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# Django Datawatch
With Django Datawatch you are able to implement arbitrary checks on data, review their status and even describe what to do to resolve them.
Think of [nagios](https://www.nagios.org/)/[icinga](https://www.icinga.org/) for data.
## Check execution backends
### Synchronous
Will execute all tasks synchronously which is not recommended but the most simple way to get started.
### Celery
Will execute the tasks asynchronously using celery as a task broker and executor.
Requires celery 5.0.0 or later.
### Other backends
Feel free to implement other task execution backends and send a pull request.
## Install
```shell
$ pip install django-datawatch
```
Add `django_datawatch` to your `INSTALLED_APPS`
## Celery beat database scheduler
If the datawatch scheduler should be run using the celery beat database scheduler, you need to install [django_celery_beat](hhttp://docs.celeryproject.org/en/latest/userguide/periodic-tasks.html#beat-custom-schedulers).
Add `django_datawatch.tasks.django_datawatch_scheduler` to the `CELERYBEAT_SCHEDULE` of your app.
This task should be executed every minute e.g. `crontab(minute='*/1')`, see example app.
## Write a custom check
Create `checks.py` inside your module.
```python
from datetime import datetime
from celery.schedules import crontab
from django_datawatch.datawatch import datawatch
from django_datawatch.base import BaseCheck, CheckResponse
from django_datawatch.models import Result
@datawatch.register
class CheckTime(BaseCheck):
run_every = crontab(minute='*/5') # scheduler will execute this check every 5 minutes
def generate(self):
yield datetime.now()
def check(self, payload):
response = CheckResponse()
if payload.hour <= 7:
response.set_status(Result.STATUS.ok)
elif payload.hour <= 12:
response.set_status(Result.STATUS.warning)
else:
response.set_status(Result.STATUS.critical)
return response
def get_identifier(self, payload):
# payload will be our datetime object that we are getting from generate method
return payload
def get_payload(self, identifier):
# as get_identifier returns the object we don't need to process it
# we can return identifier directly
return identifier
def user_forced_refresh_hook(self, payload):
payload.do_something()
```
### .generate
Must yield payloads to be checked. The check method will then be called for every payload.
### .check
Must return an instance of CheckResponse.
### .get_identifier
Must return a unique identifier for the payload.
### .user_forced_refresh_hook
A function that gets executed when the refresh is requested by a user through the `ResultRefreshView`.
This is used in checks that are purely based on triggers, e.g. when a field changes the test gets executed.
### trigger check updates
Check updates for individual payloads can also be triggered when related datasets are changed.
The map for update triggers is defined in the Check class' trigger_update attribute.
```
trigger_update = dict(subproduct=models_customer.SubProduct)
```
The key is a slug to define your trigger while the value is the model that issues the trigger when saved.
You must implement a resolver function for each entry with the name of get_<slug>_payload which returns the payload or multiple payloads (as a list) to check (same datatype as .check would expect or .generate would yield).
```
def get_subproduct_payload(self, instance):
return instance.product
```
## Exceptions
#### `DatawatchCheckSkipException`
raise this exception to skip current check. The result will not appear in the checks results.
## Run your checks
A management command is provided to queue the execution of all checks based on their schedule.
Add a crontab to run this command every minute and it will check if there's something to do.
```shell
$ ./manage.py datawatch_run_checks
$ ./manage.py datawatch_run_checks --slug=example.checks.UserHasEnoughBalance
```
## Refresh your check results
A management command is provided to forcefully refresh all existing results for a check.
This comes in handy if you changes the logic of your check and don't want to wait until the periodic execution or an update trigger.
```shell
$ ./manage.py datawatch_refresh_results
$ ./manage.py datawatch_refresh_results --slug=example.checks.UserHasEnoughBalance
```
## Get a list of registered checks
```shell
$ ./manage.py datawatch_list_checks
```
## Clean up your database
Remove the unnecessary check results and executions if you've removed the code for a check.
```shell
$ ./manage.py datawatch_clean_up
```
## Settings
```python
DJANGO_DATAWATCH_BACKEND = 'django_datawatch.backends.synchronous'
DJANGO_DATAWATCH_RUN_SIGNALS = True
```
### DJANGO_DATAWATCH_BACKEND
You can chose the backend to run the tasks. Supported are 'django_datawatch.backends.synchronous' and 'django_datawatch.backends.celery'.
