def _upload_results_to_bq(rows):
"""Upload test results to a BQ table.
Args:
rows: A list of dictionaries containing data for each row to insert
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(
bq,
_PROJECT_ID,
_DATASET_ID,
_TABLE_ID,
_RESULTS_SCHEMA,
_DESCRIPTION,
partition_type=_PARTITION_TYPE,
expiration_ms=_EXPIRATION_MS)
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID, _TABLE_ID,
rows):
break
else:
if attempt < max_retries - 1:
print('Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.')
sys.exit(1)
def _insert_result(bq, dataset_id, table_id, scenario_result, flatten=True):
if flatten:
_flatten_result_inplace(scenario_result)
_populate_metadata_inplace(scenario_result)
row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result)
return big_query_utils.insert_rows(bq, _PROJECT_ID, dataset_id, table_id,
[row])
def _insert_result(bq, dataset_id, table_id, scenario_result, flatten=True):
if flatten:
_flatten_result_inplace(scenario_result)
_populate_metadata_inplace(scenario_result)
row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result)
return big_query_utils.insert_rows(bq, _PROJECT_ID, dataset_id, table_id,
[row])
def upload_results_to_bq(resultset, bq_table, args, platform):
"""Upload test results to a BQ table.
Args:
resultset: dictionary generated by jobset.run
bq_table: string name of table to create/upload results to in BQ
args: args in run_tests.py, generated by argparse
platform: string name of platform tests were run on
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(bq, _PROJECT_ID, _DATASET_ID, bq_table, _RESULTS_SCHEMA, _DESCRIPTION,
partition_type=_PARTITION_TYPE, expiration_ms= _EXPIRATION_MS)
for shortname, results in six.iteritems(resultset):
for result in results:
test_results = {}
_get_build_metadata(test_results)
test_results['compiler'] = args.compiler
test_results['config'] = args.config
test_results['cpu_estimated'] = result.cpu_estimated
test_results['cpu_measured'] = result.cpu_measured
test_results['elapsed_time'] = '%.2f' % result.elapsed_time
test_results['iomgr_platform'] = args.iomgr_platform
# args.language is a list, but will always have one element in the contexts
# this function is used.
test_results['language'] = args.language[0]
test_results['platform'] = platform
test_results['result'] = result.state
test_results['test_name'] = shortname
test_results['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')
row = big_query_utils.make_row(str(uuid.uuid4()), test_results)
if not big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID, bq_table, [row]):
print('Error uploading result to bigquery.')
sys.exit(1)
def _upload_results_to_bq(rows):
"""Upload test results to a BQ table.
Args:
rows: A list of dictionaries containing data for each row to insert
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(bq,
_PROJECT_ID,
_DATASET_ID,
_TABLE_ID,
_RESULTS_SCHEMA,
_DESCRIPTION,
partition_type=_PARTITION_TYPE,
expiration_ms=_EXPIRATION_MS)
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID, _TABLE_ID,
rows):
break
else:
if attempt < max_retries - 1:
print('Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.')
sys.exit(1)
def upload_result(result_list, metadata):
for result in result_list:
new_result = copy.deepcopy(result)
new_result['metadata'] = metadata
bq = big_query_utils.create_big_query()
row = big_query_utils.make_row(str(uuid.uuid4()), new_result)
if not big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET,
_TABLE + "$" + _NOW, [row]):
print 'Error when uploading result', new_result
def upload_result(result_list, metadata):
for result in result_list:
new_result = copy.deepcopy(result)
new_result['metadata'] = metadata
bq = big_query_utils.create_big_query()
row = big_query_utils.make_row(str(uuid.uuid4()), new_result)
if not big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET,
_TABLE + "$" + _NOW,
[row]):
print 'Error when uploading result', new_result
def upload_results_to_bq(resultset, bq_table, args, platform):
"""Upload test results to a BQ table.
Args:
resultset: dictionary generated by jobset.run
bq_table: string name of table to create/upload results to in BQ
args: args in run_tests.py, generated by argparse
platform: string name of platform tests were run on
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(bq,
_PROJECT_ID,
_DATASET_ID,
bq_table,
_RESULTS_SCHEMA,
_DESCRIPTION,
partition_type=_PARTITION_TYPE,
expiration_ms=_EXPIRATION_MS)
for shortname, results in six.iteritems(resultset):
for result in results:
test_results = {}
_get_build_metadata(test_results)
test_results['compiler'] = args.compiler
test_results['config'] = args.config
test_results['cpu_estimated'] = result.cpu_estimated
test_results['cpu_measured'] = result.cpu_measured
test_results['elapsed_time'] = '%.2f' % result.elapsed_time
test_results['iomgr_platform'] = args.iomgr_platform
# args.language is a list, but will always have one element in the contexts
# this function is used.
test_results['language'] = args.language[0]
test_results['platform'] = platform
test_results['result'] = result.state
test_results['return_code'] = result.returncode
test_results['test_name'] = shortname
test_results['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')
row = big_query_utils.make_row(str(uuid.uuid4()), test_results)
# TODO(jtattermusch): rows are inserted one by one, very inefficient
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID,
bq_table, [row]):
break
else:
if attempt < max_retries - 1:
print(
'Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.'
