def store_performance_artifact(
self, job_ids, performance_artifact_placeholders):
"""
Store the performance data
"""
# Retrieve list of job signatures associated with the jobs
job_data = self.get_job_signatures_from_ids(job_ids)
job_ref_data_signatures = set()
map(
lambda job_guid: job_ref_data_signatures.add(
job_data[job_guid]['signature']
),
job_data.keys()
)
# Retrieve associated data in reference_data_signatures
reference_data = self.refdata_model.get_reference_data(
list(job_ref_data_signatures))
tda = TalosDataAdapter()
for perf_data in performance_artifact_placeholders:
job_guid = perf_data["job_guid"]
ref_data_signature = job_data[job_guid]['signature']
ref_data = reference_data[ref_data_signature]
if 'signature' in ref_data:
del ref_data['signature']
# adapt and load data into placeholder structures
tda.adapt_and_load(self.project, ref_data, job_data, perf_data)
def test_adapt_and_load():
talos_perf_data = SampleData.get_talos_perf_data()
tda = TalosDataAdapter()
result_count = 0
for datum in talos_perf_data:
datum = {
"job_guid": 'oqiwy0q847365qiu',
"name": "test",
"type": "test",
"blob": datum
}
job_data = {
"oqiwy0q847365qiu": {
"id": 1,
"result_set_id": 1,
"push_timestamp": 1402692388
}
}
reference_data = {
"property1": "value1",
"property2": "value2",
"property3": "value3"
}
# one extra result for the summary series
result_count += len(datum['blob']["results"]) + 1
# we create one performance series per counter
if 'talos_counters' in datum['blob']:
result_count += len(datum['blob']["talos_counters"])
# Mimic production environment, the blobs are serialized
# when the web service receives them
datum['blob'] = json.dumps({'talos_data': [datum['blob']]})
tda.adapt_and_load(reference_data, job_data, datum)
assert result_count == len(tda.performance_artifact_placeholders)
def _rewrite_series(self, jm, signature_hash, signature_properties,
subtest_signature_mapping, extra_subtest_signatures):
new_props = TalosDataAdapter._transform_signature_properties(
signature_properties,
significant_keys=Command.SIGNIFICANT_KEYS)
if 'subtest_signatures' in new_props:
suitekey = self._get_suitekey(new_props)
# rewrite a new set of subtest signatures
old_subtest_signatures = new_props['subtest_signatures']
new_subtest_signatures = set()
for old_signature in old_subtest_signatures:
try:
new_subtest_signatures.add(
subtest_signature_mapping[old_signature])
except:
# key may not exist if script interrupted, get
# suite signatures via extra_subtest_signatures
for sig in extra_subtest_signatures.get(suitekey, []):
new_subtest_signatures.add(sig)
new_props['subtest_signatures'] = sorted(new_subtest_signatures)
new_hash = TalosDataAdapter.get_series_signature(new_props)
print "%s -> %s" % (signature_hash, new_hash)
jm.set_series_signature(new_hash, new_props)
for time_interval in PerformanceTimeInterval.all_valid_time_intervals():
series_list = jm.get_performance_series_from_signatures(
[signature_hash], time_interval)
series = utils.decompress_if_needed(series_list[0]['blob'])
jm.store_performance_series(time_interval, 'talos_data',
str(new_hash), series)
jm.execute(proc='jobs.deletes.delete_performance_series',
placeholders=[signature_hash])
jm.execute(proc='jobs.deletes.delete_series_signature',
placeholders=[signature_hash])
return new_hash
def _rewrite_series(self, jm, signature_hash, signature_properties,
subtest_signature_mapping, extra_subtest_signatures):
new_props = TalosDataAdapter._transform_signature_properties(
signature_properties, significant_keys=Command.SIGNIFICANT_KEYS)
if 'subtest_signatures' in new_props:
suitekey = self._get_suitekey(new_props)
# rewrite a new set of subtest signatures
old_subtest_signatures = new_props['subtest_signatures']
new_subtest_signatures = set()