Default: 'django_datawatch.backends.synchronous'
### DJANGO_DATAWATCH_RUN_SIGNALS
Use this setting to disable running post_save updates during unittests if required.
Default: True
### celery task queue
Datawatch supported setting a specific queue in release < 0.4.0
With the switch to celery 4, you should use task routing to define the queue for your tasks, see http://docs.celeryproject.org/en/latest/userguide/routing.html
## Migrating from 3.x to 4.x
In `4.x`, the base check class supports two new methods (`get_assigned_users` and `get_assigned_groups`). This is a breaking change as the old `get_assigned_user` and `get_assigned_group` methods have been removed.
This change allows checks to assign multiple users and groups to a check. If you have implemented custom checks, you need to update your code to return a list of users and groups instead of a single user and group. e.g.
From
```python
from django_datawatch.base import BaseCheck
class CustomCheck(BaseCheck):
...
def get_assigned_user(self, payload, result) -> Optional[AbstractUser]:
return None # or a user
def get_assigned_group(self, payload, result) -> Optional[Group]:
return None # or a group
```
To
```python
from django_datawatch.base import BaseCheck
class CustomCheck(BaseCheck):
...
def get_assigned_users(self, payload, result) -> Optional[List[AbstractUser]]:
return None # or a list of users
def get_assigned_groups(self, payload, result) -> Optional[List[Group]]:
return None # or a list of groups
```
# CONTRIBUTE
## Dev environment
- Latest [Docker](https://docs.docker.com/) with the integrated compose plugin
Please make sure that no other container is using port 8000 as this is the one you're install gets exposed to:
http://localhost:8000/
## Setup
We've included an example app to show how django_datawatch works.
Start by launching the included docker container.
```bash
docker compose up -d
```
Then setup the example app environment.
```bash
docker compose run --rm app migrate
docker compose run --rm app loaddata example
```
The installed superuser is "example" with password "datawatch".
## Run checks
Open http://localhost:8000/, log in and then go back to http://localhost:8000/.
You'll be prompted with an empty dashboard. That's because we didn't run any checks yet.
Let's enqueue an update.
```bash
docker compose run --rm app datawatch_run_checks --force
```
The checks for the example app are run synchronously and should be updated immediately.
If you decide to switch to the celery backend, you should now start a celery worker to process the checks.
```bash
docker compose run --rm --entrypoint celery app -A example worker -l DEBUG
```
To execute the celery beat scheduler which runs the datawatch scheduler every minute, just run:
```bash
docker compose run --rm --entrypoint celery app -A example beat --scheduler django_celery_beat.schedulers:DatabaseScheduler
```
You will see some failed check now after you refreshed the dashboard view.
## Run the tests
```bash
docker compose run --rm app test
```
## Requirements upgrades
Check for upgradeable packages by running
```bash
docker compose up -d
docker compose exec app pip-check
```
## Translations
Collect and compile translations for all registered locales
```bash
docker compose run --rm app makemessages --no-location --all
docker compose run --rm app compilemessages
```
## Making a new release
[bumpversion](https://github.com/peritus/bumpversion) is used to manage releases.
Add your changes to the [CHANGELOG](./CHANGELOG.rst), run
```bash
docker compose exec app bumpversion <major|minor|patch>
```
then push (including tags).
