def load_starred_stories(request): user = get_user(request) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 10)) page = int(request.REQUEST.get('page', 0)) if page: offset = limit * page mstories = MStarredStory.objects(user_id=user.pk).order_by('-starred_date')[offset:offset+limit] stories = Feed.format_stories(mstories) for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) starred_date = localtime_for_timezone(story['starred_date'], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['read_status'] = 1 story['starred'] = True story['intelligence'] = { 'feed': 0, 'author': 0, 'tags': 0, 'title': 0, } logging.user(request.user, "~FCLoading starred stories: ~SB%s stories" % (len(stories))) return dict(stories=stories)
def load_starred_stories(request): user = get_user(request) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 10)) page = int(request.REQUEST.get('page', 0)) if page: offset = limit * (page - 1) mstories = MStarredStory.objects(user_id=user.pk).order_by('-starred_date')[offset:offset+limit] stories = Feed.format_stories(mstories) for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) starred_date = localtime_for_timezone(story['starred_date'], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['read_status'] = 1 story['starred'] = True story['intelligence'] = { 'feed': 0, 'author': 0, 'tags': 0, 'title': 0, } logging.user(request, "~FCLoading starred stories: ~SB%s stories" % (len(stories))) return dict(stories=stories)
def test_localtime_for_timezone(self): self.assertEqual( localtime_for_timezone( datetime(2008, 6, 25, 18, 0, 0), "America/Denver" ).strftime("%m/%d/%Y %H:%M:%S"), "06/25/2008 12:00:00" )
def load_features(request): user = get_user(request) page = int(request.REQUEST.get('page', 0)) logging.user(request, "~FBBrowse features: ~SBPage #%s" % (page+1)) features = Feature.objects.all()[page*3:(page+1)*3+1].values() features = [{ 'description': f['description'], 'date': localtime_for_timezone(f['date'], user.profile.timezone).strftime("%b %d, %Y") } for f in features] return features
return feed_classifiers classifier_feeds = sort_by_feed(MClassifierFeed.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_authors = sort_by_feed(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_titles = sort_by_feed(MClassifierTitle.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_tags = sort_by_feed(MClassifierTag.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifiers = {} for feed_id in found_feed_ids: classifiers[feed_id] = get_classifiers_for_user(user, feed_id, classifier_feeds[feed_id], classifier_authors[feed_id], classifier_titles[feed_id], classifier_tags[feed_id]) # Just need to format stories for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds[story['story_feed_id']], story['story_feed_id']), 'author': apply_classifier_authors(classifier_authors[story['story_feed_id']], story), 'tags': apply_classifier_tags(classifier_tags[story['story_feed_id']], story), 'title': apply_classifier_titles(classifier_titles[story['story_feed_id']], story), }
def localdatetime(context, date, date_format): user = get_user(context['user']) date = localtime_for_timezone(date, user.profile.timezone).strftime(date_format) return date
def load_feed_statistics(request, feed_id): user = get_user(request) timezone = user.profile.timezone stats = dict() feed = get_object_or_404(Feed, pk=feed_id) feed.update_all_statistics() feed.set_next_scheduled_update(verbose=True, skip_scheduling=True) feed.save_feed_story_history_statistics() feed.save_classifier_counts() # Dates of last and next update stats['active'] = feed.active stats['last_update'] = relative_timesince(feed.last_update) stats['next_update'] = relative_timeuntil(feed.next_scheduled_update) stats['push'] = feed.is_push if feed.is_push: try: stats['push_expires'] = localtime_for_timezone(feed.push.lease_expires, timezone).strftime("%Y-%m-%d %H:%M:%S") except PushSubscription.DoesNotExist: stats['push_expires'] = 'Missing push' feed.is_push = False feed.save() # Minutes between updates update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False) stats['update_interval_minutes'] = update_interval_minutes original_active_premium_subscribers = feed.active_premium_subscribers original_premium_subscribers = feed.premium_subscribers feed.active_premium_subscribers = max(feed.active_premium_subscribers+1, 1) feed.premium_subscribers += 1 premium_update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False, premium_speed=True) feed.active_premium_subscribers = original_active_premium_subscribers feed.premium_subscribers = original_premium_subscribers stats['premium_update_interval_minutes'] = premium_update_interval_minutes stats['errors_since_good'] = feed.errors_since_good # Stories per month - average and month-by-month breakout average_stories_per_month, story_count_history = feed.average_stories_per_month, feed.data.story_count_history stats['average_stories_per_month'] = average_stories_per_month story_count_history = story_count_history and json.decode(story_count_history) if story_count_history and isinstance(story_count_history, dict): stats['story_count_history'] = story_count_history['months'] stats['story_days_history'] = story_count_history['days'] stats['story_hours_history'] = story_count_history['hours'] else: stats['story_count_history'] = story_count_history # Rotate hours to match user's timezone offset localoffset = timezone.utcoffset(datetime.datetime.utcnow()) hours_offset = int(localoffset.total_seconds() / 3600) rotated_hours = {} for hour, value in stats['story_hours_history'].items(): rotated_hours[str(int(hour)+hours_offset)] = value stats['story_hours_history'] = rotated_hours # Subscribers stats['subscriber_count'] = feed.num_subscribers stats['num_subscribers'] = feed.num_subscribers stats['stories_last_month'] = feed.stories_last_month stats['last_load_time'] = feed.last_load_time stats['premium_subscribers'] = feed.premium_subscribers stats['active_subscribers'] = feed.active_subscribers stats['active_premium_subscribers'] = feed.active_premium_subscribers # Classifier counts stats['classifier_counts'] = json.decode(feed.data.feed_classifier_counts) # Fetch histories fetch_history = MFetchHistory.feed(feed_id, timezone=timezone) stats['feed_fetch_history'] = fetch_history['feed_fetch_history'] stats['page_fetch_history'] = fetch_history['page_fetch_history'] stats['feed_push_history'] = fetch_history['push_history'] logging.user(request, "~FBStatistics: ~SB%s" % (feed)) return stats