for old_signature in old_subtest_signatures:
try:
new_subtest_signatures.add(
subtest_signature_mapping[old_signature])
except:
# key may not exist if script interrupted, get
# suite signatures via extra_subtest_signatures
for sig in extra_subtest_signatures.get(suitekey, []):
new_subtest_signatures.add(sig)
new_props['subtest_signatures'] = sorted(new_subtest_signatures)
new_hash = TalosDataAdapter.get_series_signature(new_props)
print "%s -> %s" % (signature_hash, new_hash)
jm.set_series_signature(new_hash, new_props)
for time_interval in PerformanceTimeInterval.all_valid_time_intervals(
):
series_list = jm.get_performance_series_from_signatures(
[signature_hash], time_interval)
series = series_list[0]['blob']
jm.store_performance_series(time_interval, 'talos_data',
str(new_hash), series)
jm.jobs_execute(proc='jobs.deletes.delete_performance_series',
placeholders=[signature_hash])
jm.jobs_execute(proc='jobs.deletes.delete_series_signature',
placeholders=[signature_hash])
return new_hash
def test_performance_log_parsing():
"""
Make sure all performance data log examples validate with the
datazilla json schema.
"""
sd = SampleData()
files = sd.get_performance_logs()
tda = TalosDataAdapter()
for file_url in files:
builder = BuildbotPerformanceDataArtifactBuilder(url=file_url)
lpc = ArtifactBuilderCollection(file_url, builders=[builder])
lpc.parse()
act = lpc.artifacts[builder.name]
# Validate the data returned has the required datazilla
# json schema
validate(act['talos_data'][0], tda.datazilla_schema)
def store_performance_artifact(
self, job_ids, performance_artifact_placeholders):
"""
Store the performance data
"""
# Retrieve list of job signatures associated with the jobs
job_data = self.get_job_signatures_from_ids(job_ids)
job_ref_data_signatures = set()
map(
lambda job_guid: job_ref_data_signatures.add(
job_data[job_guid]['signature']
),
job_data.keys()
)
# Retrieve associated data in reference_data_signatures
reference_data = self.refdata_model.get_reference_data(
list(job_ref_data_signatures))
tda = TalosDataAdapter()
for perf_data in performance_artifact_placeholders:
job_guid = perf_data["job_guid"]
ref_data_signature = job_data[job_guid]['signature']
ref_data = reference_data[ref_data_signature]
if 'signature' in ref_data:
del ref_data['signature']
# adapt and load data into placeholder structures
tda.adapt_and_load(ref_data, job_data, perf_data)
self.jobs_execute(
proc="jobs.inserts.set_performance_artifact",
debug_show=self.DEBUG,
placeholders=tda.performance_artifact_placeholders,
executemany=True)
self.jobs_execute(
proc='jobs.inserts.set_series_signature',
debug_show=self.DEBUG,
placeholders=tda.signature_property_placeholders,
executemany=True)
tda.submit_tasks(self.project)
def test_adapt_and_load(self):
talos_perf_data = SampleData.get_talos_perf_data()
for talos_datum in talos_perf_data:
datum = {
"job_guid": 'oqiwy0q847365qiu',
"name": "test",
"type": "test",
"blob": talos_datum
}
job_data = {
"oqiwy0q847365qiu": {
"id": 1,
"result_set_id": 1,
"push_timestamp": 1402692388
}
}
reference_data = {
"property1": "value1",
"property2": "value2",
"property3": "value3"
}
# Mimic production environment, the blobs are serialized
# when the web service receives them
datum['blob'] = json.dumps({'talos_data': [datum['blob']]})
tda = TalosDataAdapter()
tda.adapt_and_load(reference_data, job_data, datum)
# base: subtests + one extra result for the summary series
expected_result_count = len(talos_datum["results"]) + 1
# we create one performance series per counter
if 'talos_counters' in talos_datum:
expected_result_count += len(talos_datum["talos_counters"])
# result count == number of signatures
self.assertEqual(expected_result_count, len(tda.signatures.keys()))
