from __future__ import absolute_import from typing import Any, Callable, Iterable, Tuple, Text from collections import defaultdict import datetime import six from django.db.models import Q, QuerySet from django.template import loader from django.conf import settings from zerver.lib.notifications import build_message_list, hashchange_encode, \ send_future_email, one_click_unsubscribe_link from zerver.models import UserProfile, UserMessage, Recipient, Stream, \ Subscription, get_active_streams from zerver.context_processors import common_context import logging log_format = "%(asctime)s: %(message)s" logging.basicConfig(format=log_format) formatter = logging.Formatter(log_format) file_handler = logging.FileHandler(settings.DIGEST_LOG_PATH) file_handler.setFormatter(formatter) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logger.addHandler(file_handler) # Digests accumulate 4 types of interesting traffic for a user: # 1. Missed PMs # 2. New streams # 3. New users # 4. Interesting stream traffic, as determined by the longest and most # diversely comment upon topics. def gather_hot_conversations(user_profile, stream_messages): # type: (UserProfile, QuerySet) -> List[Dict[str, Any]] # Gather stream conversations of 2 types: # 1. long conversations # 2. conversations where many different people participated # # Returns a list of dictionaries containing the templating # information for each hot conversation. conversation_length = defaultdict(int) # type: Dict[Tuple[int, Text], int] conversation_diversity = defaultdict(set) # type: Dict[Tuple[int, Text], Set[Text]] for user_message in stream_messages: if not user_message.message.sent_by_human(): # Don't include automated messages in the count. continue key = (user_message.message.recipient.type_id, user_message.message.subject) conversation_diversity[key].add( user_message.message.sender.full_name) conversation_length[key] += 1 diversity_list = list(conversation_diversity.items()) diversity_list.sort(key=lambda entry: len(entry[1]), reverse=True) length_list = list(conversation_length.items()) length_list.sort(key=lambda entry: entry[1], reverse=True) # Get up to the 4 best conversations from the diversity list # and length list, filtering out overlapping conversations. hot_conversations = [elt[0] for elt in diversity_list[:2]] for candidate, _ in length_list: if candidate not in hot_conversations: hot_conversations.append(candidate) if len(hot_conversations) >= 4: break # There was so much overlap between the diversity and length lists that we # still have < 4 conversations. Try to use remaining diversity items to pad # out the hot conversations. num_convos = len(hot_conversations) if num_convos < 4: hot_conversations.extend([elt[0] for elt in diversity_list[num_convos:4]]) hot_conversation_render_payloads = [] for h in hot_conversations: stream_id, subject = h users = list(conversation_diversity[h]) count = conversation_length[h] # We'll display up to 2 messages from the conversation. first_few_messages = [user_message.message for user_message in stream_messages.filter( message__recipient__type_id=stream_id, message__subject=subject)[:2]] teaser_data = {"participants": users, "count": count - len(first_few_messages), "first_few_messages": build_message_list( user_profile, first_few_messages)} hot_conversation_render_payloads.append(teaser_data) return hot_conversation_render_payloads def gather_new_users(user_profile, threshold): # type: (UserProfile, datetime.datetime) -> Tuple[int, List[Text]] # Gather information on users in the realm who have recently # joined. if user_profile.realm.is_zephyr_mirror_realm: new_users = [] # type: List[UserProfile] else: new_users = list(UserProfile.objects.filter( realm=user_profile.realm, date_joined__gt=threshold, is_bot=False)) user_names = [user.full_name for user in new_users] return len(user_names), user_names def gather_new_streams(user_profile, threshold): # type: (UserProfile, datetime.datetime) -> Tuple[int, Dict[str, List[Text]]] if user_profile.realm.is_zephyr_mirror_realm: new_streams = [] # type: List[Stream] else: new_streams = list(get_active_streams(user_profile.realm).filter( invite_only=False, date_created__gt=threshold)) base_url = u"%s/#narrow/stream/" % (user_profile.realm.uri,) streams_html = [] streams_plain = [] for stream in new_streams: narrow_url = base_url + hashchange_encode(stream.name) stream_link = u"%s" % (narrow_url, stream.name) streams_html.append(stream_link) streams_plain.append(stream.name) return len(new_streams), {"html": streams_html, "plain": streams_plain} def enough_traffic(unread_pms, hot_conversations, new_streams, new_users): # type: (Text, Text, int, int) -> bool if unread_pms or hot_conversations: # If you have any unread traffic, good enough. return True if new_streams and new_users: # If you somehow don't have any traffic but your realm did get # new streams and users, good enough. return True return False def send_digest_email(user_profile, subject, html_content, text_content): # type: (UserProfile, Text, Text, Text) -> None recipients = [{'email': user_profile.email, 'name': user_profile.full_name}] sender = {'email': settings.NOREPLY_EMAIL_ADDRESS, 'name': 'Zulip'} # Send now, through Mandrill. send_future_email(recipients, html_content, text_content, subject, delay=datetime.timedelta(0), sender=sender, tags=["digest-emails"]) def handle_digest_email(user_profile_id, cutoff): # type: (int, float) -> None user_profile = UserProfile.objects.get(id=user_profile_id) # Convert from epoch seconds to a datetime object. cutoff_date = datetime.datetime.utcfromtimestamp(int(cutoff)) all_messages = UserMessage.objects.filter( user_profile=user_profile, message__pub_date__gt=cutoff_date).order_by("message__pub_date") template_payload = common_context(user_profile) # Start building email template data. template_payload.update({ 'name': user_profile.full_name, 'unsubscribe_link': one_click_unsubscribe_link(user_profile, "digest") }) # Gather recent missed PMs, re-using the missed PM email logic. # You can't have an unread message that you sent, but when testing # this causes confusion so filter your messages out. pms = all_messages.filter( ~Q(message__recipient__type=Recipient.STREAM) & ~Q(message__sender=user_profile)) # Show up to 4 missed PMs. pms_limit = 4 template_payload['unread_pms'] = build_message_list( user_profile, [pm.message for pm in pms[:pms_limit]]) template_payload['remaining_unread_pms_count'] = min(0, len(pms) - pms_limit) home_view_recipients = [sub.recipient for sub in Subscription.objects.filter( user_profile=user_profile, active=True, in_home_view=True)] stream_messages = all_messages.filter( message__recipient__type=Recipient.STREAM, message__recipient__in=home_view_recipients) # Gather hot conversations. template_payload["hot_conversations"] = gather_hot_conversations( user_profile, stream_messages) # Gather new streams. new_streams_count, new_streams = gather_new_streams( user_profile, cutoff_date) template_payload["new_streams"] = new_streams template_payload["new_streams_count"] = new_streams_count # Gather users who signed up recently. new_users_count, new_users = gather_new_users( user_profile, cutoff_date) template_payload["new_users"] = new_users subject = loader.render_to_string('zerver/emails/digest/digest_email.subject').strip() text_content = loader.render_to_string( 'zerver/emails/digest/digest_email.txt', template_payload) html_content = loader.render_to_string( 'zerver/emails/digest/digest_email.html', template_payload) # We don't want to send emails containing almost no information. if enough_traffic(template_payload["unread_pms"], template_payload["hot_conversations"], new_streams_count, new_users_count): logger.info("Sending digest email for %s" % (user_profile.email,)) send_digest_email(user_profile, subject, html_content, text_content)