Once implemented, it'll allow you to get alerts and metrics to help you solve errors, detect timeouts, and prevent disruptions to your service.
RequirementsIf you're using Celery Beat to run your periodic tasks, have a look at our Celery Beat Auto Discovery documentation.
Job MonitoringUse the Python SDK to monitor and notify you if your periodic task is missed (or doesn't start when expected), if it fails due to a problem in the runtime (such as an error), or if it fails by exceeding its maximum runtime.
Use the monitor
decorator to wrap your tasks:
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import sentry_sdk
from sentry_sdk.crons import monitor
@monitor(monitor_slug='<monitor-slug>')
def tell_the_world():
print('My scheduled task...')
Alternatively, monitor
can be used as a context manager:
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import sentry_sdk
from sentry_sdk.crons import monitor
def tell_the_world():
with monitor(monitor_slug='<monitor-slug>'):
print('My scheduled task...')
Since version 1.44.1
of the SDK you can use monitor
to annotate asynchronous functions as well.
You can create and update your monitors programmatically with code rather than creating and configuring them in Sentry.io . If the monitor doesn't exist in Sentry yet, it will be created.
To create or update a monitor, use monitor
as outlined above and pass in your monitor configuration as monitor_config
. This requires SDK version 1.45.0
or higher.
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monitor_config = {
"schedule": {"type": "crontab", "value": "0 0 * * *"},
"timezone": "Europe/Vienna",
"checkin_margin": 10,
"max_runtime": 10,
"failure_issue_threshold": 5,
"recovery_threshold": 5,
}
@monitor(monitor_slug='<monitor-slug>', monitor_config=monitor_config)
def tell_the_world():
print('My scheduled task...')
If you're using manual check-ins, you can pass your monitor_config
to the capture_checkin
call:
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check_in_id = capture_checkin(
monitor_slug='<monitor-slug>',
status=MonitorStatus.IN_PROGRESS,
monitor_config=monitor_config,
)
Manual Check-Ins
Check-in monitoring allows you to track a job's progress by capturing two check-ins: one at the start of your job and another at the end of your job. This two-step process allows Sentry to notify you if your job didn't start when expected (missed) or if it exceeded its maximum runtime (failed).
If you use the monitor
decorator/context manager, the SDK will create check-ins for the wrapped code automatically.
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from sentry_sdk.crons import capture_checkin
from sentry_sdk.crons.consts import MonitorStatus
check_in_id = capture_checkin(
monitor_slug='<monitor-slug>',
status=MonitorStatus.IN_PROGRESS,
)
capture_checkin(
monitor_slug='<monitor-slug>',
check_in_id=check_in_id,
status=MonitorStatus.OK,
)
Alerts
When your recurring job fails to check in (missed), runs beyond its configured maximum runtime (failed), or manually reports a failure, Sentry will create an error event with a tag to your monitor.
To receive alerts about these events:
The event's tags match {key} {match} {value}
.Example: The event's tags match monitor.slug equals my-monitor-slug-here
Learn more in Issue Alert Configuration.
Rate LimitsTo prevent abuse and resource overuse, Crons limits check-ins to 6 per minute for each monitor environment.
For example, if you have a monitor called "database-backup" with two environments:
database-backup
in environment production
can send up to 6 check-ins per minutedatabase-backup
in environment staging
can also send up to 6 check-ins per minuteYou can verify if any check-ins are being dropped by visiting the Usage Stats page. To avoid dropped check-ins, ensure your monitors don't exceed the rate limit.
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