def test__id_datapoint(self): """Testing function _id_datapoint.""" # Initialize key variables id_agent = 'id_agent' label = 'label' index = 'index' agent_name = 'agent_name' devicename = 'devicename' # Test result = drain._id_datapoint(id_agent, label, index, agent_name, devicename) self.assertEqual( result, '9af342e9f23a5e2ff09d8a799a2b9f5234b' 'addc31f3c09b309be9dfe6801ee40')
def _sources(data): """Convert data read from cache file to format for ingester. args: data: Data read from cache file base_type: Base type to filter by returns: data_sources: List of dicts """ # Initialize key variables id_agent = data['id_agent'] agent = data['agent'] devicename = data['devicename'] primary_keys = ['timeseries', 'timefixed'] data_sources = [] # Retrieve data for primary_key in primary_keys: # Skip if there are no matching keys if primary_key not in data: continue # Process data for label, metadata in data[primary_key].items(): # Create list of dicts of data for data_item in metadata['data']: index = data_item[0] source = data_item[2] data_sources.append({ 'id_agent': id_agent, 'id_datapoint': drain._id_datapoint(id_agent, label, index, agent, devicename), 'agent_label': label, 'agent_source': source, 'description': metadata['description'], 'base_type': metadata['base_type'] }) # Return return data_sources
def _expected(data, base_type): """Convert data read from cache file to format for ingester. args: data: Data read from cache file base_type: Base type to filter by returns: expected: List of dicts """ # Initialize key variables id_agent = data['id_agent'] agent = data['agent'] devicename = data['devicename'] timestamp = data['timestamp'] expected = [] # Get all 32 bit counter values from data if base_type is not None: primary_key = 'timeseries' else: primary_key = 'timefixed' # Retrieve data if primary_key in data: for label, metadata in data[primary_key].items(): # Isolate 32 bit counter data if metadata['base_type'] == base_type: # Create list of dicts of data for data_item in metadata['data']: index = data_item[0] value = data_item[1] expected.append({ 'id_agent': id_agent, 'id_datapoint': drain._id_datapoint(id_agent, label, index, agent, devicename), 'value': value, 'timestamp': timestamp }) # Return return expected