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https://github.com/zulip/zulip.git
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446 lines
14 KiB
Python
446 lines
14 KiB
Python
import datetime
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import heapq
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import logging
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from collections import defaultdict
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from typing import Any, Collection, Dict, List, Set, Tuple
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from django.conf import settings
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from django.db import transaction
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from django.utils.timezone import now as timezone_now
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from confirmation.models import one_click_unsubscribe_link
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from zerver.context_processors import common_context
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from zerver.lib.email_notifications import build_message_list
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from zerver.lib.logging_util import log_to_file
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from zerver.lib.message import get_last_message_id
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from zerver.lib.queue import queue_json_publish
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from zerver.lib.send_email import FromAddress, send_future_email
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from zerver.lib.url_encoding import encode_stream
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from zerver.models import (
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Message,
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Realm,
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RealmAuditLog,
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Recipient,
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Stream,
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Subscription,
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UserActivityInterval,
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UserProfile,
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get_active_streams,
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)
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logger = logging.getLogger(__name__)
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log_to_file(logger, settings.DIGEST_LOG_PATH)
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DIGEST_CUTOFF = 5
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MAX_HOT_TOPICS_TO_BE_INCLUDED_IN_DIGEST = 4
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TopicKey = Tuple[int, str]
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class DigestTopic:
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def __init__(self, topic_key: TopicKey) -> None:
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self.topic_key = topic_key
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self.human_senders: Set[str] = set()
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self.sample_messages: List[Message] = []
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self.num_human_messages = 0
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def stream_id(self) -> int:
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# topic_key is (stream_id, topic_name)
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return self.topic_key[0]
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def add_message(self, message: Message) -> None:
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if len(self.sample_messages) < 2:
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self.sample_messages.append(message)
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if message.sent_by_human():
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self.human_senders.add(message.sender.full_name)
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self.num_human_messages += 1
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def length(self) -> int:
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return self.num_human_messages
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def diversity(self) -> int:
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return len(self.human_senders)
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def teaser_data(self, user: UserProfile, stream_map: Dict[int, Stream]) -> Dict[str, Any]:
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teaser_count = self.num_human_messages - len(self.sample_messages)
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first_few_messages = build_message_list(
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user=user,
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messages=self.sample_messages,
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stream_map=stream_map,
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)
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return {
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"participants": sorted(self.human_senders),
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"count": teaser_count,
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"first_few_messages": first_few_messages,
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}
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# Digests accumulate 2 types of interesting traffic for a user:
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# 1. New streams
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# 2. Interesting stream traffic, as determined by the longest and most
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# diversely comment upon topics.
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def should_process_digest(realm_str: str) -> bool:
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if realm_str in settings.SYSTEM_ONLY_REALMS:
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# Don't try to send emails to system-only realms
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return False
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return True
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# Changes to this should also be reflected in
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# zerver/worker/queue_processors.py:DigestWorker.consume()
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def queue_digest_user_ids(user_ids: List[int], cutoff: datetime.datetime) -> None:
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# Convert cutoff to epoch seconds for transit.
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event = {"user_ids": user_ids, "cutoff": cutoff.strftime("%s")}
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queue_json_publish("digest_emails", event)
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def enqueue_emails(cutoff: datetime.datetime) -> None:
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if not settings.SEND_DIGEST_EMAILS:
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return
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weekday = timezone_now().weekday()
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for realm in Realm.objects.filter(
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deactivated=False, digest_emails_enabled=True, digest_weekday=weekday
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):
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if should_process_digest(realm.string_id):
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_enqueue_emails_for_realm(realm, cutoff)
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def _enqueue_emails_for_realm(realm: Realm, cutoff: datetime.datetime) -> None:
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# This should only be called directly by tests. Use enqueue_emails
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# to process all realms that are set up for processing on any given day.
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realm_user_ids = set(
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UserProfile.objects.filter(
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realm=realm,
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is_active=True,
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is_bot=False,
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enable_digest_emails=True,
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).values_list("id", flat=True)
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)
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twelve_hours_ago = timezone_now() - datetime.timedelta(hours=12)
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recent_user_ids = set(
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RealmAuditLog.objects.filter(
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realm_id=realm.id,
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event_type=RealmAuditLog.USER_DIGEST_EMAIL_CREATED,
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event_time__gt=twelve_hours_ago,
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)
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.values_list("modified_user_id", flat=True)
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.distinct()
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)
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realm_user_ids -= recent_user_ids
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active_user_ids = set(
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UserActivityInterval.objects.filter(
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user_profile_id__in=realm_user_ids,
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end__gt=cutoff,
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)
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.values_list("user_profile_id", flat=True)
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.distinct()
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)
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user_ids = list(realm_user_ids - active_user_ids)
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user_ids.sort()
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# We process batches of 30. We want a big enough batch
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# to amortize work, but not so big that a single item
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# from the queue takes too long to process.
