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matrix-org_synapse/synapse/storage/databases/main/metrics.py
reivilibre 736199b763
Remove old R30 because R30v2 supercedes it (#10428)
R30v2 has been out since 2021-07-19 (https://github.com/matrix-org/synapse/pull/10332)
and we started collecting stats on 2021-08-16. Since it's been over a year now
(almost 2 years), this is enough grace period for us to now rip it out.
2023-05-19 11:13:44 -05:00

450 lines
17 KiB
Python

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