521 lines
20 KiB
Python
521 lines
20 KiB
Python
# Copyright 2020 The Matrix.org Foundation C.I.C.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import calendar
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import logging
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import time
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from typing import Dict
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from synapse.metrics import GaugeBucketCollector
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from synapse.metrics.background_process_metrics import wrap_as_background_process
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from synapse.storage._base import SQLBaseStore
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from synapse.storage.database import DatabasePool
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from synapse.storage.databases.main.event_push_actions import (
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EventPushActionsWorkerStore,
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)
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logger = logging.getLogger(__name__)
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# Collect metrics on the number of forward extremities that exist.
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_extremities_collecter = GaugeBucketCollector(
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"synapse_forward_extremities",
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"Number of rooms on the server with the given number of forward extremities"
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" or fewer",
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buckets=[1, 2, 3, 5, 7, 10, 15, 20, 50, 100, 200, 500],
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)
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# we also expose metrics on the "number of excess extremity events", which is
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# (E-1)*N, where E is the number of extremities and N is the number of state
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# events in the room. This is an approximation to the number of state events
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# we could remove from state resolution by reducing the graph to a single
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# forward extremity.
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_excess_state_events_collecter = GaugeBucketCollector(
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"synapse_excess_extremity_events",
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"Number of rooms on the server with the given number of excess extremity "
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"events, or fewer",
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buckets=[0] + [1 << n for n in range(12)],
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)
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class ServerMetricsStore(EventPushActionsWorkerStore, SQLBaseStore):
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"""Functions to pull various metrics from the DB, for e.g. phone home
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stats and prometheus metrics.
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"""
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def __init__(self, database: DatabasePool, db_conn, hs):
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super().__init__(database, db_conn, hs)
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# Read the extrems every 60 minutes
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if hs.config.worker.run_background_tasks:
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self._clock.looping_call(self._read_forward_extremities, 60 * 60 * 1000)
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# Used in _generate_user_daily_visits to keep track of progress
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self._last_user_visit_update = self._get_start_of_day()
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@wrap_as_background_process("read_forward_extremities")
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async def _read_forward_extremities(self):
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def fetch(txn):
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txn.execute(
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"""
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SELECT t1.c, t2.c
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FROM (
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SELECT room_id, COUNT(*) c FROM event_forward_extremities
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GROUP BY room_id
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) t1 LEFT JOIN (
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SELECT room_id, COUNT(*) c FROM current_state_events
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GROUP BY room_id
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) t2 ON t1.room_id = t2.room_id
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"""
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)
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return txn.fetchall()
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res = await self.db_pool.runInteraction("read_forward_extremities", fetch)
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_extremities_collecter.update_data(x[0] for x in res)
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_excess_state_events_collecter.update_data(
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(x[0] - 1) * x[1] for x in res if x[1]
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)
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async def count_daily_e2ee_messages(self):
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"""
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Returns an estimate of the number of messages sent in the last day.
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If it has been significantly less or more than one day since the last
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call to this function, it will return None.
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"""
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def _count_messages(txn):
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sql = """
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SELECT COALESCE(COUNT(*), 0) FROM events
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WHERE type = 'm.room.encrypted'
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AND stream_ordering > ?
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"""
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txn.execute(sql, (self.stream_ordering_day_ago,))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction("count_e2ee_messages", _count_messages)
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async def count_daily_sent_e2ee_messages(self):
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def _count_messages(txn):
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# This is good enough as if you have silly characters in your own
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# hostname then that's your own fault.
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like_clause = "%:" + self.hs.hostname
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sql = """
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SELECT COALESCE(COUNT(*), 0) FROM events
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WHERE type = 'm.room.encrypted'
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AND sender LIKE ?
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AND stream_ordering > ?
