MatrixSynapse/synapse/storage/data_stores/main/client_ips.py

586 lines
22 KiB
Python

# -*- coding: utf-8 -*-
# Copyright 2016 OpenMarket Ltd
#
# 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 logging
from six import iteritems
from twisted.internet import defer
from synapse.metrics.background_process_metrics import wrap_as_background_process
from synapse.storage._base import SQLBaseStore
from synapse.storage.database import Database
from synapse.util.caches import CACHE_SIZE_FACTOR
from synapse.util.caches.descriptors import Cache
logger = logging.getLogger(__name__)
# Number of msec of granularity to store the user IP 'last seen' time. Smaller
# times give more inserts into the database even for readonly API hits
# 120 seconds == 2 minutes
LAST_SEEN_GRANULARITY = 120 * 1000
class ClientIpBackgroundUpdateStore(SQLBaseStore):
def __init__(self, database: Database, db_conn, hs):
super(ClientIpBackgroundUpdateStore, self).__init__(database, db_conn, hs)
self.db.updates.register_background_index_update(
"user_ips_device_index",
index_name="user_ips_device_id",
table="user_ips",
columns=["user_id", "device_id", "last_seen"],
)
self.db.updates.register_background_index_update(
"user_ips_last_seen_index",
index_name="user_ips_last_seen",
table="user_ips",
columns=["user_id", "last_seen"],
)
self.db.updates.register_background_index_update(
"user_ips_last_seen_only_index",
index_name="user_ips_last_seen_only",
table="user_ips",
columns=["last_seen"],
)
self.db.updates.register_background_update_handler(
"user_ips_analyze", self._analyze_user_ip
)
self.db.updates.register_background_update_handler(
"user_ips_remove_dupes", self._remove_user_ip_dupes
)
# Register a unique index
self.db.updates.register_background_index_update(
"user_ips_device_unique_index",
index_name="user_ips_user_token_ip_unique_index",
table="user_ips",
columns=["user_id", "access_token", "ip"],
unique=True,
)
# Drop the old non-unique index
self.db.updates.register_background_update_handler(
"user_ips_drop_nonunique_index", self._remove_user_ip_nonunique
)
# Update the last seen info in devices.
self.db.updates.register_background_update_handler(
"devices_last_seen", self._devices_last_seen_update
)
@defer.inlineCallbacks
def _remove_user_ip_nonunique(self, progress, batch_size):
def f(conn):
txn = conn.cursor()
txn.execute("DROP INDEX IF EXISTS user_ips_user_ip")
txn.close()
yield self.db.runWithConnection(f)
yield self.db.updates._end_background_update("user_ips_drop_nonunique_index")
return 1
@defer.inlineCallbacks
def _analyze_user_ip(self, progress, batch_size):
# Background update to analyze user_ips table before we run the
# deduplication background update. The table may not have been analyzed
# for ages due to the table locks.
#
# This will lock out the naive upserts to user_ips while it happens, but
# the analyze should be quick (28GB table takes ~10s)
def user_ips_analyze(txn):
txn.execute("ANALYZE user_ips")
yield self.db.runInteraction("user_ips_analyze", user_ips_analyze)
yield self.db.updates._end_background_update("user_ips_analyze")
return 1
@defer.inlineCallbacks
def _remove_user_ip_dupes(self, progress, batch_size):
# This works function works by scanning the user_ips table in batches
# based on `last_seen`. For each row in a batch it searches the rest of
# the table to see if there are any duplicates, if there are then they
# are removed and replaced with a suitable row.
# Fetch the start of the batch
begin_last_seen = progress.get("last_seen", 0)
def get_last_seen(txn):
txn.execute(
"""
SELECT last_seen FROM user_ips
WHERE last_seen > ?
ORDER BY last_seen
LIMIT 1
OFFSET ?
