zulip/zerver/lib/rate_limiter.py

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import logging
import time
from abc import ABC, abstractmethod
from typing import Optional, cast
import orjson
import redis
from circuitbreaker import CircuitBreakerError, circuit
from django.conf import settings
from django.http import HttpRequest
from typing_extensions import override
from zerver.lib import redis_utils
from zerver.lib.cache import cache_with_key
from zerver.lib.exceptions import RateLimitedError
from zerver.lib.redis_utils import get_redis_client
from zerver.models import UserProfile
# Implement a rate-limiting scheme inspired by the one described here, but heavily modified
# https://www.domaintools.com/resources/blog/rate-limiting-with-redis
client = get_redis_client()
rules: dict[str, list[tuple[int, int]]] = settings.RATE_LIMITING_RULES
logger = logging.getLogger(__name__)
class RateLimiterLockingError(Exception):
pass
class RateLimitedObject(ABC):
def __init__(self, backend: Optional["type[RateLimiterBackend]"] = None) -> None:
if backend is not None:
self.backend: type[RateLimiterBackend] = backend
else:
self.backend = RedisRateLimiterBackend
def rate_limit(self) -> tuple[bool, float]:
# Returns (ratelimited, secs_to_freedom)
return self.backend.rate_limit_entity(
self.key(), self.get_rules(), self.max_api_calls(), self.max_api_window()
)
def rate_limit_request(self, request: HttpRequest) -> None:
from zerver.lib.request import RequestNotes
ratelimited, time = self.rate_limit()
request_notes = RequestNotes.get_notes(request)
request_notes.ratelimits_applied.append(
RateLimitResult(
entity=self,
secs_to_freedom=time,
remaining=0,
over_limit=ratelimited,
)
)
# Abort this request if the user is over their rate limits
if ratelimited:
# Pass information about what kind of entity got limited in the exception:
raise RateLimitedError(time)
calls_remaining, seconds_until_reset = self.api_calls_left()
request_notes.ratelimits_applied[-1].remaining = calls_remaining
request_notes.ratelimits_applied[-1].secs_to_freedom = seconds_until_reset
def block_access(self, seconds: int) -> None:
"""Manually blocks an entity for the desired number of seconds"""
self.backend.block_access(self.key(), seconds)
def unblock_access(self) -> None:
self.backend.unblock_access(self.key())
def clear_history(self) -> None:
self.backend.clear_history(self.key())
def max_api_calls(self) -> int:
"""Returns the API rate limit for the highest limit"""
return self.get_rules()[-1][1]
def max_api_window(self) -> int:
"""Returns the API time window for the highest limit"""
return self.get_rules()[-1][0]
def api_calls_left(self) -> tuple[int, float]:
"""Returns how many API calls in this range this client has, as well as when
the rate-limit will be reset to 0"""
max_window = self.max_api_window()
max_calls = self.max_api_calls()
return self.backend.get_api_calls_left(self.key(), max_window, max_calls)
def get_rules(self) -> list[tuple[int, int]]:
"""
This is a simple wrapper meant to protect against having to deal with
an empty list of rules, as it would require fiddling with that special case
all around this system. "9999 max request per seconds" should be a good proxy
for "no rules".
