we try and delete for lots of services. this includes services that
don't actually have anything to delete that day. that might be because
they had a custom data retention so we always go to check them, or
because they only sent test notifications (which we'll delete but not
include in the count in the log line). we don't really need to see log
lines saying that we didn't delete anything for that service - that's
just a long list of boring log messages that will hide the actual
interesting stuff - which services we did delete content for.
This is because that function is used both when broadcast status
is updated via API and via admin, so it's a shared resource.
Also move and update tests for updating broadcast message status
so things are tested at source and repetition is avoided.
The top-level task didn't run successfully after this was deployed
due to the worker being killed due to heavy disk usage. While the
more parallel version does log much more, it doesn't totally explain
the disk behaviour. Nonetheless, reverting it is sensible to give us
the time we need to investigate more.
If the reference from cancel CAP XML we received via API does not
match with any existing broadcast, return 404.
Do the same if service id doesn't match.
Also refactor code to cancel broadcast out into separate function
It should be a separate function that is only called by create_broadcast
function. This will prevent create_broadcast from becoming too
big and complex and doing too many things.
This follows a similar approach as [1]. Recently we've seen lots
of errors from this task, which we think are a consequence of it
doing too much work and tripping Celery's visibility timeout.
While we can optimise the query [2], it's likely the errors will
return as the number of live services grows. Parallelising the
aggregation now will make it more futureproof.
[1]: https://github.com/alphagov/notifications-api/pull/3397
[2]: https://github.com/alphagov/notifications-api/pull/3417
This is similar to the corresponding endpoint for services. However,
it is a little simpler since we don't need to worry about always having
at least one team member for an organisation.
The new dao function added, `dao_remove_user_from_organisation`, is also
simpler than `dao_remove_user_from_service` since we don't have any
organisation permissions to deal with.
Investigation with EXPLAIN and EXPLAIN ANALYZE for the notification
history table shows this is another instance of [1] but for the key
type column. Swapping "!=" for "IN" solves the problem.
[1]: https://github.com/alphagov/notifications-api/pull/3360
If a service has not sent any SMS for the financial year the free allowance was showing up as 0 rather than the number in annual billing. The query has been updated to use an outer join so that the free allow will be returned when there is no ft_billing.
There is a potential performance enhancement to only return the data for the services of the organisation in the `fetch_sms_free_allowance_remainder_until_date` subquery. I will investigate in a subsequent PR.
We want admin to send a POST request to this route if the data contains
a message recipient (a phone number or email address) so that this does
not show in the logs. This changes the route to accept both GET and POST
requests.
When running the night reporting tasks we are seeing that some tasks are failing because the query is timing out. We need to revisit how to optimise the query but this will at least let the process finish.
At the moment, when we are processing and sending an SMS we open
a DB connection at the start of the celery task and then close it
at the end of the celery task. Nice and simple.
However, during that celery task we make an HTTP call out to our
SMS providers. If our SMS providers have problems or response times
start to slow then it means we have an open DB connection sat waiting
for our SMS providers to respond which could take seconds. If our
SMS providers grind to a halt, this would cause all of the
celery tasks to hold on to their connections and we would run out
of DB connections and Notify would fall over.
We think we can solve this by closing the DB session which releases
the DB connection back to the pool.
Note, we've seen this happen in staging during load testing if our
SMS provider stub has fallen over. We've never seen it in production
and it may be less unlikely to happen as we are balancing traffic
across two providers and they generally have very good uptime.
One downside to be aware of is there could be a slight increase in
time spent to send an SMS as we will now spend a bit of extra time
closing the DB session and then reopening it again after the HTTP
request is done.
Note, there is no reason this approach couldn't be copied for our
email provider too if it appears successful.
We can't control who might be sending messages on inbound numbers
that we own i.e. this log isn't an actionable error. Looks like it
used to represent something that _was_ an error [1], but that's not
the case anymore, so it seems reasonable to downgrade it.
[1]: d99ab329eb (diff-80d123d9abb40f80a221979940657a2751cc7cb33f255aa8f352a8324023e022L125)
If the S3 object is missing [1], then that's what we want, so we
don't need such a severe log for it, but we still want to know as
it's not expected. This is separate to more general "ClientError"
exceptions, which could mean anything.
There weren't any tests to cover missing S3 objects, so I've added
one. I don't think we need a test for ClientErrors:
- If there was no handler, the task would fail and we'd learn about
it that way.
- The scope of the calling task is now much smaller, so it matters
less than it used to [2].
[1]: 81a79e56ce/app/letters/utils.py (L52)
[2]: f965322f25
we really don't gain anything by running each service delete in sequence
- we get the services, and then just loop through them deleting per
service. By deleting per service in separate tasks, we can take
advantage of parallelism. the only thing we lose is some log lines but I
don't think we're that interested in them.
only set query limit at the move_notifications dao function - the task
doesn't really care about the technical implementation of how it deletes
the notifications
This will help us monitor issues with delivery receipts and keep
track of provider performance over time.
I'm not concerned about performance here:
- The number of notifications to time out is usually small.
- This task only runs once a day.
- Calls to StatsD are quick and cheap.
A gauge is more useful as we can visualise it and combine it with
other stats - we already have other stats for the total number of
notifications sent by provider, and we can extrapolate the number
of slow notifications using this, if needed.
We also still have logs to say the task is running, as well as a
log in the calling code when we actually make a switch [1], so
we're not losing anything by removing the log here.
[1]: a9306c4557/app/celery/scheduled_tasks.py (L117)
Previously this was limited to 500K notifications. While we don't
expect to reach this limit, it's not impossible e.g. if we had a
repeat of the incident where one of our providers stopped sending
us status updates. Although that's not great, it's worse if our
code can't cope with the unexpectedly high volume.
This reuses the technique we have elsewhere [1] to keep processing
in batches until there's nothing left. Specifying a cutoff point
means the total amount of work to do can't keep growing.
[1]: 2fb432adaf/app/dao/notifications_dao.py (L441)
We're going to iterate how we use the function with a limit, so we
shouldn't say it's "temporary" anymore. We don't need to change the
default, but having it in the function parameters makes it easier to
see the funtion doesn't time out all notifications, just some.
Previously we specified the period and calculated the cutoff time
in the function. Passing it in means we can run the method multiple
times and avoid getting "new" notifications to time out in the time
it takes to process each batch.
We have been running in to the problem in
pallets/flask-sqlalchemy#518 where
our page loads very slow when viewing a single page of notifications
for a service in the admin app. Tracing this back and using SQL
explain analyze I can see that getting the notifications takes about
a second but the second query to count how many notifications there
are (to work out if there is a next page of pagination) can take up
to 100 seconds.
As suggested in that issue, we do the pagination ourselves.
Our pagination doesn't need us to know exactly how many notifications
there are, just whether there are any on the next page and that can
be done without running the slow query to count how many
notifications in total by using `count_pages=False`.
This commit is analagous to
c68d1a2f23
The only difference is that in that case, the pagination links are
used to show prev and/or next links in the admin app. In this case,
the pagination links are only used to see if there is a page 2, and
if there is, say that we are only showing the first 50 results.
