Check out what I made: A script that prunes stale metrics from Claw daily.

I've been grappling with the compounding cardinality costs in our primary observability vendor, Claw, for several quarters now. While their service is robust, their pricing model is heavily influenced by unique metric time series, and we've identified a significant source of waste: stale or abandoned metrics that continue to be ingested from our legacy services and failed deployment experiments. These series never truly die; they just accumulate silently, inflating the bill.

To address this systematically, I've developed a script that interfaces directly with Claw's API to perform daily pruning of these stale entries. The core principle is straightforward: identify any metric series that has not reported a single data point in the last 30 days (a threshold we deem safe for our retention policies) and permanently delete it from the system. This is distinct from simple downsampling or aggregation; it's a surgical removal of unused series to reduce cardinality at the source.

The script operates in two phases: discovery and execution. It first queries Claw's metadata API to fetch all active metric names, then for each metric, it performs a targeted query over the lookback window to check for any non-null values. Series that return no data are flagged for deletion. We run this as a scheduled Kubernetes CronJob during off-peak hours.

Here is the core logic of the script, written in Python for clarity:

```python
import requests
import time
from datetime import datetime, timedelta

CLAW_API_KEY = os.environ['CLAW_API_KEY']
CLAW_API_ENDPOINT = "https://api.claw.io/v1"
LOOKBACK_DAYS = 30
DRY_RUN = False # Set to True for initial validation

def fetch_metric_list():
"""Fetches the list of all metric names from Claw's metadata catalog."""
headers = {'Authorization': f'Bearer {CLAW_API_KEY}'}
response = requests.get(f"{CLAW_API_ENDPOINT}/metrics", headers=headers)
response.raise_for_status()
return response.json().get('data', [])

def is_metric_stale(metric_name):
"""Checks if a metric has reported any data in the lookback period."""
end = int(time.time())
start = end - (LOOKBACK_DAYS * 86400)
query = f"max({metric_name}) by {{}}"
params = {
'query': query,
'start': start,
'end': end,
'step': '300s'
}
headers = {'Authorization': f'Bearer {CLAW_API_KEY}'}
response = requests.get(f"{CLAW_API_ENDPOINT}/query_range", headers=headers, params=params)
data = response.json().get('data', {}).get('result', [])
# If the result list is empty, no data was reported for this series.
return len(data) == 0

def delete_metric(metric_name):
"""Initiates a deletion request for the given metric series."""
if DRY_RUN:
print(f"[DRY RUN] Would delete metric: {metric_name}")
return
headers = {'Authorization': f'Bearer {CLAW_API_KEY}'}
delete_url = f"{CLAW_API_ENDPOINT}/metrics/{metric_name}"
response = requests.delete(delete_url, headers=headers)
if response.status_code == 202:
print(f"Deletion accepted for metric: {metric_name}")
else:
print(f"Failed to delete {metric_name}: {response.status_code}")

def main():
metric_list = fetch_metric_list()
print(f"Evaluating {len(metric_list)} metrics for staleness...")
for metric in metric_list:
if is_metric_stale(metric):
delete_metric(metric)
time.sleep(0.1) # Rate limiting courtesy

if __name__ == "__main__":
main()
```

Key considerations and tradeoffs we had to make:

* **API Rate Limiting:** The script includes a small sleep between deletion calls to respect Claw's API limits. A more sophisticated version could use exponential backoff.
* **Deletion Finality:** This operation is irreversible. We run the script in `DRY_RUN` mode for a week and manually audit the list before enabling actual deletions.
* **Metric Dependencies:** We had to ensure no active dashboards or alerts were dependent on these stale metrics. We correlated the script's output with our Terraform-managed monitoring configurations.
* **Timing Window:** The 30-day window was chosen after analyzing our deployment lifecycle and incident investigation needs. A shorter window risks deleting metrics that are part of low-frequency but critical batch processes.

Since implementing this daily pruning, we've observed a 17% reduction in our active time series count over six weeks, which has translated into a measurable, albeit lagged, reduction in our monthly observability spend. The script is now a foundational part of our cost-control pipeline, acting as a garbage collector for our metric namespace. I'm interested in hearing how others approach this problem, particularly if there are strategies to prevent the creation of these stale series in the first place through better instrumentation lifecycle management.