Previous cleanups (mostly the removals of Python __future__ imports)
were done in a way that introduced leading newlines. Delete leading
newlines from all files, except static/assets/zulip-emoji/NOTICE,
which is a verbatim copy of the Apache 2.0 license.
Signed-off-by: Anders Kaseorg <anders@zulipchat.com>
This gives us access to typing_extensions.Deque, which was not added
to typing until 3.5.4.
(PROVISION_VERSION is not bumped because the transitive dependency set
in dev.txt hasn’t changed.)
Signed-off-by: Anders Kaseorg <anders@zulipchat.com>
This renames Subscription.in_home_view field to is_muted, for greater
clarity as to what it does just from seeing the setting name, without
having to look it up.
Also disabled an obsolete test_migrations test.
Fixes#10042.
This adds experimental support in /register for sending key
statistical data on the last 1000 private messages that the user is a
participant in. Because it's experimental, we require developers to
request it explicitly in production (we don't use these data yet in
the webapp, and it likely carries some perf cost).
We expect this to be extremely helpful in initializing the mobile app
user experience for showing recent private message conversations.
See the code comments, but this has been heavily optimized to be very
efficient and do all the filtering work at the database layer so that
we minimize network transit with the database.
Fixes#11944.
This commit leverages the ahocorasick algorithm to build a set of user_ids
that have their alert_words present in the message. It runs in linear time
of the order of length of the input message as opposed to number of
alert_words. This is after building a ahocorasick Automaton which runs
in O(number of alert_words in entire realm) which is usually cached.
We'll use this in the push-notifications code, in a context where
there should definitely already be UserMessage rows if everything's
gone normally... but explicitly checking at the top seems like the
right pattern from a secure-coding perspective.
For the import-data codepath, we will call
the extracted function directly in a
subsequent commit.
The do_render_markdown() function has more
required parameters, which allows for more
explicit code and also allows us to flatten
out some logic related to alert words. (We
just pass in empty sets/dicts as needed).
After the messages have been imported, set the rendered_content of the
messages instead of leaving its value to be 'None'.
This is important to ensure that:
(1) Performance for users is good after completing the import.
(2) The database's full-text indexes have all of the imported messages
(which only happens properly when Message rows have their
rendered_content field edited).
Fixes#9168.
This doesn't seem to have a huge performance downside (less than 1s
extra time for loading / on chat.zulip.org), and it means the
possibility of users having so many unreads that we get weird/buggy
behavior is much more unlikely to exist.
We'll still want a better experience for users who somehow go over
this limit, but it can be pretty firmly "you need to go mark some
things as read".
This should have no effect for now, but it'll make things a bit
simpler in case we make future changes to support public streams
without history public to subscribers (and other organization
members).
Generally emails are not written with markdown in mind and hence
sometimes render in strange ways. This commit fixes a particular
issue that was causing whitespace before paragraphs to be treated
as code block due to which email content was being rendered in a
box that scrolls in right direction a lot.
Fixes: #7045.
This refactoring doesn't change behavior, but it sets us up
to more easily handle a register setting for `client_gravatar`,
which will allow clients to tell us they're going to compute
their own gravatar URLs.
The `client_gravatar` flag already exists in our code, but it
is only used for Django views (users/messages) but not for
Zulip events.
The main change is to move the call to `set_sender_avatar` into
`finalize_payload`, which adds the boolean `client_gravatar`
parameter to that function. And then we update various callers
to supply that flag.
One small performance benefit of this change is that we now
lazily compute the client message payloads in
`event_queue.process_message_event` now, so this will improve
performance if all interested clients have the same value of
`apply_markdown`. But the change here is really preparing us
for the additional boolean parameter, which will cause us to
have four variations of the payload.
This adds the data model and bugdown support for the new UserGroup
mention feature.
Before it'll be fully operational, we'll still need:
* A backend API for making these.
* A UI for interacting with that API.
* Typeahead on the frontend.
* CSS to make them look pretty and see who's in them.
We now have a MentionData class that encapsulates
the users who are possibly mentioned in a message.
Not that the rendering code may not keep all the mentions,
since things like backticks will suppress the mention.
We populate this now in do_send_messages, so that we can use
the info earlier in the message-sending process. This info
now gets passed down the call stack as an optional parameter.
Note that bugdown.convert() still populates the data when its
callers decline to pass in a MentionData object.
This is mostly a preparatory commit, as we don't take advantage
of the data yet in do_send_messages.
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
Before this change, we populated two cache entries for each
message that we sent. The entries were largely redundant,
with the only difference being whether we sent the content
as raw markdown or as the rendered HTML.
This commit makes it so we only have one cache entry per
message, and it includes both content and rendered_content.
One legacy source on confusion here is that `content`
changes meaning when you're on the front end. Here is the
situation going forward:
database:
content = raw
rendered_contented = rendered
cache entry:
content = raw
rendered_contented = rendered
payload for the frontend:
content = raw (for apply_markdown=False)
content = rendered (for apply_markdown=True)
Clients fetching messages can now specify that they are able
to compute their avatar, and if they set client_gratavar to
True in the request (w/our normal encoding scheme), then the
backend will not compute it, and the payload will be smaller.
The fix starts with get_messages_backend. The flag gets
passed down through these functions:
* MessageDict.post_process_dicts.
* MessageDict.set_sender_avatar.
We also fix up the callers for post_process_dicts to explicitly
pass in the client_gravatar path, but for now they all just hard
code the value to False.
This change makes the cache entries smaller for message
dictionaries. It also ensures we get valid data put into
message dictionaries if, for example, the sender's avatar
changes.
After this change, all of the attributes for a message
sender are only fetched during post-processing with two
exceptions:
* We get sender_id for "free" from the message,
and it's the primary key that we need to figure
out which data to fetch in post-processing.
