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 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.
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.
Previously we showed both the value and the id of the BaseCount record,
which is confusing in a typical case where you only care about the value,
and both the value and id are smallish ints.
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 primarily implemented through altering the migration file in
order to move the columns, but also we try to make the defaults a
little better for future tables inherited from BaseCount.
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.
Type of parameter for function `is_recent`(line no.812) is `datetime`.
MyPy errors out, however, when the parameter is defined as `datetime`.
To get around, type `Any` is used.
This results in a substantial performance improvement for all of
Zulip's backend templates.
Changes in templates:
- Change `block.super` to `super()`.
- Remove `load` tag because Jinja2 doesn't support it.
- Use `minified_js()|safe` instead of `{% minified_js %}`.
- Use `compressed_css()|safe` instead of `{% compressed_css %}`.
- `forloop.first` -> `loop.first`.
- Use `{{ csrf_input }}` instead of `{% csrf_token %}`.
- Use `{# ... #}` instead of `{% comment %}`.
- Use `url()` instead of `{% url %}`.
- Use `_()` instead of `{% trans %}` because in Jinja `trans` is a block tag.
- Use `{% trans %}` instead of `{% blocktrans %}`.
- Use `{% raw %}` instead of `{% verbatim %}`.
Changes in tools:
- Check for `trans` block in `check-templates` instead of `blocktrans`
Changes in backend:
- Create custom `render_to_response` function which takes `request` objects
instead of `RequestContext` object. There are two reasons to do this:
1. `RequestContext` is not compatible with Jinja2
2. `RequestContext` in `render_to_response` is deprecated.
- Add Jinja2 related support files in zproject/jinja2 directory. It
includes a custom backend and a template renderer, compressors for js
and css and Jinja2 environment handler.
- Enable `slugify` and `pluralize` filters in Jinja2 environment.
Fixes#620.
get_realm is better in two key ways:
* It uses memcached to fetch the data from the cache and thus is faster.
* It does a case-insensitive query and thus is more safe.
To make this give accurate numbers, we need to filter out the
automated traffic from Zephyr mirroring and our internal monitoring.
(imported from commit 83642bc9a9d8d01dd9dc5dc7b3e3dee6c9705162)
This shows the number of messages sent by humans for the last
eight 24-hour periods, for each realm. "Messages sent" isn't a
perfect metric of activity, but it's easier to query with our
current data model than certain other statistics.
(imported from commit 9de3c479640a0b9dbc017b245dda21d951f4efa4)
This contains the various fixes that needed to be made in order to get
accurate statistics.
Most notably, the active_users_between function in the previous
version of zerver/lib/statistics.py was broken for end dates in the
past, because it used the UserActivity table to get its data -- so in
fact it really was querying "users last active between".
This commit isn't super clean, but I figure we're probably better off
having our latest code for historical usage data in git so it doesn't
bitrot and anyone can improve on it.
(imported from commit 24ff2f24a22e5bdc004ea8043d8da12deb97ff2f)
This makes it easier to see how many messages are being sent
by webhook bots. This assumes a 1:1 relationship between
hitting webhook endpoints and sending messages, which is probably
valid enough in the near future.
(imported from commit eb272cd38b9cabd54d317ce2dfdf12099d302fce)
For legacy reasons, this template wanted each tab's content as
a one-key dictionary, instead of a string. Each tab already has
a tuple to allow for fields like title, so this wasn't really giving us
any long term flexibility; it was just crufting up the calling
code.
(imported from commit 2a316107ec223a83efa8735f4810a6fa43107541)
Move commands related to stats collection and reporting from
zilencer to analytics. To do this, we had to make "analytics"
officially an app.
(imported from commit 63ef6c68d1b1ebb5043ee4aca999aa209e7f494d)
nikolay
bulat22101
Sidhant Bhavnani
AZtheAsian
Rafid Aslam
Rishi Gupta
Anders Kaseorg
Umair Khan
umkay
Tim Abbott
Rishi Gupta
Taranjeet
Eklavya Sharma
Hyunchel Kim
Tim Abbott
Ashish
Steven Oud
Tim Abbott
Reid Barton
Steve Howell
Leo Franchi
Kevin Mehall
Waseem Daher