API Rate Limiting in AV Systems: Throttling Solutions and Queue Management for Professional Installations
Modern audiovisual systems increasingly rely on API-based communication, creating new challenges around rate limiting and resource management. As control systems become more sophisticated and installations grow larger, managing API call frequency becomes critical for system stability and performance. This comprehensive guide explores advanced rate limiting strategies, queue management techniques, and retry mechanisms specifically designed for professional AV environments.
Table of Contents
- Understanding Rate Limiting in AV Systems
- Types of Rate Limiting Challenges
- Implementing Client-Side Throttling
- Advanced Queue Management
- Retry Strategies and Backoff Algorithms
- Backpressure Handling
- Device-Specific Rate Limiting
- Monitoring and Alerting
- Performance Optimization
- Real-World Implementation Examples
- Troubleshooting Common Issues
- Best Practices
Understanding Rate Limiting in AV Systems
Rate limiting in audiovisual systems serves multiple purposes: protecting devices from command overload, ensuring fair resource allocation across multiple controllers, and maintaining system stability during peak usage periods. Unlike traditional web APIs, AV device APIs often have unique constraints related to hardware limitations and real-time processing requirements.
Why Rate Limiting Matters in AV
Device Protection:
- Prevents command buffer overflow in legacy devices
- Protects against hardware overheating from excessive processing
- Maintains responsive user interfaces during high-traffic periods
- Preserves device longevity through controlled access patterns
System Stability:
- Ensures predictable response times across large installations
- Prevents cascade failures in distributed control systems
- Maintains quality of service for critical operations
- Enables graceful handling of traffic spikes
Resource Management:
- Optimizes network bandwidth utilization
- Balances processing load across multiple devices
- Prevents resource starvation in shared environments
- Enables fair access for multiple concurrent users
Common Rate Limiting Scenarios
AV systems encounter various rate limiting situations:
python
[object Object],
rate_limit_scenarios = {
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object], ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
}
}Types of Rate Limiting Challenges
Understanding different types of rate limiting helps in choosing appropriate mitigation strategies. AV systems face unique challenges that require specialized approaches.
Hardware-Based Limitations
Many AV devices have inherent hardware limitations that impose natural rate limits:
python
[object Object], time
,[object Object], asyncio
,[object Object], typing ,[object Object], ,[object Object],, ,[object Object],, ,[object Object],
,[object Object], dataclasses ,[object Object], dataclass
,[object Object], enum ,[object Object], Enum
,[object Object], ,[object Object],(,[object Object],):
HARDWARE = ,[object Object],
PROTOCOL = ,[object Object],
NETWORK = ,[object Object],
SOFTWARE = ,[object Object],
,[object Object],
,[object Object], ,[object Object],:
limit_type: LimitationType
max_requests: ,[object Object],
time_window: ,[object Object], ,[object Object],
burst_capacity: ,[object Object],[,[object Object],] = ,[object Object],
recovery_time: ,[object Object],[,[object Object],] = ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
DEVICE_PROFILES = {
,[object Object],: RateLimit(
limit_type=LimitationType.HARDWARE,
max_requests=,[object Object],,
time_window=,[object Object],,
recovery_time=,[object Object],
),
,[object Object],: RateLimit(
limit_type=LimitationType.PROTOCOL,
max_requests=,[object Object],,
time_window=,[object Object],,
burst_capacity=,[object Object],
),
,[object Object],: RateLimit(
limit_type=LimitationType.SOFTWARE,
max_requests=,[object Object],,
time_window=,[object Object],,
burst_capacity=,[object Object],
),
,[object Object],: RateLimit(
limit_type=LimitationType.NETWORK,
max_requests=,[object Object],,
time_window=,[object Object],,
burst_capacity=,[object Object],
)
}
,[object Object],
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[RateLimit]:
,[object Object],
,[object Object], cls.DEVICE_PROFILES.get(device_type)
,[object Object],
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
limits = cls.get_device_limits(device_type)
,[object Object], limits ,[object Object], limits.burst_capacity ,[object Object], ,[object Object], ,[object Object],Protocol-Specific Constraints
Different communication protocols impose their own rate limiting challenges:
python
[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.protocol_limits = {
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: [,[object Object],, ,[object Object],]
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
}
}
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[,[object Object],, ,[object Object],]:
,[object Object],
,[object Object], ,[object Object],.protocol_limits.get(protocol, {})
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
base_limits = ,[object Object],.get_protocol_limits(protocol)
,[object Object], protocol == ,[object Object],:
,[object Object],
max_rate = base_limits.get(,[object Object],, ,[object Object],)
actual_rate = ,[object Object],(max_rate, ,[object Object], / (device_response_time + ,[object Object],))
,[object Object], actual_rate
,[object Object], protocol == ,[object Object],:
,[object Object],
timeout = base_limits.get(,[object Object],, ,[object Object],)
,[object Object], ,[object Object],(,[object Object],, ,[object Object], / ,[object Object],(device_response_time, ,[object Object],))
,[object Object], ,[object Object], ,[object Object],Implementing Client-Side Throttling
Client-side throttling provides the first line of defense against rate limiting issues. Implementing intelligent throttling at the client level prevents unnecessary network traffic and reduces server load.
