This is quite a bit faster:
```
%timeit calendar.timegm(now.timetuple())
2.91 µs ± 361 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
%timeit int(now.timestamp())
539 ns ± 27 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
```
This is particularly important for the presence endpoint, which is a
tight loop of serializing datetimes.
Unlike the other Python datetime to Unix timestamp conversion
function (`datetime_to_timestamp`), `datetime_to_precise_timestamp`
won't drop the microseconds.
Signed-off-by: Hemanth V. Alluri <hdrive1999@gmail.com>
Fixes#2665.
Regenerated by tabbott with `lint --fix` after a rebase and change in
parameters.
Note from tabbott: In a few cases, this converts technical debt in the
form of unsorted imports into different technical debt in the form of
our largest files having very long, ugly import sequences at the
start. I expect this change will increase pressure for us to split
those files, which isn't a bad thing.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
datetime.timezone is available in Python ≥ 3.2. This also lets us
remove a pytz dependency from the PostgreSQL scripts.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
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.
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.
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.
This needs to be deployed to both staging and prod at the same
off-peak time (and the schema migration run).
At the time it is deployed, we need to make a few changes directly in
the database:
(1) UPDATE django_content_type set app_label='zerver' where app_label='zephyr';
(2) UPDATE south_migrationhistory set app_name='zerver' where app_name='zephyr';
(imported from commit eb3fd719571740189514ef0b884738cb30df1320)
