David McDonald a237162106 Reduce concurrency and prefetch count of reporting celery app
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.
2020-04-28 10:47:46 +01:00
2019-10-11 13:55:21 +01:00
2019-08-02 12:41:03 +01:00
2019-05-16 17:06:34 +01:00
2020-04-20 18:39:45 +01:00
2019-10-23 11:45:07 +01:00
2020-01-07 10:26:07 +00:00
2019-05-16 17:06:34 +01:00
2019-10-11 13:55:21 +01:00

GOV.UK Notify API

Contains:

  • the public-facing REST API for GOV.UK Notify, which teams can integrate with using our clients
  • an internal-only REST API built using Flask to manage services, users, templates, etc (this is what the admin app talks to)
  • asynchronous workers built using Celery to put things on queues and read them off to be processed, sent to providers, updated, etc

Setting Up

Python version

At the moment we run Python 3.6 in production. You will run into problems if you try to use Python 3.5 or older, or Python 3.7 or newer.

AWS credentials

To run the API you will need appropriate AWS credentials. You should receive these from whoever administrates your AWS account. Make sure you've got both an access key id and a secret access key.

Your aws credentials should be stored in a folder located at ~/.aws. Follow Amazon's instructions for storing them correctly.

Virtualenv

mkvirtualenv -p /usr/local/bin/python3 notifications-api

environment.sh

Creating the environment.sh file. Replace [unique-to-environment] with your something unique to the environment. Your AWS credentials should be set up for notify-tools (the development/CI AWS account).

Create a local environment.sh file containing the following:

echo "
export NOTIFY_ENVIRONMENT='development'

export MMG_API_KEY='MMG_API_KEY'
export FIRETEXT_API_KEY='FIRETEXT_ACTUAL_KEY'
export NOTIFICATION_QUEUE_PREFIX='YOUR_OWN_PREFIX'

export FLASK_APP=application.py
export FLASK_DEBUG=1
export WERKZEUG_DEBUG_PIN=off
"> environment.sh

NOTES:

  • Replace the placeholder key and prefix values as appropriate
  • The SECRET_KEY and DANGEROUS_SALT should match those in the notifications-admin app.
  • The unique prefix for the queue names prevents clashing with others' queues in shared amazon environment and enables filtering by queue name in the SQS interface.

Postgres

Install Postgres.app. You will need admin on your machine to do this.

Choose the version with Additional Releases - you want 9.6. Once you run the app, open the sidebar, remove the default v11 server and create and initialise a v9.6 server.

Redis

To switch redis on you'll need to install it locally. On a OSX we've used brew for this. To use redis caching you need to switch it on by changing the config for development:

    REDIS_ENABLED = True

To run the application

First, run scripts/bootstrap.sh to install dependencies and create the databases.

You need to run the api application and a local celery instance.

There are two run scripts for running all the necessary parts.

scripts/run_app.sh
scripts/run_celery.sh

Optionally you can also run this script to run the scheduled tasks:

scripts/run_celery_beat.sh

To test the application

First, ensure that scripts/bootstrap.sh has been run, as it creates the test database.

Then simply run

make test

That will run flake8 for code analysis and our unit test suite. If you wish to run our functional tests, instructions can be found in the notifications-functional-tests repository.

To update application dependencies

requirements.txt file is generated from the requirements-app.txt in order to pin versions of all nested dependencies. If requirements-app.txt has been changed (or we want to update the unpinned nested dependencies) requirements.txt should be regenerated with

make freeze-requirements

requirements.txt should be committed alongside requirements-app.txt changes.

To run one off tasks

Tasks are run through the flask command - run flask --help for more information. There are two sections we need to care about: flask db contains alembic migration commands, and flask command contains all of our custom commands. For example, to purge all dynamically generated functional test data, do the following:

Locally

flask command purge_functional_test_data -u <functional tests user name prefix>

On the server

cf run-task notify-api "flask command purge_functional_test_data -u <functional tests user name prefix>"

All commands and command options have a --help command if you need more information.

To create a new worker app

You need to:

  1. Create new entries for your app in manifest.yml.j2 and scripts/paas_app_wrapper.sh (example)
  2. Update the jenkins deployment job in the notifications-aws repo (example)
  3. Add the new worker's log group to the list of logs groups we get alerts about and we ship them to kibana (example)
  4. Optionally add it to the autoscaler (example)

Important:

Before pushing the deployment change on jenkins, read below about the first time deployment.

First time deployment of your new worker

Our deployment flow requires that the app is present in order to proceed with the deployment.

This means that the first deployment of your app must happen manually.

To do this:

  1. Ensure your code is backwards compatible
  2. From the root of this repo run CF_APP=<APP_NAME> make <cf-space> cf-push

Once this is done, you can push your deployment changes to jenkins to have your app deployed on every deployment.

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The API powering Notify.gov
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