from __future__ import absolute_import from typing import Any, Callable, Iterable, Tuple from collections import defaultdict import datetime import six from six import text_type 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 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_type], int] conversation_diversity = defaultdict(set) # type: Dict[Tuple[int, text_type], Set[text_type]] 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_type]] # 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_type]]] 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"https://%s/#narrow/stream/" % (settings.EXTERNAL_HOST,) 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_type, text_type, 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, html_content, text_content): # type: (UserProfile, text_type, text_type) -> None recipients = [{'email': user_profile.email, 'name': user_profile.full_name}] subject = "While you've been gone - Zulip" 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, int) -> 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") # Start building email template data. template_payload = { 'name': user_profile.full_name, 'external_host': settings.EXTERNAL_HOST, 'external_uri_scheme': settings.EXTERNAL_URI_SCHEME, 'server_uri': settings.SERVER_URI, 'realm_uri': user_profile.realm.uri, 'unsubscribe_link': one_click_unsubscribe_link(user_profile, "digest") } # type: Dict[str, Any] # 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 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.txt', 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, html_content, text_content)