FastAPI Backend Development Bootcamp · 课时

速率限制与机器人滥用防护

使用 Redis 实现分布式速率限制和节流，吸收流量峰值并阻止滥用客户端。

速率限制与机器人滥用防护 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 课时。这是第 2 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 FastAPI Backend Development Bootcamp 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Why Distributed Rate Limiting

A single FastAPI worker holding a rate-limit counter in process memory breaks the moment you scale horizontally. With N Uvicorn workers behind a load balancer, an abusive client gets N times the allowance because each worker counts independently.

Goal: one shared counter that every worker and every pod sees.
Tool: Redis, an atomic in-memory store that all instances connect to.
Targets: absorb legitimate traffic spikes while throttling or blocking abusive bots.

Throughout this lesson we build the algorithms in plain Python first, then wire them into FastAPI dependencies.

The Fixed Window Counter

The simplest algorithm: divide time into fixed windows (e.g. 60-second buckets) and increment a counter per client per window. When the counter exceeds the limit, reject the request.

Below is a pure-Python simulation so you can see the mechanics without Redis. Notice the boundary problem: a client can send the full quota at the end of one window and again at the start of the next, briefly doubling the effective rate.

import time

class FixedWindow:
    def __init__(self, limit, window_seconds):
        self.limit = limit
        self.window = window_seconds
        self.buckets = {}

    def allow(self, key, now):
        win = int(now // self.window)
        bucket_key = (key, win)
        count = self.buckets.get(bucket_key, 0) + 1
        self.buckets[bucket_key] = count
        return count <= self.limit

limiter = FixedWindow(limit=3, window_seconds=60)
base = 1000.0
for i in range(5):
    ok = limiter.allow('user:42', base + i)
    print(f'request {i+1}: {"ALLOW" if ok else "BLOCK"}')

The Sliding Window Log

To kill the boundary problem we track a log of timestamps per client and count only the events that fall within the trailing window relative to now. This is exact but memory-heavy: one entry per request.

The pattern below is exactly what we will translate to a Redis sorted set, where the score is the timestamp.

import time
from collections import deque

class SlidingLog:
    def __init__(self, limit, window_seconds):
        self.limit = limit
        self.window = window_seconds
        self.logs = {}

    def allow(self, key, now):
        dq = self.logs.setdefault(key, deque())
        cutoff = now - self.window
        while dq and dq[0] <= cutoff:
            dq.popleft()
        if len(dq) < self.limit:
            dq.append(now)
            return True
        return False

limiter = SlidingLog(limit=2, window_seconds=10)
for t in [0, 1, 2, 11, 12]:
    ok = limiter.allow('ip:1.2.3.4', float(t))
    print(f't={t}s -> {"ALLOW" if ok else "BLOCK"}')

Token Bucket: Absorbing Spikes

Fixed and sliding windows are strict. To absorb bursts while enforcing a long-run average, the token bucket is the standard choice.

Each client owns a bucket with a capacity (max burst) and a refill rate (tokens per second).
Each request consumes one token; if the bucket is empty, the request is throttled.
A burst of up to capacity requests passes instantly, then traffic is smoothed to the refill rate.

This is the algorithm we recommend for public APIs facing spiky-but-legitimate traffic.

class TokenBucket:
    def __init__(self, capacity, refill_per_sec):
        self.capacity = capacity
        self.refill = refill_per_sec
        self.tokens = capacity
        self.last = 0.0

    def allow(self, now):
        elapsed = now - self.last
        self.tokens = min(self.capacity, self.tokens + elapsed * self.refill)
        self.last = now
        if self.tokens >= 1:
            self.tokens -= 1
            return True
        return False

bucket = TokenBucket(capacity=5, refill_per_sec=1)
for t in [0, 0, 0, 0, 0, 0, 3]:
    print(f't={t}: {"ALLOW" if bucket.allow(float(t)) else "THROTTLE"}')

Why Atomicity Matters

Across many workers the read-modify-write of a counter is a classic race condition: two workers read count=4, both increment, both write 5, and a request that should have been blocked slips through.

Redis solves this because each command is atomic, but a rate-limit decision usually needs several commands (increment, set expiry, compare). The robust solution is a Lua script executed by EVAL: Redis runs the whole script atomically, with no other client interleaving.

The next scenes show the Redis-backed fixed window and token bucket using this approach.

Redis Fixed Window with INCR + EXPIRE

The cheapest distributed limiter: a Redis key per client per window. INCR returns the new count atomically; on the first hit we set a TTL equal to the window so the key self-cleans.

We wrap both commands in a small Lua script so the increment and the expiry are one atomic unit. This is FastAPI-side glue code, not a standalone program.

import redis.asyncio as redis

FIXED_WINDOW_LUA = """
local current = redis.call('INCR', KEYS[1])
if current == 1 then
  redis.call('EXPIRE', KEYS[1], ARGV[1])
end
return current
"""

class RedisFixedWindow:
    def __init__(self, client, limit, window):
        self.client = client
        self.limit = limit
        self.window = window
        self.script = client.register_script(FIXED_WINDOW_LUA)

    async def allow(self, identifier: str) -> bool:
        bucket = int(__import__('time').time()) // self.window
        key = f'rl:fw:{identifier}:{bucket}'
        count = await self.script(keys=[key], args=[self.window])
        return count <= self.limit

# rl = RedisFixedWindow(redis.from_url('redis://localhost'), 100, 60)

Redis Token Bucket in Lua

For burst absorption we port the token bucket to a Lua script. State lives in a Redis hash holding tokens and ts (last refill time). The script refills based on elapsed time, tries to consume one token, and writes the state back, all atomically.

