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https://issues.apache.org/jira/browse/BEAM-4065?focusedWorklogId=100986&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-100986
]
ASF GitHub Bot logged work on BEAM-4065:
----------------------------------------
Author: ASF GitHub Bot
Created on: 11/May/18 05:26
Start Date: 11/May/18 05:26
Worklog Time Spent: 10m
Work Description: chamikaramj commented on a change in pull request
#5180: [BEAM-4065] Basic performance tests analysis added.
URL: https://github.com/apache/beam/pull/5180#discussion_r187521550
##########
File path: .test-infra/jenkins/verify_performance_test_results.py
##########
@@ -0,0 +1,252 @@
+#!/usr/bin/env python
+#
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+# This script performs basic analytic of performance tests results.
+# It operates in two modes:
+# --mode=report - In this mode script iterates over list of BigQuery tables
and
+# analyses the data. This mode is intended to be run on a regulars basis,
e.g. daily.
+# Report will contain average tests execution time of given metric, its
comparison with
+# with average calculated from historical data, recent standard deviation
and standard
+# deviation calculated based on historical data.
+# --mode=validation - In this mode script will analyse single BigQuery table
and check
+# recent results.
+#
+# Other parameters are described in script. Notification is optional
parameter.
+# --send_notification - if present, script will send notification to slack
channel.
+# Requires setting env variable SLACK_WEBOOK_URL which value could be
obtained by
+# creating incoming webhook on Slack.
+#
+# This script is intended to be used only by Jenkins.
+# Example script usage:
+# verify_performance_test_results.py \
+# --bqtable='["beam_performance.avroioit_hdfs_pkb_results", \
+# "beam_performance.textioit_pkb_results"]' \
+# --metric="run_time" --mode=report --send_notification
+#
+
+import argparse, time, calendar, json, re, os, requests
+from google.cloud import bigquery
+
+### TIME SETTINGS ###########
+TIME_PATTERN = '%d-%m-%Y_%H-%M-%S'
+NOW = int(time.time())
+# First analysis time interval definition - 24h before
+TIME_POINT_1 = NOW - 1 * 86400
+# Second analysis time interval definition - week before
+TIME_POINT_2 = NOW - 7 * 86400
+##############################
+
+SLACK_USER = os.getenv('SLACK_USER', "jenkins-beam")
+SLACK_WEBHOOK_URL = os.getenv('SLACK_WEBHOOK_URL')
+SLACK_CHANNEL = os.getenv('SLACK_CHANNEL', "beam-testing")
+
+def submit_big_query_job(sql_command, return_type):
+ query_job = client.query(sql_command)
+ results = query_job.result()
+ if return_type == "list":
+ # Queries that have multiple elements in output displayed as
query_result
+ result_list = []
+ for row in results:
+ result_list.append(row.query_result)
+ return result_list
+ elif return_type == "value":
+ # All queries must have single element in output displayed as
query_result
+ for row in results:
+ return (row.query_result)
+ else:
+ print("This type is not supported")
+ return None
+
+def count_queries(table_name, time_start, time_stop, metric):
+ # This function checks how many data was inserted in time interval.
+ sql_command = 'select count(*) as query_result from {} where TIMESTAMP >
{} and TIMESTAMP < {} and METRIC=\'{}\''.format(
+ table_name,
+ time_start,
+ time_stop,
+ metric
+ )
+ count = submit_big_query_job(sql_command, "value")
+ print("Number of records inserted into {} between {} - {}: {}".format(
+ table_name,
+ time.strftime(TIME_PATTERN, time.gmtime(time_start)),
+ time.strftime(TIME_PATTERN, time.gmtime(time_stop)),
+ count))
+ return count
+
+def get_average_from(table_name, time_start, time_stop, metric):
+ # This function return average value of the provided metric in time
interval.