# verify that we have signatures for the subtests
signature_placeholders = copy.copy(
tda.signature_property_placeholders)
for (testname, results) in talos_datum["results"].iteritems():
signature_placeholder = filter(
lambda p: p[2] == testname, signature_placeholders)
self.assertEqual(len(signature_placeholder), 1)
signature_hash = signature_placeholder[0][0]
perfdata = tda.signatures[signature_hash][0]
if talos_datum.get('summary'):
# if we have a summary, ensure the subtest summary values made
# it in
for measure in ['min', 'max', 'std', 'mean', 'median']:
self.assertEqual(
round(talos_datum['summary']['subtests'][testname][measure], 2),
perfdata[measure])
else:
# this is an old style talos blob without a summary. these are going
# away, so I'm not going to bother testing the correctness. however
# let's at least verify that some values are being generated here
for measure in ['min', 'max', 'std', 'mean', 'median']:
self.assertTrue(perfdata[measure])
# filter out this signature from data to process
signature_placeholders = filter(
lambda p: p[0] != signature_hash, signature_placeholders)
# if we have counters, verify that the series for them is as expected
for (counter, results) in talos_datum.get('talos_counters',
{}).iteritems():
signature_placeholder = filter(
lambda p: p[2] == counter, signature_placeholders)
self.assertEqual(len(signature_placeholder), 1)
signature_hash = signature_placeholder[0][0]
perfdata = tda.signatures[signature_hash][0]
for measure in ['max', 'mean']:
self.assertEqual(round(float(results[measure]), 2),
perfdata[measure])
# filter out this signature from data to process
signature_placeholders = filter(
lambda p: p[0] != signature_hash, signature_placeholders)
# we should be left with just summary signature placeholders
self.assertEqual(len(signature_placeholders), 2)
perfdata = tda.signatures[signature_placeholders[0][0]][0]
if talos_datum.get('summary'):
self.assertEqual(round(talos_datum['summary']['suite'], 2),
perfdata['geomean'])
else:
# old style talos blob without summary. again, going away,
# but let's at least test that we have the 'geomean' value
# generated
self.assertTrue(perfdata['geomean'])
def test_adapt_and_load(self):
talos_perf_data = SampleData.get_talos_perf_data()
for talos_datum in talos_perf_data:
datum = {
"job_guid": 'oqiwy0q847365qiu',
"name": "test",
"type": "test",
"blob": talos_datum
}
job_data = {
"oqiwy0q847365qiu": {
"id": 1,
"result_set_id": 1,
"push_timestamp": 1402692388
}
}
reference_data = {
"property1": "value1",
"property2": "value2",
"property3": "value3"
}
# Mimic production environment, the blobs are serialized
# when the web service receives them
datum['blob'] = json.dumps({'talos_data': [datum['blob']]})
tda = TalosDataAdapter()
tda.adapt_and_load(reference_data, job_data, datum)
# base: subtests + one extra result for the summary series
expected_result_count = len(talos_datum["results"]) + 1
# we create one performance series per counter
if 'talos_counters' in talos_datum:
expected_result_count += len(talos_datum["talos_counters"])
# result count == number of signatures
self.assertEqual(expected_result_count, len(tda.signatures.keys()))
# verify that we have signatures for the subtests
signature_placeholders = copy.copy(
tda.signature_property_placeholders)
for (testname, results) in talos_datum["results"].iteritems():
signature_placeholder = filter(lambda p: p[2] == testname,
signature_placeholders)
self.assertEqual(len(signature_placeholder), 1)
signature_hash = signature_placeholder[0][0]
perfdata = tda.signatures[signature_hash][0]
if talos_datum.get('summary'):
# if we have a summary, ensure the subtest summary values made
# it in
for measure in ['min', 'max', 'std', 'mean', 'median']:
self.assertEqual(
round(
talos_datum['summary']['subtests'][testname]
