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The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
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186 lines
7.0 KiB
186 lines
7.0 KiB
#!/usr/bin/env python |
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# Copyright 2016 gRPC authors. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# Uploads performance benchmark result file to bigquery. |
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from __future__ import print_function |
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import argparse |
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import calendar |
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import json |
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import os |
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import sys |
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import time |
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import uuid |
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import massage_qps_stats |
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gcp_utils_dir = os.path.abspath( |
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os.path.join(os.path.dirname(__file__), '../../gcp/utils')) |
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sys.path.append(gcp_utils_dir) |
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import big_query_utils |
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_PROJECT_ID = 'grpc-testing' |
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def _upload_netperf_latency_csv_to_bigquery(dataset_id, table_id, result_file): |
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with open(result_file, 'r') as f: |
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(col1, col2, col3) = f.read().split(',') |
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latency50 = float(col1.strip()) * 1000 |
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latency90 = float(col2.strip()) * 1000 |
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latency99 = float(col3.strip()) * 1000 |
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scenario_result = { |
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'scenario': { |
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'name': 'netperf_tcp_rr' |
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}, |
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'summary': { |
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'latency50': latency50, |
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'latency90': latency90, |
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'latency99': latency99 |
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} |
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} |
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bq = big_query_utils.create_big_query() |
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_create_results_table(bq, dataset_id, table_id) |
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if not _insert_result( |
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bq, dataset_id, table_id, scenario_result, flatten=False): |
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print('Error uploading result to bigquery.') |
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sys.exit(1) |
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def _upload_scenario_result_to_bigquery(dataset_id, table_id, result_file): |
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with open(result_file, 'r') as f: |
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scenario_result = json.loads(f.read()) |
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bq = big_query_utils.create_big_query() |
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_create_results_table(bq, dataset_id, table_id) |
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if not _insert_result(bq, dataset_id, table_id, scenario_result): |
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print('Error uploading result to bigquery.') |
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sys.exit(1) |
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def _insert_result(bq, dataset_id, table_id, scenario_result, flatten=True): |
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if flatten: |
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_flatten_result_inplace(scenario_result) |
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_populate_metadata_inplace(scenario_result) |
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row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result) |
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return big_query_utils.insert_rows(bq, _PROJECT_ID, dataset_id, table_id, |
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[row]) |
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def _create_results_table(bq, dataset_id, table_id): |
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with open(os.path.dirname(__file__) + '/scenario_result_schema.json', |
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'r') as f: |
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table_schema = json.loads(f.read()) |
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desc = 'Results of performance benchmarks.' |
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return big_query_utils.create_table2(bq, _PROJECT_ID, dataset_id, table_id, |
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table_schema, desc) |
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def _flatten_result_inplace(scenario_result): |
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"""Bigquery is not really great for handling deeply nested data |
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and repeated fields. To maintain values of some fields while keeping |
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the schema relatively simple, we artificially leave some of the fields |
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as JSON strings. |
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""" |
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scenario_result['scenario']['clientConfig'] = json.dumps( |
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scenario_result['scenario']['clientConfig']) |
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scenario_result['scenario']['serverConfig'] = json.dumps( |
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scenario_result['scenario']['serverConfig']) |
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scenario_result['latencies'] = json.dumps(scenario_result['latencies']) |
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scenario_result['serverCpuStats'] = [] |
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for stats in scenario_result['serverStats']: |
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scenario_result['serverCpuStats'].append(dict()) |
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scenario_result['serverCpuStats'][-1]['totalCpuTime'] = stats.pop( |
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'totalCpuTime', None) |
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scenario_result['serverCpuStats'][-1]['idleCpuTime'] = stats.pop( |
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'idleCpuTime', None) |
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for stats in scenario_result['clientStats']: |
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stats['latencies'] = json.dumps(stats['latencies']) |
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stats.pop('requestResults', None) |
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scenario_result['serverCores'] = json.dumps(scenario_result['serverCores']) |
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scenario_result['clientSuccess'] = json.dumps( |
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scenario_result['clientSuccess']) |
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scenario_result['serverSuccess'] = json.dumps( |
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scenario_result['serverSuccess']) |
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scenario_result['requestResults'] = json.dumps( |
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scenario_result.get('requestResults', [])) |
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scenario_result['serverCpuUsage'] = scenario_result['summary'].pop( |
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'serverCpuUsage', None) |
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scenario_result['summary'].pop('successfulRequestsPerSecond', None) |
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scenario_result['summary'].pop('failedRequestsPerSecond', None) |
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massage_qps_stats.massage_qps_stats(scenario_result) |
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def _populate_metadata_inplace(scenario_result): |
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"""Populates metadata based on environment variables set by Jenkins.""" |
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# NOTE: Grabbing the Kokoro environment variables will only work if the |
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# driver is running locally on the same machine where Kokoro has started |
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# the job. For our setup, this is currently the case, so just assume that. |
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build_number = os.getenv('KOKORO_BUILD_NUMBER') |
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build_url = 'https://source.cloud.google.com/results/invocations/%s' % os.getenv( |
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'KOKORO_BUILD_ID') |
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job_name = os.getenv('KOKORO_JOB_NAME') |
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git_commit = os.getenv('KOKORO_GIT_COMMIT') |
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# actual commit is the actual head of PR that is getting tested |
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# TODO(jtattermusch): unclear how to obtain on Kokoro |
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git_actual_commit = os.getenv('ghprbActualCommit') |
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utc_timestamp = str(calendar.timegm(time.gmtime())) |
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metadata = {'created': utc_timestamp} |
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if build_number: |
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metadata['buildNumber'] = build_number |
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if build_url: |
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metadata['buildUrl'] = build_url |
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if job_name: |
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metadata['jobName'] = job_name |
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if git_commit: |
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metadata['gitCommit'] = git_commit |
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if git_actual_commit: |
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metadata['gitActualCommit'] = git_actual_commit |
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scenario_result['metadata'] = metadata |
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argp = argparse.ArgumentParser(description='Upload result to big query.') |
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argp.add_argument('--bq_result_table', |
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required=True, |
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default=None, |
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type=str, |
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help='Bigquery "dataset.table" to upload results to.') |
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argp.add_argument('--file_to_upload', |
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default='scenario_result.json', |
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type=str, |
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help='Report file to upload.') |
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argp.add_argument('--file_format', |
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choices=['scenario_result', 'netperf_latency_csv'], |
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default='scenario_result', |
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help='Format of the file to upload.') |
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args = argp.parse_args() |
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dataset_id, table_id = args.bq_result_table.split('.', 2) |
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if args.file_format == 'netperf_latency_csv': |
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_upload_netperf_latency_csv_to_bigquery(dataset_id, table_id, |
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args.file_to_upload) |
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else: |
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_upload_scenario_result_to_bigquery(dataset_id, table_id, |
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args.file_to_upload) |
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print('Successfully uploaded %s to BigQuery.\n' % args.file_to_upload)
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