"""
rules_list = self.rules()
return rules_list or [(1, 9999)]
@abstractmethod
def key(self) -> str:
pass
@abstractmethod
def rules(self) -> list[tuple[int, int]]:
pass
class RateLimitedUser(RateLimitedObject):
def __init__(self, user: UserProfile, domain: str = "api_by_user") -> None:
self.user_id = user.id
self.rate_limits = user.rate_limits
self.domain = domain
if settings.RUNNING_INSIDE_TORNADO and domain in settings.RATE_LIMITING_DOMAINS_FOR_TORNADO:
backend: type[RateLimiterBackend] | None = TornadoInMemoryRateLimiterBackend
else:
backend = None
super().__init__(backend=backend)
@override
def key(self) -> str:
return f"{type(self).__name__}:{self.user_id}:{self.domain}"
@override
def rules(self) -> list[tuple[int, int]]:
# user.rate_limits are general limits, applicable to the domain 'api_by_user'
if self.rate_limits != "" and self.domain == "api_by_user":
result: list[tuple[int, int]] = []
for limit in self.rate_limits.split(","):
(seconds, requests) = limit.split(":", 2)
result.append((int(seconds), int(requests)))
return result
return rules[self.domain]
class RateLimitedIPAddr(RateLimitedObject):
def __init__(self, ip_addr: str, domain: str = "api_by_ip") -> None:
self.ip_addr = ip_addr
self.domain = domain
if settings.RUNNING_INSIDE_TORNADO and domain in settings.RATE_LIMITING_DOMAINS_FOR_TORNADO:
backend: type[RateLimiterBackend] | None = TornadoInMemoryRateLimiterBackend
else:
backend = None
super().__init__(backend=backend)
@override
def key(self) -> str:
# The angle brackets are important since IPv6 addresses contain :.
return f"{type(self).__name__}:<{self.ip_addr}>:{self.domain}"
@override
def rules(self) -> list[tuple[int, int]]:
return rules[self.domain]
class RateLimiterBackend(ABC):
@classmethod
@abstractmethod
def block_access(cls, entity_key: str, seconds: int) -> None:
"""Manually blocks an entity for the desired number of seconds"""
@classmethod
@abstractmethod
def unblock_access(cls, entity_key: str) -> None:
pass
@classmethod
@abstractmethod
def clear_history(cls, entity_key: str) -> None:
pass
@classmethod
@abstractmethod
def get_api_calls_left(
cls, entity_key: str, range_seconds: int, max_calls: int
) -> tuple[int, float]:
pass
@classmethod
@abstractmethod
def rate_limit_entity(
cls, entity_key: str, rules: list[tuple[int, int]], max_api_calls: int, max_api_window: int
) -> tuple[bool, float]:
# Returns (ratelimited, secs_to_freedom)
pass
class TornadoInMemoryRateLimiterBackend(RateLimiterBackend):
# reset_times[rule][key] is the time at which the event
# request from the rate-limited key will be accepted.
reset_times: dict[tuple[int, int], dict[str, float]] = {}
# last_gc_time is the last time when the garbage was
# collected from reset_times for rule (time_window, max_count).
last_gc_time: dict[tuple[int, int], float] = {}
# timestamps_blocked_until[key] contains the timestamp
# up to which the key has been blocked manually.
timestamps_blocked_until: dict[str, float] = {}
@classmethod
def _garbage_collect_for_rule(cls, now: float, time_window: int, max_count: int) -> None:
keys_to_delete = []
reset_times_for_rule = cls.reset_times.get((time_window, max_count), None)
if reset_times_for_rule is None:
return
keys_to_delete = [
entity_key
for entity_key in reset_times_for_rule
if reset_times_for_rule[entity_key] < now
]
for entity_key in keys_to_delete:
del reset_times_for_rule[entity_key]
if not reset_times_for_rule:
del cls.reset_times[(time_window, max_count)]
@classmethod
def need_to_limit(cls, entity_key: str, time_window: int, max_count: int) -> tuple[bool, float]:
"""
Returns a tuple of `(rate_limited, time_till_free)`.
For simplicity, we have loosened the semantics here from
- each key may make at most `count * (t / window)` request within any t
time interval.
to
- each key may make at most `count * [(t / window) + 1]` request within
any t time interval.
Thus, we only need to store reset_times for each key which will be less
memory-intensive. This also has the advantage that you can only ever
lock yourself out completely for `window / count` seconds instead of
`window` seconds.
