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
