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openSUSE-release-tools/metrics.py

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Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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#!/usr/bin/python
import argparse
from collections import namedtuple
from datetime import datetime
from dateutil.parser import parse as date_parse
from influxdb import InfluxDBClient
from lxml import etree as ET
import os
import subprocess
import sys
import yaml
import osc.conf
import osc.core
import osclib.conf
from osclib.cache import Cache
from osclib.conf import Config
from osclib.stagingapi import StagingAPI
metrics: prefix release schedule file with source dir path. Otherwise, when deployed via package and run via /usr/bin the files are not found.
2017-11-08 16:42:42 -06:00
SOURCE_DIR = os.path.dirname(os.path.realpath(__file__))
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
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Point = namedtuple('Point', ['measurement', 'tags', 'fields', 'time', 'delta'])
metrics: prefix release schedule file with source dir path. Otherwise, when deployed via package and run via /usr/bin the files are not found.
2017-11-08 16:42:42 -06:00
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
# Duplicate Leap config to handle 13.2 without issue.
Another set of low-noise flake8 cleanups
2017-10-20 08:54:37 +02:00
osclib.conf.DEFAULT[
r'openSUSE:(?P<project>[\d.]+)'] = osclib.conf.DEFAULT[
r'openSUSE:(?P<project>Leap:[\d.]+)']
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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# Provide osc.core.get_request_list() that swaps out search() implementation to
# capture the generated query, paginate over and yield each request to avoid
# loading all requests at the same time. Additionally, use lxml ET to avoid
# having to re-parse to perform complex xpaths.
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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def get_request_list(*args, **kwargs):
osc.core._search = osc.core.search
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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osc.core.search = search_capture
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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osc.core._ET = osc.core.ET
osc.core.ET = ET
osc.core.get_request_list(*args, **kwargs)
osc.core.search = osc.core._search
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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query = search_capture.query
for request in search_paginated_generator(query[0], query[1], **query[2]):
# Python 3 yield from.
yield request
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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osc.core.ET = osc.core._ET
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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def search_capture(apiurl, queries=None, **kwargs):
search_capture.query = (apiurl, queries, kwargs)
return {'request': ET.fromstring('<collection matches="0"></collection>')}
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
# Provides a osc.core.search() implementation for use with get_request_list()
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
2017-11-09 01:23:03 -06:00
# that paginates in sets of 1000 and yields each request.
def search_paginated_generator(apiurl, queries=None, **kwargs):
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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if "submit/target/@project='openSUSE:Factory'" in kwargs['request']:
kwargs['request'] = osc.core.xpath_join(kwargs['request'], '@id>250000', op='and')
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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request_count = 0
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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queries['request']['limit'] = 1000
queries['request']['offset'] = 0
while True:
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
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collection = osc.core.search(apiurl, queries, **kwargs)['request']
if not request_count:
print('processing {:,} requests'.format(int(collection.get('matches'))))
for request in collection.findall('request'):
yield request
request_count += 1
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
metrics: rework request pagination to provide as generator. This prevents the memory required to process to be enough to load all parsed request element trees at once. Instead only one page of requests is loaded at a time and the memory freed after processed. The end result is the memory consumption reduced by just over 20% (current Factory drops by around 2.5GB).
2017-11-09 01:23:03 -06:00
if request_count == int(collection.get('matches')):
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
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# Stop paging once the expected number of items has been returned.
break
metrics: call ET.clear() to release unneeded memory used by search result. Roughly 1800MB per 10,000 requests saved.
