zulip/zerver/lib/rate_limiter.py

from __future__ import absolute_import

import os

from typing import Any, Iterator, List, Optional, Tuple, Text

from django.conf import settings
from zerver.lib.redis_utils import get_redis_client
from six.moves import zip

from zerver.models import UserProfile

import redis
import time
import logging

# 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 = u''

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

    def key_fragment(self):
        # type: () -> Text
        raise NotImplementedError()

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

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

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

    def rules(self):
        # type: () -> 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):
    # type: (Text) -> None
    global KEY_PREFIX
    KEY_PREFIX = test_name + u':' + Text(os.getpid()) + u':'

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

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

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

def block_access(entity, seconds):
    # type: (RateLimitedObject, 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):
    # type: (RateLimitedObject) -> None
    _, _, blocking_key = entity.get_keys()
    client.delete(blocking_key)

def clear_history(entity):
    # type: (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, range_seconds, max_calls):
    # type: (RateLimitedObject, int, 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):
    # type: (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):
    # type: (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):
    # type: (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, 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:
                    logging.error("Failed to complete incr_ratelimit transaction without interference 10 times "
                                  "in a row! Aborting rate-limit increment")
                    break
                count += 1

                continue