)
sys.exit(1)
def upload_interop_results_to_bq(resultset, bq_table, args):
"""Upload interop test results to a BQ table.
Args:
resultset: dictionary generated by jobset.run
bq_table: string name of table to create/upload results to in BQ
args: args in run_interop_tests.py, generated by argparse
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(bq,
_PROJECT_ID,
_DATASET_ID,
bq_table,
_INTEROP_RESULTS_SCHEMA,
_DESCRIPTION,
partition_type=_PARTITION_TYPE,
expiration_ms=_EXPIRATION_MS)
for shortname, results in six.iteritems(resultset):
bq_rows = []
for result in results:
test_results = {}
_get_build_metadata(test_results)
test_results['elapsed_time'] = '%.2f' % result.elapsed_time
test_results['result'] = result.state
test_results['test_name'] = shortname
test_results['suite'] = shortname.split(':')[0]
test_results['client'] = shortname.split(':')[1]
test_results['server'] = shortname.split(':')[2]
test_results['test_case'] = shortname.split(':')[3]
test_results['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')
row = big_query_utils.make_row(str(uuid.uuid4()), test_results)
bq_rows.append(row)
# BigQuery sometimes fails with large uploads, so batch 1,000 rows at a time.
for i in range((len(bq_rows) / 1000) + 1):
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(
bq, _PROJECT_ID, _DATASET_ID, bq_table,
bq_rows[i * 1000:(i + 1) * 1000]):
break
else:
if attempt < max_retries - 1:
print(
'Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.'
)
sys.exit(1)
def insert_qps_row(self, qps, recorded_at):
row_values_dict = {
'run_id': self.run_id,
'pod_name': self.pod_name,
'recorded_at': recorded_at,
'qps': qps
}
# row_unique_id is something that uniquely identifies the row (BigQuery uses
# it for duplicate detection).
row_unique_id = '%s_%s_%s' % (self.run_id, self.pod_name, recorded_at)
row = bq_utils.make_row(row_unique_id, row_values_dict)
return bq_utils.insert_rows(self.bq, self.project_id, self.dataset_id,
self.qps_table_id, [row])
def insert_qps_row(self, qps, recorded_at):
row_values_dict = {
'run_id': self.run_id,
'pod_name': self.pod_name,
'recorded_at': recorded_at,
'qps': qps
}
# row_unique_id is something that uniquely identifies the row (BigQuery uses
# it for duplicate detection).
row_unique_id = '%s_%s_%s' % (self.run_id, self.pod_name, recorded_at)
row = bq_utils.make_row(row_unique_id, row_values_dict)
return bq_utils.insert_rows(self.bq, self.project_id, self.dataset_id,
self.qps_table_id, [row])
def insert_summary_row(self, event_type, details):
row_values_dict = {
'run_id': self.run_id,
'image_type': self.image_type,
'pod_name': self.pod_name,
'event_date': datetime.datetime.now().isoformat(),
'event_type': event_type,
'details': details
}
# row_unique_id is something that uniquely identifies the row (BigQuery uses
# it for duplicate detection).
row_unique_id = '%s_%s_%s' % (self.run_id, self.pod_name, event_type)
row = bq_utils.make_row(row_unique_id, row_values_dict)
return bq_utils.insert_rows(self.bq, self.project_id, self.dataset_id,
self.summary_table_id, [row])
def insert_summary_row(self, event_type, details):
row_values_dict = {
'run_id': self.run_id,
'image_type': self.image_type,
'pod_name': self.pod_name,
'event_date': datetime.datetime.now().isoformat(),
'event_type': event_type,
'details': details
}
# row_unique_id is something that uniquely identifies the row (BigQuery uses
# it for duplicate detection).
row_unique_id = '%s_%s_%s' % (self.run_id, self.pod_name, event_type)
row = bq_utils.make_row(row_unique_id, row_values_dict)
return bq_utils.insert_rows(self.bq, self.project_id, self.dataset_id,
self.summary_table_id, [row])
def _insert_scenario_result(bq,
dataset_id,
table_id,
scenario_result,
test_metadata_file,
node_info_file,
prometheus_query_results_file,
flatten=True):
if flatten:
_flatten_result_inplace(scenario_result)
_populate_metadata_from_file(scenario_result, test_metadata_file)
_populate_node_metadata_from_file(scenario_result, node_info_file)
_populate_prometheus_query_results_from_file(scenario_result,
prometheus_query_results_file)
row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result)
return big_query_utils.insert_rows(bq, _PROJECT_ID, dataset_id, table_id,
[row])
def upload_interop_results_to_bq(resultset, bq_table, args):
"""Upload interop test results to a BQ table.
