This copies what we do to a user's email address when archiving the user
by prefixing it with `_archived_{date}`. We already prefixed the
service name and email_reply_to with `_archived`, but this didn't allow
a service with the same name to be archived more than once.
The `notifications`, `notification_history`, `templates` and `templates_history`
tables all had a check constraint on the postage column which specified
that the postage had to be `first` or `second` if the notification or
template was a letter. We now have two more options for postage -
`europe` and `rest-of-world`.
It's not possible to alter a check constraint, so the constraints have
to be dropped then recreated. We are not recreating the constraint on
the `notification_history` table since values here are always copied
from the `notifications` table.
The constraints get added as `NOT VALID` at first - this stage will lock
the tables, so updating the `notification` table and `templates` and
`templates_history` are done in separate migrations so that we don't lock
all tables at the same time. In a third migration we then run
`VALIDATE CONSTRAINT` for all tables - this will lock a row at a time,
not the whole table.
When running the purge command I found about 4 users who could not be
deleted because their user id was still referenced in the services table
as they had created the service yet they were not a member of that
service anymore.
I have fixed this by checking that if they are not a member but created
the service then we also delete the service for them.
Note, I've followed the previous convention of no tests for this
function. I've run it locally and executed the code path so there should
be no major flaws in the code. There is a small chance I wasn't able to
exactly replicate the state that existed in preview on my local but
hopefully it was close enough to be accurate.
This PR tries to parse the date, if that throws an error return now as the datereceived. This will at least allow the message to be persisted. Typically the DateReceived, provider_date, and the created_at date in the inbound_sms table are within a second of each other.
If you’ve sent a bunch of jobs from the same contact list then a handy
way to differentiate between them will be date sent, but also template
name (in effect the message you sent).
This commit extends the job response to include template name, using the
same pattern as for template type.
Because we’ll be grouping jobs under their parent contact lists it will
be useful to have a way of showing how many times a contact list has
been used. This will give the right information scent to indicate that
clicking into a contact list is where you go to see its jobs. This means
that the API needs to return a count of jobs for each contact list.
Putting this code feels very non-idiomatic for our API. So suggestions
about how to better architect it welcome…
Rather than showing all jobs that have been ‘copied’ from a contact list
I think it makes more sense to group them under the contact list. This
way it’s easier to see what messages have been sent to a given group of
contacts over time.
Part of this work means the API needs to return only jobs that have been
created from a given contact list, when asked.
We want to add another argument here, and doing so would make the line
length too long with all the arguments on one line.
Also uses the * operator to enforce keyword-only arguments.
Because we won’t be showing uploaded letters individually on the uploads
page any more we need a way of listing them. This should be by printing
day, to match how we’re grouping them on the uploads page.
The response matches a normal `get_notifications` response so we can
reuse the same code in the admin app.
Some teams have started uploading quite a lot of letters (in the
hundreds per week). They’re also uploading CSVs of emails. This means
the uploads page ends up quite jumbled.
This is because:
- there’s just a lot of items to scan through
- conceptually it’s a bit odd to have batches of things displayed
alongside individual things on the same page
So instead this commit starts grouping together uploaded letters. It
does this by the date on which we ‘start’ printing them, or in other
words the time at which they can no longer be cancelled.
This feels like a natural grouping, and it matches what we know about
people’s mental models of ‘batches’ and ‘runs’ when talking about
printing.
The code for this is a bit gnarly because:
- timezones
- the print cutoff doesn’t align with the end of a day
- we have to do this in SQL because it wouldn’t be efficient to query
thousands of letters and then do the timezone calculations on them in
Python
The standard way that we indicate that there are more results than can
be returned is by paginating. So even though we don’t intend to paginate
the search results in the admin app, it can still use the presence or
absence of a ‘next’ link to determine whether or not to show a message
about only showing the first 50 results.
we're seeing issues with email clients sniffing links, and causing them
to expire before the user gets a chance to click on them. Temporarily
disable the expiry while we work on a more permanent solution.
The link will still expire after half an hour, and sms codes aren't
affected by this change
If a service has permission to send international letters then it should
tell template preview, so that template preview knows what rule to
apply when it’s validating the address of the letter.
