We recently deleted ALL mentions and since then CPU load went down to 15-20% so pretty good but therefore the query above won’t return results. (or just a few since we deleted it)
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The query above should return results in any case, it starts from EXPLAIN VERBOSE so it returns an execution plan. When you have time, could you please copy and paste the exact query from my message, run it and share results?
Limit (cost=123.37..22204.48 rows=1000 width=40)
Output: users.id, (array_agg(ARRAY[uccm.id, c_msg.id])), uccm.user_id
-> GroupAggregate (cost=123.37..339399.59 rows=15365 width=40)
Output: users.id, array_agg(ARRAY[uccm.id, c_msg.id]), uccm.user_id
Group Key: users.id, uccm.user_id
-> Nested Loop (cost=123.37..336077.65 rows=417317 width=24)
Output: users.id, uccm.user_id, uccm.id, c_msg.id
Inner Unique: true
-> Merge Join (cost=123.23..271629.01 rows=417317 width=28)
Output: users.id, group_users.group_id, uccm.id, uccm.user_id, c_msg.id
Merge Cond: (user_options.user_id = group_users.user_id)
-> Nested Loop Left Join (cost=121.72..221430.16 rows=132835 width=28)
Output: users.id, user_options.user_id, uccm.id, uccm.user_id, c_msg.id
Filter: (((uccm.user_id = c_mentions.user_id) AND (uccm.following IS TRUE) AND ((cc.chatable_type)::text = 'Category'::text)) OR ((cc.chatable_type)::text = 'DirectMessage'::text))
-> Nested Loop (cost=121.32..149930.76 rows=132967 width=42)
Output: users.id, user_options.user_id, uccm.id, uccm.user_id, uccm.following, cc.chatable_type, c_msg.id
Join Filter: ((c_msg.user_id <> users.id) AND (cc.id = c_msg.chat_channel_id))
-> Nested Loop (cost=120.91..57868.92 rows=21066 width=54)
Output: users.id, user_options.user_id, uccm.id, uccm.user_id, uccm.chat_channel_id, uccm.last_read_message_id, uccm.last_unread_mention_when_emailed_id, uccm.following, cc.id, cc.chatable_type
Inner Unique: true
-> Nested Loop (cost=120.63..51231.73 rows=21066 width=33)
Output: users.id, user_options.user_id, uccm.id, uccm.user_id, uccm.chat_channel_id, uccm.last_read_message_id, uccm.last_unread_mention_when_emailed_id, uccm.following
Inner Unique: true
-> Merge Join (cost=120.21..36835.88 rows=21096 width=29)
Output: users.id, uccm.id, uccm.user_id, uccm.chat_channel_id, uccm.last_read_message_id, uccm.last_unread_mention_when_emailed_id, uccm.following
Inner Unique: true
Merge Cond: (uccm.user_id = users.id)
-> Index Scan using user_chat_channel_unique_memberships on public.user_chat_channel_memberships uccm (cost=0.41..12177.25 rows=21618 width=25)
Output: uccm.id, uccm.user_id, uccm.chat_channel_id, uccm.last_read_message_id, uccm.following, uccm.muted, uccm.desktop_notification_level, uccm.mobile_notification_level, uccm.created_at, uccm.updated_at, uccm.last_unread_mention_when_emailed_id, uccm.join_mode, uccm.last_viewed_at
-> Index Scan using users_pkey on public.users (cost=0.42..23823.53 rows=237544 width=4)
Output: users.id
Filter: (users.last_seen_at < '2023-11-27 08:00:00'::timestamp without time zone)
-> Index Scan using index_user_options_on_user_id on public.user_options (cost=0.42..0.68 rows=1 width=4)
Output: user_options.user_id
Index Cond: (user_options.user_id = users.id)
Filter: (user_options.chat_enabled AND (user_options.chat_email_frequency = 1))
-> Index Scan using chat_channels_pkey on public.chat_channels cc (cost=0.28..0.32 rows=1 width=21)
Output: cc.id, cc.chatable_id, cc.deleted_at, cc.deleted_by_id, cc.featured_in_category_id, cc.delete_after_seconds, cc.chatable_type, cc.created_at, cc.updated_at, cc.name, cc.description, cc.status, cc.user_count, cc.last_message_sent_at, cc.auto_join_users, cc.user_count_stale, cc.type, cc.slug, cc.allow_channel_wide_mentions, cc.messages_count, cc.threading_enabled, cc.last_message_id
Index Cond: (cc.id = uccm.chat_channel_id)
-> Index Scan using index_chat_messages_on_chat_channel_id_and_created_at on public.chat_messages c_msg (cost=0.41..4.19 rows=12 width=16)
Output: c_msg.id, c_msg.chat_channel_id, c_msg.user_id
Index Cond: ((c_msg.chat_channel_id = uccm.chat_channel_id) AND (c_msg.created_at > '2023-11-20 08:00:00'::timestamp without time zone))
Filter: ((c_msg.deleted_at IS NULL) AND ((uccm.last_read_message_id IS NULL) OR (c_msg.id > uccm.last_read_message_id)) AND ((uccm.last_unread_mention_when_emailed_id IS NULL) OR (c_msg.id > uccm.last_unread_mention_when_emailed_id)))
-> Index Only Scan using chat_mentions_index on public.chat_mentions c_mentions (cost=0.40..0.52 rows=1 width=8)
Output: c_mentions.chat_message_id, c_mentions.user_id, c_mentions.notification_id
Index Cond: (c_mentions.chat_message_id = c_msg.id)
-> Index Only Scan using index_group_users_on_user_id_and_group_id on public.group_users (cost=0.42..44019.59 rows=760157 width=8)
Output: group_users.user_id, group_users.group_id
-> Index Only Scan using groups_pkey on public.groups (cost=0.14..0.16 rows=1 width=4)
Output: groups.id
Index Cond: (groups.id = group_users.group_id)
(51 rows)
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@sok777 thank you for sharing the query plan! It’s very helpful.
Your instance contains a big amount of users and a big amount of the user_chat_channel_memberships records (as you shared here).
Before you shared the test plan, I tried to reproduce the problem on sites with big amount of users, but the query worked well. Now, I see that those sites don’t have a big amount of user_chat_channel_memberships, very likely this is what’s specific about your site and what caused so serious problems in your case.
As I understand, you don’t have problems now, because you removed all mentions at some point, but I would expect this query to become problematic again at some point, even with small amount of mentions. So I will be optimizing it in any case.
I’m currently working on fixing the underling problem that caused creating huge loads of chat_mention records in database. While it’s in progress, it’s important to keep @ all and @ here in the watched words. I’ll post an update here when I take care of that, and after that I’ll make sure to optimize this query.
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The underlying problem has been solved by
There shouldn’t be such performance issues anymore, and there is no need to keep mass mentions in watched words.
Regarding this:
It seems there are no complaints about this query, so I wasn’t optimizing it. Please don’t hesitate to report in case of problems.
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