I want to delete some datasets. How do I do that?

This article provides an in-depth overview of the tool for cleaning up unwanted datasets in bulk within Honeycomb environments. The Python script can be found on GitHub:

https://github.com/honeycombio/field-utils/blob/main/tools/hny_dataset_cleanup_tool/hny-dataset-cleanup.py

Dataset Cleanup Tool (hny-dataset-cleanup.py)

Prerequisites

• Python 3.11 or later

• Python requests library (pip install requests)

• Honeycomb API key with “Create Dataset” permission

Usage

python hny-dataset-cleanup.py -k API_KEY [options]

Required Arguments

• -k, --api-key: Honeycomb API key.

Optional Arguments

• -a, --api-host: Honeycomb API hostname (default: api.honeycomb.io).

• -m, --mode: Type of datasets targeted:

  • spammy: Datasets with spammy strings (potentially created during pentesting)

  • date: Datasets created on a specific date.

  • lastwritten: Datasets not written since a specified date.

• --date YYYY-MM-DD: Required with date or lastwritten modes.

• --dry-run: Preview datasets targeted without deleting.

Examples

• Preview spammy datasets removal:

  python hny-dataset-cleanup.py -k YOUR_API_KEY -m spammy --dry-run
  

• Delete datasets created on a specific date:

  python hny-dataset-cleanup.py -k YOUR_API_KEY -m date --date 2023-02-20
  

• Remove datasets not written to since a specific date:

  python hny-dataset-cleanup.py -k YOUR_API_KEY -m lastwritten --date 2023-01-01
  

Best Practices 

• Always perform a dry-run first to verify the targeted datasets or columns.

• Validate targets carefully, particularly when using regex patterns or date-based criteria.