Ratenbegrenzer mit gleitendem Fenster und Burst-Zulassung

import math import threading import time import unittest from collections import deque from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field # Design note: # This limiter admits a request only when two independent checks agree: # 1. a token bucket has at least cost tokens, allowing short bursts up to burst; # 2. an exact sliding-window log has room for cost more admitted units in the last # window_seconds, where the window limit is rate * window_seconds. # If the token bucket says yes but the sliding window is full, the request is # denied and no tokens are spent; if the window has room but tokens are absent, # it is also denied. The exact log stores one entry per admitted request as # (timestamp, cost), not one entry per unit, so allow is amortized O(1): each log # entry is appended once and removed once. Memory is O(number of admitted requests # in the active window) per client. A weighted two-bucket counter would use O(1) # memory but can over- or under-admit near bucket boundaries; this implementation # chooses exactness because correctness is usually more important for abuse # prevention. The client map is lock-striped; each client state has its own lock. # A caller obtains the per-client lock before mutating tokens/window state, so # concurrent allow calls cannot double-spend tokens or lose window updates. class MonotonicClock: def now(self) -> float: return time.monotonic() class ManualClock: def __init__(self, start: float = 0.0): self._now = float(start) self._lock = threading.Lock() def now(self) -> float: with self._lock: return self._now def set(self, value: float) -> None: with self._lock: self._now = float(value) def advance(self, seconds: float) -> None: with self._lock: self._now += float(seconds) @dataclass(frozen=True) class ClientConfig: rate: float burst: float window_seconds: float = 1.0 def validated(self) -> 'ClientConfig': rate = float(self.rate) burst = float(self.burst) window = float(self.window_seconds) if not math.isfinite(rate) or rate <= 0: raise ValueError('rate must be a positive finite number') if not math.isfinite(burst) or burst < 1: raise ValueError('burst must be a finite number >= 1') if not math.isfinite(window) or window <= 0: raise ValueError('window_seconds must be a positive finite number') return ClientConfig(rate, burst, window) @property def window_limit(self) -> float: return self.rate * self.window_seconds @dataclass class _ClientState: config: ClientConfig tokens: float last_refill: float last_seen: float events: deque = field(default_factory=deque) window_sum: float = 0.0 lock: threading.Lock = field(default_factory=threading.Lock) class HybridRateLimiter: def __init__( self, rate: float, burst: float, window_seconds: float = 1.0, per_client_configs=None, clock=None, stripes: int = 64, idle_ttl_seconds: float | None = None, cleanup_interval_seconds: float | None = None, ): if stripes <= 0: raise ValueError('stripes must be positive') self._default_config = ClientConfig(rate, burst, window_seconds).validated() self._configs = {} if per_client_configs: for client_id, cfg in per_client_configs.items(): self._configs[client_id] = self._coerce_config(cfg) max_window = self._default_config.window_seconds for cfg in self._configs.values(): max_window = max(max_window, cfg.window_seconds) if idle_ttl_seconds is None: idle_ttl_seconds = max(60.0, 2.0 * max_window) idle_ttl_seconds = float(idle_ttl_seconds) if not math.isfinite(idle_ttl_seconds) or idle_ttl_seconds <= 0: raise ValueError('idle_ttl_seconds must be positive and finite') if idle_ttl_seconds < max_window: raise ValueError('idle_ttl_seconds must be >= the largest window_seconds') self._idle_ttl = idle_ttl_seconds if cleanup_interval_seconds is None: cleanup_interval_seconds = min(60.0, max(1.0, idle_ttl_seconds / 2.0)) cleanup_interval_seconds = float(cleanup_interval_seconds) if not math.isfinite(cleanup_interval_seconds) or cleanup_interval_seconds <= 0: raise ValueError('cleanup_interval_seconds must be positive and finite') self._cleanup_interval = cleanup_interval_seconds self._clock = clock if clock is not None else MonotonicClock() self._clients = {} self._stripe_locks = [threading.Lock() for _ in range(stripes)] self._cleanup_mutex = threading.Lock() self._last_cleanup = self._safe_time() self._eps = 1e-12 @staticmethod def _coerce_config(cfg) -> ClientConfig: if isinstance(cfg, ClientConfig): return cfg.validated() if isinstance(cfg, dict): return ClientConfig(cfg['rate'], cfg['burst'], cfg.get('window_seconds', 1.0)).validated() rate, burst, *rest = cfg window = rest[0] if rest else 1.0 return ClientConfig(rate, burst, window).validated() def _safe_time(self) -> float: t = float(self._clock.now()) if math.isnan(t): raise ValueError('clock returned NaN') return t def _config_for(self, client_id: str) -> ClientConfig: return self._configs.get(client_id, self._default_config) def _stripe_index(self, client_id: str) -> int: return hash(client_id) % len(self._stripe_locks) def _get_state_locked(self, client_id: str) -> _ClientState: idx = self._stripe_index(client_id) stripe = self._stripe_locks[idx] stripe.acquire() try: state = self._clients.get(client_id) if state is None: cfg = self._config_for(client_id) now = self._safe_time() state = _ClientState( config=cfg, tokens=cfg.burst, last_refill=now, last_seen=now, ) self._clients[client_id] = state state.lock.acquire() return state finally: stripe.release() def _advance_and_prune_locked(self, state: _ClientState) -> float: raw_now = self._safe_time() now = max(raw_now, state.last_seen) elapsed = max(0.0, now - state.last_refill) max_useful_elapsed = state.config.burst / state.config.rate elapsed_for_tokens = min(elapsed, max_useful_elapsed) if elapsed_for_tokens > 0: state.tokens = min( state.config.burst, state.tokens + elapsed_for_tokens * state.config.rate, ) state.last_refill = now state.last_seen = now cutoff = now - state.config.window_seconds while state.events and state.events[0][0] <= cutoff + self._eps: _, old_cost = state.events.popleft() state.window_sum -= old_cost if state.window_sum < self._eps: state.window_sum = 0.0 return now def allow(self, client_id: str, cost: int = 1) -> bool: if cost <= 0: raise ValueError('cost must be a positive integer') self._maybe_cleanup() cfg = self._config_for(client_id) if cost > cfg.burst: return False state = self._get_state_locked(client_id) try: now = self._advance_and_prune_locked(state) enough_tokens = state.tokens + self._eps >= cost enough_window = state.window_sum + cost <= state.config.window_limit + self._eps if not (enough_tokens and enough_window): return False state.tokens -= cost if state.tokens < self._eps: state.tokens = 0.0 state.window_sum += cost state.events.append((now, float(cost))) return True finally: state.lock.release() def retry_after(self, client_id: str) -> float: self._maybe_cleanup() state = self._get_state_locked(client_id) try: now = self._advance_and_prune_locked(state) cfg = state.config if cfg.burst < 1 or cfg.window_limit < 1: return math.inf token_wait = 0.0 if state.tokens + self._eps >= 1.0 else (1.0 - state.tokens) / cfg.rate if state.window_sum + 1.0 <= cfg.window_limit + self._eps: window_wait = 0.0 else: window_wait = math.inf remaining = state.window_sum for ts, event_cost in state.events: remaining -= event_cost if remaining + 1.0 <= cfg.window_limit + self._eps: window_wait = max(0.0, ts + cfg.window_seconds - now) break return max(token_wait, window_wait) finally: state.lock.release() def _maybe_cleanup(self) -> None: raw_now = self._safe_time() if raw_now - self._last_cleanup < self._cleanup_interval: return if not self._cleanup_mutex.acquire(blocking=False): return try: now = max(raw_now, self._last_cleanup) if now - self._last_cleanup >= self._cleanup_interval: self._cleanup_stale_at(now) self._last_cleanup = now finally: self._cleanup_mutex.release() def cleanup_stale(self) -> int: with self._cleanup_mutex: now = max(self._safe_time(), self._last_cleanup) removed = self._cleanup_stale_at(now) self._last_cleanup = now return removed def _cleanup_stale_at(self, now: float) -> int: removed = 0 for idx, stripe in