[measure], 2), perfdata[measure])
else:
# this is an old style talos blob without a summary. these are going
# away, so I'm not going to bother testing the correctness. however
# let's at least verify that some values are being generated here
for measure in ['min', 'max', 'std', 'mean', 'median']:
self.assertTrue(perfdata[measure])
# filter out this signature from data to process
signature_placeholders = filter(
lambda p: p[0] != signature_hash, signature_placeholders)
# if we have counters, verify that the series for them is as expected
for (counter, results) in talos_datum.get('talos_counters',
{}).iteritems():
signature_placeholder = filter(lambda p: p[2] == counter,
signature_placeholders)
self.assertEqual(len(signature_placeholder), 1)
signature_hash = signature_placeholder[0][0]
perfdata = tda.signatures[signature_hash][0]
for measure in ['max', 'mean']:
self.assertEqual(round(float(results[measure]), 2),
perfdata[measure])
# filter out this signature from data to process
signature_placeholders = filter(
lambda p: p[0] != signature_hash, signature_placeholders)
# we should be left with just summary signature placeholders
self.assertEqual(len(signature_placeholders), 2)
perfdata = tda.signatures[signature_placeholders[0][0]][0]
if talos_datum.get('summary'):
self.assertEqual(round(talos_datum['summary']['suite'], 2),
perfdata['geomean'])
else:
# old style talos blob without summary. again, going away,
# but let's at least test that we have the 'geomean' value
# generated
self.assertTrue(perfdata['geomean'])
def _signature_needs_rewriting(signature_properties, signature_hash):
return (not set(signature_properties.keys()).issubset(
Command.SIGNIFICANT_KEYS) or
signature_hash != TalosDataAdapter.get_series_signature(
signature_properties))
def _get_suitekey(signature_props):
suite_signature_props = copy.copy(signature_props)
for k in ['test', 'subtest_signatures']:
if suite_signature_props.get(k):
del suite_signature_props[k]
return TalosDataAdapter.get_series_signature(suite_signature_props)
def _signature_needs_rewriting(signature_properties, signature_hash):
return (not set(signature_properties.keys()).issubset(
Command.SIGNIFICANT_KEYS) or signature_hash !=
TalosDataAdapter.get_series_signature(signature_properties))
def _get_suitekey(signature_props):
suite_signature_props = copy.copy(signature_props)
for k in ['test', 'subtest_signatures']:
if suite_signature_props.get(k):
del suite_signature_props[k]
return TalosDataAdapter.get_series_signature(suite_signature_props)
def test_adapt_and_load():
talos_perf_data = SampleData.get_talos_perf_data()
tda = TalosDataAdapter()
result_count = 0
for datum in talos_perf_data:
datum = {
"job_guid": 'oqiwy0q847365qiu',
"name": "test",
"type": "test",
"blob": datum
}
job_data = {
"oqiwy0q847365qiu": {
"id": 1,
"result_set_id": 1,
"push_timestamp": 1402692388
}
}
reference_data = {
"property1": "value1",
"property2": "value2",
"property3": "value3"
}
# one extra result for the summary series
result_count += len(datum['blob']["results"]) + 1
# we create one performance series per counter
if 'talos_counters' in datum['blob']:
result_count += len(datum['blob']["talos_counters"])
# Mimic production environment, the blobs are serialized
# when the web service receives them
datum['blob'] = json.dumps({'talos_data': [datum['blob']]})
tda.adapt_and_load(reference_data, job_data, datum)
# we upload a summary with a suite and subtest values, +1 for suite
if 'summary' in datum['blob']:
results = json.loads(zlib.decompress(tda.performance_artifact_placeholders[-1][4]))
data = json.loads(datum['blob'])['talos_data'][0]
assert results["blob"]["performance_series"]["geomean"] == data['summary']['suite']
# deal with the subtests now
for i in range(0, len(data['summary']['subtests'])):
