#!/usr/bin/env python # Copyright 2017 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 RBE results to BigQuery""" import argparse import os import json import sys import urllib2 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 _DATASET_ID = 'jenkins_test_results' _DESCRIPTION = 'Test results from master RBE builds on Kokoro' # 90 days in milliseconds _EXPIRATION_MS = 90 * 24 * 60 * 60 * 1000 _PARTITION_TYPE = 'DAY' _PROJECT_ID = 'grpc-testing' _RESULTS_SCHEMA = [ ('job_name', 'STRING', 'Name of Kokoro job'), ('build_id', 'INTEGER', 'Build ID of Kokoro job'), ('build_url', 'STRING', 'URL of Kokoro build'), ('test_target', 'STRING', 'Bazel target path'), ('test_case', 'STRING', 'Name of test case'), ('result', 'STRING', 'Test or build result'), ('timestamp', 'TIMESTAMP', 'Timestamp of test run'), ] _TABLE_ID = 'rbe_test_results' def _get_api_key(): """Returns string with API key to access ResultStore. Intended to be used in Kokoro envrionment.""" api_key_directory = os.getenv('KOKORO_GFILE_DIR') api_key_file = os.path.join(api_key_directory, 'resultstore_api_key') assert os.path.isfile(api_key_file), 'Must add --api_key arg if not on ' \ 'Kokoro or Kokoro envrionment is not set up properly.' with open(api_key_file, 'r') as f: return f.read().replace('\n', '') def _get_invocation_id(): """Returns String of Bazel invocation ID. Intended to be used in Kokoro envirionment.""" bazel_id_directory = os.getenv('KOKORO_ARTIFACTS_DIR') bazel_id_file = os.path.join(bazel_id_directory, 'bazel_invocation_ids') assert os.path.isfile(bazel_id_file), 'bazel_invocation_ids file, written ' \ 'by bazel_wrapper.py, expected but not found.' with open(bazel_id_file, 'r') as f: return f.read().replace('\n', '') def _upload_results_to_bq(rows): """Upload test results to a BQ table. Args: rows: A list of dictionaries containing data for each row to insert """ bq = big_query_utils.create_big_query() big_query_utils.create_partitioned_table( bq, _PROJECT_ID, _DATASET_ID, _TABLE_ID, _RESULTS_SCHEMA, _DESCRIPTION, partition_type=_PARTITION_TYPE, expiration_ms=_EXPIRATION_MS) max_retries = 3 for attempt in range(max_retries): if big_query_utils.insert_rows(bq, _PROJECT_ID, _DATASET_ID, _TABLE_ID, rows): break else: if attempt < max_retries - 1: print('Error uploading result to bigquery, will retry.') else: print( 'Error uploading result to bigquery, all attempts failed.') sys.exit(1) def _get_resultstore_data(api_key, invocation_id): """Returns dictionary of test results by querying ResultStore API. Args: api_key: String of ResultStore API key invocation_id: String of ResultStore invocation ID to results from """ all_actions = [] page_token = '' # ResultStore's API returns data on a limited number of tests. When we exceed # that limit, the 'nextPageToken' field is included in the request to get # subsequent data, so keep requesting until 'nextPageToken' field is omitted. while True: req = urllib2.Request( url= 'https://resultstore.googleapis.com/v2/invocations/%s/targets/-/configuredTargets/-/actions?key=%s&pageToken=%s' % (invocation_id, api_key, page_token), headers={ 'Content-Type': 'application/json' }) results = json.loads(urllib2.urlopen(req).read()) all_actions.extend(results['actions']) if 'nextPageToken' not in results: break page_token = results['nextPageToken'] return all_actions if __name__ == "__main__": # Arguments are necessary if running in a non-Kokoro environment. argp = argparse.ArgumentParser(description='Upload RBE results.') argp.add_argument('--api_key', default='', type=str) argp.add_argument('--invocation_id', default='', type=str) args = argp.parse_args() api_key = args.api_key or _get_api_key() invocation_id = args.invocation_id or _get_invocation_id() resultstore_actions = _get_resultstore_data(api_key, invocation_id) bq_rows = [] for index, action in enumerate(resultstore_actions): # Filter out non-test related data, such as build results. if 'testAction' not in action: continue # Some test results contain the fileProcessingErrors field, which indicates # an issue with parsing results individual test cases. if 'fileProcessingErrors' in action: test_cases = [{ 'testCase': { 'caseName': str(action['id']['actionId']), } }] # Test timeouts have a different dictionary structure compared to pass and # fail results. elif action['statusAttributes']['status'] == 'TIMED_OUT': test_cases = [{ 'testCase': { 'caseName': str(action['id']['actionId']), 'timedOut': True } }] # When RBE believes its infrastructure is failing, it will abort and # mark running tests as UNKNOWN. These infrastructure failures may be # related to our tests, so we should investigate if specific tests are # repeatedly being marked as UNKNOWN. elif action['statusAttributes']['status'] == 'UNKNOWN': test_cases = [{ 'testCase': { 'caseName': str(action['id']['actionId']), 'unknown': True } }] # Take the timestamp from the previous action, which should be # a close approximation. action['timing'] = { 'startTime': resultstore_actions[index - 1]['timing']['startTime'] } else: test_cases = action['testAction']['testSuite']['tests'][0][ 'testSuite']['tests'] for test_case in test_cases: if any(s in test_case['testCase'] for s in ['errors', 'failures']): result = 'FAILED' elif 'timedOut' in test_case['testCase']: result = 'TIMEOUT' elif 'unknown' in test_case['testCase']: result = 'UNKNOWN' else: result = 'PASSED' bq_rows.append({ 'insertId': str(uuid.uuid4()), 'json': { 'job_name': os.getenv('KOKORO_JOB_NAME'), 'build_id': os.getenv('KOKORO_BUILD_NUMBER'), 'build_url': 'https://source.cloud.google.com/results/invocations/%s' % invocation_id, 'test_target': action['id']['targetId'], 'test_case': test_case['testCase']['caseName'], 'result': result, 'timestamp': action['timing']['startTime'], } }) # BigQuery sometimes fails with large uploads, so batch 1,000 rows at a time. for i in range((len(bq_rows) / 1000) + 1): _upload_results_to_bq(bq_rows[i * 1000:(i + 1) * 1000])