> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mage.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Schedules

> Schedule pipelines to run periodically

<Frame>
  <img alt="Schedule pipelines to run periodically" src="https://media.tenor.com/lyU9j2EckEUAAAAd/running-out-of-time-no-time.gif" />
</Frame>

You can schedule your pipeline to run on these intervals:

* Once:  The pipeline will run only once
* Hourly: The pipeline will run at the beginning of every hour (i.e 0100, 0200, 0300, etc)
* Daily: The pipeline will run at 0000 UTC / GMT every day
* Weekly: The pipeline will run at 0000 UTC / GMT the first day (Monday) of every week
* Monthly: The pipeline will run at 0000 UTC / GMT on the first day of every month
* Always on: The pipeline will be run as soon as the previous pipeline run is finished
* Custom schedule using
  [CRON Expression](https://en.wikipedia.org/wiki/Cron#CRON_expression)
* Every minute using CRON expression: `* * * * *`
* *Streaming pipeline*: coming soon
  Learn more about how to
  [schedule pipelines](/design/data-pipeline-management#create-trigger).

## Extra runtime variables from pipeline run

Default runtime variables from pipeline run:

| Key                   | Description                                                                                      | Example              |
| --------------------- | ------------------------------------------------------------------------------------------------ | -------------------- |
| `env`                 | The value is always `prod` in pipeline runs. The value is `dev` when running blocks in notebook. | `prod`               |
| `event`               | If the pipeline is triggered by event, the event variable contains the event payload.            | `{"key1": "value1"}` |
| `execution_date`      | A datetime object that the pipeline is executed at.                                              | `86400`              |
| `execution_partition` | An automatically formatted partition of the pipeline run using the execution date.               | `10/20231225T122520` |
| `pipeline_run_id`     | The id of the pipeline run.                                                                      | `10`                 |
| `trigger_name`        | The trigger name of the pipeline run.                                                            | `test run 1`         |

If your pipeline run belongs to a trigger that is scheduled, then the following extra
variables are available in your Python block’s keyword arguments (e.g. `kwargs`).

| Key                                | Description                                                                       | Example                                      |
| ---------------------------------- | --------------------------------------------------------------------------------- | -------------------------------------------- |
| `interval_start_datetime`          | The `datetime` when the pipeline run is scheduled for.                            | `datetime.datetime(2023, 7, 23, 7, 0, 0, 0)` |
| `interval_end_datetime`            | The `datetime` when the next pipeline run is scheduled for.                       | `datetime.datetime(2023, 7, 24, 7, 0, 0, 0)` |
| `interval_seconds`                 | The number of seconds between the current pipeline run and the next pipeline run. | `86400`                                      |
| `interval_start_datetime_previous` | The `datetime` when the previous pipeline run was scheduled for.                  | `datetime.datetime(2023, 7, 22, 7, 0, 0, 0)` |

### SQL block

If you’re using a SQL block, here is an example of how you can access these variables:

```sql theme={"system"}
SELECT
    '{{ interval_start_datetime }}' AS interval_start_datetime
    , '{{ interval_end_datetime }}' AS interval_end_datetime
    , '{{ interval_seconds }}' AS interval_seconds
    , '{{ interval_start_datetime_previous }}' AS interval_start_datetime_previous
```