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.
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.
This will allow us to appropriately generalize CountStat to include
LoggingCountStat and CustomPullCountStat. It'll also make life easier when
we introduce DependentCountStat.
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.
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.
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.
Adds two things to TestCountStats.setUp():
* A realm with no messages, that generally should not show up in *Count
tables,
* Users/streams/messages created at 0, 1, 61, and 1441 (just over a day)
minutes ago (previously was 0, 60), to better test the start_time/end_time
in the queries, and the frequency/interval setting in the CountStats.
We were updating FillState with FillState.objects.filter(..).update(..),
which does not update the last_modified field (which has auto_now=True).
The correct incantation is the save() method of the actual FillState
object.
Was enabled by commit 41e8ee3 where we moved TIME_ZERO to before the realms
created by populate_db.py.
Also removes the stub for TestAggregates, since the remaining thing to be
tested was the aggregation from RealmCount to InstallationCount, and the end
to end checks provided by the TestCountStat tests should be sufficient.
In a previous design, there was no FillState table, and one could run any
CountStat at any time. This is no longer supported.
This test was making sure that if one ran a CountStat at a certain hour, and
then ran it at a previous hour, the old rows would still be there.
It seems unlikely we will need count_message_by_stream without the
UserProfile table in the future, so write count_message_by_stream_and_is_bot
in the usual query form and replace count_message_by_stream with it.
This also has the benefit of shortening our list of "special case" queries
from two to one.
The pathways of the removed test will be covered more thoroughly in the new
TestCountStats tests.
We alter the behavior of our queries to no longer write rows with 0 counts
to the db, and pad with 0s in the related views code. As a result we are
also able to combine the where and join clause conditions in the sql
queries. This new behavior is also updated in our tests.
Adds a database migration, adds a new string_id argument to the management
realm creation command, and adds a short name field to the web realm
creation form when REALMS_HAVE_SUBDOMAINS is False.
This is a major change to the analytics schema, and is the first step in a
number of refactorings and performance improvements. For instance, it allows
* Grouping sets of similar CountStats in the *Count tables. For instance,
active{_humans,_bots} will now have the same property, but have different
subgroup values.
* Combining queries that differ only in their value on 1 filter clause, so
that we make fewer passes through the zerver tables. For instance, instead
of running a query for each of messages_sent_to_public_streams and
messages_sent_to_private_streams, we can now run a single query with a
group by on Stream.invite_only, and store the group by value in the
subgroup column.
Change the CountStat object to take an is_gauge variable instead of a
smallest_interval variable. Previously, (smallest_interval, frequency)
could be any of (hour, hour), (hour, day), (hour, gauge), (day, hour),
(day, day), or (day, gauge).
The current change is equivalent to excluding (hour, day) and (day, hour)
from the list above.
This change, along with other recent changes, allows us to simplify how we
handle time intervals. This commit also removes the TimeInterval object.
Adding FillState, removing do_aggregate_hour_to_day, and disallowing unused
(interval, frequency) pairs removes the need for the nested for loops in
do_fill_count_stat_at_hour. This commit replaces that control flow with a
simpler equivalent.
Adds two simplifying assumptions to how we process analytics stats:
* Sets the atomic unit of work to: a stat processed at an hour boundary.
* For any given stat, only allows these atomic units of work to be processed
in chronological order.
Adds a table FillState that, for each stat, keeps track of the last unit of
work that was processed.
Previously, if a Realm had no users (or no streams),
do_aggregate_to_summary_table would fail to add a row with value 0. This
commit fixes the issue and also simplifies the do_aggregate_to_summary_table
logic.
Refactor the current analytics tests into the following classes:
* TestUpdateAnalyticsCounts, which will eventually test the management
command, backfilling, what happens when new tests are added, etc.
* TestProcessCountStat, which tests the ins and outs of propagating the
value of a single stat up through the various *Count tables.
* TestAggregates, which tests the do_aggregate_* methods.
* TestXByYQueries, which tests the count_X_by_Y_query SQL snippets.
* TestCountStats, which has tests for individual CountStats.
This commit does not change the name or contents of any individual test.
Many tests are structured to run some process, and then check a count in a
BaseCount record using default values for realm, property, interval, and
end_time. This commit adds a new assertCountEquals method to
AnalyticsTestCase, and simplifies other assert calls as appropriate.
Add a default_realm object to AnalyticsTestCase, created 2 days before
AnalyticsTestCase.TIME_ZERO.
Add lightweight create_user, create_stream, and create_message methods to
AnalyticsTestCase, with sensible defaults. In particular, all objects are by
default created at AnalyticsTestCase.TIME_LAST_HOUR, so that they are
included when running AnalyticsTestCase.process_last_hour.
Previously, analytics tests used timezone.now or custom datetime objects
when creating new realms, users, and streams.
This commit adds a fixed TIME_ZERO and a process_last_hour helper function
in a new AnalyticsTestCase class, and modifies the existing tests to use
them.
This is a first pass at building a framework for collecting various
stats about realms, users, streams, etc. Includes:
* New analytics tables for storing counts data
* Raw SQL queries for pulling data from zerver/models.py tables
* Aggregation functions for aggregating hourly stats into daily stats, and
aggregating user/stream level stats into realm level stats
* A management command for pulling the data
Note that counts.py was added to the linter exclude list due to errors
around %%s.
Rishi Gupta
hackerkid
Tim Abbott
hollywoodno
Raghav Jajodia
aakash-cr7
Amy Liu
anirudhjain75
bulat22101
Umair Khan
umkay
Rishi Gupta