def test_smaller_series(self):
sample1 = [20, 20, 20, 20, 20, 21, 21, 21]
sample2 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21]
simple_result = median_test(sample1, sample2, interpolate=False) # EXAMPLE OF GOING WRONG
smooth_result = median_test(sample1, sample2)
assert smooth_result["confidence"] < 0.90, "These are not different!"
def test_bimodal_series(self):
# EVEN NUMBER FROM EACH SERIES, FIRST SERIES HAS SAMPLE FROM SECOND MODE
sample1 = [ 43.35910744, 43.65596955, 43.6805196, 43.78713329, 43.54635098, 43.9086471, 43.54120044, 43.27229271, 43.35015387, 40.03955818]
sample2 = [ 40.18726543, 40.71542234, 40.15441333, 39.95611288, 38.30201645, 35.48697324, 40.16275306, 39.96934014]
smooth_result_a = median_test(sample1, sample2)
assert smooth_result_a["confidence"] > 0.997
# ODD NUMBER OF SERIES, NAIVE MEDIAN TEST WILL PICK MIDDLE VALUE (FROM
# FIRST SERIES) AND ASSUME THAT MEDIAN IS 50/50 IN EITHER MODE
sample1 = [43.41440184, 43.35910744, 43.65596955, 43.6805196, 43.78713329, 43.54635098, 43.9086471, 43.54120044, 43.27229271, 43.35015387]
sample2 = [40.03955818, 40.18726543, 40.71542234, 40.15441333, 39.95611288, 38.30201645, 35.48697324, 40.16275306, 39.96934014]
smooth_result_b = median_test(sample1, sample2)
assert smooth_result_b["confidence"] > smooth_result_a["confidence"]
def test_tight_series(self):
# MORE 20s
sample1 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21]
sample2 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21]
smooth_result = median_test(sample1, sample2)
assert smooth_result["confidence"] < 0.90, "These are not different!"
#MORE 21s
sample1 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21]
sample2 = [20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21]
smooth_result = median_test(sample1, sample2)
assert smooth_result["confidence"] < 0.90, "These are not different!"
def alert_sustained_median(settings, qb, alerts_db):
"""
find single points that deviate from the trend
"""
# OBJECTSTORE = settings.objectstore.schema + ".objectstore"
# TDAD = settings.perftest.schema + ".test_data_all_dimensions"
TDAD = settings.query["from"]
PUSH_DATE = "datazilla.date_loaded"
debug = nvl(settings.param.debug, DEBUG)
query = settings.query
def is_bad(r):
if settings.param.sustained_median.trigger < r.result.confidence:
test_param = nvl(settings.param.test[literal_field(r.Talos.Test.name)], settings.param.suite[literal_field(r.Talos.Test.suite)])
if test_param == None:
return True
if test_param.disable:
return False
if test_param.better == "higher":
diff = -r.diff
elif test_param.better == "lower":
diff = r.diff
else:
diff = abs(r.diff) # DEFAULT = ANY DIRECTION IS BAD
if test_param.min_regression:
if unicode(test_param.min_regression.strip()[-1]) == "%":
min_diff = Math.abs(r.past_stats.mean * float(test_param.min_regression.strip()[:-1]) / 100.0)
else:
min_diff = Math.abs(float(test_param.min_regression))
else:
min_diff = Math.abs(r.past_stats.mean * 0.01)
if diff > min_diff:
return True
return False
with Timer("pull combinations"):
disabled_suites = [s for s, p in settings.param.suite.items() if p.disable]
disabled_tests = [t for t, p in settings.param.test.items() if p.disable]
temp = Query({
"from": TDAD,
"select": {"name": "min_push_date", "value": PUSH_DATE, "aggregate": "min"},
"edges": query.edges,
"where": {"and": [
True if settings.args.restart else {"missing": {"field": settings.param.mark_complete}},
{"exists": {"field": "result.test_name"}},
{"range": {PUSH_DATE: {"gte": OLDEST_TS}}},
{"not": {"terms": {"Talos.Test.suite": disabled_suites}}},
{"not": {"terms": {"Talos.Test.name": disabled_tests}}}
# {"term": {"testrun.suite": "cart"}},
# {"term": {"result.test_name": "1-customize-enter.error.TART"}},
# {"term": {"test_machine.osversion": "OS X 10.8"}}
#FOR DEBUGGING SPECIFIC SERIES
# {"term": {"test_machine.type": "hamachi"}},
# {"term": {"test_machine.platform": "Gonk"}},
# {"term": {"test_machine.os": "Firefox OS"}},
# {"term": {"test_build.branch": "master"}},
# {"term": {"testrun.suite": "communications/ftu"}},
# {"term": {"result.test_name": "startup_time"}}
]},
"limit": nvl(settings.param.combo_limit, 1000)
}, qb)
new_test_points = qb.query(temp)
#BRING IN ALL NEEDED DATA
if debug:
Log.note("Pull all data for {{num}} groups:\n{{groups.name}}", {
"num": len(new_test_points),
"groups": query.edges
})
# all_min_date = Null
all_touched = set()
evaled_tests = set()
alerts = [] # PUT ALL THE EXCEPTION ITEMS HERE
for g, test_points in Q.groupby(new_test_points, query.edges):
if not test_points.min_push_date:
continue
try:
if settings.args.restart:
first_sample = OLDEST_TS
else:
first_sample = MAX(MIN(test_points.min_push_date), OLDEST_TS)
