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Honeycomb column deletion tools
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| #!/usr/bin/env python3 | |
| # usage: hny-column-cleanup.py [-h] -k API_KEY -d DATASET [-m {hidden,spammy}] | |
| # Honeycomb Dataset Column Cleanup tool | |
| # arguments: | |
| # -h, --help show this help message and exit | |
| # -k API_KEY, --api-key API_KEY | |
| # Honeycomb API key | |
| # -d DATASET, --dataset DATASET | |
| # Honeycomb Dataset | |
| # -m {hidden,spammy}, --mode {hidden,spammy} | |
| # Type of columns to clean up | |
| # --dry-run Will print out the columns it would delete without deleting them | |
| # | |
| # Prerequisites: | |
| # - Python 3.6+ | |
| # - Requests module | |
| # - A Honeycomb API key with the "Manage Queries and Columns" permission | |
| import argparse | |
| import requests | |
| import sys | |
| import signal | |
| import time | |
| HONEYCOMB_API = 'https://api.honeycomb.io/1/' # /columns/dataset_slug | |
| SPAMMY_STRINGS = ['burp', 'xml', '%', | |
| '{', '(', '*', '!', '<', '..', '|', '&', '"', '\'', '\r', '\n'] | |
| def fetch_all_columns(dataset, api_key): | |
| """ | |
| Fetch all columns in a dataset and return them all as json | |
| """ | |
| url = HONEYCOMB_API + 'columns/' + dataset | |
| response = requests.get(url, headers={"X-Honeycomb-Team": api_key}) | |
| if response.status_code != 200: | |
| print('Failure: Unable to list columns:' + response.text) | |
| return | |
| return response.json() | |
| def list_hidden_columns(dataset, api_key): | |
| """ | |
| List hidden columns in a dataset and return the list as an array of column IDs | |
| """ | |
| all_columns = fetch_all_columns(dataset, api_key) | |
| hidden_column_ids = {} | |
| for column in all_columns: | |
| if column['hidden']: | |
| hidden_column_ids[column['id']] = column['key_name'] | |
| return hidden_column_ids | |
| def list_spammy_columns(dataset, api_key): | |
| """ | |
| List spammy columns in a dataset and return the list as an array of column IDs | |
| """ | |
| all_columns = fetch_all_columns(dataset, api_key) | |
| spammy_column_ids = {} | |
| for column in all_columns: | |
| for spammy_string in SPAMMY_STRINGS: | |
| if spammy_string in column['key_name']: | |
| spammy_column_ids[column['id']] = column['key_name'] | |
| break # end the inner loop in case there's multiple matches in the same string | |
| return spammy_column_ids | |
| def delete_columns(dataset, api_key, is_dry_run, column_ids): | |
| """ | |
| Delete hidden columns in a dataset from a provided array of column IDs | |
| """ | |
| url = HONEYCOMB_API + 'columns/' + dataset | |
| headers = {"X-Honeycomb-Team": api_key} | |
| for id in column_ids.keys(): | |
| print('Deleting column ID: ' + id + | |
| ' Name: ' + column_ids[id] + '...') | |
| if not is_dry_run: | |
| response = requests.delete(url + '/' + id, headers=headers) | |
| # A tiny bit of error handling | |
| if response.status_code in [429, 500, 502, 503, 504]: | |
| print('Received a retryable error ' + | |
| response.status_code + ' sleeping and retrying...') | |
| # Put a long-ish sleep here to cope with the default rate limit of 10 requests per minute | |
| time.sleep(30) | |
| response = requests.delete(url + '/' + id, headers=headers) | |
| elif response.status_code != 204: | |
| print('Failed: Unable to delete column ID' + | |
| id + ': ' + response.text) | |
| print('Moving on to the next column...') | |
| if __name__ == "__main__": | |
| try: | |
| # parse command line arguments | |
| parser = argparse.ArgumentParser( | |
| description='Honeycomb Dataset Column Cleanup tool') | |
| parser.add_argument('-k', '--api-key', | |
| help='Honeycomb API key', required=True) | |
| parser.add_argument('-d', '--dataset', | |
| help='Honeycomb Dataset', required=True) | |
| parser.add_argument('-m', '--mode', default='hidden', | |
| choices=['hidden', 'spammy'], help='Type of columns to clean up') | |
| parser.add_argument('--dry-run', default=False, | |
| action=argparse.BooleanOptionalAction, help='Will print out the columns it would delete without deleting them') | |
| args = parser.parse_args() | |
| columns_to_delete = {} | |
| if args.mode == 'hidden': | |
| columns_to_delete = list_hidden_columns(args.dataset, args.api_key) | |
| elif args.mode == 'spammy': | |
| columns_to_delete = list_spammy_columns(args.dataset, args.api_key) | |
| if len(columns_to_delete.keys()) > 0: | |
| delete_columns(args.dataset, args.api_key, | |
| args.dry_run, columns_to_delete) | |
| print('Deleted ' + str(len(columns_to_delete.keys())) + | |
| ' ' + args.mode + ' columns! Enjoy your clean dataset!') | |
| except KeyboardInterrupt: # Suppress tracebacks on SIGINT | |
| print('\nExiting early, not done ...\n') | |
| sys.exit(128 + signal.SIGINT) # http://tldp.org/LDP/abs/html/exitcodes |
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I added a
dry-runoption so I could see what columns it would grab :D