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.
Previously, this function seemed ambivalent about whether it was generating
a series of abstract data points or a series of data points that would
correspond to times. Switch firmly to the latter, so e.g. if the frequency
changes, so will the length of the output sequence.
Fixes an error in the definition of
COUNT_STATS['messages_sent_to_stream:is_bot']. The CountStat needs a
group_by argument since it is supposed to group by UserProfile.is_bot.
This query counts the number of messages each user has sent, subgroup'd by
whether the message was a private_message (PM or sent to a huddle), sent to
a 'private_stream', or sent to a 'public_stream'.
We need to join on zerver_stream to find out whether stream messages were
sent to public streams or private streams, but it needs to be a LEFT JOIN
rather than a JOIN so that we preserve the messages sent to non-streams.
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.
interval refers to a time interval, and frequency refers to something that
semantically means something closer to 'hourly' or 'daily'.
Currently, interval can have values 'hour', 'day', or 'gauge', and frequency
can only have values 'hour' and 'day'.
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.
The filter args dictionary applies to the X table in a count X by Y query,
which in this case is the zerver_message table. This stat had an incorrect set
of arguments meant for the zerver_userprofile table.
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 count_X_by_Y_query to counts.py, similar in spirit to a
count_recipient_by_user query, where we would join on the Message,
Recipient, and UserProfile table. Here, we also join on the Stream table in
order to distinguish private and public streams, and we merge the counts for
PM and Huddle type messages into a single subgroup.
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.
For each database query made by an analytics function, log time spent and
the number of rows changed to var/logs/analytics.log.
In the spirit of write ahead logging, for each (stat, end_time)
update, log the start and end of the "transaction", as well as time
spent.
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.
The functionality provided is more naturally done in the views code. It also
allows us to aggregate using day boundaries from the local timezone, rather
than UTC.
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.
There are a number of different stats that need to be propagated from
UserCount and StreamCount to RealmCount, and from RealmCount to
InstallationCount. Stats with hour intervals also need to have their day
values propagated. This commit fixes a bug in the summary table aggregation
logic so that for a given interval on a CountStat object we pull the correct
counts for the interval as well as do the day aggregation if required. We Also
ensure that any aggregation then done from the realmcount
table to the installationcount table follows the same aggregation logic
for intervals.
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
anirudhjain75
nikolay
bulat22101
AZtheAsian
Rafid Aslam
umkay