# Deleting Events

Fiddler enables you to delete previously published production inference data using either unique identifiers or event timestamps using our Events [REST API](https://app.gitbook.com/s/rsvU8AIQ2ZL9arerribd/rest-api).

> **Note:**
>
> Event deletion is only offered using the REST API and is not currently supported in the Fiddler Python client.

Common reasons for deletion include:

* Complying with data privacy regulations ([GDPR](https://commission.europa.eu/law/law-topic/data-protection/legal-framework-eu-data-protection_en), [CCPA](https://www.oag.ca.gov/privacy/ccpa), etc.)
* Removing erroneously published data
* Correcting data inaccuracies

## Impact on Monitoring Metrics

When deleting inference data with the time range filter you can control how it affects your monitoring metrics using the `update_metrics` parameter in the Events Delete REST API:

* Preserve existing aggregated metrics: `update_metrics=False`
* Recalculate metrics to reflect the data removal (default): `update_metrics=True`

## Important Considerations

* Deletions are permanent and cannot be undone
* Pre-production (baseline) datasets do not support row-level deletion
* Large-scale deletions may temporarily impact data ingestion performance.
* By default, the event deletion API allows up to 30 requests per day.
* If the rate limit is exceeded, the API will return a `429 Too Many Requests` response, along with rate limiting headers:
  * `X-RateLimit-Limit` – The maximum number of allowed requests in the current window.
  * `X-RateLimit-Remaining` – The number of remaining requests in the current window.
  * `Retry-After` – The number of seconds to wait before retrying, or the HTTP date when the rate limit resets.
  * `X-RateLimit-Reset` – The UTC epoch time (seconds) when the current rate limiting window resets.

## Deletion Methods

Fiddler supports two filtering methods for deletion (only one method can be used per API call):

### Delete by Event ID

Delete specific inference events by providing their unique identifiers. The event ID corresponds to the `event_id_col` specified during [Model](https://github.com/fiddler-labs/fiddler/blob/release/26.7/docs/developers/api-methods-30.md#model) onboarding.

> **Note:** Event IDs are designed to be unique. If duplicate `event_id` values have been published, deleting by event ID will remove all events matching those IDs from the raw events table. If `update_metrics=True` is set, aggregated metrics may not be fully updated if duplicate events exist and [deduplication](https://docs.fiddler.ai/developers/client-library-reference/publishing-production-data/..#publish-production-data) was not enabled. You can delete up to 10,000 events per API call. If you need to delete more events in one request, please contact Fiddler support.

### Delete by Timestamp Range

Delete events that fall within a designated time range by specifying start and end timestamps. The filtering is based on the timestamp column (`event_ts_col`) defined during [Model](https://github.com/fiddler-labs/fiddler/blob/release/26.7/docs/developers/api-methods-30.md#model) onboarding. Deletions are processed in batches (default batch size: 100K events) to ensure efficiency and resilience.

> **Note:** Ensure that the `max_memory_usage` setting in ClickHouse is configured to handle the number of events specified by the batch size (controlled by the `EVENT_DELETION_BATCH_SIZE` environment variable). You can delete up to 100 million events per day using this endpoint. If you require a higher daily deletion limit, please contact Fiddler support.

**Usage params**

| Parameter       | Type            | Default | Description                                                                                                                                                                                                                                                                                         |
| --------------- | --------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| model\_id       | UUID            | -       | Unique identifier for the model from which production events are deleted.                                                                                                                                                                                                                           |
| time\_range     | Optional\[dict] | -       | Dictionary with `start_time` (inclusive) and `end_time` (exclusive) in UTC, both aligned to the hour (format: `'YYYY-MM-DD HH:00:00'`). Specify timestamps so that the entire range covers complete hours—do not use minutes or seconds. The range filter applies as `start_time` ≤ t < `end_time`. |
| event\_ids      | Optional\[list] | -       | List of event\_ids to be deleted.                                                                                                                                                                                                                                                                   |
| update\_metrics | Optional\[bool] | False   | Determines if the monitoring metrics are recalculated to reflect the changes                                                                                                                                                                                                                        |

***

## Example: Deleting Inference Events by Timestamp Range

```python
import requests
headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {token}'}
data = {'model_id': model.id,
        'time_range':{
            'start_time':'2024-09-27 17:00:00',
            'end_time':'2024-09-27 18:00:00',
            },
        'update_metrics':True,
        }
response = requests.delete(
    url=f'{url}/v3/events',
    headers=headers,
    json=data,
)

```

## Example: Deleting Inference Events by Event IDs

```python
import requests
headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {token}'}
data = {
         'model_id': model.id,
         'event_ids': ['event_id1', 'event_id2'],
         'update_metrics':False,
      }
response = requests.delete(
  url=f'{url}/v3/events',
  headers=headers,
  json=data,
)

```

> 📘 Please delete events with caution when `update_metrics=True`. We recommend not deleting events while there is an ongoing publish or update operation within the same data range.