Depends on:
- [ ] https://github.com/alphagov/notifications-template-preview/pull/445
For services that have permission to send international letters we
should not reject letters that are addressed to another country. We
should still reject letters that are badly-addressed.
The '/service/monthly-data-by-service` endpoint which is used for the
'Monthly notification statuses for live services' Platform Admin report
did not including `pending` notifications. This updates the DAO function
that the endpoint calls to group `pending` and `sending` notifications together.
We were doing this temporarily while the `normalised_to` field was not
populated for letters. Once we have a week of data in the
`normalised_to` field we can stop looking in the `to` field. This should
improve performance because:
- it’s one `WHERE` clause not two
- we had to do `ilike` on the `to` field, because we don’t lowercase its
contents – even if the two where clauses are somehow paralleized it’s
is slower than a `like` comparison, which is case-sensitive
Depends on:
- [ ] Tuesday 5 May (7 full days after deploying https://github.com/alphagov/notifications-api/pull/2814)
We have seen the reporting app run out of memory multiple times when
dealing with overnight tasks. The app runs 11 worker threads and we
reduce this to 2 worker threads to put less pressure on a single
instance.
The number 2 was chosen as most of the tasks processed by the reporting
app only take a few minutes and only one or two usually take more than
an hour. This would mean with 2 processes across our current 2
instances, a long running task should hopefully only wait behind a few
short running tasks before being picked up and therefore we shouldn't
see large increase in overall time taken to run all our overnight
reporting tasks.
On top of reducing the concurrency for the reporting app, we also set
CELERYD_PREFETCH_MULTIPLIER=1. We do this as suggested by the celery
docs because this app deals with long running tasks.
https://docs.celeryproject.org/en/3.1/userguide/optimizing.html#optimizing-prefetch-limit
The chance in prefetch multiplier should again optimise the overall time
it takes to process our tasks by ensuring that tasks are given to
instances that have (or will soon have) spare workers to deal with them,
rather than committing to putting all the tasks on certain workers in
advance.
Note, another suggestion for improving suggested by the docs for
optimising is to start setting `ACKS_LATE` on the long running tasks.
This setting would effectively change us from prefetching 1 task per
worker to prefetching 0 tasks per worker and further optimise how we
distribute our tasks across instances. However, we decided not to try
this setting as we weren't sure whether it would conflict with our
visibility_timeout. We decided not to spend the time investigating but
it may be worth revisiting in the future, as long as tasks are
idempotent.
Overall, this commit takes us from potentially having all 18 of our
reporting tasks get fetched onto a single instance to now having a
process that will ensure tasks are distributed more fairly across
instances based on when they have available workers to process the
tasks.
We've seen only some of these reporting tasks happen but with no log
messages to indicate what happened and no app crashes. This hopefully
will give us a better picture of a timeline.
Note, I've tried to make our message format very consistent and good for
searching for in kibana so I've changed that across this whole file for
consistency.
The reporting worker tasks fetch large amounts of data from the db, do
some processing then store back in the database. As the reporting worker
only processes the create nightly billing/stats table tasks, which
aren't high performance or high volume, we're fine with the performance
hit from restarting the worker between every task (which based on
limited local testing takes about a second or so).
This causes some real funky shit with the app_context (used for
accessing current_app.logger). To access flask's global state we use the
standard way of importing `from flask import current_app`. However,
processes after the first one don't have the current_app available on
shut down (they're fine during the actual task running), and are unable
to call `with current_app.app_context()` to create it. They _are_ able
to call `with app.app_context()` to create it, where `app` is the
initial app that we pass in to `NotifyCelery.init_app`.
NotifyCelery.init_app is only called once, in the master process - I
think the application state is then stored and passed to the celery
workers. But then it looks like the teardown might clear it, but it
never gets set up again for the new workers? Unsure.
To fix this, store a copy of the initial flask app on the NotifyCelery
object and then use that from within the shutdown signal logging
function.
Nothing's ever easy ¯\_(ツ)_/¯
By not having a catch-all else, it makes it clearer what we’re
expecting. And then we think it’s worth adding a comment explaining why
we normalise as we do for letters and the `None` case.