2017-11-09 01:17:27 -06:00
# Release memory as otherwise ET seems to hold onto it.
collection.clear()
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
queries['request']['offset'] += queries['request']['limit']
points = []
def point(measurement, fields, datetime, tags={}, delta=False):
global points
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
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points.append(Point(measurement, tags, fields, timestamp(datetime), delta))
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
def timestamp(datetime):
return int(datetime.strftime('%s'))
def ingest_requests(api, project):
requests = get_request_list(api.apiurl, project,
req_state=('accepted', 'revoked', 'superseded'),
exclude_target_projects=[project],
withfullhistory=True)
for request in requests:
if request.find('action').get('type') not in ('submit', 'delete'):
# TODO Handle non-stageable requests via different flow.
continue
created_at = date_parse(request.find('history').get('when'))
final_at = date_parse(request.find('state').get('when'))
final_at_history = date_parse(request.find('history[last()]').get('when'))
if final_at_history > final_at:
# Workaround for invalid dates: openSUSE/open-build-service#3858.
final_at = final_at_history
# TODO Track requests in psuedo-ignore state.
point('total', {'backlog': 1, 'open': 1}, created_at, {'event': 'create'}, True)
point('total', {'backlog': -1, 'open': -1}, final_at, {'event': 'close'}, True)
request_tags = {}
request_fields = {
'total': (final_at - created_at).total_seconds(),
'staged_count': len(request.findall('review[@by_group="factory-staging"]/history')),
}
# TODO Total time spent in backlog (ie factory-staging, but excluding when staged).
staged_first_review = request.xpath('review[contains(@by_project, "{}:Staging:")]'.format(project))
if len(staged_first_review):
by_project = staged_first_review[0].get('by_project')
request_tags['type'] = 'adi' if api.is_adi_project(by_project) else 'letter'
# TODO Determine current whitelists state based on dashboard revisions.
if project.startswith('openSUSE:Factory'):
splitter_whitelist = 'B C D E F G H I J'.split()
if splitter_whitelist:
short = api.extract_staging_short(by_project)
request_tags['whitelisted'] = short in splitter_whitelist
else:
# All letter where whitelisted since no restriction.
request_tags['whitelisted'] = request_tags['type'] == 'letter'
ready_to_accept = request.xpath('review[contains(@by_project, "{}:Staging:adi:") and @state="accepted"]/history[comment[text() = "ready to accept"]]/@when'.format(project))
if len(ready_to_accept):
ready_to_accept = date_parse(ready_to_accept[0])
request_fields['ready'] = (final_at - ready_to_accept).total_seconds()
# TODO Points with indentical timestamps are merged so this can be placed in total
# measurement, but may make sense to keep this separate and make the others follow.
point('ready', {'count': 1}, ready_to_accept, delta=True)
point('ready', {'count': -1}, final_at, delta=True)
staged_first = request.xpath('review[@by_group="factory-staging"]/history/@when')
if len(staged_first):
staged_first = date_parse(staged_first[0])
request_fields['staged_first'] = (staged_first - created_at).total_seconds()
# TODO Decide if better to break out all measurements by time most relevant to event,
# time request was created, or time request was finalized. It may also make sense to
# keep separate measurement by different times like this one.
point('request_staged_first', {'value': request_fields['staged_first']}, staged_first, request_tags)
point('request', request_fields, final_at, request_tags)
# Staging related reviews.
for number, review in enumerate(
request.xpath('review[contains(@by_project, "{}:Staging:")]'.format(project)), start=1):
staged_at = date_parse(review.get('when'))
project_type = 'adi' if api.is_adi_project(review.get('by_project')) else 'letter'
short = api.extract_staging_short(review.get('by_project'))
point('staging', {'count': 1}, staged_at,
{'id': short, 'type': project_type, 'event': 'select'}, True)
point('total', {'backlog': -1, 'staged': 1}, staged_at, {'event': 'select'}, True)
who = who_workaround(request, review)
review_tags = {'event': 'select', 'user': who, 'number': number}
review_tags.update(request_tags)
point('user', {'count': 1}, staged_at, review_tags)
history = review.find('history')
if history is not None:
unselected_at = date_parse(history.get('when'))
else:
unselected_at = final_at
# If a request is declined and re-opened it must be repaired before being re-staged. At
# which point the only possible open review should be the final one.
point('staging', {'count': -1}, unselected_at,
{'id': short, 'type': project_type, 'event': 'unselect'}, True)
point('total', {'backlog': 1, 'staged': -1}, unselected_at, {'event': 'unselect'}, True)
# No-staging related reviews.
for review in request.xpath('review[not(contains(@by_project, "{}:Staging:"))]'.format(project)):
