requests-ratelimiter

Requests-Ratelimiter

This package is a simple wrapper around pyrate-limiter v2 that adds convenient integration with the requests library.

Full project documentation can be found at requests-ratelimiter.readthedocs.io.

Features

• pyrate-limiter is a general-purpose rate-limiting library that implements the leaky bucket algorithm, supports multiple rate limits, and has optional persistence with SQLite and Redis backends
• requests-ratelimiter adds some conveniences for sending rate-limited HTTP requests with the requests library
• It can be used as either a session or a transport adapter
• It can also be used as a mixin, for compatibility with other requests-based libraries
• Rate limits are tracked separately per host
• Different rate limits can optionally be applied to different hosts

Installation

pip install requests-ratelimiter

Usage

Usage Options

There are three ways to use requests-ratelimiter:

Session

The simplest option is LimiterSession, which can be used as a drop-in replacement for requests.Session.

Note: By default, each session will perform rate limiting independently. If you are using a multi-threaded environment or multiple processes, you should use a persistent backend like SQLite or Redis which can persist the rate limit across threads, processes, and/or application restarts. When using requests-ratelimiter as part of a web application, it is recommended to use a persistent backend to ensure that the rate limit is shared across all requests.

Example:

from requests_ratelimiter import LimiterSession
from time import time

# Apply a rate limit of 5 requests per second to all requests
session = LimiterSession(per_second=5)
start = time()

# Send requests that stay within the defined rate limit
for i in range(20):
    response = session.get('https://httpbin.org/get')
    print(f'[t+{time()-start:.2f}] Sent request {i+1}')

Example output:

[t+0.22] Sent request 1
[t+0.26] Sent request 2
[t+0.30] Sent request 3
[t+0.34] Sent request 4
[t+0.39] Sent request 5
[t+1.24] Sent request 6
[t+1.28] Sent request 7
[t+1.32] Sent request 8
[t+1.37] Sent request 9
[t+1.41] Sent request 10
[t+2.04] Sent request 11
...

Adapter

For more advanced usage, LimiterAdapter is available to be used as a transport adapter.

Example:

from requests import Session
from requests_ratelimiter import LimiterAdapter

session = Session()

# Apply a rate-limit (5 requests per second) to all requests
adapter = LimiterAdapter(per_second=5)
session.mount('http://', adapter)
session.mount('https://', adapter)

# Send rate-limited requests
for user_id in range(100):
    response = session.get(f'https://api.some_site.com/v1/users/{user_id}')
    print(response.json())

Mixin

Finally, LimiterMixin is available for advanced use cases in which you want add rate-limiting features to a custom session or adapter class. See Custom Session Example below for an example.

Rate Limit Settings

Basic Settings

The following parameters are available for the most common rate limit intervals:

• per_second: Max requests per second
• per_minute: Max requests per minute
• per_hour: Max requests per hour
• per_day: Max requests per day
• per_month: Max requests per month
• burst: Max number of consecutive requests allowed before applying per-second rate-limiting

Advanced Settings

If you need to define more complex rate limits, you can create a Limiter object instead:

from pyrate_limiter import Duration, RequestRate, Limiter
from requests_ratelimiter import LimiterSession

nanocentury_rate = RequestRate(10, Duration.SECOND * 3.156)
fortnight_rate = RequestRate(1000, Duration.DAY * 14)
trimonthly_rate = RequestRate(10000, Duration.MONTH * 3)
limiter = Limiter(nanocentury_rate, fortnight_rate, trimonthly_rate)

session = LimiterSession(limiter=limiter)

See pyrate-limiter docs for more Limiter usage details.

Backends

By default, rate limits are tracked in memory and are not persistent. You can optionally use either SQLite or Redis to persist rate limits across threads, processes, and/or application restarts. You can specify which backend to use with the bucket_class argument. For example, to use SQLite:

from pyrate_limiter import SQLiteBucket
from requests_ratelimiter import LimiterSession

session = LimiterSession(per_second=5, bucket_class=SQLiteBucket)

See pyrate-limiter docs for more details.

