Hey everyone, I'm trying to sync some CrowdStrike Intel indicators into my own system using their API. I've got a Python script that runs in a container, but I keep hitting the rate limits and my job takes forever or just fails.
Here's the basic loop I'm using:
```python
import requests
from time import sleep
url = "https://api.crowdstrike.com/intel/indicators/v1/search"
headers = {"Authorization": "Bearer "}
params = {"limit": 100, "offset": 0}
all_indicators = []
while True:
resp = requests.get(url, headers=headers, params=params)
data = resp.json()
all_indicators.extend(data.get('resources', []))
if not data.get('meta', {}).get('pagination', {}).get('next'):
break
params['offset'] += params['limit']
# sleep a bit?
```
The problem is after a few pages, I get throttled. I added a `sleep(1)` but it still seems too fast. What's the actual limit? Is it requests per minute or per second?
How do you guys structure your sync jobs to play nice with the API? Do you use a specific backoff strategy or maybe a different endpoint? I'm running this in a Docker container on a schedule, so I need it to be reliable. Thanks!
Containers are magic, but I want to know how the magic works.
Yeah, that sleep(1) might not be enough depending on their limits. Have you checked the response headers? They often stuff the rate limit info (like `X-RateLimit-Remaining` and `X-RateLimit-Reset`) in there. You could check for a 429 and then parse the `Retry-After` header.
You could also try using a library like `tenacity` for retries with backoff, it's saved me a few headaches. Simple example:
```python
from tenacity import retry, wait_exponential, stop_after_attempt
@retry(wait=wait_exponential(multiplier=1, min=4, max=10), stop=stop_after_attempt(5))
def make_request(url, headers, params):
resp = requests.get(url, headers=headers, params=params)
resp.raise_for_status()
return resp
```
Just wrap your call in that. It'll wait longer after each failure.
Great question! The decorator only handles failures (429s). You're right, respecting X-RateLimit-Remaining on successful calls is a separate loop management thing. You'd need to check that header after each successful request and pause if you're getting close to zero. I usually stash the reset timestamp and calculate a sleep time.
It gets a bit messy, but managing that in the main pagination loop is the way to go.
Yeah, the trick is you've got to handle both the reactive backoff on 429s and the proactive pacing to avoid hitting the limit. The `sleep(1)` is a guess and their limit is almost certainly per-minute, not per-second.
You need to parse the headers. Here's a rough sketch to drop into your loop. It checks the remaining limit and sleeps until the next window if you're near the bottom. Combine this with the retry decorator others mentioned for the 429s.
```python
import time
while True:
resp = requests.get(url, headers=headers, params=params)
# Check rate limit headers
remaining = int(resp.headers.get('X-RateLimit-Remaining', 100))
reset_time = int(resp.headers.get('X-RateLimit-Reset', 0)) # Often a Unix timestamp
if remaining <= 5: # Too close to the limit
now = time.time()
sleep_for = max(reset_time - now, 0)
time.sleep(sleep_for + 1) # Add a small buffer
# ... your existing processing logic ...
```
Also, for a scheduled container job, consider writing your progress (like the last successful offset) to a persistent volume. That way if you get killed by a limit, you can resume instead of starting over.
yaml is my native language