255 строки
8.3 KiB
Python
255 строки
8.3 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2013 Google Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License")
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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__author__ = 'ericbidelman@chromium.org (Eric Bidelman)'
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import datetime
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import json
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import logging
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from framework import users
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from framework import basehandlers
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from internals import metrics_models
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from framework import rediscache
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import settings
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CACHE_AGE = 86400 # 24hrs
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ROUNDING = 8 # 8 decimal places because all percents are < 1.0.
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def _is_googler(user):
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return user and user.email().endswith('@google.com')
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def _datapoints_to_json_dicts(datapoints):
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user = users.get_current_user()
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# Don't show raw percentages if user is not a googler.
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full_precision = _is_googler(user)
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json_dicts = [
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{'bucket_id': dp.bucket_id,
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'date': str(dp.date), # YYYY-MM-DD
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'day_percentage':
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(dp.day_percentage if full_precision else
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round(dp.day_percentage, ROUNDING)),
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'property_name': dp.property_name,
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}
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for dp in datapoints]
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return json_dicts
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class TimelineHandler(basehandlers.FlaskHandler):
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HTTP_CACHE_TYPE = 'private'
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JSONIFY = True
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CACHE_PREFIX = 'metrics|'
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def make_query(self, bucket_id):
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query = self.MODEL_CLASS.query()
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query = query.filter(self.MODEL_CLASS.bucket_id == bucket_id)
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# The switch to new UMA data changed the semantics of the CSS animated
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# properties. Since showing the historical data alongside the new data
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# does not make sense, filter out everything before the 2017-10-26 switch.
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# See https://github.com/GoogleChrome/chromium-dashboard/issues/414
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if self.MODEL_CLASS == metrics_models.AnimatedProperty:
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query = query.filter(
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self.MODEL_CLASS.date >= datetime.datetime(2017, 10, 26))
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return query
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def get_template_data(self, **kwargs):
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bucket_id = self.get_int_arg('bucket_id')
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if bucket_id is None:
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# TODO(jrobbins): Why return [] instead of 400?
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return []
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cache_key = '%s|%s' % (self.CACHE_KEY, bucket_id)
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datapoints = rediscache.get(cache_key)
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if not datapoints:
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query = self.make_query(bucket_id)
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query = query.order(self.MODEL_CLASS.date)
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datapoints = query.fetch(None) # All matching results.
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# Remove outliers if percentage is not between 0-1.
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#datapoints = filter(lambda x: 0 <= x.day_percentage <= 1, datapoints)
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rediscache.set(cache_key, datapoints, time=CACHE_AGE)
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return _datapoints_to_json_dicts(datapoints)
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class PopularityTimelineHandler(TimelineHandler):
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CACHE_KEY = TimelineHandler.CACHE_PREFIX + 'css_pop_timeline'
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MODEL_CLASS = metrics_models.StableInstance
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def get_template_data(self, **kwargs):
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return super(PopularityTimelineHandler, self).get_template_data()
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class AnimatedTimelineHandler(TimelineHandler):
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CACHE_KEY = TimelineHandler.CACHE_PREFIX + 'css_animated_timeline'
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MODEL_CLASS = metrics_models.AnimatedProperty
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def get_template_data(self, **kwargs):
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return super(AnimatedTimelineHandler, self).get_template_data()
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class FeatureObserverTimelineHandler(TimelineHandler):
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CACHE_KEY = TimelineHandler.CACHE_PREFIX + 'featureob_timeline'
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MODEL_CLASS = metrics_models.FeatureObserver
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def get_template_data(self, **kwargs):
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return super(FeatureObserverTimelineHandler, self).get_template_data()
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class FeatureHandler(basehandlers.FlaskHandler):
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HTTP_CACHE_TYPE = 'private'
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JSONIFY = True
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CACHE_PREFIX = 'metrics|'
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def __query_metrics_for_properties(self):
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datapoints = []
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buckets_future = self.PROPERTY_CLASS.query().fetch_async(None)
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# First, grab a bunch of recent datapoints in a batch.
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# That operation is fast and makes most of the iterations
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# of the main loop become in-RAM operations.