Raw data
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"author": "Jens Nistler <opensource@jensnistler.de>, Bogdan Radko <bogdan.radko@regiohelden.de>",
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"description": "[](https://badge.fury.io/py/django-datawatch)\n[](https://github.com/RegioHelden/django-datawatch/actions)\n[](https://github.com/ellerbrock/open-source-badges/)\n[](https://opensource.org/licenses/mit-license.php)\n\n# Django Datawatch\n\nWith Django Datawatch you are able to implement arbitrary checks on data, review their status and even describe what to do to resolve them.\nThink of [nagios](https://www.nagios.org/)/[icinga](https://www.icinga.org/) for data.\n\n## Check execution backends\n\n### Synchronous\n\nWill execute all tasks synchronously which is not recommended but the most simple way to get started.\n\n### Celery\n\nWill execute the tasks asynchronously using celery as a task broker and executor.\nRequires celery 5.0.0 or later.\n\n### Other backends\n\nFeel free to implement other task execution backends and send a pull request.\n\n## Install\n\n```shell\n$ pip install django-datawatch\n```\n\nAdd `django_datawatch` to your `INSTALLED_APPS`\n\n## Celery beat database scheduler\n\nIf the datawatch scheduler should be run using the celery beat database scheduler, you need to install [django_celery_beat](hhttp://docs.celeryproject.org/en/latest/userguide/periodic-tasks.html#beat-custom-schedulers).\n\nAdd `django_datawatch.tasks.django_datawatch_scheduler` to the `CELERYBEAT_SCHEDULE` of your app.\nThis task should be executed every minute e.g. `crontab(minute='*/1')`, see example app.\n\n## Write a custom check\n\nCreate `checks.py` inside your module.\n\n```python\nfrom datetime import datetime\n\nfrom celery.schedules import crontab\n\nfrom django_datawatch.datawatch import datawatch\nfrom django_datawatch.base import BaseCheck, CheckResponse\nfrom django_datawatch.models import Result\n\n\n@datawatch.register\nclass CheckTime(BaseCheck):\n run_every = crontab(minute='*/5') # scheduler will execute this check every 5 minutes\n\n def generate(self):\n yield datetime.now()\n\n def check(self, payload):\n response = CheckResponse()\n if payload.hour <= 7:\n response.set_status(Result.STATUS.ok)\n elif payload.hour <= 12:\n response.set_status(Result.STATUS.warning)\n else:\n response.set_status(Result.STATUS.critical)\n return response\n\n def get_identifier(self, payload):\n # payload will be our datetime object that we are getting from generate method\n return payload\n\n def get_payload(self, identifier):\n # as get_identifier returns the object we don't need to process it\n # we can return identifier directly\n return identifier\n\n def user_forced_refresh_hook(self, payload):\n payload.do_something()\n```\n\n### .generate\n\nMust yield payloads to be checked. The check method will then be called for every payload.\n\n### .check\n\nMust return an instance of CheckResponse.\n\n### .get_identifier\n\nMust return a unique identifier for the payload. \n\n### .user_forced_refresh_hook\n\nA function that gets executed when the refresh is requested by a user through the `ResultRefreshView`.\n\nThis is used in checks that are purely based on triggers, e.g. when a field changes the test gets executed.\n\n### trigger check updates\n\nCheck updates for individual payloads can also be triggered when related datasets are changed.\nThe map for update triggers is defined in the Check class' trigger_update attribute.\n\n```\ntrigger_update = dict(subproduct=models_customer.SubProduct)\n```\n\nThe key is a slug to define your trigger while the value is the model that issues the trigger when saved.\nYou must implement a resolver function for each entry with the name of get_<slug>_payload which returns the payload or multiple payloads (as a list) to check (same datatype as .check would expect or .generate would yield).\n```\ndef get_subproduct_payload(self, instance):\n return instance.product\n```\n\n## Exceptions\n\n#### `DatawatchCheckSkipException`\nraise this exception to skip current check. The result will not appear in the checks results. \n\n## Run your checks\n\nA management command is provided to queue the execution of all checks based on their schedule.\nAdd a crontab to run this command every minute and it will check if there's something to do.\n\n```shell\n$ ./manage.py datawatch_run_checks\n$ ./manage.py datawatch_run_checks --slug=example.checks.UserHasEnoughBalance\n```\n\n## Refresh your check results\n\nA management command is provided to forcefully refresh all existing results for a check.\nThis comes in handy if you changes the logic of your check and don't want to wait until the periodic execution or an update trigger.