enumerate(self._stripe_locks): with stripe: for client_id, state in list(self._clients.items()): if self._stripe_index(client_id) != idx: continue if not state.lock.acquire(blocking=False): continue try: if self._clients.get(client_id) is state and now - state.last_seen >= self._idle_ttl: del self._clients[client_id] removed += 1 finally: state.lock.release() return removed def client_count(self) -> int: total = 0 for stripe in self._stripe_locks: with stripe: total += len(self._clients) break return len(self._clients) class HybridRateLimiterTests(unittest.TestCase): def test_basic_allow_deny_and_retry_after(self): clock = ManualClock() limiter = HybridRateLimiter(rate=1, burst=2, window_seconds=10, clock=clock) self.assertTrue(limiter.allow('alice')) self.assertTrue(limiter.allow('alice')) self.assertFalse(limiter.allow('alice')) self.assertAlmostEqual(limiter.retry_after('alice'), 1.0, places=9) def test_burst_draining_and_fractional_refill(self): clock = ManualClock() limiter = HybridRateLimiter(rate=2, burst=3, window_seconds=10, clock=clock) self.assertTrue(limiter.allow('alice')) self.assertTrue(limiter.allow('alice')) self.assertTrue(limiter.allow('alice')) self.assertFalse(limiter.allow('alice')) clock.advance(0.25) self.assertFalse(limiter.allow('alice')) self.assertAlmostEqual(limiter.retry_after('alice'), 0.25, places=9) clock.advance(0.25) self.assertTrue(limiter.allow('alice')) def test_sliding_window_caps_even_when_bucket_refilled(self): clock = ManualClock() limiter = HybridRateLimiter(rate=10, burst=10, window_seconds=1, clock=clock) for _ in range(10): self.assertTrue(limiter.allow('abuser')) clock.advance(0.5) self.assertFalse(limiter.allow('abuser')) self.assertAlmostEqual(limiter.retry_after('abuser'), 0.5, places=9) clock.advance(0.5) self.assertTrue(limiter.allow('abuser')) def test_cost_larger_than_burst_is_rejected_without_state_creation(self): clock = ManualClock() limiter = HybridRateLimiter(rate=10, burst=5, window_seconds=10, clock=clock) self.assertFalse(limiter.allow('alice', cost=6)) self.assertEqual(limiter.client_count(), 0) self.assertTrue(limiter.allow('alice', cost=5)) self.assertFalse(limiter.allow('alice')) def test_concurrent_contention_obeys_token_bucket_bound(self): clock = ManualClock() rate = 5 burst = 5 limiter = HybridRateLimiter(rate=rate, burst=burst, window_seconds=100, clock=clock, stripes=8) successes = 0 for tick in range(11): clock.set(tick / 10.0) with ThreadPoolExecutor(max_workers=32) as pool: results = list(pool.map(lambda _: limiter.allow('hot'), range(32))) successes += sum(1 for r in results if r) bound = rate * 1.0 + burst self.assertLessEqual(successes, bound) def test_stale_client_eviction(self): clock = ManualClock() limiter = HybridRateLimiter( rate=10, burst=10, window_seconds=1, clock=clock, idle_ttl_seconds=5, cleanup_interval_seconds=1, ) self.assertTrue(limiter.allow('old-client')) self.assertEqual(limiter.client_count(), 1) clock.advance(6) self.assertTrue(limiter.allow('new-client')) self.assertEqual(limiter.client_count(), 1) def test_clock_going_backwards_is_clamped(self): clock = ManualClock(10) limiter = HybridRateLimiter(rate=1, burst=1, window_seconds=10, clock=clock) self.assertTrue(limiter.allow('alice')) clock.set(5) self.assertFalse(limiter.allow('alice')) self.assertAlmostEqual(limiter.retry_after('alice'), 1.0, places=9) def test_large_pause_refills_only_to_burst(self): clock = ManualClock() limiter = HybridRateLimiter(rate=1, burst=3, window_seconds=1000, clock=clock) for _ in range(3): self.assertTrue(limiter.allow('alice')) clock.advance(10 ** 9) for _ in range(3): self.assertTrue(limiter.allow('alice')) self.assertFalse(limiter.allow('alice')) if __name__ == '__main__': unittest.main()