def load_river_stories(request): limit = 18 offset = 0 start = datetime.datetime.utcnow() user = get_user(request) feed_ids = [int(feed_id) for feed_id in request.REQUEST.getlist('feeds') if feed_id] original_feed_ids = list(feed_ids) page = int(request.REQUEST.get('page', 0))+1 read_stories_count = int(request.REQUEST.get('read_stories_count', 0)) bottom_delta = datetime.timedelta(days=settings.DAYS_OF_UNREAD) if not feed_ids: logging.user(request.user, "~FCLoading empty river stories: page %s" % (page)) return dict(stories=[]) # Fetch all stories at and before the page number. # Not a single page, because reading stories can move them up in the unread order. # `read_stories_count` is an optimization, works best when all 25 stories before have been read. limit = limit * page - read_stories_count # Read stories to exclude read_stories = MUserStory.objects(user_id=user.pk, feed_id__in=feed_ids).only('story') read_stories = [rs.story.id for rs in read_stories] # Determine mark_as_read dates for all feeds to ignore all stories before this date. # max_feed_count = 0 feed_counts = {} feed_last_reads = {} for feed_id in feed_ids: try: usersub = UserSubscription.objects.get(feed__pk=feed_id, user=user) except UserSubscription.DoesNotExist: continue if not usersub: continue feed_counts[feed_id] = (usersub.unread_count_negative * 1 + usersub.unread_count_neutral * 10 + usersub.unread_count_positive * 20) # if feed_counts[feed_id] > max_feed_count: # max_feed_count = feed_counts[feed_id] feed_last_reads[feed_id] = int(time.mktime(usersub.mark_read_date.timetuple())) feed_counts = sorted(feed_counts.items(), key=itemgetter(1))[:50] feed_ids = [f[0] for f in feed_counts] feed_last_reads = dict([(str(feed_id), feed_last_reads[feed_id]) for feed_id in feed_ids]) feed_counts = dict(feed_counts) # After excluding read stories, all that's left are stories # past the mark_read_date. Everything returned is guaranteed to be unread. mstories = MStory.objects( id__nin=read_stories, story_feed_id__in=feed_ids, story_date__gte=start - bottom_delta ).map_reduce("""function() { var d = feed_last_reads[this[~story_feed_id]]; if (this[~story_date].getTime()/1000 > d) { emit(this[~id], this); } }""", """function(key, values) { return values[0]; }""", output='inline', scope={ 'feed_last_reads': feed_last_reads } ) mstories = [story.value for story in mstories] mstories = sorted(mstories, cmp=lambda x, y: cmp(story_score(y, bottom_delta), story_score(x, bottom_delta))) # story_feed_counts = defaultdict(int) # mstories_pruned = [] # for story in mstories: # print story['story_title'], story_feed_counts[story['story_feed_id']] # if story_feed_counts[story['story_feed_id']] >= 3: continue # mstories_pruned.append(story) # story_feed_counts[story['story_feed_id']] += 1 stories = [] for i, story in enumerate(mstories): if i < offset: continue if i >= offset + limit: break stories.append(bunch(story)) stories = Feed.format_stories(stories) found_feed_ids = list(set([story['story_feed_id'] for story in stories])) # Find starred stories starred_stories = MStarredStory.objects( user_id=user.pk, story_feed_id__in=found_feed_ids ).only('story_guid', 'starred_date') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) # Intelligence classifiers for all feeds involved def sort_by_feed(classifiers): feed_classifiers = defaultdict(list) for classifier in classifiers: feed_classifiers[classifier.feed_id].append(classifier) return feed_classifiers classifier_feeds = sort_by_feed(MClassifierFeed.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_authors = sort_by_feed(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_titles = sort_by_feed(MClassifierTitle.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_tags = sort_by_feed(MClassifierTag.objects(user_id=user.pk, feed_id__in=found_feed_ids)) # Just need to format stories for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds[story['story_feed_id']], story['story_feed_id']), 'author': apply_classifier_authors(classifier_authors[story['story_feed_id']], story), 'tags': apply_classifier_tags(classifier_tags[story['story_feed_id']], story), 'title': apply_classifier_titles(classifier_titles[story['story_feed_id']], story), } diff = datetime.datetime.utcnow() - start timediff = float("%s.%.2s" % (diff.seconds, (diff.microseconds / 1000))) logging.user(request.user, "~FCLoading river stories: page %s - ~SB%s/%s " "stories ~SN(%s/%s/%s feeds) ~FB(%s seconds)" % (page, len(stories), len(mstories), len(found_feed_ids), len(feed_ids), len(original_feed_ids), timediff)) return dict(stories=stories)
def load_social_page(request, user_id, username=None, **kwargs): start = time.time() user = request.user social_user_id = int(user_id) social_user = get_object_or_404(User, pk=social_user_id) offset = int(request.REQUEST.get("offset", 0)) limit = int(request.REQUEST.get("limit", 6)) page = request.REQUEST.get("page") format = request.REQUEST.get("format", None) has_next_page = False feed_id = kwargs.get("feed_id") or request.REQUEST.get("feed_id") if page: offset = limit * (int(page) - 1) user_social_profile = None user_social_services = None if user.is_authenticated(): user_social_profile = MSocialProfile.get_user(user.pk) user_social_services = MSocialServices.get_user(user.pk) social_profile = MSocialProfile.get_user(social_user_id) params = dict(user_id=social_user.pk) if feed_id: params["story_feed_id"] = feed_id mstories = MSharedStory.objects(**params).order_by("-shared_date")[offset : offset + limit + 1] stories = Feed.format_stories(mstories) if len(stories) > limit: has_next_page = True stories = stories[:-1] checkpoint1 = time.time() if not stories: params = { "user": user, "stories": [], "feeds": {}, "social_user": social_user, "social_profile": social_profile, "user_social_services": user_social_services, "user_social_profile": json.encode(user_social_profile and user_social_profile.page()), } template = "social/social_page.xhtml" return render_to_response(template, params, context_instance=RequestContext(request)) story_feed_ids = list(set(s["story_feed_id"] for s in stories)) feeds = Feed.objects.filter(pk__in=story_feed_ids) feeds = dict((feed.pk, feed.canonical(include_favicon=False)) for feed in feeds) for story in stories: if story["story_feed_id"] in feeds: # Feed could have been deleted. story["feed"] = feeds[story["story_feed_id"]] shared_date = localtime_for_timezone(story["shared_date"], social_user.profile.timezone) story["shared_date"] = shared_date stories, profiles = MSharedStory.stories_with_comments_and_profiles(stories, social_user.pk, check_all=True) checkpoint2 = time.time() if user.is_authenticated(): for story in stories: if user.pk in story["share_user_ids"]: story["shared_by_user"] = True shared_story = MSharedStory.objects.get( user_id=user.pk, story_feed_id=story["story_feed_id"], story_guid=story["id"] ) story["user_comments"] = shared_story.comments stories = MSharedStory.attach_users_to_stories(stories, profiles) params = { "social_user": social_user, "stories": stories, "user_social_profile": user_social_profile, "user_social_profile_page": json.encode(user_social_profile and user_social_profile.page()), "user_social_services": user_social_services, "user_social_services_page": json.encode(user_social_services and user_social_services.to_json()), "social_profile": social_profile, "feeds": feeds, "user_profile": hasattr(user, "profile") and user.profile, "has_next_page": has_next_page, "holzer_truism": random.choice(jennyholzer.TRUISMS), # if not has_next_page else None } diff1 = checkpoint1 - start diff2 = checkpoint2 - start timediff = time.time() - start logging.user( request, "~FYLoading ~FMsocial page~FY: ~SB%s%s ~SN(%.4s seconds, ~SB%.4s/%.4s~SN)" % (social_profile.title[:22], ("~SN/p%s" % page) if page > 1 else "", timediff, diff1, diff2), ) if format == "html": template = "social/social_stories.xhtml" else: template = "social/social_page.xhtml" return render_to_response(template, params, context_instance=RequestContext(request))
def load_single_feed(request, feed_id): start = time.time() user = get_user(request) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 12)) page = int(request.REQUEST.get('page', 1)) dupe_feed_id = None userstories_db = None if page: offset = limit * (page-1) if not feed_id: raise Http404 try: feed = Feed.objects.get(id=feed_id) except Feed.DoesNotExist: feed_address = request.REQUEST.get('feed_address') dupe_feed = DuplicateFeed.objects.filter(duplicate_address=feed_address) if dupe_feed: feed = dupe_feed[0].feed dupe_feed_id = feed_id else: raise Http404 stories = feed.get_stories(offset, limit) # Get intelligence classifier for user classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, feed_id=feed_id)) classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, feed_id=feed_id)) classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, feed_id=feed_id)) classifier_tags = list(MClassifierTag.objects(user_id=user.pk, feed_id=feed_id)) checkpoint1 = time.time() usersub = UserSubscription.objects.get(user=user, feed=feed) userstories = [] if usersub: userstories_db = MUserStory.objects(user_id=user.pk, feed_id=feed.pk, read_date__gte=usersub.mark_read_date) starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id=feed_id).only('story_guid', 'starred_date') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) for us in userstories_db: if hasattr(us.story, 'story_guid') and isinstance(us.story.story_guid, unicode): userstories.append(us.story.story_guid) elif hasattr(us.story, 'id') and isinstance(us.story.id, unicode): userstories.append(us.story.id) # TODO: Remove me after migration from story.id->guid checkpoint2 = time.time() for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) if usersub: if story['id'] in userstories: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] < usersub.mark_read_date: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] > usersub.last_read_date: story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) else: story['read_status'] = 1 story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds, feed), 'author': apply_classifier_authors(classifier_authors, story), 'tags': apply_classifier_tags(classifier_tags, story), 'title': apply_classifier_titles(classifier_titles, story), } checkpoint3 = time.time() # Intelligence feed_tags = json.decode(feed.data.popular_tags) if feed.data.popular_tags else [] feed_authors = json.decode(feed.data.popular_authors) if feed.data.popular_authors else [] classifiers = get_classifiers_for_user(user, feed_id, classifier_feeds, classifier_authors, classifier_titles, classifier_tags) if usersub: usersub.feed_opens += 1 usersub.save() timediff = time.time()-start last_update = relative_timesince(feed.last_update) logging.user(request.user, "~FYLoading feed: ~SB%s%s ~SN(%.4s seconds)" % ( feed, ('~SN/p%s' % page) if page > 1 else '', timediff)) FeedLoadtime.objects.create(feed=feed, loadtime=timediff) if timediff >= 1: diff1 = checkpoint1-start diff2 = checkpoint2-start diff3 = checkpoint3-start logging.user(request.user, "~FYSlow feed load: ~SB%.4s/%.4s(%s)/%.4s" % ( diff1, diff2, userstories_db and userstories_db.count(), diff3)) data = dict(stories=stories, feed_tags=feed_tags, feed_authors=feed_authors, classifiers=classifiers, last_update=last_update, feed_id=feed.pk) if dupe_feed_id: data['dupe_feed_id'] = dupe_feed_id if not usersub: data.update(feed.canonical()) return data
def assemble_statistics(user, feed_id): user_timezone = user.profile.timezone stats = dict() feed = get_object_or_404(Feed, pk=feed_id) feed.update_all_statistics() feed.set_next_scheduled_update(verbose=True, skip_scheduling=True) feed.save_feed_story_history_statistics() feed.save_classifier_counts() # Dates of last and next update stats['active'] = feed.active stats['last_update'] = relative_timesince(feed.last_update) stats['next_update'] = relative_timeuntil(feed.next_scheduled_update) stats['push'] = feed.is_push if feed.is_push: try: stats['push_expires'] = localtime_for_timezone( feed.push.lease_expires, user_timezone).strftime("%Y-%m-%d %H:%M:%S") except PushSubscription.DoesNotExist: stats['push_expires'] = 'Missing push' feed.is_push = False feed.save() # Minutes between updates update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False) stats['update_interval_minutes'] = update_interval_minutes original_active_premium_subscribers = feed.active_premium_subscribers original_premium_subscribers = feed.premium_subscribers feed.active_premium_subscribers = max(feed.active_premium_subscribers + 1, 1) feed.premium_subscribers += 1 premium_update_interval_minutes = feed.get_next_scheduled_update( force=True, verbose=False, premium_speed=True) feed.active_premium_subscribers = original_active_premium_subscribers feed.premium_subscribers = original_premium_subscribers stats['premium_update_interval_minutes'] = premium_update_interval_minutes stats['errors_since_good'] = feed.errors_since_good # Stories per month - average and month-by-month breakout average_stories_per_month, story_count_history = feed.average_stories_per_month, feed.data.story_count_history stats['average_stories_per_month'] = average_stories_per_month story_count_history = story_count_history and json.decode( story_count_history) if story_count_history and isinstance(story_count_history, dict): stats['story_count_history'] = story_count_history['months'] stats['story_days_history'] = story_count_history['days'] stats['story_hours_history'] = story_count_history['hours'] else: stats['story_count_history'] = story_count_history # Rotate hours to match user's timezone offset localoffset = user_timezone.utcoffset(datetime.datetime.utcnow()) hours_offset = int(localoffset.total_seconds() / 3600) rotated_hours = {} for hour, value in stats['story_hours_history'].items(): rotated_hours[str(int(hour) + hours_offset)] = value stats['story_hours_history'] = rotated_hours # Subscribers stats['subscriber_count'] = feed.num_subscribers stats['num_subscribers'] = feed.num_subscribers stats['stories_last_month'] = feed.stories_last_month stats['last_load_time'] = feed.last_load_time stats['premium_subscribers'] = feed.premium_subscribers stats['active_subscribers'] = feed.active_subscribers stats['active_premium_subscribers'] = feed.active_premium_subscribers # Classifier counts stats['classifier_counts'] = json.decode(feed.data.feed_classifier_counts) # Fetch histories fetch_history = MFetchHistory.feed(feed_id, timezone=user_timezone) stats['feed_fetch_history'] = fetch_history['feed_fetch_history'] stats['page_fetch_history'] = fetch_history['page_fetch_history'] stats['feed_push_history'] = fetch_history['push_history'] return stats