\n\n```shell\n$ ./manage.py datawatch_refresh_results\n$ ./manage.py datawatch_refresh_results --slug=example.checks.UserHasEnoughBalance\n```\n\n## Get a list of registered checks\n\n```shell\n$ ./manage.py datawatch_list_checks\n```\n\n## Clean up your database\n\nRemove the unnecessary check results and executions if you've removed the code for a check.\n\n```shell\n$ ./manage.py datawatch_clean_up\n```\n\n## Settings\n\n```python\nDJANGO_DATAWATCH_BACKEND = 'django_datawatch.backends.synchronous'\nDJANGO_DATAWATCH_RUN_SIGNALS = True\n```\n\n### DJANGO_DATAWATCH_BACKEND\n\nYou can chose the backend to run the tasks. Supported are 'django_datawatch.backends.synchronous' and 'django_datawatch.backends.celery'.\n\nDefault: 'django_datawatch.backends.synchronous'\n\n### DJANGO_DATAWATCH_RUN_SIGNALS\n\nUse this setting to disable running post_save updates during unittests if required.\n\nDefault: True\n\n### celery task queue\n\nDatawatch supported setting a specific queue in release < 0.4.0\n\nWith the switch to celery 4, you should use task routing to define the queue for your tasks, see http://docs.celeryproject.org/en/latest/userguide/routing.html\n\n## Migrating from 3.x to 4.x\n\nIn `4.x`, the base check class supports two new methods (`get_assigned_users` and `get_assigned_groups`). This is a breaking change as the old `get_assigned_user` and `get_assigned_group` methods have been removed.\n\nThis change allows checks to assign multiple users and groups to a check. If you have implemented custom checks, you need to update your code to return a list of users and groups instead of a single user and group. e.g.\n\nFrom\n\n```python\nfrom django_datawatch.base import BaseCheck\n\nclass CustomCheck(BaseCheck):\n ...\n def get_assigned_user(self, payload, result) -> Optional[AbstractUser]:\n return None # or a user\n def get_assigned_group(self, payload, result) -> Optional[Group]:\n return None # or a group\n```\n\nTo\n\n```python\nfrom django_datawatch.base import BaseCheck\n\nclass CustomCheck(BaseCheck):\n ...\n def get_assigned_users(self, payload, result) -> Optional[List[AbstractUser]]:\n return None # or a list of users\n def get_assigned_groups(self, payload, result) -> Optional[List[Group]]:\n return None # or a list of groups\n```\n\n# CONTRIBUTE\n\n## Dev environment\n- Latest [Docker](https://docs.docker.com/) with the integrated compose plugin\n\nPlease make sure that no other container is using port 8000 as this is the one you're install gets exposed to:\nhttp://localhost:8000/\n\n## Setup\n\nWe've included an example app to show how django_datawatch works.\nStart by launching the included docker container.\n```bash\ndocker compose up -d\n```\n\nThen setup the example app environment.\n```bash\ndocker compose run --rm app migrate\ndocker compose run --rm app loaddata example\n```\nThe installed superuser is \"example\" with password \"datawatch\".\n\n## Run checks\n\nOpen http://localhost:8000/, log in and then go back to http://localhost:8000/.\nYou'll be prompted with an empty dashboard. That's because we didn't run any checks yet.\nLet's enqueue an update.\n```bash\ndocker compose run --rm app datawatch_run_checks --force\n```\n\nThe checks for the example app are run synchronously and should be updated immediately.\nIf you decide to switch to the celery backend, you should now start a celery worker to process the checks.\n```bash\ndocker compose run --rm --entrypoint celery app -A example worker -l DEBUG\n```\n\nTo execute the celery beat scheduler which runs the datawatch scheduler every minute, just run:\n```bash\ndocker compose run --rm --entrypoint celery app -A example beat --scheduler django_celery_beat.schedulers:DatabaseScheduler\n```\n\nYou will see some failed check now after you refreshed the dashboard view.\n\n![Django Datawatch dashboard](http://static.jensnistler.de/django_datawatch.png \"Django Datawatch dashboard\")\n\n![Django Datawatch detail view](http://static.jensnistler.de/django_datawatch_details.png \"Django Datawatch detail view\")\n\n## Run the tests\n```bash\ndocker compose run --rm app test\n```\n\n## Requirements upgrades\n\nCheck for upgradeable packages by running \n```bash\ndocker compose up -d\ndocker compose exec app pip-check\n```\n\n## Translations\n\nCollect and compile translations for all registered locales\n\n```bash\ndocker compose run --rm app makemessages --no-location --all\ndocker compose run --rm app compilemessages\n```\n\n## Making a new release\n\n[bumpversion](https://github.com/peritus/bumpversion) is used to manage releases.\n\nAdd your changes to the [CHANGELOG](./CHANGELOG.rst), run\n```bash\ndocker compose exec app bumpversion <major|minor|patch>\n```\nthen push (including tags).\n",
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