| #!/usr/bin/env python |
| # Copyright 2016, Google Inc. |
| # All rights reserved. |
| # |
| # Redistribution and use in source and binary forms, with or without |
| # modification, are permitted provided that the following conditions are |
| # met: |
| # |
| # * Redistributions of source code must retain the above copyright |
| # notice, this list of conditions and the following disclaimer. |
| # * Redistributions in binary form must reproduce the above |
| # copyright notice, this list of conditions and the following disclaimer |
| # in the documentation and/or other materials provided with the |
| # distribution. |
| # * Neither the name of Google Inc. nor the names of its |
| # contributors may be used to endorse or promote products derived from |
| # this software without specific prior written permission. |
| # |
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS |
| # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT |
| # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR |
| # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT |
| # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, |
| # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT |
| # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, |
| # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY |
| # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT |
| # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE |
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| |
| # 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 |
| |
| |
| 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): |
| 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_result(bq, dataset_id, table_id, scenario_result): |
| 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 _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) |
| |
| |
| def _populate_metadata_inplace(scenario_result): |
| """Populates metadata based on environment variables set by Jenkins.""" |
| # NOTE: Grabbing the Jenkins environment variables will only work if the |
| # driver is running locally on the same machine where Jenkins has started |
| # the job. For our setup, this is currently the case, so just assume that. |
| build_number = os.getenv('BUILD_NUMBER') |
| build_url = os.getenv('BUILD_URL') |
| job_name = os.getenv('JOB_NAME') |
| git_commit = os.getenv('GIT_COMMIT') |
| # actual commit is the actual head of PR that is getting tested |
| 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 |
| |
| |
| 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('--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) |
| print('Successfully uploaded %s to BigQuery.\n' % args.file_to_upload) |