"""
now = time.time()
# Remove all timestamps from `reset_times` that are too old.
if cls.last_gc_time.get((time_window, max_count), 0) <= now - time_window / max_count:
cls.last_gc_time[(time_window, max_count)] = now
cls._garbage_collect_for_rule(now, time_window, max_count)
reset_times_for_rule = cls.reset_times.setdefault((time_window, max_count), {})
new_reset = max(reset_times_for_rule.get(entity_key, now), now) + time_window / max_count
if new_reset > now + time_window:
# Compute for how long the bucket will remain filled.
time_till_free = new_reset - time_window - now
return True, time_till_free
reset_times_for_rule[entity_key] = new_reset
return False, 0.0
@classmethod
@override
def get_api_calls_left(
cls, entity_key: str, range_seconds: int, max_calls: int
) -> tuple[int, float]:
now = time.time()
if (range_seconds, max_calls) in cls.reset_times and entity_key in cls.reset_times[
(range_seconds, max_calls)
]:
reset_time = cls.reset_times[(range_seconds, max_calls)][entity_key]
else:
return max_calls, 0
calls_remaining = (now + range_seconds - reset_time) * max_calls // range_seconds
return int(calls_remaining), reset_time - now
@classmethod
@override
def block_access(cls, entity_key: str, seconds: int) -> None:
now = time.time()
cls.timestamps_blocked_until[entity_key] = now + seconds
@classmethod
@override
def unblock_access(cls, entity_key: str) -> None:
del cls.timestamps_blocked_until[entity_key]
@classmethod
@override
def clear_history(cls, entity_key: str) -> None:
for reset_times_for_rule in cls.reset_times.values():
reset_times_for_rule.pop(entity_key, None)
cls.timestamps_blocked_until.pop(entity_key, None)
@classmethod
@override
def rate_limit_entity(
cls, entity_key: str, rules: list[tuple[int, int]], max_api_calls: int, max_api_window: int
) -> tuple[bool, float]:
now = time.time()
if entity_key in cls.timestamps_blocked_until:
# Check whether the key is manually blocked.
if now < cls.timestamps_blocked_until[entity_key]:
blocking_ttl = cls.timestamps_blocked_until[entity_key] - now
return True, blocking_ttl
else:
del cls.timestamps_blocked_until[entity_key]
assert rules
for time_window, max_count in rules:
ratelimited, time_till_free = cls.need_to_limit(entity_key, time_window, max_count)
if ratelimited:
break
return ratelimited, time_till_free
class RedisRateLimiterBackend(RateLimiterBackend):
@classmethod
def get_keys(cls, entity_key: str) -> list[str]:
return [
f"{redis_utils.REDIS_KEY_PREFIX}ratelimit:{entity_key}:{keytype}"
for keytype in ["list", "zset", "block"]
]
@classmethod
@override
def block_access(cls, entity_key: str, seconds: int) -> None:
"""Manually blocks an entity for the desired number of seconds"""
_, _, blocking_key = cls.get_keys(entity_key)
with client.pipeline() as pipe:
pipe.set(blocking_key, 1)
pipe.expire(blocking_key, seconds)
pipe.execute()
@classmethod
@override
def unblock_access(cls, entity_key: str) -> None:
_, _, blocking_key = cls.get_keys(entity_key)
client.delete(blocking_key)
@classmethod
@override
def clear_history(cls, entity_key: str) -> None:
for key in cls.get_keys(entity_key):
client.delete(key)
@classmethod
@override
def get_api_calls_left(
cls, entity_key: str, range_seconds: int, max_calls: int
) -> tuple[int, float]:
list_key, set_key, _ = cls.get_keys(entity_key)
# Count the number of values in our sorted set
# that are between now and the cutoff
now = time.time()
boundary = now - range_seconds
with client.pipeline() as pipe:
# Count how many API calls in our range have already been made
pipe.zcount(set_key, boundary, now)
# Get the newest call so we can calculate when the ratelimit
# will reset to 0
pipe.lindex(list_key, 0)
results = pipe.execute()
count: int = results[0]