* We need sender_realm_id to be able to cache topic
links, and a sender's realm id will never change,
so it's not a concern for invalidating cache rows.
All the other attributes are either likely to change (e.g.
sender avatar_version) and/or impact the size of cache
entries more severely than the two small id fields above.
This change should improve our overall system performance
by reducing the amount of memory used by every N message
rows we cache, and typically N will be in the thousands or
so on a large realm.
The other major implication of this change is that when
a user changes their avatar, and then later messages that
the user sent are fetched, all of the fields that go into
computing the avatar url will be pulled from the database,
not from cache.
Message.get_raw_db_rows is moved to MessageDict, since its
implementation details are highly coupled to other methods
in MessageDict.
And then sew_messages_and_reactions comes along for the
ride.
We eventually want to move Reaction.get_raw_db_rows to there
as well.
We now populate the avatar url as part of the post
processing step of building message dictionaries,
so that the avatar url is no longer in cache.
This change makes the cache slimmer, because instead
of caching the avatar url (which often includes a long
hash), we just cache the smaller fields that are used
to compute the url.
Note that this commit still has the problem that we're
essentially computing the avatar url from cached fields
that can be invalid. We will address that a few commits
later.
An immediate benefit of this change is that how we compute
avatar urls (or whether we compute them all) is now decoupled
from caching concerns. We will address this later as
well. (Some clients will be capable of computing their
own gravatar urls, for example.)
We're about to have multiple post-processing stages for building
message dictionaries. Rather than having individual "hydration"
methods remove intermediate values, we just wait until the end.
This decouples the hyrdration steps. The potentional problem
here is that we may have a field like sender_is_mirror_dummy
that isn't part of the final payload, but we need it for
calculating display recipients and avatars. We don't want to
delete it too early from the objects.
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.
This removes sender names from the message cache, since
they aren't guaranteed to be valid, and they're inexpensive
to add.
This commit will make the message cache entries smaller
by removing sender___full_name and sender__short_name
fields.
Then we add in the sender fields to the message payloads
by doing a query against the unique sender ids of the
messages we are processing.
This change leads to 2 extra database hops for most of
our message-related codepaths. The reason there are 2 hops
instead of 1 is that we basically re-calculate way too
much data to get a no-markdown dictionary.
Introduce MessageDict.post_process_dicts() will allow us
the ability to do the following:
* use less memory in the cache for repeated data
* prevent cache invalidation
* format data according to different client needs
The first use of this function is pretty inconsequential, but
it sets us up for more consequential changes.
In this commit we defer the MessageDict.hydrate_recipient_info
step until after we pull data out of the cache. This impacts
cache size as follows:
* streams - negligibly bigger
* PMs/huddles - slimmer due to not needing to repeat
sender data like email/full_name
Again, the main point of this change is to start setting up
the infrastructure to do post-processing.
This is a first step to eventually slimming the message cache,
but there are still some moving parts there to be worked through.
The more immediate benefit of extracting this function is that
we can put tests on it. Also, it isolates some functionality
that may go away as our clients gets smarter.
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 have two helper functions:
* get_raw_unread_data
* aggregate_unread_data
Separating the concerns is nice. The first function does
all the data collection. The second function should be fast,
and it only re-organizes the data into an aggregated form
that makes the page_params payload smaller and easier for
clients to work with.
For the first function, we try to return data structures
that are easier to manipulate than the end result. This
will allow us to apply events more easily, in a subsequent
commit.
There is no reason for either render_incoming_message() or
render_markdown() to require full UserProfile objects just to
triage alert words.
By only asking for user_ids, we save extra queries in two
callpaths and we make it easier to start using user_ids in
do_send_messages().
This never made sense to be a flag on the UserMessage table, since
it's not per-user state. And in fact it doesn't need to be in a
database at all, since it's easily computed from content anyway.
Fixes#1099.
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.
This field is convenient for bankruptcy checks. Clients could
calculate it from page_params.unread_msgs before this change, but
it would kind of a painful calculation.
To add count, we had to simplify the mypy annotations, which weren't
really accurate before.
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.
We are adding a new list of unread message ids grouped by
conversation to the queue registration result. This will allow
clients to show accurate unread badges without needing to load an
unbound number of historic messages.
Jason started this commit, and then Steve Howell finished it.
We only identify conversations using stream_id/user_id info;
we may need a subsequent version that includes things like
stream names and user emails/names for API clients that don't
have data structures to map ids -> attributes.
Instead of using dict's `get()` method use the subscript syntax
so that we can assert correctly that the reaction row contains
all the fields and if not raise the `KeyError` instead of silently
returning None.
This fixes a major performance issue, where we would fetch
user_profile objects inside a code path that had already bulk-fetched
the necessary user objects.
Like the similar related changes we just made, the fix is to marshall
and pass the data into the avatar library directly.
Also puts them into a processing queue, though the queue processor
does nothing.
Rewritten by tabbott to avoid unnecessary database queries in
do_send_messages.
In some cases here we simplify things by calling avatar_url()
instead of get_avatar_url(), when we have a user_profile record
handy. For other cases we pass in an extra avatar_version
parameter to get_avatar_url(), including from avatar_url().
Anders Kaseorg
Tim Abbott
Roman Godov
Bennet Sunder
Greg Price
Anders Kaseorg
Vishnu Ks
Steve Howell
Joshua Pan
Rhea Parekh
Shubham Dhama
jkiely
Aditya Bansal
Nikhil Kumar Mishra
neiljp (Neil Pilgrim)
rht
Harshit Bansal
Umair Khan
Steve Howell
Jason Michalski
Vaida Plankyte
Elliott Jin
Christian Hudon
vaibhav
Rishi Gupta
Susan Salituro
Raghav Jajodia