The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
https://grpc.io/
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89 lines
3.5 KiB
89 lines
3.5 KiB
# 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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# utilities for exporting benchmark results |
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import json |
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import os |
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import sys |
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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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_DATASET_ID='test_dataset' |
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_RESULTS_TABLE_ID='scenario_results' |
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def upload_scenario_result_to_bigquery(result_file): |
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bq = big_query_utils.create_big_query() |
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_create_results_table(bq) |
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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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_insert_result(bq, scenario_result) |
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def _insert_result(bq, scenario_result): |
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_flatten_result_inplace(scenario_result) |
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# TODO: handle errors... |
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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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_RESULTS_TABLE_ID, |
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[row]) |
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def _create_results_table(bq): |
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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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_RESULTS_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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scenario_result['serverCores'] = json.dumps(scenario_result['serverCores'])
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