mirror of https://github.com/zulip/zulip.git
264 lines
8.9 KiB
Python
264 lines
8.9 KiB
Python
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import os
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from typing import List, Optional, Tuple
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from django.conf import settings
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from zerver.lib.redis_utils import get_redis_client
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from zerver.lib.utils import statsd
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from zerver.models import UserProfile
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import logging
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import redis
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import time
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# Implement a rate-limiting scheme inspired by the one described here, but heavily modified
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# http://blog.domaintools.com/2013/04/rate-limiting-with-redis/
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client = get_redis_client()
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rules = settings.RATE_LIMITING_RULES # type: List[Tuple[int, int]]
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KEY_PREFIX = ''
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logger = logging.getLogger(__name__)
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class RateLimiterLockingException(Exception):
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pass
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class RateLimitedObject:
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def get_keys(self) -> List[str]:
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key_fragment = self.key_fragment()
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return ["{}ratelimit:{}:{}".format(KEY_PREFIX, key_fragment, keytype)
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for keytype in ['list', 'zset', 'block']]
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def key_fragment(self) -> str:
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raise NotImplementedError()
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def rules(self) -> List[Tuple[int, int]]:
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raise NotImplementedError()
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def __str__(self) -> str:
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raise NotImplementedError()
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class RateLimitedUser(RateLimitedObject):
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def __init__(self, user: UserProfile, domain: str='all') -> None:
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self.user = user
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self.domain = domain
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def __str__(self) -> str:
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return "Id: {}".format(self.user.id)
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def key_fragment(self) -> str:
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return "{}:{}:{}".format(type(self.user), self.user.id, self.domain)
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def rules(self) -> List[Tuple[int, int]]:
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if self.user.rate_limits != "":
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result = [] # type: List[Tuple[int, int]]
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for limit in self.user.rate_limits.split(','):
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(seconds, requests) = limit.split(':', 2)
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result.append((int(seconds), int(requests)))
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return result
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return rules
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def bounce_redis_key_prefix_for_testing(test_name: str) -> None:
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global KEY_PREFIX
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KEY_PREFIX = test_name + ':' + str(os.getpid()) + ':'
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def max_api_calls(entity: RateLimitedObject) -> int:
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"Returns the API rate limit for the highest limit"
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return entity.rules()[-1][1]
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def max_api_window(entity: RateLimitedObject) -> int:
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"Returns the API time window for the highest limit"
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return entity.rules()[-1][0]
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def add_ratelimit_rule(range_seconds: int, num_requests: int) -> None:
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"Add a rate-limiting rule to the ratelimiter"
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global rules
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rules.append((range_seconds, num_requests))
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rules.sort(key=lambda x: x[0])
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def remove_ratelimit_rule(range_seconds: int, num_requests: int) -> None:
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global rules
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rules = [x for x in rules if x[0] != range_seconds and x[1] != num_requests]
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def block_access(entity: RateLimitedObject, seconds: int) -> None:
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"Manually blocks an entity for the desired number of seconds"
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_, _, blocking_key = entity.get_keys()
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with client.pipeline() as pipe:
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pipe.set(blocking_key, 1)
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pipe.expire(blocking_key, seconds)
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pipe.execute()
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def unblock_access(entity: RateLimitedObject) -> None:
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_, _, blocking_key = entity.get_keys()
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client.delete(blocking_key)
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def clear_history(entity: RateLimitedObject) -> None:
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'''
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This is only used by test code now, where it's very helpful in
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allowing us to run tests quickly, by giving a user a clean slate.