import threading import time import collections # Injectable clock for deterministic testing class MonotonicClock: def time(self): return time.monotonic() class TestClock: def __init__(self, initial_time=0.0): self._time = initial_time self._lock = threading.Lock() def time(self): with self._lock: return self._time def advance(self, seconds): with self._lock: self._time += seconds def set_time(self, new_time): with self._lock: self._time = new_time # Client state for the rate limiter class ClientState: def __init__(self, rate: float, burst: int, window_seconds: int, current_time: float): self.lock = threading.Lock() self.tokens = float(burst) # Current tokens in the bucket self.last_refill_time = current_time # Last time tokens were refilled self.burst = burst # Max tokens self.rate = rate # Tokens per second # Sliding window log: stores (timestamp, cost) tuples of allowed requests # We use a deque for efficient O(1) append and popleft self.request_timestamps = collections.deque() self.current_window_cost = 0 # Sum of costs in the current window self.window_seconds = window_seconds self.last_access_time = current_time # For stale client eviction def _refill_tokens(self, current_time: float): # Clamp last_refill_time to current_time to prevent negative time delta # and thus unbounded token grants if clock goes backwards (though monotonic clock prevents this) # or if there's a huge pause (e.g., suspended VM). if current_time < self.last_refill_time: self.last_refill_time = current_time time_delta = current_time - self.last_refill_time if time_delta > 0: refill_amount = time_delta * self.rate self.tokens = min(self.burst, self.tokens + refill_amount) self.last_refill_time = current_time def _clean_window(self, current_time: float): # Remove (timestamp, cost) tuples older than window_seconds while self.request_timestamps and self.request_timestamps[0][0] <= current_time - self.window_seconds: _, removed_cost = self.request_timestamps.popleft() self.current_window_cost -= removed_cost class RateLimiter: def __init__(self, rate: float, burst: int, window_seconds: int = 60, clock=None, cleanup_interval: float = 300.0, client_ttl_multiplier: float = 2.0): if rate <= 0 or burst < 0 or window_seconds <= 0: raise ValueError("Rate must be positive, burst non-negative, window_seconds positive.") self.rate = rate self.burst = burst self.window_seconds = window_seconds self.clock = clock if clock is not None else MonotonicClock() self.clients = {} # client_id -> ClientState self._clients_lock = threading.Lock() # Protects access to the clients dictionary # Stale client cleanup self.cleanup_interval = cleanup_interval self.client_ttl_multiplier = client_ttl_multiplier # e.g., 2.0 * window_seconds self._cleanup_thread = None self._stop_cleanup_event = threading.Event() self._start_cleanup_thread() def _get_or_create_client_state(self, client_id: str) -> ClientState: with self._clients_lock: if client_id not in self.clients: self.clients[client_id] = ClientState(self.rate, self.burst, self.window_seconds, self.clock.time()) return self.clients[client_id] def allow(self, client_id: str, cost: int = 1) -> bool: if cost <= 0: return True # Zero or negative cost requests are always allowed (or could raise error) client_state = self._get_or_create_client_state(client_id) current_time = self.clock.time() with client_state.lock: # Edge case: cost > burst. A single request cannot exceed the max capacity of the bucket. if cost > client_state.burst: return False # Update last access time for cleanup client_state.last_access_time = current_time # Token Bucket check client_state._refill_tokens(current_time) if client_state.tokens < cost: return False # Not enough tokens # Sliding Window check client_state._clean_window(current_time) # Window capacity is `rate * window_seconds` units. if client_state.current_window_cost + cost > self.rate * self.window_seconds: return False # Window capacity exceeded # Both checks passed. Consume tokens and log request. client_state.tokens -= cost client_state.request_timestamps.append((current_time, cost)) client_state.current_window_cost += cost return True def retry_after(self, client_id: str) -> float: client_state = self._get_or_create_client_state(client_id) current_time = self.clock.time() with client_state.lock: # Update last access time for cleanup client_state.last_access_time = current_time client_state._refill_tokens(current_time) client_state._clean_window(current_time) # Check