tags = {
# who_added is non-trivial due to openSUSE/open-build-service#3898.
'state': review.get('state'),
}
opened_at = date_parse(review.get('when'))
history = review.find('history')
if history is not None:
completed_at = date_parse(history.get('when'))
tags['who_completed'] = history.get('who')
else:
completed_at = final_at
# Does not seem to make sense to mirror user responsible for making final state
# change as the user who completed the review.
tags['key'] = []
tags['type'] = []
for name, value in sorted(review.items(), reverse=True):
if name.startswith('by_'):
tags[name] = value
tags['key'].append(value)
tags['type'].append(name[3:])
tags['type'] = '_'.join(tags['type'])
point('review', {'open_for': (completed_at - opened_at).total_seconds()}, completed_at, tags)
point('review_count', {'count': 1}, opened_at, tags, True)
point('review_count', {'count': -1}, completed_at, tags, True)
found = []
for set_priority in request.xpath('history[description[contains(text(), "Request got a new priority:")]]'):
parts = set_priority.find('description').text.rsplit(' ', 3)
priority_previous = parts[1]
priority = parts[3]
if priority == priority_previous:
continue
changed_at = date_parse(set_priority.get('when'))
if priority_previous != 'moderate':
point('priority', {'count': -1}, changed_at, {'level': priority_previous}, True)
if priority != 'moderate':
point('priority', {'count': 1}, changed_at, {'level': priority}, True)
found.append(priority)
# Ensure a final removal entry is created when request is finalized.
priority = request.find('priority')
if priority is not None and priority.text != 'moderate':
if priority.text in found:
point('priority', {'count': -1}, final_at, {'level': priority.text}, True)
else:
print('unable to find priority history entry for {} to {}'.format(request.get('id'), priority.text))
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
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print('finalizing {:,} points'.format(len(points)))
return walk_points(points, project)
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
def who_workaround(request, review, relax=False):
# Super ugly workaround for incorrect and missing data:
# - openSUSE/open-build-service#3857
# - openSUSE/open-build-service#3898
global who_workaround_swap, who_workaround_miss
who = review.get('who') # All that should be required (used as fallback).
when = review.get('when')
if relax:
# Super hack, chop off seconds to relax in hopes of finding potential.
when = when[:-2]
who_real = request.xpath(
'history[contains(@when, "{}") and comment[contains(text(), "{}")]]/@who'.format(
when, review.get('by_project')))
if len(who_real):
who = who_real[0]
who_workaround_swap += 1
elif not relax:
return who_workaround(request, review, True)
else:
who_workaround_miss += 1
return who
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
# Walk data points in order by time, adding up deltas and merging points at
# the same time. Data is converted to dict() and written to influx batches to
# avoid extra memory usage required for all data in dict() and avoid influxdb
# allocating memory for entire incoming data set at once.
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
def walk_points(points, target):
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
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global client
# Wait until just before writing to drop database.
client.drop_database(client._database)
client.create_database(client._database)
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
counters = {}
final = []
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
time_last = None
wrote = 0
for point in sorted(points, key=lambda l: l.time):
if point.time != time_last and len(final) >= 1000:
# Write final point in batches of ~1000, but guard against writing
# when in the middle of points at the same time as they may end up
# being merged. As such the previous time should not match current.
client.write_points(final, 's')
wrote += len(final)
final = []
time_last = point.time
if not point.delta:
final.append(dict(point._asdict()))
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
continue
# A more generic method like 'key' which ended up being needed is likely better.
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
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measurement = counters_tag_key = point.measurement
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
if measurement == 'staging':
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
counters_tag_key += point.tags['id']
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
elif measurement == 'review_count':
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
counters_tag_key += '_'.join(point.tags['key'])
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
elif measurement == 'priority':
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
counters_tag_key += point.tags['level']
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
counters_tag = counters.setdefault(counters_tag_key, {'last': None, 'values': {}})
values = counters_tag['values']
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
for key, value in point.fields.items():
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
values[key] = values.setdefault(key, 0) + value
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
if counters_tag['last'] and point.time == counters_tag['last']['time']:
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
point = counters_tag['last']
else:
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
point = dict(point._asdict())
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
counters_tag['last'] = point
final.append(point)
point['fields'].update(counters_tag['values'])
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
# Write any remaining final points.
client.write_points(final, 's')
return wrote + len(final)
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
def ingest_release_schedule(project):
points = []
release_schedule = {}
metrics: prefix release schedule file with source dir path. Otherwise, when deployed via package and run via /usr/bin the files are not found.
