Reduce user_ips bloat during dedupe background update

The background update to remove duplicate rows naively deleted and
reinserted the duplicates. For large tables with a large number of
duplicates this causes a lot of bloat (with postgres), as the inserted
rows are appended to the table, since deleted rows will not be
overwritten until a VACUUM has happened.

This should hopefully also help ensure that the query in the last batch
uses the correct index, as inserting a large number of new rows without
analyzing will upset the query planner.
pull/14/head
Erik Johnston 6 years ago
parent 46b8a79b3a
commit 362d80b770
  1. 63
      synapse/storage/client_ips.py

@ -167,12 +167,16 @@ class ClientIpStore(background_updates.BackgroundUpdateStore):
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)
MAX(device_id), MAX(user_agent), MAX(last_seen),
COUNT(*)
FROM (
SELECT user_id, access_token, ip
SELECT DISTINCT user_id, access_token, ip
FROM user_ips
WHERE {}
) c
@ -186,7 +190,60 @@ class ClientIpStore(background_updates.BackgroundUpdateStore):
# We've got some duplicates
for i in res:
user_id, access_token, ip, device_id, user_agent, last_seen = i
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(

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