| #!/usr/bin/env python |
| # Copyright 2016 gRPC authors. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| # Uploads performance benchmark result file to bigquery. |
| |
| from __future__ import print_function |
| |
| import argparse |
| import calendar |
| import json |
| import os |
| import sys |
| import time |
| import uuid |
| |
| import massage_qps_stats |
| |
| gcp_utils_dir = os.path.abspath( |
| os.path.join(os.path.dirname(__file__), '../../gcp/utils')) |
| sys.path.append(gcp_utils_dir) |
| import big_query_utils |
| |
| _PROJECT_ID = 'grpc-testing' |
| |
| |
| def _upload_netperf_latency_csv_to_bigquery(dataset_id, table_id, result_file): |
| with open(result_file, 'r') as f: |
| (col1, col2, col3) = f.read().split(',') |
| latency50 = float(col1.strip()) * 1000 |
| latency90 = float(col2.strip()) * 1000 |
| latency99 = float(col3.strip()) * 1000 |
| |
| scenario_result = { |
| 'scenario': { |
| 'name': 'netperf_tcp_rr' |
| }, |
| 'summary': { |
| 'latency50': latency50, |
| 'latency90': latency90, |
| 'latency99': latency99 |
| } |
| } |
| |
| bq = big_query_utils.create_big_query() |
| _create_results_table(bq, dataset_id, table_id) |
| |
| if not _insert_result( |
| bq, dataset_id, table_id, scenario_result, flatten=False): |
| print('Error uploading result to bigquery.') |
| sys.exit(1) |
| |
| |
| def _upload_scenario_result_to_bigquery(dataset_id, table_id, result_file, |
| metadata_file, node_info_file): |
| with open(result_file, 'r') as f: |
| scenario_result = json.loads(f.read()) |
| |
| bq = big_query_utils.create_big_query() |
| _create_results_table(bq, dataset_id, table_id) |
| |
| if not _insert_scenario_result(bq, dataset_id, table_id, scenario_result, |
| metadata_file, node_info_file): |
| print('Error uploading result to bigquery.') |
| 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_scenario_result(bq, |
| dataset_id, |
| table_id, |
| scenario_result, |
| test_metadata_file, |
| node_info_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) |
| 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 _create_results_table(bq, dataset_id, table_id): |
| with open(os.path.dirname(__file__) + '/scenario_result_schema.json', |
| 'r') as f: |
| table_schema = json.loads(f.read()) |
| desc = 'Results of performance benchmarks.' |
| return big_query_utils.create_table2(bq, _PROJECT_ID, dataset_id, table_id, |
| table_schema, desc) |
| |
| |
| def _flatten_result_inplace(scenario_result): |
| """Bigquery is not really great for handling deeply nested data |
| and repeated fields. To maintain values of some fields while keeping |
| the schema relatively simple, we artificially leave some of the fields |
| as JSON strings. |
| """ |
| scenario_result['scenario']['clientConfig'] = json.dumps( |
| scenario_result['scenario']['clientConfig']) |
| scenario_result['scenario']['serverConfig'] = json.dumps( |
| scenario_result['scenario']['serverConfig']) |
| scenario_result['latencies'] = json.dumps(scenario_result['latencies']) |
| scenario_result['serverCpuStats'] = [] |
| for stats in scenario_result['serverStats']: |
| scenario_result['serverCpuStats'].append(dict()) |
| scenario_result['serverCpuStats'][-1]['totalCpuTime'] = stats.pop( |
| 'totalCpuTime', None) |
| scenario_result['serverCpuStats'][-1]['idleCpuTime'] = stats.pop( |
| 'idleCpuTime', None) |
| for stats in scenario_result['clientStats']: |
| stats['latencies'] = json.dumps(stats['latencies']) |
| stats.pop('requestResults', None) |
| scenario_result['serverCores'] = json.dumps(scenario_result['serverCores']) |
| scenario_result['clientSuccess'] = json.dumps( |
| scenario_result['clientSuccess']) |
| scenario_result['serverSuccess'] = json.dumps( |
| scenario_result['serverSuccess']) |
| scenario_result['requestResults'] = json.dumps( |
| scenario_result.get('requestResults', [])) |
| scenario_result['serverCpuUsage'] = scenario_result['summary'].pop( |
| 'serverCpuUsage', None) |
| scenario_result['summary'].pop('successfulRequestsPerSecond', None) |
| scenario_result['summary'].pop('failedRequestsPerSecond', None) |
| massage_qps_stats.massage_qps_stats(scenario_result) |
| |
| |
| def _populate_metadata_inplace(scenario_result): |
| """Populates metadata based on environment variables set by Jenkins.""" |
| # NOTE: Grabbing the Kokoro environment variables will only work if the |
| # driver is running locally on the same machine where Kokoro has started |
| # the job. For our setup, this is currently the case, so just assume that. |
