# Deleting Events Fiddler enables you to delete previously published production inference data using either unique identifiers or event timestamps using our Events [REST API](/api/rest-api/rest-api.md). > **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/26.9/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](/developers/client-library-reference/publishing-production-data.md#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/26.9/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`. 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