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"""
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txn.execute(sql, (like_clause, self.stream_ordering_day_ago))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction(
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"count_daily_sent_e2ee_messages", _count_messages
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)
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async def count_daily_active_e2ee_rooms(self):
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def _count(txn):
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sql = """
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SELECT COALESCE(COUNT(DISTINCT room_id), 0) FROM events
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WHERE type = 'm.room.encrypted'
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AND stream_ordering > ?
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"""
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txn.execute(sql, (self.stream_ordering_day_ago,))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction(
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"count_daily_active_e2ee_rooms", _count
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)
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async def count_daily_messages(self):
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"""
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Returns an estimate of the number of messages sent in the last day.
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If it has been significantly less or more than one day since the last
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call to this function, it will return None.
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"""
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def _count_messages(txn):
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sql = """
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SELECT COALESCE(COUNT(*), 0) FROM events
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WHERE type = 'm.room.message'
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AND stream_ordering > ?
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"""
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txn.execute(sql, (self.stream_ordering_day_ago,))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction("count_messages", _count_messages)
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async def count_daily_sent_messages(self):
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def _count_messages(txn):
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# This is good enough as if you have silly characters in your own
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# hostname then that's your own fault.
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like_clause = "%:" + self.hs.hostname
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sql = """
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SELECT COALESCE(COUNT(*), 0) FROM events
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WHERE type = 'm.room.message'
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AND sender LIKE ?
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AND stream_ordering > ?
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"""
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txn.execute(sql, (like_clause, self.stream_ordering_day_ago))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction(
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"count_daily_sent_messages", _count_messages
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)
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async def count_daily_active_rooms(self):
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def _count(txn):
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sql = """
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SELECT COALESCE(COUNT(DISTINCT room_id), 0) FROM events
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WHERE type = 'm.room.message'
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AND stream_ordering > ?
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"""
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txn.execute(sql, (self.stream_ordering_day_ago,))
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(count,) = txn.fetchone()
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return count
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return await self.db_pool.runInteraction("count_daily_active_rooms", _count)
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async def count_daily_users(self) -> int:
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"""
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Counts the number of users who used this homeserver in the last 24 hours.
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"""
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yesterday = int(self._clock.time_msec()) - (1000 * 60 * 60 * 24)
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return await self.db_pool.runInteraction(
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"count_daily_users", self._count_users, yesterday
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)
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async def count_monthly_users(self) -> int:
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"""
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Counts the number of users who used this homeserver in the last 30 days.
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Note this method is intended for phonehome metrics only and is different
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from the mau figure in synapse.storage.monthly_active_users which,
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amongst other things, includes a 3 day grace period before a user counts.
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"""
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thirty_days_ago = int(self._clock.time_msec()) - (1000 * 60 * 60 * 24 * 30)
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return await self.db_pool.runInteraction(
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"count_monthly_users", self._count_users, thirty_days_ago
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)
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def _count_users(self, txn, time_from):
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"""
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Returns number of users seen in the past time_from period
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"""
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sql = """
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SELECT COALESCE(count(*), 0) FROM (
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SELECT user_id FROM user_ips
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WHERE last_seen > ?
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GROUP BY user_id
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) u
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"""
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txn.execute(sql, (time_from,))
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(count,) = txn.fetchone()
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return count
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async def count_r30_users(self) -> Dict[str, int]:
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"""
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Counts the number of 30 day retained users, defined as:-
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* Users who have created their accounts more than 30 days ago
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* Where last seen at most 30 days ago
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* Where account creation and last_seen are > 30 days apart
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Returns:
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A mapping of counts globally as well as broken out by platform.