subresults = json.loads(zlib.decompress(tda.performance_artifact_placeholders[-1 - i][4]))
if 'subtest_signatures' in subresults["blob"]['signature_properties']:
# ignore summary signatures
continue
subdata = data['summary']['subtests'][subresults["blob"]['signature_properties']['test']]
for datatype in ['min', 'max', 'mean', 'median', 'std']:
assert subdata[datatype] == subresults["blob"]["performance_series"][datatype]
if 'value' in subdata.keys():
assert subdata['value'] == subresults["blob"]["performance_series"]['value']
else:
# FIXME: the talos data blob we're currently using contains datums with summaries and those without
# we should probably test non-summarized data as well
pass
assert result_count == len(tda.performance_artifact_placeholders)
def test_adapt_and_load(self):
talos_perf_data = SampleData.get_talos_perf_data()
for talos_datum in talos_perf_data:
# delete any previously-created perf objects
# FIXME: because of https://bugzilla.mozilla.org/show_bug.cgi?id=1133273
# this can be really slow if we have a dev database with lots of
# performance data in it (if the test succeeds, the transaction
# will be rolled back so at least it won't pollute the production
# database)
PerformanceSignature.objects.all().delete()
PerformanceDatum.objects.all().delete()
datum = {
"job_guid": 'oqiwy0q847365qiu',
"name": "test",
"type": "test",
"blob": talos_datum
}
job_data = {
"oqiwy0q847365qiu": {
"id": 1,
"result_set_id": 1,
"push_timestamp": 1402692388
}
}
reference_data = {
"option_collection_hash": self.OPTION_HASH,
"machine_platform": self.MACHINE_PLATFORM,
"property1": "value1",
"property2": "value2",
"property3": "value3"
}
# Mimic production environment, the blobs are serialized
# when the web service receives them
datum['blob'] = json.dumps({'talos_data': [datum['blob']]})
tda = TalosDataAdapter()
tda.adapt_and_load(self.REPO_NAME, reference_data, job_data, datum)
# base: subtests + one extra result for the summary series
expected_result_count = len(talos_datum["results"]) + 1
# we create one performance series per counter
if 'talos_counters' in talos_datum:
expected_result_count += len(talos_datum["talos_counters"])
# result count == number of signatures
self.assertEqual(expected_result_count,
PerformanceSignature.objects.all().count())
# verify that we have signatures for the subtests
for (testname, results) in talos_datum["results"].iteritems():
signature = PerformanceSignature.objects.get(test=testname)
datum = PerformanceDatum.objects.get(signature=signature)
if talos_datum.get('summary'):
# if we have a summary, ensure the subtest summary values made
# it in
self.assertEqual(
round(talos_datum['summary']['subtests'][testname]['filtered'], 2),
datum.value)
else:
# this is an old style talos blob without a summary. these are going
# away, so I'm not going to bother testing the correctness. however
# let's at least verify that some values are being generated here
self.assertTrue(datum.value)
# if we have counters, verify that the series for them is as expected
for (counter, results) in talos_datum.get('talos_counters',
{}).iteritems():
signature = PerformanceSignature.objects.get(test=counter)
datum = PerformanceDatum.objects.get(signature=signature)
self.assertEqual(round(float(results['mean']), 2),
datum.value)
# we should be left with just the summary series
signature = PerformanceSignature.objects.get(
test='',
suite=talos_datum['testrun']['suite'])
datum = PerformanceDatum.objects.get(signature=signature)
if talos_datum.get('summary'):
self.assertEqual(round(talos_datum['summary']['suite'], 2),
datum.value)
else:
# old style talos blob without summary. again, going away,
# but let's at least test that we have the value
self.assertTrue(datum.value)
