def test_match_only(self):
config = TemplateMinerConfig()
config.drain_extra_delimiters = ["_"]
mi = MaskingInstruction(
"((?<=[^A-Za-z0-9])|^)([\\-\\+]?\\d+)((?=[^A-Za-z0-9])|$)", "NUM")
config.masking_instructions.append(mi)
tm = TemplateMiner(None, config)
res = tm.add_log_message("aa aa aa")
print(res)
res = tm.add_log_message("aa aa bb")
print(res)
res = tm.add_log_message("xx yy zz")
print(res)
res = tm.add_log_message("rrr qqq 123")
print(res)
c = tm.match("aa aa tt")
self.assertEqual(1, c.cluster_id)
c = tm.match("aa aa 12")
self.assertEqual(1, c.cluster_id)
c = tm.match("xx yy zz")
self.assertEqual(2, c.cluster_id)
c = tm.match("xx yy rr")
self.assertIsNone(c)
c = tm.match("nothing")
self.assertIsNone(c)
c = tm.match("rrr qqq 456 ")
self.assertEqual(3, c.cluster_id)
c = tm.match("rrr qqq 555.2")
self.assertIsNone(c)
c = tm.match("rrr qqq num")
self.assertIsNone(c)
def test_match_strategies(self):
miner = TemplateMiner()
print(miner.add_log_message("training4Model start"))
print(miner.add_log_message("loadModel start"))
print(miner.add_log_message("loadModel stop"))
print(miner.add_log_message("this is a test"))
miner.drain.print_tree()
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="fallback"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="always"))
self.assertIsNone(
miner.match("loadModel start", full_search_strategy="never"))
print(miner.add_log_message("loadModel start"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="fallback"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="always"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="never"))
config = TemplateMinerConfig()
config.parametrize_numeric_tokens = False
miner = TemplateMiner(config=config)
print(miner.add_log_message("training4Model start"))
print(miner.add_log_message("loadModel start"))
print(miner.add_log_message("loadModel stop"))
print(miner.add_log_message("this is a test"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="fallback"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="always"))
self.assertIsNotNone(
miner.match("loadModel start", full_search_strategy="never"))
self.assertIsNone(miner.match("", full_search_strategy="never"))
self.assertIsNone(miner.match("", full_search_strategy="always"))
self.assertIsNone(miner.match("", full_search_strategy="fallback"))
print(miner.add_log_message(""))
self.assertIsNotNone(miner.match("", full_search_strategy="never"))
self.assertIsNotNone(miner.match("", full_search_strategy="always"))
self.assertIsNotNone(miner.match("", full_search_strategy="fallback"))
log_line = input("> ")
if log_line == 'q':
break
result = template_miner.add_log_message(log_line)
result_json = json.dumps(result)
print(result_json)
template = result["template_mined"]
params = template_miner.extract_parameters(template, log_line)
print("Parameters: " + str(params))
print("Training done. Mined clusters:")
for cluster in template_miner.drain.clusters:
print(cluster)
print(
f"Starting inference mode, matching to pre-trained clusters. Input log lines or 'q' to finish"
)
while True:
log_line = input("> ")
if log_line == 'q':
break
cluster = template_miner.match(log_line)
if cluster is None:
print(f"No match found")
else:
template = cluster.get_template()
print(f"Matched template #{cluster.cluster_id}: {template}")
print(
f"Parameters: {template_miner.get_parameter_list(template, log_line)}"
)