Do you call get_recipient(Recipient.STREAM, stream_id) or
get_recipient(stream_id, Recipient.STREAM)? I could never
remember, and it was not very type safe, since both parameters
are integers.
We mostly introduce these functions (as part of a big
code sweep):
send_stream_message
send_personal_message
send_huddle_message
In two cases, where we want to specifically manipulate
queue ids, we now call check_send_message directly. (The
above three functions deliberately don't support kwargs
to ensure simple code and better type safety.)
In do_send_messages, we only produce one dictionary for
the event queues, instead of different flavors for text
vs. html. This prevents two unnecessary queries to the
database.
It also means we only put one dictionary on the "message"
event queue instead of two, albeit a wider one that has
some values that won't be sent to the actual clients.
This wider dictionary from MessageDict.wide_dict is also
used for the `feedback_messages` queue and service bot
queues. Since the extra fields are possibly useful down
the road, and they'll just be ignored for now, we don't
bother to remove them. Also, those queue processors won't
have access to `content_type`, which they shouldn't need.
Fixes#6947
We make a few things cleaner for populating `realm_users`
in `do_event_register` and `apply_events`:
* We have a `raw_users` intermediate dictionary that
makes event updates O(1) and cleaner to read.
* We extract an `is_me` section for all updates that
apply to the current user.
* For `update` events, we do a more surgical copying
of fields from the event into our dict. This
prevents us from mutating fields in the event,
which was sketchy (at least in test mode). In
particular, this allowed us to remove some ugly
`del` code related to avatars.
* We introduce local vars `was_admin` and `now_admin`.
The cleanup had two test implications:
* We no longer need to normalize `realm_users`, since
`apply_events` now sees `raw_users` instead. Since
`raw_users` is a dict, there is no need to normalize
it, unlike lists with possibly random order.
* We updated the schema for avatar updates to include
the two fields that we used to hackily delete from
an event.
This new test solves the problem that when we
made changes to the page-load codepath in the past,
it's been hard to identify what new code caused
more database queries. Now you can see query
counts broken out by event type.
This requires a small, harmless change to extract
an `always_want` function in `lib/events.py`.
This field would get overwritten with an improper value when
we looped over multiple clients, due to not making full copies
of the message dictionary. This failure would be somewhat
random depending on how clients were ordered in the loop.
The only consumers of this field were the mobile app and the
apply-events-to-unread-counts logic. Both of these will now
use `flags` instead.
The `is_mentioned` flag in message events was buggy. We now
look directly at flags.
We will kill off `is_mentioned` in a subsequent commit.
We also remove some debugging code in the test that was failing
before this fix. The test would only fail when `is_mentioned`
was wrong, which never happened when you ran a single test, and
which would happen randomly when you ran multiple tests.
It's fairly difficult to debug tests that use
EventsRegisterTest.do_test, and when they fail on
Travis, it's particularly challengning. Now we make
the main diff less noisy, and we also include
the events that were applied.
The logic to apply events to page_params['unread_msgs'] was
complicated due to the aggregated data structures that we pass
down to the client.
Now we defer the aggregation logic until after we apply the
events. This leads to some simplifications in that codepath,
as well as some performance enhancements.
The intermediate data structure has sets and dictionaries that
generally are keyed by message_id, so most message-related
updates are O(1) in nature.
Also, by waiting to compute the counts until the end, it's a
bit less messy to try to keep track of increments/decrements.
Instead, we just update the dictionaries and sets during the
event-apply phase.
This change also fixes some corner cases:
* We now respect mutes when updating counts.
* For message updates, instead of bluntly updating
the whole topic bucket, we update individual
message ids.
Unfortunately, this change doesn't seem to address the pesky
test that fails sporadically on Travis, related to mention
updates. It will change the symptom, slightly, though.
We now do push notifications and missed message emails
for offline users who are subscribed to the stream for
a message that has been edited, but we short circuit
the offline-notification logic for any user who presumably
would have already received a notification on the original
message.
This effectively boils down to sending notifications to newly
mentioned users. The motivating use case here is that you
forget to mention somebody in a message, and then you edit
the message to mention the person. If they are offline, they
will now get pushed notifications and missed message emails,
with some minor caveats.
We try to mostly use the same techniques here as the
send-message code path, and we share common code with the
send-message path once we get to the Tornado layer and call
maybe_enqueue_notifications.
The major places where we differ are in a function called
maybe_enqueue_notifications_for_message_update, and the top
of that function short circuits a bunch of cases where we
can mostly assume that the original message had an offline
notification.
We can expect a couple changes in the future:
* Requirements may change here, and it might make sense
to send offline notifications on the update side even
in circumstances where the original message had a
notification.
* We may track more notifications in a DB model, which
may simplify our short-circuit logic.
In the view/action layer, we already had two separate codepaths
for send-message and update-message, but this mostly echoes
what the send-message path does in terms of collecting data
about recipients.
This class encapsulates the mapping of stream ids to
recipient ids, and it is optimized for bulk use and
repeated use (i.e. it remembers values it already fetched).
This particular commit barely improves the performance
of gather_subscriptions_helper, but it sets us up for
further optimizations.
Long term, we may try to denormalize stream_id on to the
Subscriber table or otherwise modify the database so we
don't have to jump through hoops to do this kind of mapping.
This commit will help enable those changes, because we
isolate the mapping to this one new class.
This commit completely switches us over to using a
dedicated model called MutedTopic to track which topics
a user has muted.
This includes the necessary migrations to create the
table and populate it from legacy data in UserProfile.
A subsequent commit will actually remove the old field
in UserProfile.
This function optimizes marking streams and topics as read,
by using UserMessage.where_unread(), which uses a partial
index on the "read" flag.
This also simplifies the code path for ordinary message
flag updates.
In order to keep 100% line coverage, I simplified the
logging in update_message_flags, so now all requests
will show the "actually" format.
This is an interim step toward creating dedicated endpoints
for marking streams/topics as reads, so we do error checking
with asserts for flag/operation, so we don't introduce a
temporary translation string.
This is the first part of a larger migration to convert Zulip's
reactions storage to something based on the codepoint, not the emoji
name that the user typed in, so that we don't need to worry about
changes in the names we're using breaking the emoji storage.
We were exiting this function in certain cases before updating
mentions. This bug was always there, but it was flaky in terms
of database setup whether the tests would fail, so now the
relevant test sends three consecutive messages.
We also avoid putting duplicate message ids in mentions.
The "all" option for 'message/flags' was dangerous, as it could
apply to any of our flags. The only flag it made sense for, the
"read" flag, now has a dedicated endpoint.
Harshit Bansal
rht
Steve Howell
Tim Abbott
Rishi Gupta
Sarah
Umair Khan
Aditya Bansal
neiljp (Neil Pilgrim)
Jack Zhang
Steve Howell