Limitação de taxa e proteção contra abuso de bots
Implemente limitação de taxa e controle de fluxo distribuídos com Redis para absorver picos e bloquear clientes abusivos.
Limitação de taxa e proteção contra abuso de bots é uma aula grátis de FastAPI Backend Development Bootcamp no CoddyKit. Esta é a aula 2 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de FastAPI Backend Development Bootcamp, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de FastAPI Backend Development Bootcamp inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
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
capacityrequests 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-Aftertells the client how many seconds to wait.X-RateLimit-Limit,X-RateLimit-Remaining, andX-RateLimit-Resetlet 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
GETtolerates 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
EVALto avoid read-modify-write races. - Expose limiters as FastAPI dependencies keyed on authenticated identity first, IP as fallback.
- Respond honestly with 429,
Retry-After, andX-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.
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Implemente limitação de taxa e controle de fluxo distribuídos com Redis para absorver picos e bloquear clientes abusivos. Você pratica FastAPI Backend Development Bootcamp com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
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Todas as aulas deste curso
- Mitigação dos 10 principais riscos de segurança de APIs da OWASP
- Limitação de taxa e proteção contra abuso de bots
- Gerenciamento de segredos e rotação de chaves
- CORS, CSP e políticas de cabeçalhos seguros