newest_call: bytes | None = results[1]
calls_left = max_calls - count
if newest_call is not None:
time_reset = now + (range_seconds - (now - float(newest_call)))
else:
time_reset = now
return calls_left, time_reset - now
@classmethod
def is_ratelimited(cls, entity_key: str, rules: list[tuple[int, int]]) -> tuple[bool, float]:
"""Returns a tuple of (rate_limited, time_till_free)"""
assert rules
list_key, set_key, blocking_key = cls.get_keys(entity_key)
# Go through the rules from shortest to longest,
# seeing if this user has violated any of them. First
# get the timestamps for each nth items
with client.pipeline() as pipe:
for _, request_count in rules:
pipe.lindex(list_key, request_count - 1) # 0-indexed list
# Get blocking info
pipe.get(blocking_key)
pipe.ttl(blocking_key)
rule_timestamps: list[bytes | None] = pipe.execute()
# Check if there is a manual block on this API key
blocking_ttl_b = rule_timestamps.pop()
key_blocked = rule_timestamps.pop()
if key_blocked is not None:
# We are manually blocked. Report for how much longer we will be
if blocking_ttl_b is None: # nocoverage # defensive code, this should never happen
blocking_ttl = 0.5
else:
blocking_ttl = int(blocking_ttl_b)
return True, blocking_ttl
now = time.time()
for timestamp, (range_seconds, num_requests) in zip(rule_timestamps, rules, strict=False):
# Check if the nth timestamp is newer than the associated rule. If so,
# it means we've hit our limit for this rule
if timestamp is None:
continue
boundary = float(timestamp) + range_seconds
if boundary >= now:
free = boundary - now
return True, free
return False, 0.0
@classmethod
def incr_ratelimit(cls, entity_key: str, max_api_calls: int, max_api_window: int) -> None:
"""Increases the rate-limit for the specified entity"""
list_key, set_key, _ = cls.get_keys(entity_key)
now = time.time()
# Start Redis transaction
with client.pipeline() as pipe:
count = 0
while True:
try:
# To avoid a race condition between getting the element we might trim from our list
# and removing it from our associated set, we abort this whole transaction if
# another agent manages to change our list out from under us
# When watching a value, the pipeline is set to Immediate mode
pipe.watch(list_key)
# Get the last elem that we'll trim (so we can remove it from our sorted set)
last_val = cast( # mypy doesnt know the pipe is in immediate mode
bytes | None, pipe.lindex(list_key, max_api_calls - 1)
)
# Restart buffered execution
pipe.multi()
# Add this timestamp to our list
pipe.lpush(list_key, now)
# Trim our list to the oldest rule we have
pipe.ltrim(list_key, 0, max_api_calls - 1)
# Add our new value to the sorted set that we keep
# We need to put the score and val both as timestamp,
# as we sort by score but remove by value
pipe.zadd(set_key, {str(now): now})
# Remove the trimmed value from our sorted set, if there was one
if last_val is not None:
pipe.zrem(set_key, last_val)
# Set the TTL for our keys as well
api_window = max_api_window
pipe.expire(list_key, api_window)
pipe.expire(set_key, api_window)
pipe.execute()
# If no exception was raised in the execution, there were no transaction conflicts
break
except redis.WatchError: # nocoverage # Ideally we'd have a test for this.
if count > 10:
raise RateLimiterLockingError
count += 1
continue
@classmethod
@override
def rate_limit_entity(
cls, entity_key: str, rules: list[tuple[int, int]], max_api_calls: int, max_api_window: int
) -> tuple[bool, float]:
ratelimited, time = cls.is_ratelimited(entity_key, rules)
if not ratelimited:
try:
cls.incr_ratelimit(entity_key, max_api_calls, max_api_window)
except RateLimiterLockingError:
logger.warning("Deadlock trying to incr_ratelimit for %s", entity_key)