| build_number = os.getenv('KOKORO_BUILD_NUMBER') |
| build_url = 'https://source.cloud.google.com/results/invocations/%s' % os.getenv( |
| 'KOKORO_BUILD_ID') |
| job_name = os.getenv('KOKORO_JOB_NAME') |
| git_commit = os.getenv('KOKORO_GIT_COMMIT') |
| # actual commit is the actual head of PR that is getting tested |
| # TODO(jtattermusch): unclear how to obtain on Kokoro |
| git_actual_commit = os.getenv('ghprbActualCommit') |
| |
| utc_timestamp = str(calendar.timegm(time.gmtime())) |
| metadata = {'created': utc_timestamp} |
| |
| if build_number: |
| metadata['buildNumber'] = build_number |
| if build_url: |
| metadata['buildUrl'] = build_url |
| if job_name: |
| metadata['jobName'] = job_name |
| if git_commit: |
| metadata['gitCommit'] = git_commit |
| if git_actual_commit: |
| metadata['gitActualCommit'] = git_actual_commit |
| |
| scenario_result['metadata'] = metadata |
| |
| |
| def _populate_metadata_from_file(scenario_result, test_metadata_file): |
| utc_timestamp = str(calendar.timegm(time.gmtime())) |
| metadata = {'created': utc_timestamp} |
| |
| _annotation_to_bq_metadata_key_map = { |
| 'ci_' + key: key for key in ( |
| 'buildNumber', |
| 'buildUrl', |
| 'jobName', |
| 'gitCommit', |
| 'gitActualCommit', |
| ) |
| } |
| |
| if os.access(test_metadata_file, os.R_OK): |
| with open(test_metadata_file, 'r') as f: |
| test_metadata = json.loads(f.read()) |
| |
| # eliminate managedFields from metadata set |
| if 'managedFields' in test_metadata: |
| del test_metadata['managedFields'] |
| |
| annotations = test_metadata.get('annotations', {}) |
| |
| # if use kubectl apply ..., kubectl will append current configuration to |
| # annotation, the field is deleted since it includes a lot of irrelevant |
| # information |
| if 'kubectl.kubernetes.io/last-applied-configuration' in annotations: |
| del annotations['kubectl.kubernetes.io/last-applied-configuration'] |
| |
| # dump all metadata as JSON to testMetadata field |
| scenario_result['testMetadata'] = json.dumps(test_metadata) |
| for key, value in _annotation_to_bq_metadata_key_map.items(): |
| if key in annotations: |
| metadata[value] = annotations[key] |
| |
| scenario_result['metadata'] = metadata |
| |
| |
| def _populate_node_metadata_from_file(scenario_result, node_info_file): |
| node_metadata = {'driver': {}, 'servers': [], 'clients': []} |
| _node_info_to_bq_node_metadata_key_map = { |
| 'Name': 'name', |
| 'PodIP': 'podIP', |
| 'NodeName': 'nodeName', |
| } |
| with open(node_info_file, 'r') as f: |
| file_metadata = json.loads(f.read()) |
| for key, value in _node_info_to_bq_node_metadata_key_map.items(): |
| node_metadata['driver'][value] = file_metadata['Driver'][key] |
| for clientNodeInfo in file_metadata['Clients']: |
| node_metadata['clients'].append({ |
| value: clientNodeInfo[key] for key, value in |
| _node_info_to_bq_node_metadata_key_map.items() |
| }) |
| for serverNodeInfo in file_metadata['Servers']: |
| node_metadata['servers'].append({ |
| value: serverNodeInfo[key] for key, value in |
| _node_info_to_bq_node_metadata_key_map.items() |
| }) |
| scenario_result['nodeMetadata'] = node_metadata |
| |
| |
| argp = argparse.ArgumentParser(description='Upload result to big query.') |
| argp.add_argument('--bq_result_table', |
| required=True, |
| default=None, |
| type=str, |
| help='Bigquery "dataset.table" to upload results to.') |
| argp.add_argument('--file_to_upload', |
| default='scenario_result.json', |
| type=str, |
| help='Report file to upload.') |
| argp.add_argument('--metadata_file_to_upload', |
| default='metadata.json', |
| type=str, |
| help='Metadata file to upload.') |
| argp.add_argument('--node_info_file_to_upload', |
| default='node_info.json', |
| type=str, |
| help='Node information file to upload.') |
| argp.add_argument('--file_format', |
| choices=['scenario_result', 'netperf_latency_csv'], |
| default='scenario_result', |
| help='Format of the file to upload.') |
| |
| args = argp.parse_args() |
| |
| dataset_id, table_id = args.bq_result_table.split('.', 2) |
| |
| if args.file_format == 'netperf_latency_csv': |
| _upload_netperf_latency_csv_to_bigquery(dataset_id, table_id, |
| args.file_to_upload) |
| else: |
| _upload_scenario_result_to_bigquery(dataset_id, table_id, |
| args.file_to_upload, |
| args.metadata_file_to_upload, |
| args.node_info_file_to_upload) |
| print('Successfully uploaded %s, %s and %s to BigQuery.\n' % |
| (args.file_to_upload, args.metadata_file_to_upload, |
| args.node_info_file_to_upload)) |