Migrate all `ids` of anything which does not have a foreign key from
the Message or UserMessage table (and would thus require walking
those) to be `bigint`. This is done by removing explicit
`BigAutoField`s, trading them for explicit `AutoField`s on the tables
to not be migrated, while updating `DEFAULT_AUTO_FIELD` to the new
default.
In general, the tables adjusted in this commit are small tables -- at
least compared to Messages and UserMessages.
Many-to-many tables without their own model class are adjusted by a
custom Operation, since they do not automatically pick up migrations
when `DEFAULT_AUTO_FIELD` changes[^1].
Note that this does multiple scans over tables to update foreign
keys[^2]. Large installs may wish to hand-optimize this using the
output of `./manage.py sqlmigrate` to join multiple `ALTER TABLE`
statements into one, to speed up the migration. This is unfortunately
not possible to do generically, as constraint names may differ between
installations.
This leaves the following primary keys as non-`bigint`:
- `auth_group.id`
- `auth_group_permissions.id`
- `auth_permission.id`
- `django_content_type.id`
- `django_migrations.id`
- `otp_static_staticdevice.id`
- `otp_static_statictoken.id`
- `otp_totp_totpdevice.id`
- `two_factor_phonedevice.id`
- `zerver_archivedmessage.id`
- `zerver_client.id`
- `zerver_message.id`
- `zerver_realm.id`
- `zerver_recipient.id`
- `zerver_userprofile.id`
[^1]: https://code.djangoproject.com/ticket/32674
[^2]: https://code.djangoproject.com/ticket/24203
This helps prevent wraparound on exceedingly large and old installs,
particularly Zulip Cloud. These are relatively simple migrations
since they are not referenced by any other tables; however, they are
quite large, and are actively used from Django by running servers,
making this not a migration which is possible to run without stopping
the server.
Use the escape hatch in the previous commit to temporarily pause
analytics writes while the migration happens. This should make the
migration transparent to users, at the small cost of an artificial dip
in statistics (specifically, to push notification counts, and unread
message counts) while the migration runs.
The Django convention is for __repr__ to include the type and __str__
to omit it. In fact its default __repr__ implementation for models
automatically adds a type prefix to __str__, which has resulted in the
type being duplicated:
>>> UserProfile.objects.first()
<UserProfile: <UserProfile: emailgateway@zulip.com <Realm: zulipinternal 1>>>
Signed-off-by: Anders Kaseorg <anders@zulip.com>
Previously, we type the model fields with explicit type annotations
manually with the approximate types. This was because the lack of types
for Django.
django-stubs provides more specific types for all these fields that
incompatible with our previous approximate annotations. So now we can
remove the inline type annotations and rely on the types defined in the
stubs. This allows mypy to infer the types of the model fields for us.
Signed-off-by: Zixuan James Li <p359101898@gmail.com>
This is a prep commit. Currenty we only pass CountStat.property
to last_successful_fill function. But it needs access to
CountStat.time_increment as well. We can pass the entire CountStat
object to the function as a workaround. But making last_successful_fill
a property of CountStat seems to be much more cleaner.
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>
Automatically generated by the following script, based on the output
of lint with flake8-comma:
import re
import sys
last_filename = None
last_row = None
lines = []
for msg in sys.stdin:
m = re.match(
r"\x1b\[35mflake8 \|\x1b\[0m \x1b\[1;31m(.+):(\d+):(\d+): (\w+)", msg
)
if m:
filename, row_str, col_str, err = m.groups()
row, col = int(row_str), int(col_str)
if filename == last_filename:
assert last_row != row
else:
if last_filename is not None:
with open(last_filename, "w") as f:
f.writelines(lines)
with open(filename) as f:
lines = f.readlines()
last_filename = filename
last_row = row
line = lines[row - 1]
if err in ["C812", "C815"]:
lines[row - 1] = line[: col - 1] + "," + line[col - 1 :]
elif err in ["C819"]:
assert line[col - 2] == ","
lines[row - 1] = line[: col - 2] + line[col - 1 :].lstrip(" ")
if last_filename is not None:
with open(last_filename, "w") as f:
f.writelines(lines)
Signed-off-by: Anders Kaseorg <anders@zulipchat.com>
Replaced unique_together with UniqueConstraint in models that
covered nullable fields as in unique_together database indexes
don't work where subgroup=None. So added conditional unique
index handling invalid duplicate Count data.
Added 0015_clear_duplicate_counts migration to handle existing
data that violates the constraints.
Also corrected a test case in test_counts.py which didn't clear its
state properly and thus was accidentally taking advantage of this
database schema bug.
This field wasn't used for anything, and I think it has very limited
use for debugging, since fundamentally, it'll almost always have a
value within the hour of the actual timestamp in FillState, and any
more fine-grained logging we might want would be available in the
analytics job's own logs.
The proximal reason to remove it is that apparently Django's
model_to_dict doesn't support auto_now fields, and that caused some
trouble when working on adding more complete import/export support for
analytics data.
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.
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.
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.
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 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.
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.
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.
Alex Vandiver
Anders Kaseorg
Zixuan James Li
Vishnu KS
Anders Kaseorg
arpit551
Tim Abbott
Anders Kaseorg
Rishi Gupta
Aditya Bansal
rht
Christian Hudon
hackerkid
anirudhjain75
umkay
Rishi Gupta