Args:
resultset: dictionary generated by jobset.run
bq_table: string name of table to create/upload results to in BQ
args: args in run_interop_tests.py, generated by argparse
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_partitioned_table(
bq,
_PROJECT_ID,
_DATASET_ID,
bq_table,
_INTEROP_RESULTS_SCHEMA,
_DESCRIPTION,
partition_type=_PARTITION_TYPE,
expiration_ms=_EXPIRATION_MS)
for shortname, results in six.iteritems(resultset):
for result in results:
test_results = {}
_get_build_metadata(test_results)
test_results['elapsed_time'] = '%.2f' % result.elapsed_time
test_results['result'] = result.state
test_results['test_name'] = shortname
test_results['suite'] = shortname.split(':')[0]
test_results['client'] = shortname.split(':')[1]
test_results['server'] = shortname.split(':')[2]
test_results['test_case'] = shortname.split(':')[3]
test_results['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')
row = big_query_utils.make_row(str(uuid.uuid4()), test_results)
# TODO(jtattermusch): rows are inserted one by one, very inefficient
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID,
bq_table, [row]):
break
else:
if attempt < max_retries - 1:
print('Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.'
)
sys.exit(1)
def _insert_rows_with_retries(bq, bq_table, bq_rows):
"""Insert rows to bq table. Retry on error."""
# BigQuery sometimes fails with large uploads, so batch 1,000 rows at a time.
for i in range((len(bq_rows) / 1000) + 1):
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID,
bq_table,
bq_rows[i * 1000:(i + 1) * 1000]):
break
else:
if attempt < max_retries - 1:
print('Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.'
)
sys.exit(1)
def _insert_rows_with_retries(bq, bq_table, bq_rows):
"""Insert rows to bq table. Retry on error."""
# BigQuery sometimes fails with large uploads, so batch 1,000 rows at a time.
for i in range((len(bq_rows) / 1000) + 1):
max_retries = 3
for attempt in range(max_retries):
if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID,
bq_table,
bq_rows[i * 1000:(i + 1) * 1000]):
break
else:
if attempt < max_retries - 1:
print('Error uploading result to bigquery, will retry.')
else:
print(
'Error uploading result to bigquery, all attempts failed.'
)
sys.exit(1)
def upload_results_to_bq(resultset, bq_table, args, platform):
"""Upload test results to a BQ table.
Args:
resultset: dictionary generated by jobset.run
bq_table: string name of table to create/upload results to in BQ
args: args in run_tests.py, generated by argparse
platform: string name of platform tests were run on
"""
bq = big_query_utils.create_big_query()
big_query_utils.create_table(bq, _PROJECT_ID, _DATASET_ID, bq_table,
_RESULTS_SCHEMA, _DESCRIPTION)
for shortname, results in six.iteritems(resultset):
for result in results:
test_results = {}
_get_build_metadata(test_results)
test_results['compiler'] = args.compiler
test_results['config'] = args.config
test_results['cpu_estimated'] = result.cpu_estimated
test_results['cpu_measured'] = result.cpu_measured
test_results['elapsed_time'] = '%.2f' % result.elapsed_time
test_results['iomgr_platform'] = args.iomgr_platform
# args.language is a list, but will always have one element in the contexts
# this function is used.
test_results['language'] = args.language[0]
test_results['platform'] = platform
test_results['result'] = result.state
test_results['test_name'] = shortname
test_results['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')
row = big_query_utils.make_row(str(uuid.uuid4()), test_results)
if not big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID,
bq_table, [row]):
print('Error uploading result to bigquery.')
sys.exit(1)
last_complete_build_number = job.get_last_completed_buildnumber()
# To avoid processing all builds for a project never looked at. In this case,
# only examine 10 latest builds.
starting_build_number = max(last_processed_build_number+1,
last_complete_build_number-9)
for build_number in xrange(starting_build_number,
last_complete_build_number+1):
print('====> Processing %s build %d.' % (build_name, build_number))
build = None
try:
build = job.get_build_metadata(build_number)
except KeyError:
print('====> Build %s is missing. Skip.' % build_number)
continue
build_result = {'build_number': build_number,
'timestamp': str(build.get_timestamp())}
url_base = json_url = '%s/%s/%d' % (_URL_BASE, build_name, build_number)
if _BUILDS[build_name]: # The build has matrix, such as gRPC_master.
build_result['matrix'] = _process_matrix(build, url_base)
else:
json_url = '%s/testReport/api/json' % url_base
console_url = '%s/consoleFull' % url_base
build_result['duration'] = build.get_duration().total_seconds()
build_result.update(_process_build(json_url, console_url))
rows = [big_query_utils.make_row(build_number, build_result)]
if not big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID, build_name,
rows):
print '====> Error uploading result to bigquery.'
sys.exit(1)