This deletes the unused Subscription.notifications field and removes
it from some testing and analytics code (which should not have been
using it in the first place).
Fixes#10042.
This fixes a subtle bug where if you reran populate_analytics_db
directly, we'd end up in a weird state where memcached fetched the
"old" pre-flush UserProfile object for shylock when loading /stats,
which ultimately would result in /stats appearing totally broken.
This code is going to end up pretty complex -- each stat has multiple levels
of aggregation (UserCount, RealmCount, InstallationCount), and refinement
(subgroups), and soon we'll have charts that take data from multiple stats
as input.
Not sure what the best way to present it is, but hopefully this simplifies
it a bit.
We use "Everyone" for the button labels already.
Soon we'll support "Everyone" meaning either the installation or the realm,
depending on the URL route used to access the stats.
In this commit:
Two new URLs are added, to make all realms accessible for server
admins. One is for the stats page itself and another for getting
chart data i.e. chart data API requests.
For the above two new URLs corresponding two view functions are
added.
The name `create_logger` suggests something much bigger than what this
function actually does -- the logger doesn't any more or less exist
after the function is called than before. Its one real function is to
send logs to a specific file.
So, pull out that logic to an appropriately-named function just for
it. We already use `logging.getLogger` in a number of places to
simply get a logger by name, and the old `create_logger` callsites can
do the same.
This is already the loglevel we set on the root logger, so this has no
effect -- except in tests, where `test_settings.py` attempts to set
some of these same loggers to higher loglevels. Because the
`create_logger` call generally runs after we've configured settings,
it clobbers that effect.
The code in `test_settings.py` that tries to suppress logs only works
because it also sets `propagate=False`, which has nothing to do with
loglevels but does cause logs at this logger (and descendants) to be
dropped completely unless we've configured handlers for this logger
(or one of its relevant descendants.)
I've wanted this when looking at a tab from the day before.
Also provides the date and time in UTC, which is handy for
interpreting some of the data.
Pretty sure this is not the world's cleanest way to do this in the
front-end code. It'll do for now.
Substantively, this makes the table more readable by grouping users
into expanding sets by level of activity: active in last day, active
in last week, have an account at all. The class "active in last week",
as opposed to "active in last week but not in last day", makes more
natural comparisons both between realms and for one realm through time,
and it's less sensitive to the details of our definitions.
This also makes the terminology more standard. We already made that
change in the display, in the previous commit; as we go through the
logic here, we adjust the terminology in the code too.
Except in:
- docs/writing-bots-guide.md, because bots are supposed to be Python 2
compatible
- puppet/zulip_ops/files/zulip-ec2-configure-interfaces, because this
script is still on python2.7
- tools/lint
- tools/linter_lib
- tools/lister.py
For the latter two, because they might be yanked away to a separate repo
for general use with other FLOSS projects.
Sort of a hacky hammer, but
* The original design of the analytics system mistakenly attempted to play
nicely with non-UTC datetimes.
* Timezone errors are really hard to find and debug, and don't jump out that
easily when reading code.
I don't know of any outstanding errors, but putting a few "assert this
timezone is in UTC" around will hopefully reduce the chance that there are
any current or future timezone errors.
Note that none of these functions are called outside of the analytics code
(and tests). This commit also doesn't change any current behavior, assuming
a database where all datetimes have been being stored in UTC.
Previously, entering a non-UTC end time for a daily stat would give you
incorrect results. This is because:
* All daily stats are collected at and have end_times in the database in
midnight UTC.
* For daily stats, time_range returns a list of datetimes at midnight in the
timezone of its end argument. These datetimes are the only ones we look
for when looking for rows corresponding to the stat in the database.
* Previously, we passed on the end argument from the API to time_range,
without modification.
This consists of the `zulip_ops::stats` Puppet class, which has apparently
not been used since 2014, and a number of files that I believe were
only used for that. Also a couple of tiny loose ends in other files.
These are no longer useful, with our spiffy new analytics framework,
and we haven't in fact been using them for some time, while the
`active-user-stats` cron job does cause regular mail from cron.
Just delete them.
sort_client_labels sorts first by total, and then to ensure deterministic
outcomes, sorts (reverse) alphabetically by label.
Fixes regression introduced in 0c0e539.
Previously we showed the total number of users with an active account. This
changes it to show only the number of users that have logged in in the past
two weeks.
Rename 'zulip_internal' decorator to 'require_server_admin', add
documentation for 'server_admin', explaining how to give permission
for ./activity page.
Fixes: #1463.
Previously we would update FillState for daily stats on hourly boundaries as
well. This would create two extra queries on the FillState table every hour
(for each CountStat), which adds roughly 50ms of extra processing for each
CountStat each day, as well as two extra lines each hour in the analytics
log. This can be a minor annoyance when backfilling stats.
I think this is more pythonic?
We could also get rid of LoggingCountStats altogether, since it's now just a
special case of CountStat (is_logging == data_collector.pull_function is None).
But I think it's nice to keep the distinction since they behave so differently.
Removes the circular dependency of CountStat containing a DataCollector, and
DataCollector containing a function that takes a CountStat as an argument.
This will allow us to appropriately generalize CountStat to include
LoggingCountStat and CustomPullCountStat. It'll also make life easier when
we introduce DependentCountStat.
The previous zerver_* names were unwieldy and not very readable. This also
puts more of the useful information in one place; in particular, makes it
easier to skim a CountStat declaration and see if we're collecting it at a
user/stream granularity or a realm granularity.