""",
(begin_last_seen, batch_size),
)
row = txn.fetchone()
if row:
return row[0]
else:
return None
# Get a last seen that has roughly `batch_size` since `begin_last_seen`
end_last_seen = yield self.db.runInteraction(
"user_ips_dups_get_last_seen", get_last_seen
)
# If it returns None, then we're processing the last batch
last = end_last_seen is None
logger.info(
"Scanning for duplicate 'user_ips' rows in range: %s <= last_seen < %s",
begin_last_seen,
end_last_seen,
)
def remove(txn):
# This works by looking at all entries in the given time span, and
# then for each (user_id, access_token, ip) tuple in that range
# checking for any duplicates in the rest of the table (via a join).
# It then only returns entries which have duplicates, and the max
# last_seen across all duplicates, which can the be used to delete
# all other duplicates.
# It is efficient due to the existence of (user_id, access_token,
# ip) and (last_seen) indices.
# Define the search space, which requires handling the last batch in
# a different way
if last:
clause = "? <= last_seen"
args = (begin_last_seen,)
else:
clause = "? <= last_seen AND last_seen < ?"
args = (begin_last_seen, end_last_seen)
# (Note: The DISTINCT in the inner query is important to ensure that
# the COUNT(*) is accurate, otherwise double counting may happen due
# to the join effectively being a cross product)
txn.execute(
"""
SELECT user_id, access_token, ip,
MAX(device_id), MAX(user_agent), MAX(last_seen),
COUNT(*)
FROM (
SELECT DISTINCT user_id, access_token, ip
FROM user_ips
WHERE {}
) c
INNER JOIN user_ips USING (user_id, access_token, ip)
GROUP BY user_id, access_token, ip
HAVING count(*) > 1
""".format(
clause
),
args,
)
res = txn.fetchall()
# We've got some duplicates
for i in res:
user_id, access_token, ip, device_id, user_agent, last_seen, count = i
# We want to delete the duplicates so we end up with only a
# single row.
#
# The naive way of doing this would be just to delete all rows
# and reinsert a constructed row. However, if there are a lot of
# duplicate rows this can cause the table to grow a lot, which
# can be problematic in two ways:
# 1. If user_ips is already large then this can cause the
# table to rapidly grow, potentially filling the disk.
# 2. Reinserting a lot of rows can confuse the table
# statistics for postgres, causing it to not use the
# correct indices for the query above, resulting in a full
# table scan. This is incredibly slow for large tables and
# can kill database performance. (This seems to mainly
# happen for the last query where the clause is simply `? <
# last_seen`)
#
# So instead we want to delete all but *one* of the duplicate
# rows. That is hard to do reliably, so we cheat and do a two
# step process:
# 1. Delete all rows with a last_seen strictly less than the
# max last_seen. This hopefully results in deleting all but
# one row the majority of the time, but there may be
# duplicate last_seen
# 2. If multiple rows remain, we fall back to the naive method
# and simply delete all rows and reinsert.
#
# Note that this relies on no new duplicate rows being inserted,
# but if that is happening then this entire process is futile
# anyway.
# Do step 1:
txn.execute(
"""
DELETE FROM user_ips
WHERE user_id = ? AND access_token = ? AND ip = ? AND last_seen < ?
""",
(user_id, access_token, ip, last_seen),
)
if txn.rowcount == count - 1:
# We deleted all but one of the duplicate rows, i.e. there
# is exactly one remaining and so there is nothing left to
# do.
continue
elif txn.rowcount >= count:
raise Exception(
"We deleted more duplicate rows from 'user_ips' than expected"
)
# The previous step didn't delete enough rows, so we fallback to
# step 2:
# Drop all the duplicates
txn.execute(
"""
DELETE FROM user_ips
WHERE user_id = ? AND access_token = ? AND ip = ?
""",
(user_id, access_token, ip),
)
# Add in one to be the last_seen
txn.execute(
"""
INSERT INTO user_ips
(user_id, access_token, ip, device_id, user_agent, last_seen)
VALUES (?, ?, ?, ?, ?, ?)
