本文整理汇总了Python中elastalert.ruletypes.SpikeRule类的典型用法代码示例。如果您正苦于以下问题：Python SpikeRule类的具体用法？Python SpikeRule怎么用？Python SpikeRule使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了SpikeRule类的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_spike_deep_key
def test_spike_deep_key():
rules = {'threshold_ref': 10,
'spike_height': 2,
'timeframe': datetime.timedelta(seconds=10),
'spike_type': 'both',
'timestamp_field': '@timestamp',
'query_key': 'foo.bar.baz'}
rule = SpikeRule(rules)
rule.add_data([{'@timestamp': ts_to_dt('2015'), 'foo': {'bar': {'baz': 'LOL'}}}])
assert 'LOL' in rule.cur_windows示例2: test_spike_query_key
def test_spike_query_key():
events = hits(100, timestamp_field='ts', username='qlo')
# Constant rate, doesn't match
rules = {'threshold_ref': 10,
'spike_height': 2,
'timeframe': datetime.timedelta(seconds=10),
'spike_type': 'both',
'use_count_query': False,
'timestamp_field': 'ts',
'query_key': 'username'}
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 0
# Double the rate of events, but with a different usename
events_bob = hits(100, timestamp_field='ts', username='bob')
events2 = events[:50]
for num in range(50, 99):
events2.append(events_bob[num])
events2.append(events[num])
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Double the rate of events, with the same username
events2 = events[:50]
for num in range(50, 99):
events2.append(events_bob[num])
events2.append(events[num])
events2.append(events[num])
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1示例3: test_spike_query_key
def test_spike_query_key():
events = hits(100, timestamp_field="ts", username="qlo")
# Constant rate, doesn't match
rules = {
"threshold_ref": 10,
"spike_height": 2,
"timeframe": datetime.timedelta(seconds=10),
"spike_type": "both",
"use_count_query": False,
"timestamp_field": "ts",
"query_key": "username",
}
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 0
# Double the rate of events, but with a different usename
events_bob = hits(100, timestamp_field="ts", username="bob")
events2 = events[:50]
for num in range(50, 99):
events2.append(events_bob[num])
events2.append(events[num])
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Double the rate of events, with the same username
events2 = events[:50]
for num in range(50, 99):
events2.append(events_bob[num])
events2.append(events[num])
events2.append(events[num])
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1示例4: test_spike_deep_key
def test_spike_deep_key():
rules = {
"threshold_ref": 10,
"spike_height": 2,
"timeframe": datetime.timedelta(seconds=10),
"spike_type": "both",
"timestamp_field": "@timestamp",
"query_key": "foo.bar.baz",
}
rule = SpikeRule(rules)
rule.add_data([{"@timestamp": ts_to_dt("2015"), "foo": {"bar": {"baz": "LOL"}}}])
assert "LOL" in rule.cur_windows示例5: test_spike_terms
def test_spike_terms():
rules = {'threshold_ref': 5,
'spike_height': 2,
'timeframe': datetime.timedelta(minutes=10),
'spike_type': 'both',
'use_count_query': False,
'timestamp_field': 'ts',
'query_key': 'username',
'use_term_query': True}
terms1 = {ts_to_dt('2014-01-01T00:01:00Z'): [{'key': 'userA', 'doc_count': 10},
{'key': 'userB', 'doc_count': 5}]}
terms2 = {ts_to_dt('2014-01-01T00:10:00Z'): [{'key': 'userA', 'doc_count': 22},
{'key': 'userB', 'doc_count': 5}]}
terms3 = {ts_to_dt('2014-01-01T00:25:00Z'): [{'key': 'userA', 'doc_count': 25},
{'key': 'userB', 'doc_count': 27}]}
terms4 = {ts_to_dt('2014-01-01T00:27:00Z'): [{'key': 'userA', 'doc_count': 10},
{'key': 'userB', 'doc_count': 12},
{'key': 'userC', 'doc_count': 100}]}
terms5 = {ts_to_dt('2014-01-01T00:30:00Z'): [{'key': 'userD', 'doc_count': 100},
{'key': 'userC', 'doc_count': 100}]}
rule = SpikeRule(rules)
# Initial input
rule.add_terms_data(terms1)
assert len(rule.matches) == 0
# No spike for UserA because windows not filled
rule.add_terms_data(terms2)
assert len(rule.matches) == 0
# Spike for userB only
rule.add_terms_data(terms3)
assert len(rule.matches) == 1
assert rule.matches[0].get('username') == 'userB'
# Test no alert for new user over threshold
rules.pop('threshold_ref')
rules['threshold_cur'] = 50
rule = SpikeRule(rules)
rule.add_terms_data(terms1)
rule.add_terms_data(terms2)
rule.add_terms_data(terms3)
rule.add_terms_data(terms4)
assert len(rule.matches) == 0
# Test alert_on_new_data
rules['alert_on_new_data'] = True
