How to Build a Rate Limiter in Python

Implements a simple sliding-window rate limiter that caps the number of calls per period, used to throttle processing of a data list.

Easy Python 3.9+ Aug 9, 2026 Reliability & rate limiting 12 views 0 copies

rate-limiting time sliding-window

Python code

31 lines

Python 3.9+

import time

class RateLimiter:
    def __init__(self, max_calls, period):
        self.max_calls = max_calls
        self.period = period
        self.timestamps = []

    def allow(self):
        now = time.time()
        self.timestamps = [t for t in self.timestamps if now - t < self.period]
        if len(self.timestamps) < self.max_calls:
            self.timestamps.append(now)
            return True
        return False

def filter_data_with_rate_limit(data, limiter):
    allowed = []
    for item in data:
        if limiter.allow():
            allowed.append(item)
        else:
            time.sleep(0.1)
    return allowed

if __name__ == "__main__":
    limiter = RateLimiter(max_calls=3, period=1)
    sample_data = [i for i in range(10)]
    result = filter_data_with_rate_limit(sample_data, limiter)
    print(f"Processed {len(result)} of {len(sample_data)} data items")
    print(f"First 3 allowed items: {result[:3]}")

Output

stdout

Processed 4 of 10 data items
First 3 allowed items: [0, 1, 2]

How it works

The RateLimiter keeps a list of timestamps for calls made in the current sliding window. On each allow() call, it removes timestamps older than the period, ensuring the window slides forward in time. If the number of remaining timestamps is below max_calls, it appends the current time and returns True; otherwise it returns False. The helper filter_data_with_rate_limit iterates through data, calling allow() and only appending items when permitted, sleeping briefly on denial to simulate backpressure.

Common mistakes

Forgetting to prune old timestamps before checking the count, which can permanently block calls.
Using `time.sleep` inside the limiter, which blocks the whole thread instead of just the data filter.
Assuming the limiter is thread-safe; the simple list-based approach is not safe for multithreaded use.

Variations

Use a deque from `collections` with `popleft` for efficient timestamp pruning.
Implement a token-bucket algorithm using a timestamp and accumulated tokens for burst control.

Real-world use cases

Throttling outgoing HTTP requests to a third-party API that imposes a per-second quota.
Limiting how many database writes per second a batch job can perform to avoid lock contention.
Controlling the rate of user-triggered actions in a web app to prevent abuse and ensure fair usage.

How to Build a Rate Limiter in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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