2017-11-08 16:42:42 -06:00
release_schedule_file = os.path.join(SOURCE_DIR, 'metrics/annotation/{}.yaml'.format(project))
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
if project.endswith('Factory'):
# Extract Factory "release schedule" from Tumbleweed snapshot list.
command = 'rsync rsync.opensuse.org::opensuse-full/opensuse/tumbleweed/iso/Changes.* | ' \
'grep -oP "Changes\.\K\d{5,}"'
snapshots = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE).communicate()[0]
for date in snapshots.split():
release_schedule[datetime.strptime(date, '%Y%m%d')] = 'Snapshot {}'.format(date)
elif os.path.isfile(release_schedule_file):
# Load release schedule for non-rolling releases from yaml file.
with open(release_schedule_file, 'r') as stream:
release_schedule = yaml.safe_load(stream)
for date, description in release_schedule.items():
points.append({
'measurement': 'release_schedule',
'fields': {'description': description},
'time': timestamp(date),
})
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
client.write_points(points, 's')
return len(points)
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
def main(args):
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
global client
client = InfluxDBClient(args.host, args.port, args.user, args.password, args.project)
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
osc.conf.get_config(override_apiurl=args.apiurl)
osc.conf.config['debug'] = args.debug
# Use separate cache since it is persistent.
Cache.CACHE_DIR = os.path.expanduser('~/.cache/osc-plugin-factory-metrics')
Cache.PATTERNS['/search/request'] = sys.maxint
Cache.init()
Config(args.project)
api = StagingAPI(osc.conf.config['apiurl'], args.project)
global who_workaround_swap, who_workaround_miss
who_workaround_swap = who_workaround_miss = 0
points_requests = ingest_requests(api, args.project)
points_schedule = ingest_release_schedule(args.project)
print('who_workaround_swap', who_workaround_swap)
print('who_workaround_miss', who_workaround_miss)
metrics: rework to store points as named tuple and write in batches. Savings of around 400MB per 10,000 requests. Using a named tuple was the original approach for this reason, but the influxdb interface requires dict()s and it seemed silly to spend time converting them. Additionally, influxdb client already does batching. Unfortunately, with the amount of data processed for Factory that will continue to grow this approach is necessary. The dict() final structures are buffered up to ~1000 before being written and released. Another benefit of the batching is that influxdb does not allocate memory for the entire incoming batch.
2017-11-09 01:29:57 -06:00
print('wrote {:,} points and {:,} annotation points to db'.format(
points_requests, points_schedule))
Initial metrics ingestion, processing, write to influxdb, and grafana dashboards. A lot of room for improvement and additional metrics that can be extracted. Including non-final state requests would allow for analyzing the current staging state instead of only historical state. Additionally, the current state can be used to present an activity log. Handling incremental updates is non-trivial given the deltas are evaluated and stored in sum state. A few possible approaches, but likely not worth the hassle given the relatively short processing time and infrequent desire to update data (daily at minimum).
2017-10-04 00:58:43 -05:00
if __name__ == '__main__':
description = 'Ingest relevant OBS and annotation data to generate insightful metrics.'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('-A', '--apiurl', help='OBS instance API URL')
parser.add_argument('-d', '--debug', action='store_true', help='print useful debugging info')
parser.add_argument('-p', '--project', default='openSUSE:Factory', help='OBS project')
parser.add_argument('--host', default='localhost', help='InfluxDB host')
parser.add_argument('--port', default=8086, help='InfluxDB post')
parser.add_argument('--user', default='root', help='InfluxDB user')
parser.add_argument('--password', default='root', help='InfluxDB password')
args = parser.parse_args()
sys.exit(main(args))
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