How to avoid HTTP error 429 (Too Many Requests) python

Receiving a status 429 is not an error, it is the other server "kindly" asking you to please stop spamming requests. Obviously, your rate of requests has been too high and the server is not willing to accept this.

You should not seek to "dodge" this, or even try to circumvent server security settings by trying to spoof your IP, you should simply respect the server's answer by not sending too many requests.

If everything is set up properly, you will also have received a "Retry-after" header along with the 429 response. This header specifies the number of seconds you should wait before making another call. The proper way to deal with this "problem" is to read this header and to sleep your process for that many seconds.

You can find more information on status 429 here: https://www.rfc-editor.org/rfc/rfc6585#page-3

Writing this piece of code fixed my problem:

requests.get(link, headers = {'User-agent': 'your bot 0.1'})

As MRA said, you shouldn't try to dodge a 429 Too Many Requests but instead handle it accordingly. You have several options depending on your use-case:

1) Sleep your process. The server usually includes a Retry-after header in the response with the number of seconds you are supposed to wait before retrying. Keep in mind that sleeping a process might cause problems, e.g. in a task queue, where you should instead retry the task at a later time to free up the worker for other things.

2) Exponential backoff. If the server does not tell you how long to wait, you can retry your request using increasing pauses in between. The popular task queue Celery has this feature built right-in.

3) Token bucket. This technique is useful if you know in advance how many requests you are able to make in a given time. Each time you access the API you first fetch a token from the bucket. The bucket is refilled at a constant rate. If the bucket is empty, you know you'll have to wait before hitting the API again. Token buckets are usually implemented on the other end (the API) but you can also use them as a proxy to avoid ever getting a 429 Too Many Requests. Celery's rate_limit feature uses a token bucket algorithm.

Here is an example of a Python/Celery app using exponential backoff and rate-limiting/token bucket:

class TooManyRequests(Exception):"""Too many requests"""@task(   rate_limit='10/s',   autoretry_for=(ConnectTimeout, TooManyRequests,),   retry_backoff=True)def api(*args, **kwargs):  r = requests.get('placeholder-external-api')  if r.status_code == 429:    raise TooManyRequests()