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"""
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def _count_r30_users(txn):
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thirty_days_in_secs = 86400 * 30
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now = int(self._clock.time())
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thirty_days_ago_in_secs = now - thirty_days_in_secs
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sql = """
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SELECT platform, COALESCE(count(*), 0) FROM (
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SELECT
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users.name, platform, users.creation_ts * 1000,
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MAX(uip.last_seen)
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FROM users
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INNER JOIN (
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SELECT
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user_id,
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last_seen,
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CASE
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WHEN user_agent LIKE '%%Android%%' THEN 'android'
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WHEN user_agent LIKE '%%iOS%%' THEN 'ios'
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WHEN user_agent LIKE '%%Electron%%' THEN 'electron'
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WHEN user_agent LIKE '%%Mozilla%%' THEN 'web'
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WHEN user_agent LIKE '%%Gecko%%' THEN 'web'
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ELSE 'unknown'
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END
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AS platform
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FROM user_ips
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) uip
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ON users.name = uip.user_id
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AND users.appservice_id is NULL
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AND users.creation_ts < ?
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AND uip.last_seen/1000 > ?
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AND (uip.last_seen/1000) - users.creation_ts > 86400 * 30
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GROUP BY users.name, platform, users.creation_ts
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) u GROUP BY platform
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"""
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results = {}
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txn.execute(sql, (thirty_days_ago_in_secs, thirty_days_ago_in_secs))
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for row in txn:
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if row[0] == "unknown":
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pass
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results[row[0]] = row[1]
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sql = """
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SELECT COALESCE(count(*), 0) FROM (
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SELECT users.name, users.creation_ts * 1000,
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MAX(uip.last_seen)
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FROM users
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INNER JOIN (
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SELECT
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user_id,
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last_seen
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FROM user_ips
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) uip
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ON users.name = uip.user_id
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AND appservice_id is NULL
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AND users.creation_ts < ?
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AND uip.last_seen/1000 > ?
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AND (uip.last_seen/1000) - users.creation_ts > 86400 * 30
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GROUP BY users.name, users.creation_ts
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) u
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"""
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txn.execute(sql, (thirty_days_ago_in_secs, thirty_days_ago_in_secs))
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(count,) = txn.fetchone()
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results["all"] = count
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return results
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return await self.db_pool.runInteraction("count_r30_users", _count_r30_users)
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async def count_r30v2_users(self) -> Dict[str, int]:
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"""
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Counts the number of 30 day retained users, defined as users that:
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- Appear more than once in the past 60 days
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- Have more than 30 days between the most and least recent appearances that
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occurred in the past 60 days.
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(This is the second version of this metric, hence R30'v2')
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Returns:
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A mapping from client type to the number of 30-day retained users for that client.
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The dict keys are:
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- "all" (a combined number of users across any and all clients)
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- "android" (Element Android)
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- "ios" (Element iOS)
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- "electron" (Element Desktop)
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- "web" (any web application -- it's not possible to distinguish Element Web here)
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"""
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def _count_r30v2_users(txn):
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thirty_days_in_secs = 86400 * 30
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now = int(self._clock.time())
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sixty_days_ago_in_secs = now - 2 * thirty_days_in_secs
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one_day_from_now_in_secs = now + 86400
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# This is the 'per-platform' count.
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sql = """
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SELECT
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client_type,
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count(client_type)
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FROM
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(
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SELECT
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user_id,
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CASE
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WHEN
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LOWER(user_agent) LIKE '%%riot%%' OR
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LOWER(user_agent) LIKE '%%element%%'
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THEN CASE
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WHEN
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LOWER(user_agent) LIKE '%%electron%%'
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THEN 'electron'
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WHEN
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LOWER(user_agent) LIKE '%%android%%'
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THEN 'android'
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WHEN
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LOWER(user_agent) LIKE '%%ios%%'
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THEN 'ios'
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ELSE 'unknown'
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END
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WHEN
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LOWER(user_agent) LIKE '%%mozilla%%' OR
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LOWER(user_agent) LIKE '%%gecko%%'
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THEN 'web'
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ELSE 'unknown'
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END as client_type
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FROM
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user_daily_visits
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WHERE
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timestamp > ?
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AND
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timestamp < ?
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GROUP BY
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user_id,
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client_type
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HAVING
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max(timestamp) - min(timestamp) > ?