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'''
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for key in entity.get_keys():
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client.delete(key)
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def _get_api_calls_left(entity: RateLimitedObject, range_seconds: int, max_calls: int) -> Tuple[int, float]:
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list_key, set_key, _ = entity.get_keys()
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# Count the number of values in our sorted set
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# that are between now and the cutoff
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now = time.time()
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boundary = now - range_seconds
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with client.pipeline() as pipe:
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# Count how many API calls in our range have already been made
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pipe.zcount(set_key, boundary, now)
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# Get the newest call so we can calculate when the ratelimit
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# will reset to 0
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pipe.lindex(list_key, 0)
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results = pipe.execute()
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count = results[0] # type: int
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newest_call = results[1] # type: Optional[bytes]
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calls_left = max_calls - count
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if newest_call is not None:
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time_reset = now + (range_seconds - (now - float(newest_call)))
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else:
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time_reset = now
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return calls_left, time_reset
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def api_calls_left(entity: RateLimitedObject) -> Tuple[int, float]:
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"""Returns how many API calls in this range this client has, as well as when
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the rate-limit will be reset to 0"""
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max_window = max_api_window(entity)
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max_calls = max_api_calls(entity)
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return _get_api_calls_left(entity, max_window, max_calls)
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def is_ratelimited(entity: RateLimitedObject) -> Tuple[bool, float]:
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"Returns a tuple of (rate_limited, time_till_free)"
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list_key, set_key, blocking_key = entity.get_keys()
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rules = entity.rules()
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if len(rules) == 0:
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return False, 0.0
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# Go through the rules from shortest to longest,
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# seeing if this user has violated any of them. First
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# get the timestamps for each nth items
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with client.pipeline() as pipe:
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for _, request_count in rules:
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pipe.lindex(list_key, request_count - 1) # 0-indexed list
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# Get blocking info
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pipe.get(blocking_key)
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pipe.ttl(blocking_key)
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rule_timestamps = pipe.execute() # type: List[Optional[bytes]]
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# Check if there is a manual block on this API key
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blocking_ttl_b = rule_timestamps.pop()
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key_blocked = rule_timestamps.pop()
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if key_blocked is not None:
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# We are manually blocked. Report for how much longer we will be
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if blocking_ttl_b is None:
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blocking_ttl = 0.5
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else:
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blocking_ttl = int(blocking_ttl_b)
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return True, blocking_ttl
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now = time.time()
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for timestamp, (range_seconds, num_requests) in zip(rule_timestamps, rules):
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# Check if the nth timestamp is newer than the associated rule. If so,
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# it means we've hit our limit for this rule
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if timestamp is None:
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continue
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boundary = float(timestamp) + range_seconds
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if boundary > now:
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free = boundary - now
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return True, free
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# No api calls recorded yet
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return False, 0.0
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def incr_ratelimit(entity: RateLimitedObject) -> None:
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"""Increases the rate-limit for the specified entity"""
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list_key, set_key, _ = entity.get_keys()
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now = time.time()
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# If we have no rules, we don't store anything
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if len(rules) == 0:
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return
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# Start redis transaction
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with client.pipeline() as pipe:
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count = 0
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while True:
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try:
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# To avoid a race condition between getting the element we might trim from our list
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# and removing it from our associated set, we abort this whole transaction if
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# another agent manages to change our list out from under us
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# When watching a value, the pipeline is set to Immediate mode
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pipe.watch(list_key)
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# Get the last elem that we'll trim (so we can remove it from our sorted set)
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last_val = pipe.lindex(list_key, max_api_calls(entity) - 1)
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# Restart buffered execution
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pipe.multi()
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# Add this timestamp to our list
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pipe.lpush(list_key, now)
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# Trim our list to the oldest rule we have
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pipe.ltrim(list_key, 0, max_api_calls(entity) - 1)
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# Add our new value to the sorted set that we keep
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# We need to put the score and val both as timestamp,
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# as we sort by score but remove by value
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pipe.zadd(set_key, now, now)
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# Remove the trimmed value from our sorted set, if there was one
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if last_val is not None:
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pipe.zrem(set_key, last_val)
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# Set the TTL for our keys as well
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api_window = max_api_window(entity)
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pipe.expire(list_key, api_window)
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pipe.expire(set_key, api_window)
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pipe.execute()
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# If no exception was raised in the execution, there were no transaction conflicts
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break
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except redis.WatchError:
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if count > 10:
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raise RateLimiterLockingException()
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count += 1
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continue
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def rate_limit_entity(entity: RateLimitedObject) -> Tuple[bool, float]:
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# Returns (ratelimited, secs_to_freedom)
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ratelimited, time = is_ratelimited(entity)
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if ratelimited:
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statsd.incr("ratelimiter.limited.%s.%s" % (type(entity), str(entity)))
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else:
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try:
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incr_ratelimit(entity)
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except RateLimiterLockingException:
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logger.warning("Deadlock trying to incr_ratelimit for %s:%s" % (
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type(entity).__name__, str(entity)))
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# rate-limit users who are hitting the API so hard we can't update our stats.
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ratelimited = True
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return ratelimited, time
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