
benchmarked_func = Workflow.benchmark(func)
source = set_workflow_config[1]
destination = set_workflow_config[2]
source["input_path"] = input_path
destination["output_path"] = output_path
# Create new workflow with source and destination configurations
tb = spy(
TestWorkflowImpl(source=source, destination=destination, name="test-workflow")
)
benchmarked_func(tb.run_workflow)
# Verify that run_workflow was not called, instead expect that benchmark wrapper function will be called
verify(tb, times=0).run_workflow(...)