rule = SpikeRule(rules)
rule.add_terms_data(terms1)
rule.add_terms_data(terms2)
rule.add_terms_data(terms3)
rule.add_terms_data(terms4)
assert len(rule.matches) == 1
# Test that another alert doesn't fire immediately for userC but it does for userD
rule.matches = []
rule.add_terms_data(terms5)
assert len(rule.matches) == 1
assert rule.matches[0]['username'] == 'userD'示例6: test_spike
def test_spike():
# Events are 1 per second
events = hits(100, timestamp_field='ts')
# Constant rate, doesn't match
rules = {'threshold_ref': 10,
'spike_height': 2,
'timeframe': datetime.timedelta(seconds=10),
'spike_type': 'both',
'use_count_query': False,
'timestamp_field': 'ts'}
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 0
# Double the rate of events after [50:]
events2 = events[:50]
for event in events[50:]:
events2.append(event)
events2.append({'ts': event['ts'] + datetime.timedelta(milliseconds=1)})
rules['spike_type'] = 'up'
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1
# Doesn't match
rules['spike_height'] = 3
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Downward spike
events = events[:50] + events[75:]
rules['spike_type'] = 'down'
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 1
# Doesn't meet threshold_ref
# When ref hits 11, cur is only 20
rules['spike_height'] = 2
rules['threshold_ref'] = 11
rules['spike_type'] = 'up'
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Doesn't meet threshold_cur
# Maximum rate of events is 20 per 10 seconds
rules['threshold_ref'] = 10
rules['threshold_cur'] = 30
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Alert on new data
# (At least 25 events occur before 30 seconds has elapsed)
rules.pop('threshold_ref')
rules['timeframe'] = datetime.timedelta(seconds=30)
rules['threshold_cur'] = 25
rules['spike_height'] = 2
rules['alert_on_new_data'] = True
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1示例7: test_spike_count
def test_spike_count():
rules = {'threshold_ref': 10,
'spike_height': 2,
'timeframe': datetime.timedelta(seconds=10),
'spike_type': 'both',
'timestamp_field': '@timestamp'}
rule = SpikeRule(rules)
# Double rate of events at 20 seconds
rule.add_count_data({ts_to_dt('2014-09-26T00:00:00'): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt('2014-09-26T00:00:10'): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt('2014-09-26T00:00:20'): 20})
assert len(rule.matches) == 1
# Downward spike
rule = SpikeRule(rules)
rule.add_count_data({ts_to_dt('2014-09-26T00:00:00'): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt('2014-09-26T00:00:10'): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt('2014-09-26T00:00:20'): 0})
assert len(rule.matches) == 1示例8: test_spike_terms
def test_spike_terms():
rules = {
"threshold_ref": 5,
"spike_height": 2,
"timeframe": datetime.timedelta(minutes=10),
"spike_type": "both",
"use_count_query": False,
"timestamp_field": "ts",
"query_key": "username",
"use_term_query": True,
}
terms1 = {ts_to_dt("2014-01-01T00:01:00Z"): [{"key": "userA", "doc_count": 10}, {"key": "userB", "doc_count": 5}]}
terms2 = {ts_to_dt("2014-01-01T00:10:00Z"): [{"key": "userA", "doc_count": 22}, {"key": "userB", "doc_count": 5}]}
terms3 = {ts_to_dt("2014-01-01T00:25:00Z"): [{"key": "userA", "doc_count": 25}, {"key": "userB", "doc_count": 27}]}
terms4 = {
ts_to_dt("2014-01-01T00:27:00Z"): [
{"key": "userA", "doc_count": 10},
{"key": "userB", "doc_count": 12},
{"key": "userC", "doc_count": 100},
]
}
terms5 = {
ts_to_dt("2014-01-01T00:30:00Z"): [{"key": "userD", "doc_count": 100}, {"key": "userC", "doc_count": 100}]
}
rule = SpikeRule(rules)
# Initial input
rule.add_terms_data(terms1)
assert len(rule.matches) == 0
# No spike for UserA because windows not filled
rule.add_terms_data(terms2)
assert len(rule.matches) == 0
# Spike for userB only
rule.add_terms_data(terms3)
assert len(rule.matches) == 1
assert rule.matches[0].get("username") == "userB"
# Test no alert for new user over threshold
rules.pop("threshold_ref")
rules["threshold_cur"] = 50
rule = SpikeRule(rules)
rule.add_terms_data(terms1)
rule.add_terms_data(terms2)
rule.add_terms_data(terms3)
rule.add_terms_data(terms4)
assert len(rule.matches) == 0
# Test alert_on_new_data
rules["alert_on_new_data"] = True
rule = SpikeRule(rules)
rule.add_terms_data(terms1)