token bucket first for 1 unit of cost if client_state.tokens >= 1: # Token bucket allows, now check sliding window for 1 unit if client_state.current_window_cost + 1 <= self.rate * self.window_seconds: return 0.0 # Currently allowed for 1 unit else: # Sliding window denies for 1 unit. Find when oldest request in window expires. needed_to_remove = (client_state.current_window_cost + 1) - (self.rate * self.window_seconds) if needed_to_remove <= 0: # Should not happen if we are in this else branch return 0.0 removed_sum = 0 for ts, c in client_state.request_timestamps: removed_sum += c if removed_sum >= needed_to_remove: return max(0.0, (ts + self.window_seconds) - current_time) return 0.0 # Should not be reached if window denies else: # Token bucket denies. Calculate time until enough tokens are available for 1 unit. tokens_needed = 1 - client_state.tokens time_to_refill = tokens_needed / self.rate return max(0.0, time_to_refill) def _cleanup_clients(self): while not self._stop_cleanup_event.wait(self.cleanup_interval): current_time = self.clock.time() stale_threshold = current_time - (self.window_seconds * self.client_ttl_multiplier) clients_to_remove = [] with self._clients_lock: for client_id, state in self.clients.items(): # Check last_access_time without acquiring client's lock to avoid potential deadlocks # with concurrent `allow` calls. A slight inaccuracy in cleanup timing is acceptable. if state.last_access_time < stale_threshold: clients_to_remove.append(client_id) for client_id in clients_to_remove: # Remove from the dictionary. Subsequent `allow` calls will re-create if needed. if client_id in self.clients: # Check again in case another thread already removed it del self.clients[client_id] def _start_cleanup_thread(self): if self._cleanup_thread is None or not self._cleanup_thread.is_alive(): self._cleanup_thread = threading.Thread(target=self._cleanup_clients, daemon=True) self._cleanup_thread.start() def stop_cleanup_thread(self): self._stop_cleanup_event.set() if self._cleanup_thread: self._cleanup_thread.join() self._cleanup_thread = None # Design Note: # This rate limiter combines a token bucket with a sliding-window log to provide robust rate limiting. # # Algorithm Justification: # The **token bucket** handles burst tolerance, allowing requests to exceed the average rate temporarily # up to the `burst` capacity. Tokens refill at `rate` per second. This prevents short-term spikes. # The **sliding-window log** (implemented with a `collections.deque` storing `(timestamp, cost)` tuples) # prevents sustained abuse that a pure token bucket might allow after refills. It ensures that the # total `cost` of requests within any `window_seconds` period does not exceed `rate * window_seconds`. # This hybrid approach ensures both short-term burstiness and long-term rate adherence. # # Data Structure Choice for Sliding Window: # An **exact sliding-window log** is used. While it consumes more memory than a weighted two-bucket # approximation (like Cloudflare's), it offers perfect accuracy within the defined window. For a # per-client limiter, the memory footprint `O(rate * window_seconds)` per active client (storing # `(timestamp, cost)` tuples) is generally acceptable, especially with the stale client eviction # mechanism. The `deque` provides `O(1)` amortized time for adding and removing elements from its ends, # making window maintenance efficient. # # Concurrency: # Thread safety is achieved using **per-client locks**. Each `ClientState` object has its own `threading.Lock`. # This prevents a single global lock from becoming a bottleneck, allowing concurrent `allow` calls for # different `client_id`s to proceed in parallel. For a specific `client_id`, its lock ensures that # operations on its `tokens`, `last_refill_time`, `request_timestamps`, and `current_window_cost` are atomic. # This guarantees no double-spend of tokens or lost updates for that client. The `self._clients_lock` # protects the `self.clients` dictionary itself, ensuring safe addition/removal of client states. # # Semantics when algorithms