""",
(user_id, access_token, ip, device_id, user_agent, last_seen),
)
self.db.updates._background_update_progress_txn(
txn, "user_ips_remove_dupes", {"last_seen": end_last_seen}
)
yield self.db.runInteraction("user_ips_dups_remove", remove)
if last:
yield self.db.updates._end_background_update("user_ips_remove_dupes")
return batch_size
@defer.inlineCallbacks
def _devices_last_seen_update(self, progress, batch_size):
"""Background update to insert last seen info into devices table
"""
last_user_id = progress.get("last_user_id", "")
last_device_id = progress.get("last_device_id", "")
def _devices_last_seen_update_txn(txn):
# This consists of two queries:
#
# 1. The sub-query searches for the next N devices and joins
# against user_ips to find the max last_seen associated with
# that device.
# 2. The outer query then joins again against user_ips on
# user/device/last_seen. This *should* hopefully only
# return one row, but if it does return more than one then
# we'll just end up updating the same device row multiple
# times, which is fine.
if self.database_engine.supports_tuple_comparison:
where_clause = "(user_id, device_id) > (?, ?)"
where_args = [last_user_id, last_device_id]
else:
# We explicitly do a `user_id >= ? AND (...)` here to ensure
# that an index is used, as doing `user_id > ? OR (user_id = ? AND ...)`
# makes it hard for query optimiser to tell that it can use the
# index on user_id
where_clause = "user_id >= ? AND (user_id > ? OR device_id > ?)"
where_args = [last_user_id, last_user_id, last_device_id]
sql = """
SELECT
last_seen, ip, user_agent, user_id, device_id
FROM (
SELECT
user_id, device_id, MAX(u.last_seen) AS last_seen
FROM devices
INNER JOIN user_ips AS u USING (user_id, device_id)
WHERE %(where_clause)s
GROUP BY user_id, device_id
ORDER BY user_id ASC, device_id ASC
LIMIT ?
) c
INNER JOIN user_ips AS u USING (user_id, device_id, last_seen)
""" % {
"where_clause": where_clause
}
txn.execute(sql, where_args + [batch_size])
rows = txn.fetchall()
if not rows:
return 0
sql = """
UPDATE devices
SET last_seen = ?, ip = ?, user_agent = ?
WHERE user_id = ? AND device_id = ?
"""
txn.execute_batch(sql, rows)
_, _, _, user_id, device_id = rows[-1]
self.db.updates._background_update_progress_txn(
txn,
"devices_last_seen",
{"last_user_id": user_id, "last_device_id": device_id},
)
return len(rows)
updated = yield self.db.runInteraction(
"_devices_last_seen_update", _devices_last_seen_update_txn
)
if not updated:
yield self.db.updates._end_background_update("devices_last_seen")
return updated
class ClientIpStore(ClientIpBackgroundUpdateStore):
def __init__(self, database: Database, db_conn, hs):
self.client_ip_last_seen = Cache(
name="client_ip_last_seen", keylen=4, max_entries=50000 * CACHE_SIZE_FACTOR
)
super(ClientIpStore, self).__init__(database, db_conn, hs)
self.user_ips_max_age = hs.config.user_ips_max_age
# (user_id, access_token, ip,) -> (user_agent, device_id, last_seen)
self._batch_row_update = {}
self._client_ip_looper = self._clock.looping_call(
self._update_client_ips_batch, 5 * 1000
)
self.hs.get_reactor().addSystemEventTrigger(
"before", "shutdown", self._update_client_ips_batch
)
if self.user_ips_max_age:
self._clock.looping_call(self._prune_old_user_ips, 5 * 1000)
@defer.inlineCallbacks
def insert_client_ip(
self, user_id, access_token, ip, user_agent, device_id, now=None
):
if not now:
now = int(self._clock.time_msec())
key = (user_id, access_token, ip)
try:
last_seen = self.client_ip_last_seen.get(key)
except KeyError:
last_seen = None
yield self.populate_monthly_active_users(user_id)
# Rate-limited inserts
if last_seen is not None and (now - last_seen) < LAST_SEEN_GRANULARITY:
return
self.client_ip_last_seen.prefill(key, now)
self._batch_row_update[key] = (user_agent, device_id, now)
@wrap_as_background_process("update_client_ips")
def _update_client_ips_batch(self):
# If the DB pool has already terminated, don't try updating
if not self.db.is_running():
return
to_update = self._batch_row_update
self._batch_row_update = {}
return self.db.runInteraction(
"_update_client_ips_batch", self._update_client_ips_batch_txn, to_update
)
def _update_client_ips_batch_txn(self, txn, to_update):
if "user_ips" in self.db._unsafe_to_upsert_tables or (
not self.database_engine.can_native_upsert
):
self.database_engine.lock_table(txn, "user_ips")
for entry in iteritems(to_update):
(user_id, access_token, ip), (user_agent, device_id, last_seen) = entry
try:
self.db.simple_upsert_txn(
txn,
table="user_ips",
keyvalues={
"user_id": user_id,
"access_token": access_token,
"ip": ip,
},
values={
"user_agent": user_agent,
"device_id": device_id,
"last_seen": last_seen,
},
lock=False,
)