It turned out to not be that useful once we added subgroup. The previous
design of the CountStat object also assumed more reuseability of the *_query
strings than what ended up happening.
The filter_args also had some carrying costs:
* It's hard to be confident that filter_args other than the ones explicitly
in our tests would have had expected behavior.
* The filter_args/join_args system is the most complex part of the CountStat
object, and makes understanding the *_query strings unnecessarily
difficult for a new contributor.
Groundwork for allowing stats like "Monthly Active Users".
CountStat.interval is no longer as clean a value as before, so removed it
from views.get_chart_data. It wasn't being used by the frontend anyway.
Removing interval from logger calls in counts.py is not a big loss since we
now include the frequency (which is typically also the interval) in
CountStat.property.
The code in fixtures.py is only called from populate_analytics_db, and is
only used for generating pretty fixture data for manual testing. This commit
adds tests for a few things that were easy to add tests for, and provides
some minimal coverage of the file, but is not meant to be comprehensive.
Originally, all the client names in populate_analytics_db started with
underscores to make it easy to selectively delete and regenerate them when
re-running populate_analytics_db.
We eventually want to merge populate_analytics_db into populate_db though,
in which case it makes more sense for them to share client names, and not
worry about the case where we run (or re-run) populate_analytics_db
independently of populate_db.
It will simplify the logic needed to process the "Sent by Me" view in
Messages Sent Over Time in stats.js.
Also, we gzip the data sent from our server, so there is little additional
network usage by doing this.
Django 1.10 has changed the implementation of this function to
match our custom implementation; in addition to this, we prefer
render().
Fixes#1914 via #4093.
count_message_type_by_user_query is in a different format (no WHERE clause)
from the rest since I'm having a hard time reasoning about how that would
interact with the LEFT JOIN, especially given that there are %(join_args)s.
Analytics database tables are getting big, and so we're likely moving to a
model where ~all stats are day stats, and we keep hourly stats only for the
last N days.
Also changed the name because:
* messages_sent_* suggests the counts (summed over subgroup) should be the
same as the other messages_sent stats, but they are different (these don't
include PMs).
* messages_sent_by_stream:is_bot:day is longer than 32 characters, the max
allowable length for a BaseCount.property.
Includes a database migration to remove the old stat from the analytics
tables.
When you pass a naive datetime to the Django ORM, it uses settings.TIME_ZONE
for the time zone. In the development environment, both settings.TIME_ZONE
and datetime.now() use 'America/New_York', so there is no change in behavior
there. (fromtimestamp with no tz argument uses the same timezone as
datetime.now)
We are soon going to change settings.TIME_ZONE to UTC, so need to remove
naive datetimes from queries to the ORM.
This actually fixes previously broken behavior, since 'date' here gets
turned into the 'day' argument of seconds_active_during_day(day), where
tzinfo is set to UTC.
API: Adds a "display_order" to the response, which is a suggested order of
importance for the clients or recipient types respectively.
frontend: Changes messages_sent_by_{client,recipient_type} to use a fixed
order for any given user.
Also includes a number of changes to messages_sent_by_recipient_type that
were convenient to do at the same time, since the two charts share a lot of
code.
This adds a frontend for the analytics system we've had for a few
months, showing several graphs of the data in Zulip.
There's a ton more that we can do with this tooling, but this initial
version is enough to provide users with a pretty good experience.
Fixes#2052.
Makes a number of simplications to the analytics views code. The main one is
that we now return the entire data series, even if the data is eventually
going to go into a pie chart. This was prompted by us wanting several
different pie charts for each stat (one for last 30 days, one for all time,
etc), but I think it is also a more natural API. The total amount of data
being sent for the pie charts now is maybe half of what is being sent for
our single 'hourly' stat, or maybe up to 10,000 ints per year the
organization has been around.
The other big change is that the data being sent back is now always explicit
about whether it is data about the realm (stored in data['realm'], or data
about the user (stored in data['user']).
Having both messages_sent:hour and messages_sent:is_bot:day is confusing,
since a single messages_sent:is_bot:hour would have a superset of the
information and take less total space. This commit and its parent together
replace the two stats with a single messages_sent:is_bot:hour.
Includes a database migration. The interval field was originally there to
facilitate time aggregation (e.g. aggregate_hour_to_day), but we now do such
aggregations in views code or in the frontend.
A few reasons:
* Our two other subgroup'd message stats in UserCount are at CountStat.DAY
frequency (messages_sent:is_bot and messages_sent:message_type).
* Keeping this stat at hourly frequency would likely double the size of our
analytics table, given the current stats. (Counterpoint: if there are
roughly as many active streams as active users, and we keep
messages_sent_to_stream:is_bot at hourly frequency, then maybe this stat
is only a 30% or 50% increase).
* We're currently only showing this on the frontend as a pie chart anyway.
Steve Howell
Yashashvi Dave
Rishi Gupta
Tim Abbott
Vishnu Ks
Roman Godov
Anders Kaseorg
Eeshan Garg
Nikhil Kumar Mishra
Yago González
Aditya Bansal
Shubham Dhama
neiljp (Neil Pilgrim)
Archana BS
Aman Jain
rht
Greg Price
derAnfaenger
Umair Khan
Greg Price
Vishnu Ks
umkay
Christian Hudon
Maxim Averin
hollywoodno
Raghav Jajodia
umkay
Amy Liu