disagree: # A request is permitted **only if both** the token bucket check and the sliding-window log check pass. # If the token bucket has enough capacity but the sliding window indicates too many units have been # consumed recently, the request is denied. Conversely, if the sliding window has space but the # token bucket is empty, the request is also denied. This ensures the stricter of the two limits # is always enforced. # # Time Complexity: # - `allow`: Amortized O(1). Token bucket operations are O(1). Sliding window `deque` operations # (`append`, `popleft`) are O(1) amortized. In the worst case, `_clean_window` might iterate # through many elements if the clock jumps significantly, but this is amortized over time. # - `retry_after`: Amortized O(N) in the worst case for the sliding window part, where N is the number # of requests in the window, as it might iterate through the `deque` to find the expiration point. # However, N is bounded by `rate * window_seconds`. # # Memory Complexity: # - Per client: O(rate * window_seconds) for storing `(timestamp, cost)` tuples in the sliding window log. # Plus constant memory for token bucket state and lock. # - Total: O(Number of active clients * rate * window_seconds). Stale client cleanup prevents unbounded growth. # Unit Tests import unittest import time import threading import collections # Re-define TestClock here for self-contained test file class TestClock: def __init__(self, initial_time=0.0): self._time = initial_time self._lock = threading.Lock() def time(self): with self._lock: return self._time def advance(self, seconds): with self._lock: self._time += seconds def set_time(self, new_time): with self._lock: self._time = new_time class RateLimiterTest(unittest.TestCase): def setUp(self): self.clock = TestClock() # Use a short cleanup interval for tests to make eviction testable self.limiter = RateLimiter(rate=10, burst=5, window_seconds=10, clock=self.clock, cleanup_interval=0.1, client_ttl_multiplier=1.5) def tearDown(self): self.limiter.stop_cleanup_thread() def test_basic_allow_deny(self): client_id = "client1" # Initial burst of 5 + 10 tokens/sec * 0 sec = 5 tokens # Allow 5 requests for _ in range(5): self.assertTrue(self.limiter.allow(client_id)) # Deny 6th request (burst exhausted) self.assertFalse(self.limiter.allow(client_id)) self.assertGreater(self.limiter.retry_after(client_id), 0) # Advance time to allow refill self.clock.advance(0.1) # 1 token refilled (10 tokens/sec * 0.1 sec) self.assertTrue(self.limiter.allow(client_id)) # Should allow 1 self.assertFalse(self.limiter.allow(client_id)) # Deny 2nd self.clock.advance(0.9) # 9 more tokens refilled, total 10 tokens refilled since initial burst exhausted for _ in range(9): self.assertTrue(self.limiter.allow(client_id)) self.assertFalse(self.limiter.allow(client_id)) # Deny 10th def test_burst_draining_and_refill(self): client_id = "client2" # Burst: 5, Rate: 10/s # Drain burst for _ in range(5): self.assertTrue(self.limiter.allow(client_id)) self.assertFalse(self.limiter.allow(client_id)) # Burst exhausted # Advance time for partial refill self.clock.advance(0.2) # 2 tokens refilled (10 * 0.2) self.assertTrue(self.limiter.allow(client_id)) self.assertTrue(self.limiter.allow(client_id)) self.assertFalse(self.limiter.allow(client_id)) # Refill exhausted # Advance time for full refill (up to burst capacity) self.clock.advance(1.0) # 10 tokens refilled. Bucket should be capped at burst (5). self.assertTrue(self.limiter.allow(client_id)) self.assertTrue(self.limiter.allow(client_id)) self.assertTrue(self.limiter.allow(client_id)) self.assertTrue(self.limiter.allow(client_id)) self.assertTrue(self.limiter.allow(client_id)) self.assertFalse(self.limiter.allow(client_id)) # Burst exhausted again def test_sliding_window_cap_independent_of_bucket_refill(self): # Reset limiter and client for this specific test scenario self.limiter.stop_cleanup_thread() self.limiter = RateLimiter(rate=10, burst=10, window_seconds=1, clock=self.clock, cleanup_interval=0.1, client_ttl_multiplier=1.5) self.clock.set_time(0.0) client_id = "client_window_test" # Allow 10 requests within a very short time (burst allows this) for _ in range(10): self.assertTrue(self.limiter.allow(client_id)) # Window capacity is 10 units (10 units/s * 1s). # We have 10 requests in the window, total cost 10. # Next request should be denied by window, even if bucket has tokens. self.assertFalse(self.limiter.allow(client_id)) self.assertGreater(self.limiter.retry_after(client_id), 0) # Advance time by 0.5s. Still denied by window. self.clock.advance(0.5) self.assertFalse(self.limiter.allow(client_id)) # Advance time by 0.5s more. Total 1.0s since the first request (at time 0.0). # The first request (at time 0.0) should now be out of the window. self.clock.advance(0.5) self.assertTrue(self.limiter.allow(client_id)) # Should allow, as one request expired from window. self.assertEqual(self.limiter.clients[client_id].current_window_cost, 10) # 10 requests in window, 10 units. def test_cost_greater_than_burst(self): client_id = "client4" # Burst is 5. Cost 6 should be rejected. self.assertFalse(self.limiter.allow(client_id, cost=6)) self.assertEqual(self.limiter.clients[client_id].tokens, 5) # Tokens should not be consumed self.assertEqual(self.limiter.clients[client_id].current_window_cost, 0) # Window should be empty # Cost 5 should be allowed self.assertTrue(self.limiter.allow(client_id, cost=5)) self.assertEqual(self.limiter.clients[client_id].tokens, 0) self.assertEqual(self.limiter.clients[client_id].current_window_cost, 5) def test_concurrent_contention_on_one_client(self): self.limiter.stop_cleanup_thread() self.limiter = RateLimiter(rate=10, burst=5, window_seconds=10, clock=self.clock, cleanup_interval=0.1, client_ttl_multiplier=1.5) self.clock.set_time(0.0) client_id = "client_fixed_duration" test_duration = 2.0 # seconds num_threads = 10 allowed_count = [0] # Use a list to make it mutable in nested scope def fixed_duration_worker(): start_time = self.clock.time() while self.clock.time() < start_time + test_duration: if self.limiter.allow(client_id): with threading.Lock(): # Protect shared counter allowed_count[0] += 1 # Simulate some work and advance clock slightly self.clock.advance(0.001) # Each attempt takes 1ms threads = [] for _ in range(num_threads): t = threading.Thread(target=fixed_duration_worker) threads.append(t) t.start() for t in threads: t.join() # Max allowed by token bucket: burst + rate * test_duration = 5 + 10 * 2 = 25. # Max allowed by window: rate * window_seconds = 10 * 10 = 100. # So, the token bucket is the stricter bottleneck here. Expected max allowed is 25. self.assertLessEqual(allowed_count[0], 25) # Due to non-deterministic scheduling and clock advancement by multiple threads, # asserting a precise lower bound is difficult. We primarily verify the upper bound. self.assertGreater(allowed_count[0], 15) # Should allow a significant number of requests def test_stale_client_eviction(self): client_id_active = "active_client" client_id_stale = "stale_client" self.limiter.stop_cleanup_thread() self.limiter = RateLimiter(rate=10, burst=5, window_seconds=10, clock=self.clock, cleanup_interval=0.1, client_ttl_multiplier=1.5) self.clock.set_time(0.0) # Reset clock # Access active client at time 0.0 self.assertTrue(self.limiter.allow(client_id_active)) # Access stale client at time 0.0 self.assertTrue(self.limiter.allow(client_id_stale)) # Advance time to just before stale_threshold for stale_client # Stale threshold is 1.5 * 10 = 15 seconds after last access. # So, stale_client will be stale at time 15.0. self.clock.advance(14.0) # Time is now 14.0 # Access active client again to update its last_access_time self.assertTrue(self.limiter.allow(client_id_active)) # last_access_time for active_client is now 14.0 # Advance time past stale_threshold for stale_client self.clock.advance(1.5) # Time is now 15.5. stale_client's last_access_time (0.0) is < 15.5 - 15 = 0.5. time.sleep(self.limiter.cleanup_interval * 2) # Give cleanup thread time to run self.assertNotIn(client_id_stale, self.limiter.clients) self.assertIn(client_id_active, self.limiter.clients) # Active client should still be there def test_retry_after(self): client_id = "client_retry" # Initial state: 5 burst tokens, 0 window cost. self.assertAlmostEqual(self.limiter.retry_after(client_id), 0.0) # Consume burst for _ in range(5): self.limiter.allow(client_id) # No tokens left. Need 1 token. Rate is 10/s. So, 0.1s needed. self.assertAlmostEqual(self.limiter.retry_after(client_id), 0.1) self.clock.advance(0.05) # Advance half the time needed self.assertAlmostEqual(self.limiter.retry_after(client_id), 0.05) self.clock.advance(0.05) # Advance remaining time. Now 0.1s passed. 