tsearch_extras returns search offsets in bytes but our highlight
function treated them as character offsets. Added a check to subtract
extra bytes if the tsearch search backend is being used.
Fixes#4084.
Fixes#7021.
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.
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.
Add this field to the Stream model will prevent us from having
to look at realm data for several types of stream operations, which
can be prone to either doing extra database lookups or making
our cached data bloated.
Going forward, we'll set stream.is_zephyr to True whenever the
realm's string id is "zephyr".
Instead of using `unified_reactions` mapping start using
`name_to_codepoint` mapping for converting emoji name to
codepoints. We were using `unified_reactions` mapping
because prior to emoji web PR `name_to_codepoint` mapping
was generated using emoji_map.json which contained old
codepoints but for reactions new codepoints were required
to display them using sprite sheets.
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 is mostly pure code extraction.
It also removes some dead code in update_muted_topic, where
were updating muted_topics spuriously before calling
do_update_muted_topic.
For filters like has:link, where the web app doesn't necessarily
want to guess whether incoming messages meet the criteria of the
filter, the server is asked to query rows that match the query.
Usually these queries are search queries, which have fields for
content_matches and subject_matches. Our logic was handling those
correctly.
Non-search queries were throwing an exception related to tuple
unpacking. Now we recognize when those fields are absent and
do the proper thing.
There are probably situations where the web app should stop hitting
this endpoint and just use its own filters. We are making the most
defensive fix first.
Fixes#6118
Before this change, server searches for both
`is:mentioned` and `is:alerted` would return all messages
where the user is specifically mentioned (but not
at-all mentions).
Now we follow the JS semantics:
is:mentioned -- all mentions, including wildcards
is:alerted -- has an alert word
Here is one relevant JS snippet:
} else if (operand === 'mentioned') {
return message.mentioned;
} else if (operand === 'alerted') {
return message.alerted;
And here you see that `mentioned` is OR'ed over both mention flags:
message.mentioned = convert_flag('mentioned') || convert_flag('wildcard_mentioned');
The `alerted` flag on the JS side is a simple mapping:
message.alerted = convert_flag('has_alert_word');
Fixes#5020
We apparently were using the default of num_before=1, not
num_before=0, which meant that if the very last randomly generated
message was one by cordelia mentioning lunch,
test_get_messages_with_search would fail because there were actually 3
matches.
We recently changed the populate_db data set to include more variable
message content, which happened to include the possibility of the word
"lunch" appearing in the test messages. This caused occasional
failures of the search tests that looked for messages containing
"lunch" starting at the beginning of time, not the beginning of the
test.
This completes the major endpoint migrations to eliminate legacy API
endpoints from Zulip.
There's a few other things that will happen naturally, so I believe
this fixes#611.
The example_user() function is specifically designed for
AARON, hamlet, cordelia, and friends, and it allows a concise
way of using their built-in user profiles. Eventually, the
widespread use of example_user() should help us with refactorings
such as moving the tests users out of the "zulip.com" realm
and deprecating get_user_profile_by_email.
This is a better solution to the problem of how _pg_re_escape should
handle the null character. There's really no good reason to have a
null character in a stream name.
neiljp (Neil Pilgrim)
Sampriti Panda
Tim Abbott
Steve Howell
rht
derAnfaenger
Harshit Bansal
Umair Khan
Cory Lynch
Aditya Bansal
Vishnu Ks
Rick Chern
umkay
Steve Howell
sinwar
Raghav Jajodia
Abhijeet Kaur