A small functional change here was to eliminate an enormous "Usage"
headline that was already implicit from the tabs. It would have
complicated the refactoring to try to preserve it, and I don't think
anyone will miss it.
Extracting this template will give us a little more flexibility
to customize future tabs in the /activity page.
(imported from commit bdb0b7030c8ec1e20d4451dc059830c3f5ea7632)
We are still showing the same data points, but the logic to drill
down on details for a particular realm is now all server side,
not client side, and we are smarter about omitting fields. In
summary mode, we don't show empty Name or Email columns. In
detailed mode, we show the realm as a headline instead of a column.
In this version you do lose the ability to see all system users in
the same view, but Waseem is ok with this.
(imported from commit edd2e646ab4cf5783ea64232d0cd621debece8d4)
When you load the activity report, it will just show summary
counts for realms, but if you click on a realm, you will see
details about users in the realms. You can also click "Show all"
to see an interleaved view of realms and users.
(imported from commit b106557b1fae64d525071afc124b5a8aed319086)
Add rows to the activity report that roll up counts for all
users on each realm, to go along with individual users.
(imported from commit 8104f3ef7fbe406fe0fd2ba1bb00ce76a1ccbee5)
get_subscribers_backend() now calls the new get_subscriber_emails()
function, which just queries the email field:
"zerver_userprofile"."email"
...instead of querying about 40 fields that it never uses.
I was able to verify the query slimming by watching my postgres server log.
Also, you can verify that the ORM does roughly 16x less work using values():
>>> def f(): return [sub.user_profile.email for sub in list(Subscription.objects.all().select_related())]
...
>>> def g(): return [row['user_profile__email'] for row in list(Subscription.objects.all().values('user_profile__email'))]
...
>>> def timeit(func): t = time.time(); func(); return time.time() - t
...
>>> timeit(f)
0.045198917388916016
>>> timeit(g)
0.002752065658569336
(imported from commit a69f690a96d076b323fdfc2f4821b0548bdfac7f)
These engagement data will be useful both for making pretty graphs of
how addicted our users are as well as for allowing us to check whether
a new deployment is actually using the product or not.
This measures "number of minutes during which each user had checked
the app within the previous 15 minutes". It should correctly not
count server-initiated reloads.
It's possible that we should use something less aggressive than
mousemove; I'm a little torn on that because you really can check the
app for new messages without doing anything active.
This is somewhat tested but there are a few outstanding issues:
* Mobile apps don't report these data. It should be as easy as having
them send in update_active_status queries with new_user_input=true.
* The semantics of this should be better documented (e.g. the
management script should print out the spec above)x.
(imported from commit ec8b2dc96b180e1951df00490707ae916887178e)
The new version is more accurate (doesn't rely on UserMessage IDs
being sorted, which they aren't necessarily) and simpler.
(imported from commit 671dd89dc8881ae2dcb8d0e804fd65458e074a29)
Steve Howell
Jessica McKellar
Waseem Daher
Leo Franchi
Tim Abbott
Scott Feeney
Kevin Mehall