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) AS temp
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GROUP BY
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client_type
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;
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"""
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# We initialise all the client types to zero, so we get an explicit
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# zero if they don't appear in the query results
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results = {"ios": 0, "android": 0, "web": 0, "electron": 0}
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txn.execute(
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sql,
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(
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sixty_days_ago_in_secs * 1000,
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one_day_from_now_in_secs * 1000,
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thirty_days_in_secs * 1000,
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),
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)
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for row in txn:
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if row[0] == "unknown":
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continue
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results[row[0]] = row[1]
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# This is the 'all users' count.
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sql = """
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SELECT COUNT(*) FROM (
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SELECT
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1
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FROM
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user_daily_visits
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WHERE
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timestamp > ?
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AND
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timestamp < ?
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GROUP BY
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user_id
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HAVING
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max(timestamp) - min(timestamp) > ?
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) AS r30_users
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"""
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txn.execute(
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sql,
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(
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sixty_days_ago_in_secs * 1000,
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one_day_from_now_in_secs * 1000,
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thirty_days_in_secs * 1000,
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),
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)
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row = txn.fetchone()
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if row is None:
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results["all"] = 0
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else:
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results["all"] = row[0]
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return results
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return await self.db_pool.runInteraction(
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"count_r30v2_users", _count_r30v2_users
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)
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def _get_start_of_day(self):
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"""
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Returns millisecond unixtime for start of UTC day.
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"""
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now = time.gmtime(self._clock.time())
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today_start = calendar.timegm((now.tm_year, now.tm_mon, now.tm_mday, 0, 0, 0))
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return today_start * 1000
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@wrap_as_background_process("generate_user_daily_visits")
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async def generate_user_daily_visits(self) -> None:
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"""
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Generates daily visit data for use in cohort/ retention analysis
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"""
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def _generate_user_daily_visits(txn):
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logger.info("Calling _generate_user_daily_visits")
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today_start = self._get_start_of_day()
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a_day_in_milliseconds = 24 * 60 * 60 * 1000
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now = self._clock.time_msec()
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# A note on user_agent. Technically a given device can have multiple
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# user agents, so we need to decide which one to pick. We could have
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# handled this in number of ways, but given that we don't care
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# _that_ much we have gone for MAX(). For more details of the other
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# options considered see
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# https://github.com/matrix-org/synapse/pull/8503#discussion_r502306111
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sql = """
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INSERT INTO user_daily_visits (user_id, device_id, timestamp, user_agent)
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SELECT u.user_id, u.device_id, ?, MAX(u.user_agent)
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FROM user_ips AS u
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LEFT JOIN (
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SELECT user_id, device_id, timestamp FROM user_daily_visits
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WHERE timestamp = ?
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) udv
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ON u.user_id = udv.user_id AND u.device_id=udv.device_id
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INNER JOIN users ON users.name=u.user_id
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WHERE ? <= last_seen AND last_seen < ?
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AND udv.timestamp IS NULL AND users.is_guest=0
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AND users.appservice_id IS NULL
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GROUP BY u.user_id, u.device_id
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"""
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# This means that the day has rolled over but there could still
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# be entries from the previous day. There is an edge case
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# where if the user logs in at 23:59 and overwrites their
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# last_seen at 00:01 then they will not be counted in the
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# previous day's stats - it is important that the query is run
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# often to minimise this case.
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if today_start > self._last_user_visit_update:
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yesterday_start = today_start - a_day_in_milliseconds
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txn.execute(
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sql,
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(
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yesterday_start,
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yesterday_start,
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self._last_user_visit_update,
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today_start,
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),
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)
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self._last_user_visit_update = today_start
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txn.execute(
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sql, (today_start, today_start, self._last_user_visit_update, now)
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)
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# Update _last_user_visit_update to now. The reason to do this
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# rather just clamping to the beginning of the day is to limit
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# the size of the join - meaning that the query can be run more
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# frequently
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self._last_user_visit_update = now
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await self.db_pool.runInteraction(
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"generate_user_daily_visits", _generate_user_daily_visits
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)
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