# rate-limit users who are hitting the API so hard we can't update our stats.
ratelimited = True
return ratelimited, time
class RateLimitResult:
def __init__(
self, entity: RateLimitedObject, secs_to_freedom: float, over_limit: bool, remaining: int
) -> None:
if over_limit:
assert not remaining
self.entity = entity
self.secs_to_freedom = secs_to_freedom
self.over_limit = over_limit
self.remaining = remaining
class RateLimitedSpectatorAttachmentAccessByFile(RateLimitedObject):
def __init__(self, path_id: str) -> None:
self.path_id = path_id
super().__init__()
@override
def key(self) -> str:
return f"{type(self).__name__}:{self.path_id}"
@override
def rules(self) -> list[tuple[int, int]]:
return settings.RATE_LIMITING_RULES["spectator_attachment_access_by_file"]
def rate_limit_spectator_attachment_access_by_file(path_id: str) -> None:
ratelimited, _ = RateLimitedSpectatorAttachmentAccessByFile(path_id).rate_limit()
if ratelimited:
raise RateLimitedError
def is_local_addr(addr: str) -> bool:
return addr in ("127.0.0.1", "::1")
@cache_with_key(lambda: "tor_ip_addresses:", timeout=60 * 60)
@circuit(failure_threshold=2, recovery_timeout=60 * 10)
def get_tor_ips() -> set[str]:
if not settings.RATE_LIMIT_TOR_TOGETHER:
return set()
# Cron job in /etc/cron.d/fetch-tor-exit-nodes fetches this
# hourly; we cache it in memcached to prevent going to disk on
# every unauth'd request. In case of failures to read, we
# circuit-break so 2 failures cause a 10-minute backoff.
with open(settings.TOR_EXIT_NODE_FILE_PATH, "rb") as f:
exit_node_list = orjson.loads(f.read())
# This should always be non-empty; if it's empty, assume something
# went wrong with writing and treat it as a non-existent file.
# Circuit-breaking will ensure that we back off on re-reading the
# file.
if len(exit_node_list) == 0:
raise OSError("File is empty")
return set(exit_node_list)
def client_is_exempt_from_rate_limiting(request: HttpRequest) -> bool:
from zerver.lib.request import RequestNotes
# Don't rate limit requests from Django that come from our own servers,
# and don't rate-limit dev instances
client = RequestNotes.get_notes(request).client
return (client is not None and client.name.lower() == "internal") and (
is_local_addr(request.META["REMOTE_ADDR"]) or settings.DEBUG_RATE_LIMITING
)
def rate_limit_user(request: HttpRequest, user: UserProfile, domain: str) -> None:
"""Returns whether or not a user was rate limited. Will raise a RateLimitedError exception
if the user has been rate limited, otherwise returns and modifies request to contain
the rate limit information"""
if not should_rate_limit(request):
return
RateLimitedUser(user, domain=domain).rate_limit_request(request)
def rate_limit_request_by_ip(request: HttpRequest, domain: str) -> None:
if not should_rate_limit(request):
return
# REMOTE_ADDR is set by SetRemoteAddrFromRealIpHeader in conjunction
# with the nginx configuration to guarantee this to be *the* correct
# IP address to use - without worrying we'll grab the IP of a proxy.
ip_addr = request.META["REMOTE_ADDR"]
assert ip_addr
try:
# We lump all TOR exit nodes into one bucket; this prevents
# abuse from TOR, while still allowing some access to these
# endpoints for legitimate users. Checking for local
# addresses is a shortcut somewhat for ease of testing without
# mocking the TOR endpoint in every test.
if is_local_addr(ip_addr):
pass
elif ip_addr in get_tor_ips():
ip_addr = "tor-exit-node"
except (OSError, CircuitBreakerError) as err:
# In the event that we can't get an updated list of TOR exit
# nodes, assume the IP is _not_ one, and leave it unchanged.
# We log a warning so that this endpoint being taken out of
# service doesn't silently remove this functionality.
logger.warning("Failed to fetch TOR exit node list: %s", err)
RateLimitedIPAddr(ip_addr, domain=domain).rate_limit_request(request)
def should_rate_limit(request: HttpRequest) -> bool:
if not settings.RATE_LIMITING:
return False
if client_is_exempt_from_rate_limiting(request):
return False
return True