Returning 1 means allowed, 0 means throttled. We also set a TTL so idle clients free their memory.

TOKEN_BUCKET_LUA = """
local key = KEYS[1]
local capacity = tonumber(ARGV[1])
local refill = tonumber(ARGV[2])
local now = tonumber(ARGV[3])
local state = redis.call('HMGET', key, 'tokens', 'ts')
local tokens = tonumber(state[1]) or capacity
local ts = tonumber(state[2]) or now
local delta = math.max(0, now - ts)
tokens = math.min(capacity, tokens + delta * refill)
local allowed = 0
if tokens >= 1 then
  tokens = tokens - 1
  allowed = 1
end
redis.call('HMSET', key, 'tokens', tokens, 'ts', now)
redis.call('EXPIRE', key, math.ceil(capacity / refill) * 2)
return allowed
"""
# Invoked with EVAL via redis-py: script(keys=[key], args=[cap, refill, time.time()])

A Reusable FastAPI Dependency

We expose the limiter as a dependency so any route can opt in. The dependency derives the client identity, runs the limiter, and raises HTTPException(429) when the quota is exhausted.

Prefer an authenticated identity (API key, user id) over raw IP, because IPs are shared behind NAT and trivially rotated by bots. Fall back to IP only for anonymous traffic.

from fastapi import Request, HTTPException, Depends

def client_identity(request: Request) -> str:
    api_key = request.headers.get('x-api-key')
    if api_key:
        return f'key:{api_key}'
    return f'ip:{request.client.host}'

def rate_limit(limit: int, window: int):
    async def dependency(request: Request):
        ident = client_identity(request)
        limiter = request.app.state.limiter
        if not await limiter.allow(f'{ident}:{limit}:{window}'):
            raise HTTPException(status_code=429, detail='Rate limit exceeded')
    return dependency

# @app.get('/search', dependencies=[Depends(rate_limit(30, 60))])
# async def search(): ...

Honest 429s: Retry-After and Headers

A correct limiter is also a polite one. Well-behaved clients respect standard signals; sending them reduces retries and support tickets.

429 Too Many Requests is the only correct status; never use 403 or 503 for throttling.
Retry-After tells the client how many seconds to wait.
X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset let clients self-pace.

Have your Lua script also return the remaining count and reset time so you can populate these headers without extra round-trips.

from fastapi import Request, HTTPException

async def enforce(request: Request, limit: int, window: int):
    limiter = request.app.state.limiter
    allowed, remaining, reset_in = await limiter.check(client_identity(request))
    if not allowed:
        raise HTTPException(
            status_code=429,
            detail='Rate limit exceeded',
            headers={
                'Retry-After': str(reset_in),
                'X-RateLimit-Limit': str(limit),
                'X-RateLimit-Remaining': '0',
                'X-RateLimit-Reset': str(reset_in),
            },
        )

Tiered Limits and Abuse Escalation

One global limit is blunt. Production systems layer several:

Per-route limits: a cheap GET tolerates far more traffic than an expensive search or login endpoint.
Tiered identities: anonymous IPs get a tight quota, authenticated users more, paid plans the most.
Escalation: when a client repeatedly hits 429, write it to a Redis denylist with an exponential TTL so abusive bots get progressively longer bans.

The helper below computes a doubling ban duration capped at one hour.

def next_ban_seconds(strikes: int) -> int:
    base = 60  # 1 minute
    cap = 3600  # 1 hour
    return min(cap, base * (2 ** strikes))

for s in range(8):
    print(f'strike {s}: ban for {next_ban_seconds(s)}s')

Detecting Bots Beyond Counting

Rate limiting caps volume, but sophisticated bots stay just under the limit. Combine throttling with cheap behavioral signals:

Missing or junk headers: absent User-Agent, or a UA on a known bad list.
Login failure ratio: a high failed-to-successful auth ratio per IP signals credential stuffing.
Path entropy: rapid hits across many unrelated endpoints suggest scraping.
Proof of work / CAPTCHA as a gate when a score crosses a threshold, rather than an outright block.

Feed these into a per-client risk score in Redis and tighten the token-bucket capacity dynamically for high-risk clients.

Quick Check: Choosing the Algorithm

Your public FastAPI API runs across many Uvicorn workers and several pods. Legitimate clients sometimes send short legitimate bursts, but you must enforce a steady long-run average and keep the decision consistent across all instances. Which design fits best?

Recap and Production Checklist

You can now build distributed, abuse-resistant throttling for FastAPI:

Centralize state in Redis so every worker and pod shares one counter.
Pick the algorithm by need: fixed window for simple caps, sliding log for exactness, token bucket to absorb bursts with an enforced average.
Guarantee atomicity with Lua scripts via EVAL to avoid read-modify-write races.
Expose limiters as FastAPI dependencies keyed on authenticated identity first, IP as fallback.
Respond honestly with 429, Retry-After, and X-RateLimit-* headers.
Layer defenses: per-route and per-tier limits, exponential-backoff denylists, and behavioral bot scoring beyond raw counting.

Always fail open carefully: if Redis is unreachable, decide deliberately whether to allow or block, and alert on it.

常见问题解答

「速率限制与机器人滥用防护」课时是免费的吗？

是的 — 「速率限制与机器人滥用防护」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 FastAPI Backend Development Bootcamp 课程的其余内容，请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

「速率限制与机器人滥用防护」这节课中我会学到什么？

使用 Redis 实现分布式速率限制和节流，吸收流量峰值并阻止滥用客户端。你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

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无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 2 节课，共 4 节。

「速率限制与机器人滥用防护」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

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