def load_river_stories(request): limit = 18 offset = 0 start = datetime.datetime.utcnow() user = get_user(request) feed_ids = [int(feed_id) for feed_id in request.REQUEST.getlist('feeds') if feed_id] original_feed_ids = list(feed_ids) page = int(request.REQUEST.get('page', 1)) read_stories_count = int(request.REQUEST.get('read_stories_count', 0)) new_flag = request.REQUEST.get('new_flag', False) bottom_delta = datetime.timedelta(days=settings.DAYS_OF_UNREAD) if not feed_ids: logging.user(request, "~FCLoading empty river stories: page %s" % (page)) return dict(stories=[]) # Fetch all stories at and before the page number. # Not a single page, because reading stories can move them up in the unread order. # `read_stories_count` is an optimization, works best when all 25 stories before have been read. limit = limit * page - read_stories_count # Read stories to exclude read_stories = MUserStory.objects(user_id=user.pk, feed_id__in=feed_ids).only('story_id') read_stories = [rs.story_id for rs in read_stories] # Determine mark_as_read dates for all feeds to ignore all stories before this date. # max_feed_count = 0 feed_counts = {} feed_last_reads = {} for feed_id in feed_ids: try: usersub = UserSubscription.objects.get(feed__pk=feed_id, user=user) except UserSubscription.DoesNotExist: continue if not usersub: continue feed_counts[feed_id] = (usersub.unread_count_negative * 1 + usersub.unread_count_neutral * 10 + usersub.unread_count_positive * 20) # if feed_counts[feed_id] > max_feed_count: # max_feed_count = feed_counts[feed_id] feed_last_reads[feed_id] = int(time.mktime(usersub.mark_read_date.timetuple())) feed_counts = sorted(feed_counts.items(), key=itemgetter(1))[:50] feed_ids = [f[0] for f in feed_counts] feed_last_reads = dict([(str(feed_id), feed_last_reads[feed_id]) for feed_id in feed_ids if feed_id in feed_last_reads]) feed_counts = dict(feed_counts) # After excluding read stories, all that's left are stories # past the mark_read_date. Everything returned is guaranteed to be unread. mstories = MStory.objects( story_guid__nin=read_stories, story_feed_id__in=feed_ids, # story_date__gte=start - bottom_delta ).map_reduce("""function() { var d = feed_last_reads[this[~story_feed_id]]; if (this[~story_date].getTime()/1000 > d) { emit(this[~id], this); } }""", """function(key, values) { return values[0]; }""", output='inline', scope={ 'feed_last_reads': feed_last_reads } ) mstories = [story.value for story in mstories if story and story.value] mstories = sorted(mstories, cmp=lambda x, y: cmp(story_score(y, bottom_delta), story_score(x, bottom_delta))) # story_feed_counts = defaultdict(int) # mstories_pruned = [] # for story in mstories: # print story['story_title'], story_feed_counts[story['story_feed_id']] # if story_feed_counts[story['story_feed_id']] >= 3: continue # mstories_pruned.append(story) # story_feed_counts[story['story_feed_id']] += 1 stories = [] for i, story in enumerate(mstories): if i < offset: continue if i >= offset + limit: break stories.append(bunch(story)) stories = Feed.format_stories(stories) found_feed_ids = list(set([story['story_feed_id'] for story in stories])) # Find starred stories starred_stories = MStarredStory.objects( user_id=user.pk, story_feed_id__in=found_feed_ids ).only('story_guid', 'starred_date') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) # Intelligence classifiers for all feeds involved def sort_by_feed(classifiers): feed_classifiers = defaultdict(list) for classifier in classifiers: feed_classifiers[classifier.feed_id].append(classifier) return feed_classifiers classifier_feeds = sort_by_feed(MClassifierFeed.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_authors = sort_by_feed(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_titles = sort_by_feed(MClassifierTitle.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_tags = sort_by_feed(MClassifierTag.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifiers = {} for feed_id in found_feed_ids: classifiers[feed_id] = get_classifiers_for_user(user, feed_id, classifier_feeds[feed_id], classifier_authors[feed_id], classifier_titles[feed_id], classifier_tags[feed_id]) # Just need to format stories for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds[story['story_feed_id']], story['story_feed_id']), 'author': apply_classifier_authors(classifier_authors[story['story_feed_id']], story), 'tags': apply_classifier_tags(classifier_tags[story['story_feed_id']], story), 'title': apply_classifier_titles(classifier_titles[story['story_feed_id']], story), } diff = datetime.datetime.utcnow() - start timediff = float("%s.%.2s" % (diff.seconds, (diff.microseconds / 1000))) logging.user(request, "~FCLoading river stories: page %s - ~SB%s/%s " "stories ~SN(%s/%s/%s feeds) ~FB(%s seconds)" % (page, len(stories), len(mstories), len(found_feed_ids), len(feed_ids), len(original_feed_ids), timediff)) if new_flag: return dict(stories=stories, classifiers=classifiers) else: logging.user(request, "~BR~FCNo new flag on river") return dict(stories=stories)
