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#!/usr/bin/env python2.7
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# Copyright 2016, Google Inc.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following disclaimer
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# in the documentation and/or other materials provided with the
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# distribution.
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# * Neither the name of Google Inc. nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# Uploads performance benchmark result file to bigquery.
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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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gcp_utils_dir = os.path.abspath(os.path.join(
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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(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,
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_PROJECT_ID,
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dataset_id,
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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', '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,
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table_id, 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(scenario_result['scenario']['clientConfig'])
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scenario_result['scenario']['serverConfig'] = json.dumps(scenario_result['scenario']['serverConfig'])
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scenario_result['latencies'] = json.dumps(scenario_result['latencies'])
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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(scenario_result['clientSuccess'])
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scenario_result['serverSuccess'] = json.dumps(scenario_result['serverSuccess'])
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scenario_result['requestResults'] = json.dumps(scenario_result.get('requestResults', []))
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scenario_result['summary'].pop('successfulRequestsPerSecond', None)
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scenario_result['summary'].pop('failedRequestsPerSecond', None)
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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 Jenkins environment variables will only work if the
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# driver is running locally on the same machine where Jenkins 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('BUILD_NUMBER')
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build_url = os.getenv('BUILD_URL')
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job_name = os.getenv('JOB_NAME')
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git_commit = os.getenv('GIT_COMMIT')
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# actual commit is the actual head of PR that is getting tested
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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', required=True, default=None, type=str,
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help='Bigquery "dataset.table" to upload results to.')
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argp.add_argument('--file_to_upload', default='scenario_result.json', 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, args.file_to_upload)
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else:
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_upload_scenario_result_to_bigquery(dataset_id, table_id, args.file_to_upload)
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print 'Successfully uploaded %s to BigQuery.\n' % args.file_to_upload
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