+ sql_command = 'select avg(value) as query_result from {} where TIMESTAMP >
{} and TIMESTAMP < {} and METRIC=\'{}\''.format(
+ table_name,
+ time_start,
+ time_stop,
+ metric
+ )
+ average = submit_big_query_job(sql_command, "value")
+ return average
+
+def get_stddev_from(table_name, time_start, time_stop, metric):
+ # This function return standard deviation of the provided metric in time
interval.
+ sql_command = 'select stddev(value) as query_result from {} where
TIMESTAMP > {} and TIMESTAMP < {} and METRIC=\'{}\''.format(
+ table_name,
+ time_start,
+ time_stop,
+ metric
+ )
+ stddev = submit_big_query_job(sql_command, "value")
+ return stddev
+
+def get_records_from(table_name, time_start, time_stop, metric,
number_of_records):
+ # This function checks how many data was inserted in time interval.
+ sql_command = 'select value as query_result from {} where TIMESTAMP > {}
and TIMESTAMP < {} and METRIC=\'{}\' ORDER BY TIMESTAMP DESC LIMIT {}'.format(
+ table_name,
+ time_start,
+ time_stop,
+ metric,
+ number_of_records
+ )
+ list_of_records = submit_big_query_job(sql_command, "list")
+ return list_of_records
+
+def calculate_historical_data(bq_table_name, TIME_POINT_2, TIME_POINT_1,
metric, nb_older_records, average_recent):
+ average_old = get_average_from(bq_table_name, TIME_POINT_2, TIME_POINT_1,
metric)
+ if nb_older_records > 1:
+ stddev_old = get_stddev_from(bq_table_name, TIME_POINT_2,
TIME_POINT_1, metric)
+ else:
+ # Standard deviation is 0 when there is only single value.
+ stddev_old = 0
+ percentage_change = 100*(average_recent - average_old)/average_old
+ return percentage_change, stddev_old
+
+def update_messages(bq_table_name, metric, average_recent, percentage_change,
stddev_recent, stddev_old,
+ report_message, slack_report_message):
+ report_message = report_message + '{} - {}, avg_time {:.2f}s, change
{:+.3f}%, stddev {:.2f}, stddev_old {:.2f} \n'.format(
+ bq_table_name, metric, average_recent, percentage_change,
stddev_recent, stddev_old)
+ slack_report_message = slack_report_message + format_slack_message(
+ bq_table_name, metric, average_recent, percentage_change,
stddev_recent, stddev_old)
+ return report_message, slack_report_message
+
+def create_report(bqtables, metric, send_notification):
+ # This function create a report message from tables.
+ report_message = ''
+ slack_report_message = ''
+
+ for bqtable in bqtables:
+ # Get raw table name
+ bq_table_name = re.sub(r'\"|\[|\]', '', bqtable).strip()
+
+ # Make sure there was data records inserted
+ nb_recent_records = count_queries(bq_table_name, TIME_POINT_1, NOW,
metric)
+ if nb_recent_records == 0:
+ report_message = report_message + '{} there were no tests results
uploaded in recent 24h. \n'.format(bq_table_name)
+ slack_report_message = slack_report_message + '`{}` there were no
tests results uploaded in recent 24h. :bangbang:\n'.format(bq_table_name)
Review comment:
s/tests results/test results
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Issue Time Tracking
-------------------
Worklog Id: (was: 100986)
Time Spent: 7h (was: 6h 50m)
> Performance Tests Results Analysis and Regression Detection
> -----------------------------------------------------------
>
> Key: BEAM-4065
> URL: https://issues.apache.org/jira/browse/BEAM-4065
> Project: Beam
> Issue Type: Improvement
> Components: build-system
> Reporter: Kamil Szewczyk
> Assignee: Kamil Szewczyk
> Priority: Major
> Time Spent: 7h
> Remaining Estimate: 0h
>
> Performance tests are running on Jenkins on regular basis and results are
> pushed to BigQuery. However there is no automatic regression detection or
> daily reports with results.
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