rule.add_terms_data(terms2)
rule.add_terms_data(terms3)
rule.add_terms_data(terms4)
assert len(rule.matches) == 1
# Test that another alert doesn't fire immediately for userC but it does for userD
rule.matches = []
rule.add_terms_data(terms5)
assert len(rule.matches) == 1
assert rule.matches[0]["username"] == "userD"示例9: test_spike
def test_spike():
# Events are 1 per second
events = hits(100, timestamp_field="ts")
# Constant rate, doesn't match
rules = {
"threshold_ref": 10,
"spike_height": 2,
"timeframe": datetime.timedelta(seconds=10),
"spike_type": "both",
"use_count_query": False,
"timestamp_field": "ts",
}
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 0
# Double the rate of events after [50:]
events2 = events[:50]
for event in events[50:]:
events2.append(event)
events2.append({"ts": event["ts"] + datetime.timedelta(milliseconds=1)})
rules["spike_type"] = "up"
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1
# Doesn't match
rules["spike_height"] = 3
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Downward spike
events = events[:50] + events[75:]
rules["spike_type"] = "down"
rule = SpikeRule(rules)
rule.add_data(events)
assert len(rule.matches) == 1
# Doesn't meet threshold_ref
# When ref hits 11, cur is only 20
rules["spike_height"] = 2
rules["threshold_ref"] = 11
rules["spike_type"] = "up"
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Doesn't meet threshold_cur
# Maximum rate of events is 20 per 10 seconds
rules["threshold_ref"] = 10
rules["threshold_cur"] = 30
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 0
# Alert on new data
# (At least 25 events occur before 30 seconds has elapsed)
rules.pop("threshold_ref")
rules["timeframe"] = datetime.timedelta(seconds=30)
rules["threshold_cur"] = 25
rules["spike_height"] = 2
rules["alert_on_new_data"] = True
rule = SpikeRule(rules)
rule.add_data(events2)
assert len(rule.matches) == 1示例10: test_spike_count
def test_spike_count():
rules = {
"threshold_ref": 10,
"spike_height": 2,
"timeframe": datetime.timedelta(seconds=10),
"spike_type": "both",
"timestamp_field": "@timestamp",
}
rule = SpikeRule(rules)
# Double rate of events at 20 seconds
rule.add_count_data({ts_to_dt("2014-09-26T00:00:00"): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt("2014-09-26T00:00:10"): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt("2014-09-26T00:00:20"): 20})
assert len(rule.matches) == 1
# Downward spike
rule = SpikeRule(rules)
rule.add_count_data({ts_to_dt("2014-09-26T00:00:00"): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt("2014-09-26T00:00:10"): 10})
assert len(rule.matches) == 0
rule.add_count_data({ts_to_dt("2014-09-26T00:00:20"): 0})
assert len(rule.matches) == 1示例11: test_spike_terms_query_key_alert_on_new_data
def test_spike_terms_query_key_alert_on_new_data():
rules = {'spike_height': 1.5,
'timeframe': datetime.timedelta(minutes=10),
'spike_type': 'both',
'use_count_query': False,
'timestamp_field': 'ts',
'query_key': 'username',
'use_term_query': True,
'alert_on_new_data': True}
terms1 = {ts_to_dt('2014-01-01T00:01:00Z'): [{'key': 'userA', 'doc_count': 10}]}
terms2 = {ts_to_dt('2014-01-01T00:06:00Z'): [{'key': 'userA', 'doc_count': 10}]}
terms3 = {ts_to_dt('2014-01-01T00:11:00Z'): [{'key': 'userA', 'doc_count': 10}]}
terms4 = {ts_to_dt('2014-01-01T00:21:00Z'): [{'key': 'userA', 'doc_count': 20}]}
terms5 = {ts_to_dt('2014-01-01T00:26:00Z'): [{'key': 'userA', 'doc_count': 20}]}
terms6 = {ts_to_dt('2014-01-01T00:31:00Z'): [{'key': 'userA', 'doc_count': 20}]}
terms7 = {ts_to_dt('2014-01-01T00:36:00Z'): [{'key': 'userA', 'doc_count': 20}]}
terms8 = {ts_to_dt('2014-01-01T00:41:00Z'): [{'key': 'userA', 'doc_count': 20}]}
rule = SpikeRule(rules)
# Initial input
rule.add_terms_data(terms1)
assert len(rule.matches) == 0
# No spike for UserA because windows not filled
rule.add_terms_data(terms2)
assert len(rule.matches) == 0
rule.add_terms_data(terms3)
assert len(rule.matches) == 0
rule.add_terms_data(terms4)
assert len(rule.matches) == 0
# Spike
rule.add_terms_data(terms5)
assert len(rule.matches) == 1
rule.matches[:] = []
# There will be no more spikes since all terms have the same doc_count
rule.add_terms_data(terms6)
assert len(rule.matches) == 0
rule.add_terms_data(terms7)
assert len(rule.matches) == 0
rule.add_terms_data(terms8)
assert len(rule.matches) == 0