# FOR THIS g, HOW FAR BACK IN TIME MUST WE GO TO COVER OUR WINDOW_SIZE?
first_in_window = qb.query({
"select": {"name": "min_date", "value": "push_date", "aggregate": "min"},
"from": {
"from": TDAD,
"select": {"name": "push_date", "value": PUSH_DATE},
"where": {"and": [
{"term": g},
{"range": {PUSH_DATE: {"lt": first_sample}}}
]},
"sort": {"field": PUSH_DATE, "sort": -1},
"limit": settings.param.sustained_median.window_size * 2
}
})
if len(first_in_window) > settings.param.sustained_median.window_size * 2:
do_all = False
else:
do_all = True
min_date = MIN(first_sample, first_in_window.min_date)
#LOAD TEST RESULTS FROM DATABASE
test_results = qb.query({
"from": {
"from": "talos",
"select": [{"name": "push_date", "value": PUSH_DATE}] +
query.select +
query.edges,
"where": {"and": [
{"term": g},
{"range": {PUSH_DATE: {"gte": min_date}}}
]},
},
"sort": "push_date"
})
Log.note("{{num}} test results found for {{group}} dating back no further than {{start_date}}", {
"num": len(test_results),
"group": g,
"start_date": CNV.milli2datetime(min_date)
})
if debug:
Log.note("Find sustained_median exceptions")
#APPLY WINDOW FUNCTIONS
stats = Q.run({
"from": {
"from": test_results,
"where": {"exists": {"field": "value"}}
},
"window": [
{
# WE DO NOT WANT TO CONSIDER THE POINTS BEFORE FULL WINDOW SIZE
"name": "ignored",
"value": lambda r, i: False if do_all or i > settings.param.sustained_median.window_size else True
}, {
# SO WE CAN SHOW A DATAZILLA WINDOW
"name": "push_date_min",
"value": lambda r: r.push_date,
"sort": "push_date",
"aggregate": windows.Min,
"range": {"min": -settings.param.sustained_median.window_size, "max": 0}
}, {
# SO WE CAN SHOW A DATAZILLA WINDOW
"name": "push_date_max",
"value": lambda r: r.push_date,
"sort": "push_date",
"aggregate": windows.Max,
"range": {"min": 0, "max": settings.param.sustained_median.window_size}
}, {
"name": "past_revision",
"value": lambda r, i, rows: rows[i - 1].Talos.Revision,
"sort": "push_date"
}, {
"name": "past_stats",
"value": lambda r: r.value,
"sort": "push_date",
"aggregate": windows.Stats(middle=0.60),
"range": {"min": -settings.param.sustained_median.window_size, "max": 0}
}, {
"name": "future_stats",
"value": lambda r: r.value,
"sort": "push_date",
"aggregate": windows.Stats(middle=0.60),
"range": {"min": 0, "max": settings.param.sustained_median.window_size}
}, {
"name": "result",
"value": lambda r, i, rows: median_test(
rows[-settings.param.sustained_median.window_size + i:i:].value,
rows[i:settings.param.sustained_median.window_size + i:].value,
interpolate=False
),
"sort": "push_date"
}, {
"name": "diff",
"value": lambda r: r.future_stats.mean - r.past_stats.mean
}, {
"name": "diff_percent",
"value": lambda r: (r.future_stats.mean - r.past_stats.mean) / r.past_stats.mean
}, {
"name": "is_diff",
"value": is_bad
}, {
#USE THIS TO FILL CONFIDENCE HOLES
#WE CAN MARK IT is_diff KNOWING THERE IS A HIGHER CONFIDENCE
"name": "future_is_diff",
"value": lambda r, i, rows: rows[i - 1].is_diff and r.result.confidence < rows[i - 1].result.confidence,
"sort": "push_date"
}, {
#WE CAN MARK IT is_diff KNOWING THERE IS A HIGHER CONFIDENCE
"name": "past_is_diff",
"value": lambda r, i, rows: rows[i - 1].is_diff and r.result.confidence < rows[i - 1].result.confidence,
"sort": {"value": "push_date", "sort": -1}
},
]
})
#PICK THE BEST SCORE FOR EACH is_diff==True REGION
for g2, data in Q.groupby(stats, "is_diff", contiguous=True):
if g2.is_diff:
best = Q.sort(data, ["result.confidence", "diff"]).last()
best["pass"] = True
all_touched.update(Q.select(test_results, ["test_run_id", "Talos.Test"]))
# TESTS THAT HAVE BEEN (RE)EVALUATED GIVEN THE NEW INFORMATION
evaled_tests.update(Q.run({
"from": test_results,
"select": ["test_run_id", "Talos.Test"],
"where": {"term": {"ignored": False}}
}))
File("test_values.txt").write(CNV.list2tab(Q.select(stats, [
{"name": "push_date", "value": lambda x: CNV.datetime2string(CNV.milli2datetime(x.push_date), "%d-%b-%Y %H:%M:%S")},
"value",
{"name": "revision", "value": "Talos.Revision"},
{"name": "confidence", "value": "result.confidence"},
"pass"
])))
#TESTS THAT HAVE SHOWN THEMSELVES TO BE EXCEPTIONAL
new_exceptions = Q.filter(stats, {"term": {"pass": True}})
for v in new_exceptions:
if v.ignored:
continue
alert = Struct(
status="new",
create_time=CNV.milli2datetime(v.push_date),
tdad_id={"test_run_id": v.test_run_id, "Talos": {"Test": v.Talos.Test}},
reason=REASON,
revision=v.Talos.Revision,
details=v,
severity=SEVERITY,
confidence=v.result.confidence
)
alerts.append(alert)
if debug:
Log.note("{{num}} new exceptions found", {"num": len(new_exceptions)})
except Exception, e:
Log.warning("Problem with alert identification, continue to log existing alerts and stop cleanly", e)
