zulip/zerver/lib/rate_limiter.py

import os

from typing import List, Optional, Tuple

from django.conf import settings
from django.http import HttpRequest
from zerver.lib.exceptions import RateLimited
from zerver.lib.redis_utils import get_redis_client
from zerver.lib.utils import statsd

from zerver.models import UserProfile

import logging
import redis
import time

# Implement a rate-limiting scheme inspired by the one described here, but heavily modified
# http://blog.domaintools.com/2013/04/rate-limiting-with-redis/

client = get_redis_client()
rules = settings.RATE_LIMITING_RULES  # type: List[Tuple[int, int]]

KEY_PREFIX = ''

logger = logging.getLogger(__name__)

class RateLimiterLockingException(Exception):
    pass

class RateLimitedObject:
    def get_keys(self) -> List[str]:
        key_fragment = self.key_fragment()
        return ["{}ratelimit:{}:{}".format(KEY_PREFIX, key_fragment, keytype)
                for keytype in ['list', 'zset', 'block']]

    def key_fragment(self) -> str:
        raise NotImplementedError()

    def rules(self) -> List[Tuple[int, int]]:
        raise NotImplementedError()

    def __str__(self) -> str:
        raise NotImplementedError()

class RateLimitedUser(RateLimitedObject):
    def __init__(self, user: UserProfile, domain: str='all') -> None:
        self.user = user
        self.domain = domain

    def __str__(self) -> str:
        return "Id: {}".format(self.user.id)

    def key_fragment(self) -> str:
        return "{}:{}:{}".format(type(self.user), self.user.id, self.domain)

    def rules(self) -> List[Tuple[int, int]]:
        if self.user.rate_limits != "":
            result = []  # type: List[Tuple[int, int]]
            for limit in self.user.rate_limits.split(','):
                (seconds, requests) = limit.split(':', 2)
                result.append((int(seconds), int(requests)))
            return result
        return rules

def bounce_redis_key_prefix_for_testing(test_name: str) -> None:
    global KEY_PREFIX
    KEY_PREFIX = test_name + ':' + str(os.getpid()) + ':'

def max_api_calls(entity: RateLimitedObject) -> int:
    "Returns the API rate limit for the highest limit"
    return entity.rules()[-1][1]

def max_api_window(entity: RateLimitedObject) -> int:
    "Returns the API time window for the highest limit"
    return entity.rules()[-1][0]

def add_ratelimit_rule(range_seconds: int, num_requests: int) -> None:
    "Add a rate-limiting rule to the ratelimiter"
    global rules

    rules.append((range_seconds, num_requests))
    rules.sort(key=lambda x: x[0])

def remove_ratelimit_rule(range_seconds: int, num_requests: int) -> None:
    global rules
    rules = [x for x in rules if x[0] != range_seconds and x[1] != num_requests]

def block_access(entity: RateLimitedObject, seconds: int) -> None:
    "Manually blocks an entity for the desired number of seconds"
    _, _, blocking_key = entity.get_keys()
    with client.pipeline() as pipe:
        pipe.set(blocking_key, 1)
        pipe.expire(blocking_key, seconds)
        pipe.execute()

def unblock_access(entity: RateLimitedObject) -> None:
    _, _, blocking_key = entity.get_keys()
    client.delete(blocking_key)

def clear_history(entity: RateLimitedObject) -> None:
    '''
    This is only used by test code now, where it's very helpful in
    allowing us to run tests quickly, by giving a user a clean slate.
    '''
    for key in entity.get_keys():
        client.delete(key)

def _get_api_calls_left(entity: RateLimitedObject, range_seconds: int, max_calls: int) -> Tuple[int, float]:
    list_key, set_key, _ = entity.get_keys()
    # 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 = results[0]  # type: int
    newest_call = results[1]  # type: Optional[bytes]

    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

def api_calls_left(entity: RateLimitedObject) -> 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 = max_api_window(entity)
    max_calls = max_api_calls(entity)
    return _get_api_calls_left(entity, max_window, max_calls)

def is_ratelimited(entity: RateLimitedObject) -> Tuple[bool, float]:
    "Returns a tuple of (rate_limited, time_till_free)"
    list_key, set_key, blocking_key = entity.get_keys()

    rules = entity.rules()

    if len(rules) == 0:
        return False, 0.0

    # 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 = pipe.execute()  # type: List[Optional[bytes]]

    # 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:
            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):
        # 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

    # No api calls recorded yet
    return False, 0.0

def incr_ratelimit(entity: RateLimitedObject) -> None:
    """Increases the rate-limit for the specified entity"""
    list_key, set_key, _ = entity.get_keys()
    now = time.time()

    # If we have no rules, we don't store anything
    if len(rules) == 0:
        return

    # 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 = pipe.lindex(list_key, max_api_calls(entity) - 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(entity) - 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(entity)
                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:
                if count > 10:
                    raise RateLimiterLockingException()
                count += 1

                continue

def rate_limit_entity(entity: RateLimitedObject) -> Tuple[bool, float]:
    # Returns (ratelimited, secs_to_freedom)
    ratelimited, time = is_ratelimited(entity)

    if ratelimited:
        statsd.incr("ratelimiter.limited.%s.%s" % (type(entity), str(entity)))

    else:
        try:
            incr_ratelimit(entity)
        except RateLimiterLockingException:
            logger.warning("Deadlock trying to incr_ratelimit for %s:%s" % (
                           type(entity).__name__, str(entity)))
            # rate-limit users who are hitting the API so hard we can't update our stats.
            ratelimited = True

    return ratelimited, time

def rate_limit_request_by_entity(request: HttpRequest, entity: RateLimitedObject) -> None:
    ratelimited, time = rate_limit_entity(entity)

    entity_type = type(entity).__name__
    if not hasattr(request, '_ratelimit'):
        request._ratelimit = {}
    request._ratelimit[entity_type] = {}
    request._ratelimit[entity_type]['applied_limits'] = True
    request._ratelimit[entity_type]['secs_to_freedom'] = time
    request._ratelimit[entity_type]['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 RateLimited(entity_type)

    calls_remaining, time_reset = api_calls_left(entity)

    request._ratelimit[entity_type]['remaining'] = calls_remaining
    request._ratelimit[entity_type]['secs_to_freedom'] = time_reset