ratehawk 0.1.8

A flexible API rate limiting library for Python

Ratehawk

Ratehawk is a flexible API rate limiting library for Python. It allows you to easily implement rate limiting in your applications to prevent abuse and ensure fair usage of your APIs.

Installation

You can install Ratehawk using pip:

pip install ratehawk

Usage

Here are some examples of how to use Ratehawk in your Python applications:

Basic Usage

from ratehawk import RateLimiter

# Create a rate limiter with a limit of 10 requests per minute
rate_limiter = RateLimiter(limits=[(10, 60)])

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Redis Storage

from ratehawk import RateLimiter
from ratehawk.storage.redis import RedisStorage
from redis import Redis

# Create a Redis client
redis_client = Redis(host='localhost', port=6379, db=0)

# Create a rate limiter with Redis storage
rate_limiter = RateLimiter(limits=[(10, 60)], storage=RedisStorage(redis_client))

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Postgres Storage

from ratehawk import RateLimiter
from ratehawk.storage.postgres import PostgresStorage

# Create a rate limiter with Postgres storage
rate_limiter = RateLimiter(limits=[(10, 60)], storage=PostgresStorage(dsn="postgresql://localhost/ratehawk"))

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using SQLite Storage

from ratehawk import RateLimiter
from ratehawk.storage.sqlite import SQLiteStorage

# Create a rate limiter with SQLite storage
rate_limiter = RateLimiter(limits=[(10, 60)], storage=SQLiteStorage(db_path="ratehawk.db"))

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using RateLimitMetrics

from ratehawk import RateLimiter
from ratehawk.monitoring.metrics import RateLimitMetrics

# Create a rate limiter with metrics monitoring
rate_limiter = RateLimiter(limits=[(10, 60)])
rate_limiter.metrics = RateLimitMetrics()

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using RateLimitLogger

from ratehawk import RateLimiter
from ratehawk.monitoring.logging import RateLimitLogger

# Create a rate limiter with logging
rate_limiter = RateLimiter(limits=[(10, 60)])
rate_limiter.logger = RateLimitLogger()

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using RateLimitEvent

from ratehawk import RateLimiter
from ratehawk.monitoring.events import RateLimitEvent, EventEmitter

# Create a rate limiter with event monitoring
rate_limiter = RateLimiter(limits=[(10, 60)])
rate_limiter.events = EventEmitter()

# Register event handlers
def on_limit_exceeded(key, retry_after):
    print(f"Rate limit exceeded for key: {key}, retry after: {retry_after}s")

def on_near_limit(key, current, limit):
    print(f"Near rate limit for key: {key}, current: {current}, limit: {limit}")

def on_reset(key):
    print(f"Rate limit reset for key: {key}")

rate_limiter.events.on(RateLimitEvent.LIMIT_EXCEEDED, on_limit_exceeded)
rate_limiter.events.on(RateLimitEvent.NEAR_LIMIT, on_near_limit)
rate_limiter.events.on(RateLimitEvent.RESET, on_reset)

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Multiple Rate Limits per Key

from ratehawk import RateLimiter

# Create a rate limiter with multiple rate limits per key
rate_limiter = RateLimiter(limits=[(10, 60), (100, 3600)])

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Rate Limit Groups and Hierarchies

from ratehawk import RateLimiter

# Create a rate limiter with rate limit groups and hierarchies
rate_limiter = RateLimiter(
    limits=[(10, 60)],
    burst_limits=[(20, 15)],
    quota_limits=[(1000, 86400)]
)

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Dynamic Rate Limits

from ratehawk import RateLimiter

# Function to dynamically determine rate limits based on user attributes or time of day
def dynamic_limits_func():
    # Example: Different rate limits for different user roles
    user_role = get_user_role()
    if user_role == "premium":
        return {"limits": [(100, 60)], "burst_limits": [(200, 15)], "quota_limits": [(5000, 86400)]}
    else:
        return {"limits": [(10, 60)], "burst_limits": [(20, 15)], "quota_limits": [(1000, 86400)]}

# Create a rate limiter with dynamic rate limits
rate_limiter = RateLimiter(
    limits=[(10, 60)],
    dynamic_limits_func=dynamic_limits_func
)

# Apply dynamic rate limits
await rate_limiter.apply_dynamic_limits()

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

Using Rate Limit Quotas

from ratehawk import RateLimiter

# Create a rate limiter with rate limit quotas over longer periods
rate_limiter = RateLimiter(
    limits=[(10, 60)],
    quota_limits=[(1000, 86400)]
)

# Check if the rate limit is exceeded
if await rate_limiter.check():
    print("Rate limit not exceeded")
else:
    print("Rate limit exceeded")

# Increment the rate limit counter
try:
    await rate_limiter.increment()
    print("Request allowed")
except RateLimitExceeded:
    print("Rate limit exceeded, try again later")

# Check if the quota limit is exceeded
if await rate_limiter.check_quota():
    print("Quota limit not exceeded")
else:
    print("Quota limit exceeded")

# Increment the quota limit counter
try:
    await rate_limiter.increment_quota()
    print("Request allowed")
except RateLimitExceeded:
    print("Quota limit exceeded, try again later")

Examples

For more detailed examples, please refer to the following files in the docs folder:

• Basic Example
• FastAPI Example
• Flask Example

License

This project is licensed under the MIT License. See the LICENSE file for more details.