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batch_datapoints_query = self.MODEL_CLASS.query()
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batch_datapoints_query = batch_datapoints_query.order(
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-self.MODEL_CLASS.date)
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batch_datapoints_list = batch_datapoints_query.fetch(5000)
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logging.info('batch query found %r recent datapoints',
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len(batch_datapoints_list))
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batch_datapoints_dict = {}
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for dp in batch_datapoints_list:
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if dp.bucket_id not in batch_datapoints_dict:
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batch_datapoints_dict[dp.bucket_id] = dp
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logging.info('batch query found datapoints for %r buckets',
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len(batch_datapoints_dict))
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# For every css property, fetch latest day_percentage.
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buckets = buckets_future.get_result()
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futures = []
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for b in buckets:
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if b.bucket_id in batch_datapoints_dict:
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datapoints.append(batch_datapoints_dict[b.bucket_id])
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else:
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query = self.MODEL_CLASS.query()
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query = query.filter(self.MODEL_CLASS.bucket_id == b.bucket_id)
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query = query.order(-self.MODEL_CLASS.date)
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futures.append(query.get_async())
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for f in futures:
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last_result = f.result()
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if last_result:
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datapoints.append(last_result)
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# Sort list by percentage. Highest first.
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datapoints.sort(key=lambda x: x.day_percentage, reverse=True)
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return datapoints
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def get_template_data(self, **kwargs):
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num = self.get_int_arg('num')
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if num and not self.should_refresh():
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feature_observer_key = self.get_top_num_cache_key(num)
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properties = rediscache.get(feature_observer_key)
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if properties is not None:
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return _datapoints_to_json_dicts(properties)
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# Get all datapoints in sorted order.
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properties = self.fetch_all_datapoints()
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if num:
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feature_observer_key = self.get_top_num_cache_key(num)
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# Cache top `num` properties.
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properties = properties[:num]
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rediscache.set(feature_observer_key, properties, time=CACHE_AGE)
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return _datapoints_to_json_dicts(properties)
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def get_top_num_cache_key(self, num):
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return self.CACHE_KEY + '_' + str(num)
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def fetch_all_datapoints(self):
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properties = rediscache.get(self.CACHE_KEY)
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logging.info(
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'looked at cache %r and found %s', self.CACHE_KEY,
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repr(properties)[:settings.MAX_LOG_LINE])
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if (properties is None) or self.should_refresh():
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logging.info('Loading properties from datastore')
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properties = self.__query_metrics_for_properties()
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rediscache.set(self.CACHE_KEY, properties, time=CACHE_AGE)
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logging.info('before filtering: %s',
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repr(properties)[:settings.MAX_LOG_LINE])
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return properties
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def should_refresh(self):
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return (self.MODEL_CLASS == metrics_models.FeatureObserver and
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self.request.args.get('refresh'))
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class CSSPopularityHandler(FeatureHandler):
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CACHE_KEY = FeatureHandler.CACHE_PREFIX + 'css_popularity'
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MODEL_CLASS = metrics_models.StableInstance
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PROPERTY_CLASS = metrics_models.CssPropertyHistogram
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def get_template_data(self, **kwargs):
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return super(CSSPopularityHandler, self).get_template_data()
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class CSSAnimatedHandler(FeatureHandler):
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CACHE_KEY = FeatureHandler.CACHE_PREFIX + 'css_animated'
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MODEL_CLASS = metrics_models.AnimatedProperty
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PROPERTY_CLASS = metrics_models.CssPropertyHistogram
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def get_template_data(self, **kwargs):
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return super(CSSAnimatedHandler, self).get_template_data()
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class FeatureObserverPopularityHandler(FeatureHandler):
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CACHE_KEY = FeatureHandler.CACHE_PREFIX + 'featureob_popularity'
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MODEL_CLASS = metrics_models.FeatureObserver
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PROPERTY_CLASS = metrics_models.FeatureObserverHistogram
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def get_template_data(self, **kwargs):
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return super(FeatureObserverPopularityHandler, self).get_template_data()
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class FeatureBucketsHandler(basehandlers.FlaskHandler):
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HTTP_CACHE_TYPE = 'private'
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JSONIFY = True
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def get_template_data(self, **kwargs):
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prop_type = kwargs.get('prop_type', None)
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if prop_type == 'cssprops':
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properties = sorted(
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metrics_models.CssPropertyHistogram.get_all().items(),
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key=lambda x:x[1])
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else:
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properties = sorted(
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metrics_models.FeatureObserverHistogram.get_all().items(),
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key=lambda x:x[1])
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return properties
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