def load_social_page(request, user_id, username=None, **kwargs): start = time.time() user = request.user social_user_id = int(user_id) social_user = get_object_or_404(User, pk=social_user_id) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 6)) page = request.REQUEST.get('page') format = request.REQUEST.get('format', None) has_next_page = False feed_id = kwargs.get('feed_id') or request.REQUEST.get('feed_id') if page: offset = limit * (int(page) - 1) user_social_profile = None if user.is_authenticated(): user_social_profile = MSocialProfile.get_user(user.pk) social_profile = MSocialProfile.get_user(social_user_id) params = dict(user_id=social_user.pk) if feed_id: params['story_feed_id'] = feed_id mstories = MSharedStory.objects(**params).order_by('-shared_date')[offset:offset+limit+1] stories = Feed.format_stories(mstories) if len(stories) > limit: has_next_page = True stories = stories[:-1] checkpoint1 = time.time() if not stories: params = { "user": user, "stories": [], "feeds": {}, "social_user": social_user, "social_profile": social_profile, 'user_social_profile' : json.encode(user_social_profile and user_social_profile.page()), } template = 'social/social_page.xhtml' return render_to_response(template, params, context_instance=RequestContext(request)) story_feed_ids = list(set(s['story_feed_id'] for s in stories)) feeds = Feed.objects.filter(pk__in=story_feed_ids) feeds = dict((feed.pk, feed.canonical(include_favicon=False)) for feed in feeds) for story in stories: if story['story_feed_id'] in feeds: # Feed could have been deleted. story['feed'] = feeds[story['story_feed_id']] shared_date = localtime_for_timezone(story['shared_date'], social_user.profile.timezone) story['shared_date'] = shared_date stories, profiles = MSharedStory.stories_with_comments_and_profiles(stories, social_user.pk, check_all=True) checkpoint2 = time.time() if user.is_authenticated(): for story in stories: if user.pk in story['shared_by_friends'] or user.pk in story['shared_by_public']: story['shared_by_user'] = True shared_story = MSharedStory.objects.get(user_id=user.pk, story_feed_id=story['story_feed_id'], story_guid=story['id']) story['user_comments'] = shared_story.comments stories = MSharedStory.attach_users_to_stories(stories, profiles) params = { 'social_user' : social_user, 'stories' : stories, 'user_social_profile' : json.encode(user_social_profile and user_social_profile.page()), 'social_profile': social_profile, 'feeds' : feeds, 'user_profile' : hasattr(user, 'profile') and user.profile, 'has_next_page' : has_next_page, 'holzer_truism' : random.choice(jennyholzer.TRUISMS) #if not has_next_page else None } diff1 = checkpoint1-start diff2 = checkpoint2-start timediff = time.time()-start logging.user(request, "~FYLoading ~FMsocial page~FY: ~SB%s%s ~SN(%.4s seconds, ~SB%.4s/%.4s~SN)" % ( social_profile.title[:22], ('~SN/p%s' % page) if page > 1 else '', timediff, diff1, diff2)) if format == 'html': template = 'social/social_stories.xhtml' else: template = 'social/social_page.xhtml' return render_to_response(template, params, context_instance=RequestContext(request))
def localtime(value, timezone): return localtime_for_timezone(value, timezone)
classifier_feeds = sort_by_feed(MClassifierFeed.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_authors = sort_by_feed(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_titles = sort_by_feed(MClassifierTitle.objects(user_id=user.pk, feed_id__in=found_feed_ids)) classifier_tags = sort_by_feed(MClassifierTag.objects(user_id=user.pk, feed_id__in=found_feed_ids)) except OperationFailure: logging.info(" ***> Classifiers failure") else: for feed_id in found_feed_ids: classifiers[feed_id] = get_classifiers_for_user(user, feed_id, classifier_feeds[feed_id], classifier_authors[feed_id], classifier_titles[feed_id], classifier_tags[feed_id]) # Just need to format stories for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds[story['story_feed_id']], story['story_feed_id']), 'author': apply_classifier_authors(classifier_authors[story['story_feed_id']], story), 'tags': apply_classifier_tags(classifier_tags[story['story_feed_id']], story), 'title': apply_classifier_titles(classifier_titles[story['story_feed_id']], story), }
def load_single_feed(request, feed_id): start = time.time() user = get_user(request) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 12)) page = int(request.REQUEST.get('page', 1)) dupe_feed_id = None userstories_db = None if page: offset = limit * (page-1) if not feed_id: raise Http404 try: feed = Feed.objects.get(id=feed_id) except Feed.DoesNotExist: feed_address = request.REQUEST.get('feed_address') dupe_feed = DuplicateFeed.objects.filter(duplicate_address=feed_address) if dupe_feed: feed = dupe_feed[0].feed dupe_feed_id = feed_id else: raise Http404 stories = feed.get_stories(offset, limit) # Get intelligence classifier for user classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, feed_id=feed_id)) classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, feed_id=feed_id)) classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, feed_id=feed_id)) classifier_tags = list(MClassifierTag.objects(user_id=user.pk, feed_id=feed_id)) checkpoint1 = time.time() usersub = UserSubscription.objects.get(user=user, feed=feed) userstories = [] if usersub and stories: story_ids = [story['id'] for story in stories] userstories_db = MUserStory.objects(user_id=user.pk, feed_id=feed.pk, story_id__in=story_ids).only('story_id') starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id=feed_id, story_guid__in=story_ids).only('story_guid', 'starred_date') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) userstories = set(us.story_id for us in userstories_db) checkpoint2 = time.time() for story in stories: story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) if usersub: if story['id'] in userstories: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] < usersub.mark_read_date: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] > usersub.last_read_date: story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) else: story['read_status'] = 1 story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds, feed), 'author': apply_classifier_authors(classifier_authors, story), 'tags': apply_classifier_tags(classifier_tags, story), 'title': apply_classifier_titles(classifier_titles, story), } checkpoint3 = time.time() # Intelligence feed_tags = json.decode(feed.data.popular_tags) if feed.data.popular_tags else [] feed_authors = json.decode(feed.data.popular_authors) if feed.data.popular_authors else [] classifiers = get_classifiers_for_user(user, feed_id, classifier_feeds, classifier_authors, classifier_titles, classifier_tags) if usersub: usersub.feed_opens += 1 usersub.save() diff1 = checkpoint1-start diff2 = checkpoint2-start diff3 = checkpoint3-start timediff = time.time()-start last_update = relative_timesince(feed.last_update) logging.user(request, "~FYLoading feed: ~SB%s%s ~SN(%.4s seconds, ~SB%.4s/%.4s(%s)/%.4s~SN)" % ( feed.feed_title[:32], ('~SN/p%s' % page) if page > 1 else '', timediff, diff1, diff2, userstories_db and userstories_db.count() or '~SN0~SB', diff3)) FeedLoadtime.objects.create(feed=feed, loadtime=timediff) data = dict(stories=stories, feed_tags=feed_tags, feed_authors=feed_authors, classifiers=classifiers, last_update=last_update, feed_id=feed.pk) if dupe_feed_id: data['dupe_feed_id'] = dupe_feed_id if not usersub: data.update(feed.canonical()) return data