Other Features

Per-Host Rate Limit Tracking

With either LimiterSession or LimiterAdapter, rate limits are tracked separately for each host. In other words, requests sent to one host will not count against the rate limit for any other hosts:

session = LimiterSession(per_second=5)

# Make requests for two different hosts
for _ in range(10):
    response = session.get(f'https://httpbin.org/get')
    print(response.json())
    session.get(f'https://httpbingo.org/get')
    print(response.json())

If you have a case where multiple hosts share the same rate limit, you can disable this behavior with the per_host option:

session = LimiterSession(per_second=5, per_host=False)

Per-Host Rate Limit Definitions

With LimiterAdapter, you can apply different rate limits to different hosts or URLs:

# Apply a different set of rate limits (2/second and 100/minute) to a specific host
adapter_2 = LimiterAdapter(per_second=2, per_minute=100)
session.mount('https://api.some_site.com', adapter_2)

Behavior for matching requests is the same as other transport adapters: requests will use the adapter with the most specific (i.e., longest) URL prefix that matches a given request. For example:

session.mount('https://api.some_site.com/v1', adapter_3)
session.mount('https://api.some_site.com/v1/users', adapter_4)

# This request will use adapter_3
session.get('https://api.some_site.com/v1/')

# This request will use adapter_4
session.get('https://api.some_site.com/v1/users/1234')

Custom Tracking

For advanced use cases, you can define your own custom tracking behavior with the bucket option. For example, an API that enforces rate limits based on a tenant ID, this feature can be used to track rate limits per tenant. If bucket is specified, host tracking is disabled.

Note: It is advisable to use SQLite or Redis backends when using custom tracking because using the default backend each session will track rate limits independently, even if both sessions call the same URL.

sessionA = LimiterSession(per_second=5, bucket='tenant1')
sessionB = LimiterSession(per_second=5, bucket='tenant2')

Rate Limit Error Handling

Sometimes, server-side rate limiting may not behave exactly as documented (or may not be documented at all). Or you might encounter other scenarios where your client-side limit gets out of sync with the server-side limit. Typically, a server will send a 429: Too Many Requests response for an exceeded rate limit.

When this happens, requests-ratelimiter will adjust its request log in an attempt to catch up to the server-side limit. If a server sends a different status code other than 429 to indicate an exceeded limit, you can set this with limit_statuses:

session = LimiterSession(per_second=5, limit_statuses=[429, 500])

Or if you would prefer to disable this behavior and handle it yourself:

session = LimiterSession(per_second=5, limit_statuses=[])

Compatibility

There are many other useful libraries out there that add features to requests, most commonly by extending or modifying requests.Session or requests.HTTPAdapter.

To use requests-ratelimiter with one of these libraries, you have a few different options:

1. If the library provides a custom Session class, mount a LimiterAdapter on it
2. Or use LimiterMixin to create a custom Session class with features from both libraries
3. If the library provides a custom Adapter class, use LimiterMixin to create a custom Adapter class with features from both libraries

Custom Session Example: Requests-Cache

For example, to combine with requests-cache, which also includes a separate mixin class:

from requests import Session
from requests_cache import CacheMixin
from requests_ratelimiter import LimiterMixin, SQLiteBucket


class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
    """
    Session class with caching and rate-limiting behavior. Accepts arguments for both
    LimiterSession and CachedSession.
    """


# Optionally use SQLite as both the bucket backend and the cache backend
session = CachedLimiterSession(
    per_second=5,
    cache_name='cache.db',
    bucket_class=SQLiteBucket,
    bucket_kwargs={
        "path": "cache.db",
        'isolation_level': "EXCLUSIVE",
        'check_same_thread': False,
    },
)

This example has an extra benefit: cache hits won't count against your rate limit!