1 token refilled. self.assertAlmostEqual(self.limiter.retry_after(client_id), 0.0) self.assertTrue(self.limiter.allow(client_id)) # Consume the token # Now test retry_after for window limit self.limiter.stop_cleanup_thread() self.limiter = RateLimiter(rate=10, burst=10, window_seconds=1, clock=self.clock, cleanup_interval=0.1, client_ttl_multiplier=1.5) self.clock.set_time(0.0) # Fill up the window (10 units in 1 second window) for i in range(10): self.clock.advance(0.01) # Small advance to make timestamps distinct self.limiter.allow(client_id) # Current time is approx 0.1s. All 10 requests are in the window. # Window capacity is 10 units. # Next request (cost 1) should be denied by window. self.assertGreater(self.limiter.retry_after(client_id), 0.0) # The first request was at time ~0.01. It will expire at ~0.01 + 1 = ~1.01. # Current time is ~0.1. So, retry_after should be ~1.01 - ~0.1 = ~0.91. first_req_ts = self.limiter.clients[client_id].request_timestamps[0][0] expected_retry = (first_req_ts + self.limiter.window_seconds) - self.clock.time() self.assertAlmostEqual(self.limiter.retry_after(client_id), expected_retry, places=2) # Advance time until the first request expires self.clock.advance(expected_retry + 0.001) # Advance slightly past expiration self.assertAlmostEqual(self.limiter.retry_after(client_id), 0.0, places=2) self.assertTrue(self.limiter.allow(client_id)) # Should now be allowed def test_clock_going_backwards_or_large_pauses(self): # Monotonic clock prevents going backwards, but large pauses are handled by clamping. # TestClock can simulate backwards movement. self.limiter.stop_cleanup_thread() self.clock = TestClock(initial_time=100.0) self.limiter = RateLimiter(rate=10, burst=5, window_seconds=10, clock=self.clock) client_id = "client_time_warp" self.assertTrue(self.limiter.allow(client_id)) # Time 100.0, tokens 4, window cost 1 self.clock.set_time(50.0) # Clock goes backwards # Refill should clamp last_refill_time to current_time (50.0), no extra tokens granted. # Tokens should still be 4 (from previous allow). The refill logic will see time_delta=0. self.assertTrue(self.limiter.allow(client_id)) # Should still allow, tokens 3. self.assertEqual(self.limiter.clients[client_id].tokens, 3) # Now advance time normally, tokens should refill from 50.0 self.clock.set_time(51.0) # 1 second passed from clamped time self.assertTrue(self.limiter.allow(client_id)) # Tokens 3 + 10 (refill) - 1 (cost) = 12. Capped at burst 5. So 5 - 1 = 4. self.assertEqual(self.limiter.clients[client_id].tokens, 4) def test_first_ever_request_lazy_initialization(self): client_id = "new_client" self.assertNotIn(client_id, self.limiter.clients) self.assertTrue(self.limiter.allow(client_id)) self.assertIn(client_id, self.limiter.clients) self.assertEqual(self.limiter.clients[client_id].tokens, 4) # 5 (burst) - 1 (cost) self.assertEqual(self.limiter.clients[client_id].current_window_cost, 1) def test_fractional_tokens_sub_millisecond_timing(self): self.limiter.stop_cleanup_thread() self.limiter = RateLimiter(rate=1, burst=1, window_seconds=10, clock=self.clock) client_id = "fractional_client" self.assertTrue(self.limiter.allow(client_id)) # Tokens 0 self.assertFalse(self.limiter.allow(client_id)) # No tokens self.clock.advance(0.0001) # 0.0001 tokens refilled self.assertFalse(self.limiter.allow(client_id)) # Not enough for cost 1 self.clock.advance(0.9999) # Total 1.0s passed. 1 token refilled. self.assertTrue(self.limiter.allow(client_id)) # Allowed. Tokens 0.