def load_river_blurblog(request): limit = 10 start = time.time() user = get_user(request) social_user_ids = [int(uid) for uid in request.REQUEST.getlist("social_user_ids") if uid] original_user_ids = list(social_user_ids) page = int(request.REQUEST.get("page", 1)) order = request.REQUEST.get("order", "newest") read_filter = request.REQUEST.get("read_filter", "unread") relative_user_id = request.REQUEST.get("relative_user_id", None) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) UNREAD_CUTOFF = datetime.datetime.utcnow() - datetime.timedelta(days=settings.DAYS_OF_UNREAD) if not relative_user_id: relative_user_id = get_user(request).pk if not social_user_ids: socialsubs = MSocialSubscription.objects.filter(user_id=user.pk) social_user_ids = [s.subscription_user_id for s in socialsubs] offset = (page - 1) * limit limit = page * limit - 1 story_ids, story_dates = MSocialSubscription.feed_stories( user.pk, social_user_ids, offset=offset, limit=limit, order=order, read_filter=read_filter ) mstories = MStory.objects(id__in=story_ids) story_id_to_dates = dict(zip(story_ids, story_dates)) def sort_stories_by_id(a, b): return int(story_id_to_dates[str(b.id)]) - int(story_id_to_dates[str(a.id)]) sorted_mstories = sorted(mstories, cmp=sort_stories_by_id) stories = Feed.format_stories(sorted_mstories) for s, story in enumerate(stories): story["story_date"] = datetime.datetime.fromtimestamp(story_dates[s]) stories, user_profiles = MSharedStory.stories_with_comments_and_profiles(stories, relative_user_id, check_all=True) story_feed_ids = list(set(s["story_feed_id"] for s in stories)) usersubs = UserSubscription.objects.filter(user__pk=user.pk, feed__pk__in=story_feed_ids) usersubs_map = dict((sub.feed_id, sub) for sub in usersubs) unsub_feed_ids = list(set(story_feed_ids).difference(set(usersubs_map.keys()))) unsub_feeds = Feed.objects.filter(pk__in=unsub_feed_ids) unsub_feeds = [feed.canonical(include_favicon=False) for feed in unsub_feeds] # Find starred stories if story_feed_ids: story_ids = [story["id"] for story in stories] starred_stories = MStarredStory.objects(user_id=user.pk, story_guid__in=story_ids).only( "story_guid", "starred_date" ) starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) shared_stories = MSharedStory.objects(user_id=user.pk, story_guid__in=story_ids).only( "story_guid", "shared_date", "comments" ) shared_stories = dict( [ (story.story_guid, dict(shared_date=story.shared_date, comments=story.comments)) for story in shared_stories ] ) userstories_db = MUserStory.objects(user_id=user.pk, feed_id__in=story_feed_ids, story_id__in=story_ids).only( "story_id" ) userstories = set(us.story_id for us in userstories_db) else: starred_stories = {} shared_stories = {} userstories = [] # Intelligence classifiers for all feeds involved if story_feed_ids: classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_tags = list(MClassifierTag.objects(user_id=user.pk, feed_id__in=story_feed_ids)) else: classifier_feeds = [] classifier_authors = [] classifier_titles = [] classifier_tags = [] classifiers = sort_classifiers_by_feed( user=user, feed_ids=story_feed_ids, classifier_feeds=classifier_feeds, classifier_authors=classifier_authors, classifier_titles=classifier_titles, classifier_tags=classifier_tags, ) # Just need to format stories for story in stories: if story["id"] in userstories: story["read_status"] = 1 elif story["story_date"] < UNREAD_CUTOFF: story["read_status"] = 1 else: story["read_status"] = 0 story_date = localtime_for_timezone(story["story_date"], user.profile.timezone) story["short_parsed_date"] = format_story_link_date__short(story_date, now) story["long_parsed_date"] = format_story_link_date__long(story_date, now) if story["id"] in starred_stories: story["starred"] = True starred_date = localtime_for_timezone(starred_stories[story["id"]], user.profile.timezone) story["starred_date"] = format_story_link_date__long(starred_date, now) story["intelligence"] = { "feed": apply_classifier_feeds(classifier_feeds, story["story_feed_id"]), "author": apply_classifier_authors(classifier_authors, story), "tags": apply_classifier_tags(classifier_tags, story), "title": apply_classifier_titles(classifier_titles, story), } if story["id"] in shared_stories: story["shared"] = True shared_date = localtime_for_timezone(shared_stories[story["id"]]["shared_date"], user.profile.timezone) story["shared_date"] = format_story_link_date__long(shared_date, now) story["shared_comments"] = strip_tags(shared_stories[story["id"]]["comments"]) diff = time.time() - start timediff = round(float(diff), 2) logging.user( request, "~FYLoading ~FCriver blurblogs stories~FY: ~SBp%s~SN (%s/%s " "stories, ~SN%s/%s/%s feeds)" % (page, len(stories), len(mstories), len(story_feed_ids), len(social_user_ids), len(original_user_ids)), ) return { "stories": stories, "user_profiles": user_profiles, "feeds": unsub_feeds, "classifiers": classifiers, "elapsed_time": timediff, }
def load_social_stories(request, user_id, username=None): start = time.time() user = get_user(request) social_user_id = int(user_id) social_user = get_object_or_404(User, pk=social_user_id) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 6)) page = request.REQUEST.get('page') order = request.REQUEST.get('order', 'newest') read_filter = request.REQUEST.get('read_filter', 'all') stories = [] if page: offset = limit * (int(page) - 1) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) UNREAD_CUTOFF = datetime.datetime.utcnow() - datetime.timedelta(days=settings.DAYS_OF_UNREAD) social_profile = MSocialProfile.get_user(social_user.pk) try: socialsub = MSocialSubscription.objects.get(user_id=user.pk, subscription_user_id=social_user_id) except MSocialSubscription.DoesNotExist: socialsub = None mstories = MSharedStory.objects(user_id=social_user.pk).order_by('-shared_date')[offset:offset+limit] stories = Feed.format_stories(mstories) if socialsub and (read_filter == 'unread' or order == 'oldest'): story_ids = socialsub.get_stories(order=order, read_filter=read_filter, offset=offset, limit=limit) story_date_order = "%sshared_date" % ('' if order == 'oldest' else '-') if story_ids: mstories = MSharedStory.objects(user_id=social_user.pk, story_db_id__in=story_ids).order_by(story_date_order) stories = Feed.format_stories(mstories) else: mstories = MSharedStory.objects(user_id=social_user.pk).order_by('-shared_date')[offset:offset+limit] stories = Feed.format_stories(mstories) if not stories: return dict(stories=[]) checkpoint1 = time.time() stories, user_profiles = MSharedStory.stories_with_comments_and_profiles(stories, user.pk, check_all=True) story_feed_ids = list(set(s['story_feed_id'] for s in stories)) usersubs = UserSubscription.objects.filter(user__pk=user.pk, feed__pk__in=story_feed_ids) usersubs_map = dict((sub.feed_id, sub) for sub in usersubs) unsub_feed_ids = list(set(story_feed_ids).difference(set(usersubs_map.keys()))) unsub_feeds = Feed.objects.filter(pk__in=unsub_feed_ids) unsub_feeds = [feed.canonical(include_favicon=False) for feed in unsub_feeds] date_delta = UNREAD_CUTOFF if socialsub and date_delta < socialsub.mark_read_date: date_delta = socialsub.mark_read_date # Get intelligence classifier for user classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, social_user_id=social_user_id)) classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, social_user_id=social_user_id)) classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, social_user_id=social_user_id)) classifier_tags = list(MClassifierTag.objects(user_id=user.pk, social_user_id=social_user_id)) # Merge with feed specific classifiers classifier_feeds = classifier_feeds + list(MClassifierFeed.