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chunk_size = 30
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for i in range(0, len(user_ids), chunk_size):
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chunk_user_ids = user_ids[i : i + chunk_size]
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queue_digest_user_ids(chunk_user_ids, cutoff)
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logger.info(
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"Queuing user_ids for potential digest: %s",
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chunk_user_ids,
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)
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def get_recent_topics(
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stream_ids: List[int],
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cutoff_date: datetime.datetime,
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) -> List[DigestTopic]:
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# Gather information about topic conversations, then
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# classify by:
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# * topic length
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# * number of senders
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messages = (
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Message.objects.filter(
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recipient__type=Recipient.STREAM,
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recipient__type_id__in=stream_ids,
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date_sent__gt=cutoff_date,
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)
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.order_by(
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"id", # we will sample the first few messages
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)
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.select_related(
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"recipient", # we need stream_id
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"sender", # we need the sender's full name
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"sending_client", # for Message.sent_by_human
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)
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)
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digest_topic_map: Dict[TopicKey, DigestTopic] = {}
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for message in messages:
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topic_key = (message.recipient.type_id, message.topic_name())
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if topic_key not in digest_topic_map:
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digest_topic_map[topic_key] = DigestTopic(topic_key)
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digest_topic_map[topic_key].add_message(message)
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topics = list(digest_topic_map.values())
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return topics
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def get_hot_topics(
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all_topics: List[DigestTopic],
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stream_ids: Set[int],
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) -> List[DigestTopic]:
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topics = [topic for topic in all_topics if topic.stream_id() in stream_ids]
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hot_topics = heapq.nlargest(2, topics, key=DigestTopic.diversity)
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for topic in heapq.nlargest(
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MAX_HOT_TOPICS_TO_BE_INCLUDED_IN_DIGEST, topics, key=DigestTopic.length
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):
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if topic not in hot_topics:
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hot_topics.append(topic)
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if len(hot_topics) == MAX_HOT_TOPICS_TO_BE_INCLUDED_IN_DIGEST:
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break
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return hot_topics
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def get_recent_streams(realm: Realm, threshold: datetime.datetime) -> List[Stream]:
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fields = ["id", "name", "is_web_public", "invite_only"]
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return list(get_active_streams(realm).filter(date_created__gt=threshold).only(*fields))
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def gather_new_streams(
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realm: Realm,
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recent_streams: List[Stream], # streams only need id and name
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can_access_public: bool,
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) -> Tuple[int, Dict[str, List[str]]]:
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if can_access_public:
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new_streams = [stream for stream in recent_streams if not stream.invite_only]
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else:
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new_streams = [stream for stream in recent_streams if stream.is_web_public]
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base_url = f"{realm.uri}/#narrow/stream/"
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streams_html = []
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streams_plain = []
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for stream in new_streams:
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narrow_url = base_url + encode_stream(stream.id, stream.name)
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stream_link = f"<a href='{narrow_url}'>{stream.name}</a>"
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streams_html.append(stream_link)
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streams_plain.append(stream.name)
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return len(new_streams), {"html": streams_html, "plain": streams_plain}
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def enough_traffic(hot_conversations: str, new_streams: int) -> bool:
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return bool(hot_conversations or new_streams)
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def get_user_stream_map(user_ids: List[int]) -> Dict[int, Set[int]]:
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rows = Subscription.objects.filter(
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user_profile_id__in=user_ids,
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recipient__type=Recipient.STREAM,
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active=True,
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is_muted=False,
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).values("user_profile_id", "recipient__type_id")
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# maps user_id -> {stream_id, stream_id, ...}
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dct: Dict[int, Set[int]] = defaultdict(set)
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for row in rows:
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dct[row["user_profile_id"]].add(row["recipient__type_id"])
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return dct
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def get_slim_stream_map(stream_ids: Set[int]) -> Dict[int, Stream]:
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# This can be passed to build_message_list.
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streams = Stream.objects.filter(
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id__in=stream_ids,
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).only("id", "name")
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return {stream.id: stream for stream in streams}
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def bulk_get_digest_context(
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users: Collection[UserProfile], cutoff: float
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) -> Dict[int, Dict[str, Any]]:
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# We expect a non-empty list of users all from the same realm.
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assert users
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realm = next(iter(users)).realm
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for user in users:
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assert user.realm_id == realm.id
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# Convert from epoch seconds to a datetime object.
