Fixes#1727.
With the server down, apply migrations 0245 and 0246. 0246 will remove
the pub_date column, so it's essential that the previous migrations
ran correctly to copy data before running this.
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.)
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 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.
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.
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.
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.
Mateusz Mandera
Anders Kaseorg
Rishi Gupta
Vishnu Ks
Nikhil Kumar Mishra
Aditya Bansal
Greg Price
rht
Tim Abbott
umkay
hackerkid
hollywoodno
Raghav Jajodia
anirudhjain75
nikolay
bulat22101
AZtheAsian
Rafid Aslam
umkay