# Technically an access token might not be associated with
# a device so we need to check.
if device_id:
# this is always an update rather than an upsert: the row should
# already exist, and if it doesn't, that may be because it has been
# deleted, and we don't want to re-create it.
self.db.simple_update_txn(
txn,
table="devices",
keyvalues={"user_id": user_id, "device_id": device_id},
updatevalues={
"user_agent": user_agent,
"last_seen": last_seen,
"ip": ip,
},
)
except Exception as e:
# Failed to upsert, log and continue
logger.error("Failed to insert client IP %r: %r", entry, e)
@defer.inlineCallbacks
def get_last_client_ip_by_device(self, user_id, device_id):
"""For each device_id listed, give the user_ip it was last seen on
Args:
user_id (str)
device_id (str): If None fetches all devices for the user
Returns:
defer.Deferred: resolves to a dict, where the keys
are (user_id, device_id) tuples. The values are also dicts, with
keys giving the column names
"""
keyvalues = {"user_id": user_id}
if device_id is not None:
keyvalues["device_id"] = device_id
res = yield self.db.simple_select_list(
table="devices",
keyvalues=keyvalues,
retcols=("user_id", "ip", "user_agent", "device_id", "last_seen"),
)
ret = {(d["user_id"], d["device_id"]): d for d in res}
for key in self._batch_row_update:
uid, access_token, ip = key
if uid == user_id:
user_agent, did, last_seen = self._batch_row_update[key]
if not device_id or did == device_id:
ret[(user_id, device_id)] = {
"user_id": user_id,
"access_token": access_token,
"ip": ip,
"user_agent": user_agent,
"device_id": did,
"last_seen": last_seen,
}
return ret
@defer.inlineCallbacks
def get_user_ip_and_agents(self, user):
user_id = user.to_string()
results = {}
for key in self._batch_row_update:
uid, access_token, ip, = key
if uid == user_id:
user_agent, _, last_seen = self._batch_row_update[key]
results[(access_token, ip)] = (user_agent, last_seen)
rows = yield self.db.simple_select_list(
table="user_ips",
keyvalues={"user_id": user_id},
retcols=["access_token", "ip", "user_agent", "last_seen"],
desc="get_user_ip_and_agents",
)
results.update(
((row["access_token"], row["ip"]), (row["user_agent"], row["last_seen"]))
for row in rows
)
return [
{
"access_token": access_token,
"ip": ip,
"user_agent": user_agent,
"last_seen": last_seen,
}
for (access_token, ip), (user_agent, last_seen) in iteritems(results)
]
@wrap_as_background_process("prune_old_user_ips")
async def _prune_old_user_ips(self):
"""Removes entries in user IPs older than the configured period.
"""
if self.user_ips_max_age is None:
# Nothing to do
return
if not await self.db.updates.has_completed_background_update(
"devices_last_seen"
):
# Only start pruning if we have finished populating the devices
# last seen info.
return
# We do a slightly funky SQL delete to ensure we don't try and delete
# too much at once (as the table may be very large from before we
# started pruning).
#
# This works by finding the max last_seen that is less than the given
# time, but has no more than N rows before it, deleting all rows with
# a lesser last_seen time. (We COALESCE so that the sub-SELECT always
# returns exactly one row).
sql = """
DELETE FROM user_ips
WHERE last_seen <= (
SELECT COALESCE(MAX(last_seen), -1)
FROM (
SELECT last_seen FROM user_ips
WHERE last_seen <= ?
ORDER BY last_seen ASC
LIMIT 5000
) AS u
)
"""
timestamp = self.clock.time_msec() - self.user_ips_max_age
def _prune_old_user_ips_txn(txn):
txn.execute(sql, (timestamp,))
await self.db.runInteraction("_prune_old_user_ips", _prune_old_user_ips_txn)