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_authors = classifier_authors + list(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_titles = classifier_titles + list(MClassifierTitle.objects(user_id=user.pk, feed_id__in=story_feed_ids)) classifier_tags = classifier_tags + list(MClassifierTag.objects(user_id=user.pk, feed_id__in=story_feed_ids)) checkpoint2 = time.time() story_ids = [story['id'] for story in stories] userstories_db = MUserStory.objects(user_id=user.pk, feed_id__in=story_feed_ids, story_id__in=story_ids).only('story_id') userstories = set(us.story_id for us in userstories_db) starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id__in=story_feed_ids, story_guid__in=story_ids).only('story_guid', 'starred_date') shared_stories = MSharedStory.objects(user_id=user.pk, story_feed_id__in=story_feed_ids, story_guid__in=story_ids)\ .only('story_guid', 'shared_date', 'comments') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) shared_stories = dict([(story.story_guid, dict(shared_date=story.shared_date, comments=story.comments)) for story in shared_stories]) for story in stories: story['social_user_id'] = social_user_id story_feed_id = story['story_feed_id'] # story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) shared_date = localtime_for_timezone(story['shared_date'], user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(shared_date, now) story['long_parsed_date'] = format_story_link_date__long(shared_date, now) if not socialsub: story['read_status'] = 1 elif story['id'] in userstories: story['read_status'] = 1 elif story['shared_date'] < date_delta: story['read_status'] = 1 elif not usersubs_map.get(story_feed_id): story['read_status'] = 0 elif not story.get('read_status') and story['story_date'] < usersubs_map[story_feed_id].mark_read_date: story['read_status'] = 1 elif not story.get('read_status') and story['shared_date'] < date_delta: story['read_status'] = 1 # elif not story.get('read_status') and socialsub and story['shared_date'] > socialsub.last_read_date: # story['read_status'] = 0 else: story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) if story['id'] in shared_stories: story['shared'] = True shared_date = localtime_for_timezone(shared_stories[story['id']]['shared_date'], user.profile.timezone) story['shared_date'] = format_story_link_date__long(shared_date, now) story['shared_comments'] = strip_tags(shared_stories[story['id']]['comments']) story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds, story['story_feed_id'], social_user_id=social_user_id), 'author': apply_classifier_authors(classifier_authors, story), 'tags': apply_classifier_tags(classifier_tags, story), 'title': apply_classifier_titles(classifier_titles, story), } classifiers = sort_classifiers_by_feed(user=user, feed_ids=story_feed_ids, classifier_feeds=classifier_feeds, classifier_authors=classifier_authors, classifier_titles=classifier_titles, classifier_tags=classifier_tags) if socialsub: socialsub.feed_opens += 1 socialsub.save() diff1 = checkpoint1-start diff2 = checkpoint2-start logging.user(request, "~FYLoading ~FMshared stories~FY: ~SB%s%s ~SN(~SB%.4ss/%.4ss~SN)" % ( social_profile.title[:22], ('~SN/p%s' % page) if page > 1 else '', diff1, diff2)) return { "stories": stories, "user_profiles": user_profiles, "feeds": unsub_feeds, "classifiers": classifiers, }
def test_localtime_for_timezone(self): self.assertEqual( localtime_for_timezone( datetime(2008, 6, 25, 18, 0, 0), "America/Denver").strftime("%m/%d/%Y %H:%M:%S"), "06/25/2008 12:00:00")
def load_feed_statistics(request, feed_id): user = get_user(request) timezone = user.profile.timezone stats = dict() feed = get_object_or_404(Feed, pk=feed_id) feed.update_all_statistics() feed.set_next_scheduled_update(verbose=True, skip_scheduling=True) feed.save_feed_story_history_statistics() feed.save_classifier_counts() # Dates of last and next update stats["active"] = feed.active stats["last_update"] = relative_timesince(feed.last_update) stats["next_update"] = relative_timeuntil(feed.next_scheduled_update) stats["push"] = feed.is_push if feed.is_push: try: stats["push_expires"] = localtime_for_timezone(feed.push.lease_expires, timezone).strftime( "%Y-%m-%d %H:%M:%S" ) except PushSubscription.DoesNotExist: stats["push_expires"] = "Missing push" feed.is_push = False feed.save() # Minutes between updates update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False) stats["update_interval_minutes"] = update_interval_minutes original_active_premium_subscribers = feed.active_premium_subscribers original_premium_subscribers = feed.premium_subscribers feed.active_premium_subscribers = max(feed.active_premium_subscribers + 1, 1) feed.premium_subscribers += 1 premium_update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False, premium_speed=True) feed.active_premium_subscribers = original_active_premium_subscribers feed.premium_subscribers = original_premium_subscribers stats["premium_update_interval_minutes"] = premium_update_interval_minutes stats["errors_since_good"] = feed.errors_since_good # Stories per month - average and month-by-month breakout average_stories_per_month, story_count_history = feed.average_stories_per_month, feed.data.story_count_history stats["average_stories_per_month"] = average_stories_per_month stats["story_count_history"] = story_count_history and json.decode(story_count_history) # Subscribers stats["subscriber_count"] = feed.num_subscribers stats["num_subscribers"] = feed.num_subscribers stats["stories_last_month"] = feed.stories_last_month stats["last_load_time"] = feed.last_load_time stats["premium_subscribers"] = feed.premium_subscribers stats["active_subscribers"] = feed.active_subscribers stats["active_premium_subscribers"] = feed.active_premium_subscribers # Classifier counts stats["classifier_counts"] = json.decode(feed.data.feed_classifier_counts) # Fetch histories fetch_history = MFetchHistory.feed(feed_id, timezone=timezone) stats["feed_fetch_history"] = fetch_history["feed_fetch_history"] stats["page_fetch_history"] = fetch_history["page_fetch_history"] stats["feed_push_history"] = fetch_history["push_history"] logging.user(request, "~FBStatistics: ~SB%s" % (feed)) return stats