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cutoff_date = datetime.datetime.fromtimestamp(int(cutoff), tz=datetime.timezone.utc)
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result: Dict[int, Dict[str, Any]] = {}
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user_ids = [user.id for user in users]
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user_stream_map = get_user_stream_map(user_ids)
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recently_modified_streams = get_modified_streams(user_ids, cutoff_date)
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all_stream_ids = set()
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for user in users:
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stream_ids = user_stream_map[user.id]
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stream_ids -= recently_modified_streams.get(user.id, set())
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all_stream_ids |= stream_ids
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# Get all the recent topics for all the users. This does the heavy
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# lifting of making an expensive query to the Message table. Then
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# for each user, we filter to just the streams they care about.
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recent_topics = get_recent_topics(sorted(all_stream_ids), cutoff_date)
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stream_map = get_slim_stream_map(all_stream_ids)
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recent_streams = get_recent_streams(realm, cutoff_date)
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for user in users:
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stream_ids = user_stream_map[user.id]
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hot_topics = get_hot_topics(recent_topics, stream_ids)
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context = common_context(user)
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# Start building email template data.
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unsubscribe_link = one_click_unsubscribe_link(user, "digest")
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context.update(unsubscribe_link=unsubscribe_link)
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# Get context data for hot conversations.
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context["hot_conversations"] = [
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hot_topic.teaser_data(user, stream_map) for hot_topic in hot_topics
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]
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# Gather new streams.
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new_streams_count, new_streams = gather_new_streams(
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realm=realm,
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recent_streams=recent_streams,
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can_access_public=user.can_access_public_streams(),
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)
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context["new_streams"] = new_streams
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context["new_streams_count"] = new_streams_count
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result[user.id] = context
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return result
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def get_digest_context(user: UserProfile, cutoff: float) -> Dict[str, Any]:
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return bulk_get_digest_context([user], cutoff)[user.id]
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@transaction.atomic
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def bulk_handle_digest_email(user_ids: List[int], cutoff: float) -> None:
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# We go directly to the database to get user objects,
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# since inactive users are likely to not be in the cache.
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users = (
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UserProfile.objects.filter(id__in=user_ids, is_active=True, realm__deactivated=False)
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.order_by("id")
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.select_related("realm")
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)
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context_map = bulk_get_digest_context(users, cutoff)
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digest_users = []
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for user in users:
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context = context_map[user.id]
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# We don't want to send emails containing almost no information.
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if enough_traffic(context["hot_conversations"], context["new_streams_count"]):
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digest_users.append(user)
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logger.info("Sending digest email for user %s", user.id)
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# Send now, as a ScheduledEmail
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send_future_email(
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"zerver/emails/digest",
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user.realm,
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to_user_ids=[user.id],
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from_name="Zulip Digest",
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from_address=FromAddress.no_reply_placeholder,
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context=context,
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)
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bulk_write_realm_audit_logs(digest_users)
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def bulk_write_realm_audit_logs(users: List[UserProfile]) -> None:
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if not users:
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return
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# We write RealmAuditLog rows for auditing, and we will also
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# use these rows during the next run to possibly exclude the
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# users (if insufficient time has passed).
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last_message_id = get_last_message_id()
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now = timezone_now()
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log_rows = [
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RealmAuditLog(
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realm_id=user.realm_id,
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modified_user_id=user.id,
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event_last_message_id=last_message_id,
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event_time=now,
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event_type=RealmAuditLog.USER_DIGEST_EMAIL_CREATED,
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)
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for user in users
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]
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RealmAuditLog.objects.bulk_create(log_rows)
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def get_modified_streams(
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user_ids: List[int], cutoff_date: datetime.datetime
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) -> Dict[int, Set[int]]:
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"""Skipping streams where the user's subscription status has changed
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when constructing digests is critical to ensure correctness for
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streams without shared history, guest users, and long-term idle
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users, because it means that every user has the same view of the
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history of a given stream whose message history is being included
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(and thus we can share a lot of work).
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The downside is that newly created streams are never included in
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the first digest email after their creation. Should we wish to
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change that, we will need to be very careful to avoid creating
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bugs for any of those classes of users.
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"""
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events = [
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RealmAuditLog.SUBSCRIPTION_CREATED,
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RealmAuditLog.SUBSCRIPTION_ACTIVATED,
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RealmAuditLog.SUBSCRIPTION_DEACTIVATED,
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]
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# Get rows where the users' subscriptions have changed.
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rows = (
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RealmAuditLog.objects.filter(
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modified_user_id__in=user_ids,
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event_time__gt=cutoff_date,
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event_type__in=events,
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)
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.values("modified_user_id", "modified_stream_id")
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.distinct()
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)
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result: Dict[int, Set[int]] = defaultdict(set)
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for row in rows:
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user_id = row["modified_user_id"]
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stream_id = row["modified_stream_id"]
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result[user_id].add(stream_id)
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return result
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