def load_feed_statistics(request, feed_id): user = get_user(request) timezone = user.profile.timezone stats = dict() feed = get_object_or_404(Feed, pk=feed_id) feed.update_all_statistics() feed.set_next_scheduled_update(verbose=True, skip_scheduling=True) feed.save_feed_story_history_statistics() feed.save_classifier_counts() # Dates of last and next update stats['active'] = feed.active stats['last_update'] = relative_timesince(feed.last_update) stats['next_update'] = relative_timeuntil(feed.next_scheduled_update) stats['push'] = feed.is_push if feed.is_push: try: stats['push_expires'] = localtime_for_timezone( feed.push.lease_expires, timezone).strftime("%Y-%m-%d %H:%M:%S") except PushSubscription.DoesNotExist: stats['push_expires'] = 'Missing push' feed.is_push = False feed.save() # Minutes between updates update_interval_minutes = feed.get_next_scheduled_update(force=True, verbose=False) stats['update_interval_minutes'] = update_interval_minutes original_active_premium_subscribers = feed.active_premium_subscribers original_premium_subscribers = feed.premium_subscribers feed.active_premium_subscribers = max(feed.active_premium_subscribers + 1, 1) feed.premium_subscribers += 1 premium_update_interval_minutes = feed.get_next_scheduled_update( force=True, verbose=False, premium_speed=True) feed.active_premium_subscribers = original_active_premium_subscribers feed.premium_subscribers = original_premium_subscribers stats['premium_update_interval_minutes'] = premium_update_interval_minutes stats['errors_since_good'] = feed.errors_since_good # Stories per month - average and month-by-month breakout average_stories_per_month, story_count_history = feed.average_stories_per_month, feed.data.story_count_history stats['average_stories_per_month'] = average_stories_per_month stats['story_count_history'] = story_count_history and json.decode( story_count_history) # Subscribers stats['subscriber_count'] = feed.num_subscribers stats['num_subscribers'] = feed.num_subscribers stats['stories_last_month'] = feed.stories_last_month stats['last_load_time'] = feed.last_load_time stats['premium_subscribers'] = feed.premium_subscribers stats['active_subscribers'] = feed.active_subscribers stats['active_premium_subscribers'] = feed.active_premium_subscribers # Classifier counts stats['classifier_counts'] = json.decode(feed.data.feed_classifier_counts) # Fetch histories fetch_history = MFetchHistory.feed(feed_id, timezone=timezone) stats['feed_fetch_history'] = fetch_history['feed_fetch_history'] stats['page_fetch_history'] = fetch_history['page_fetch_history'] stats['feed_push_history'] = fetch_history['push_history'] logging.user(request, "~FBStatistics: ~SB%s" % (feed)) return stats
def load_single_feed(request, feed_id): start = datetime.datetime.utcnow() user = get_user(request) offset = int(request.REQUEST.get('offset', 0)) limit = int(request.REQUEST.get('limit', 12)) page = int(request.REQUEST.get('page', 1)) if page: offset = limit * (page-1) dupe_feed_id = None if not feed_id: raise Http404 try: feed = Feed.objects.get(id=feed_id) except Feed.DoesNotExist: feed_address = request.REQUEST.get('feed_address') dupe_feed = DuplicateFeed.objects.filter(duplicate_address=feed_address) if dupe_feed: feed = dupe_feed[0].feed dupe_feed_id = feed_id else: raise Http404 stories = feed.get_stories(offset, limit) # Get intelligence classifier for user classifier_feeds = MClassifierFeed.objects(user_id=user.pk, feed_id=feed_id) classifier_authors = MClassifierAuthor.objects(user_id=user.pk, feed_id=feed_id) classifier_titles = MClassifierTitle.objects(user_id=user.pk, feed_id=feed_id) classifier_tags = MClassifierTag.objects(user_id=user.pk, feed_id=feed_id) usersub = UserSubscription.objects.get(user=user, feed=feed) userstories = [] if usersub: userstories_db = MUserStory.objects(user_id=user.pk, feed_id=feed.pk, read_date__gte=usersub.mark_read_date) starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id=feed_id).only('story_guid', 'starred_date') starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories]) for us in userstories_db: if hasattr(us.story, 'story_guid') and isinstance(us.story.story_guid, unicode): userstories.append(us.story.story_guid) elif hasattr(us.story, 'id') and isinstance(us.story.id, unicode): userstories.append(us.story.id) # TODO: Remove me after migration from story.id->guid for story in stories: [x.rewind() for x in [classifier_feeds, classifier_authors, classifier_tags, classifier_titles]] story_date = localtime_for_timezone(story['story_date'], user.profile.timezone) now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone) story['short_parsed_date'] = format_story_link_date__short(story_date, now) story['long_parsed_date'] = format_story_link_date__long(story_date, now) if usersub: if story['id'] in userstories: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] < usersub.mark_read_date: story['read_status'] = 1 elif not story.get('read_status') and story['story_date'] > usersub.last_read_date: story['read_status'] = 0 if story['id'] in starred_stories: story['starred'] = True starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone) story['starred_date'] = format_story_link_date__long(starred_date, now) else: story['read_status'] = 1 story['intelligence'] = { 'feed': apply_classifier_feeds(classifier_feeds, feed), 'author': apply_classifier_authors(classifier_authors, story), 'tags': apply_classifier_tags(classifier_tags, story), 'title': apply_classifier_titles(classifier_titles, story), } # Intelligence feed_tags = json.decode(feed.data.popular_tags) if feed.data.popular_tags else [] feed_authors = json.decode(feed.data.popular_authors) if feed.data.popular_authors else [] classifiers = get_classifiers_for_user(user, feed_id, classifier_feeds, classifier_authors, classifier_titles, classifier_tags) if usersub: usersub.feed_opens += 1 usersub.save() diff = datetime.datetime.utcnow()-start timediff = float("%s.%.2s" % (diff.seconds, (diff.microseconds / 1000))) last_update = relative_timesince(feed.last_update) logging.user(request.user, "~FYLoading feed: ~SB%s%s ~SN(%s seconds)" % ( feed, ('~SN/p%s' % page) if page > 1 else '', timediff)) FeedLoadtime.objects.create(feed=feed, loadtime=timediff) data = dict(stories=stories, feed_tags=feed_tags, feed_authors=feed_authors, classifiers=classifiers, last_update=last_update, feed_id=feed.pk) if dupe_feed_id: data['dupe_feed_id'] = dupe_feed_id if not usersub: data.update(feed.canonical()) return data