Token Bucket Algorithm Implementation
The token bucket algorithm provides smooth rate limiting with burst capability:
python
[object Object], time
,[object Object], threading
,[object Object], typing ,[object Object], ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.capacity = capacity
,[object Object],.refill_rate = refill_rate ,[object Object],
,[object Object],.tokens = initial_tokens ,[object Object], initial_tokens ,[object Object], ,[object Object], ,[object Object], ,[object Object], capacity
,[object Object],.last_refill = time.time()
,[object Object],.lock = threading.Lock()
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object],.lock:
,[object Object],._refill()
,[object Object], ,[object Object],.tokens >= tokens:
,[object Object],.tokens -= tokens
,[object Object], ,[object Object],
,[object Object], ,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
now = time.time()
elapsed = now - ,[object Object],.last_refill
tokens_to_add = elapsed * ,[object Object],.refill_rate
,[object Object],.tokens = ,[object Object],(,[object Object],.capacity, ,[object Object],.tokens + tokens_to_add)
,[object Object],.last_refill = now
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object],.lock:
,[object Object],._refill()
,[object Object], ,[object Object],(,[object Object],.tokens)
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object],.lock:
,[object Object],._refill()
,[object Object], ,[object Object],.tokens >= tokens:
,[object Object], ,[object Object],
tokens_needed = tokens - ,[object Object],.tokens
,[object Object], tokens_needed / ,[object Object],.refill_rate
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.device_id = device_id
,[object Object],.limits = device_limits
,[object Object],
capacity = device_limits.burst_capacity ,[object Object], device_limits.max_requests
refill_rate = device_limits.max_requests / device_limits.time_window
,[object Object],.bucket = TokenBucket(capacity, refill_rate)
,[object Object],.last_request_time = ,[object Object],
,[object Object],.consecutive_failures = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object],
,[object Object], ,[object Object],.limits.recovery_time:
time_since_last = time.time() - ,[object Object],.last_request_time
,[object Object], time_since_last < ,[object Object],.limits.recovery_time:
,[object Object], asyncio.sleep(,[object Object],.limits.recovery_time - time_since_last)
,[object Object],
tokens_needed = ,[object Object], ,[object Object], priority == ,[object Object], ,[object Object], ,[object Object],
,[object Object], ,[object Object],.bucket.consume(tokens_needed):
,[object Object],.last_request_time = time.time()
,[object Object], ,[object Object],
,[object Object],
wait_time = ,[object Object],.bucket.time_until_available(tokens_needed)
,[object Object], wait_time <= ,[object Object],: ,[object Object],
,[object Object], asyncio.sleep(wait_time)
,[object Object], ,[object Object],.bucket.consume(tokens_needed)
,[object Object], ,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],.consecutive_failures += ,[object Object],
,[object Object],
,[object Object], ,[object Object],.consecutive_failures > ,[object Object],:
slowdown_factor = ,[object Object],(,[object Object],.consecutive_failures / ,[object Object],, ,[object Object],)
,[object Object],.bucket.refill_rate /= slowdown_factor
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object],.consecutive_failures > ,[object Object],:
,[object Object],.consecutive_failures = ,[object Object],
,[object Object],
original_rate = ,[object Object],.limits.max_requests / ,[object Object],.limits.time_window
,[object Object],.bucket.refill_rate = original_rateSliding Window Rate Limiter
For more precise rate limiting, implement a sliding window approach:
python
[object Object], collections ,[object Object], deque
,[object Object], asyncio
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.max_requests = max_requests
,[object Object],.window_size = window_size
,[object Object],.requests = deque()
,[object Object],.lock = asyncio.Lock()
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
now = time.time()
,[object Object],
,[object Object], ,[object Object],.requests ,[object Object], ,[object Object],.requests[,[object Object],] <= now - ,[object Object],.window_size:
,[object Object],.requests.popleft()
,[object Object],
,[object Object], ,[object Object],(,[object Object],.requests) < ,[object Object],.max_requests:
,[object Object],.requests.append(now)
,[object Object], ,[object Object],
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
start_time = time.time()
,[object Object], time.time() - start_time < timeout:
,[object Object], ,[object Object], ,[object Object],.is_allowed():
,[object Object], ,[object Object],
,[object Object],
,[object Object], ,[object Object],.requests:
oldest_request = ,[object Object],.requests[,[object Object],]
wait_time = oldest_request + ,[object Object],.window_size - time.time()
wait_time = ,[object Object],(,[object Object],, ,[object Object],(wait_time, ,[object Object],))
,[object Object], asyncio.sleep(wait_time)
,[object Object],:
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.base_rate = initial_rate
,[object Object],.current_rate = initial_rate
,[object Object],.window_size = window_size
,[object Object],.limiter = SlidingWindowLimiter(initial_rate, window_size)
,[object Object],.success_count = ,[object Object],
,[object Object],.failure_count = ,[object Object],
,[object Object],.last_adjustment = time.time()
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
allowed = ,[object Object], ,[object Object],.limiter.is_allowed()
,[object Object], allowed:
,[object Object],.success_count += ,[object Object],
,[object Object],:
,[object Object],.failure_count += ,[object Object],
,[object Object],
,[object Object], time.time() - ,[object Object],.last_adjustment > ,[object Object],:
,[object Object], ,[object Object],._adjust_rate()
,[object Object], allowed
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
total_requests = ,[object Object],.success_count + ,[object Object],.failure_count
,[object Object], total_requests > ,[object Object],:
success_rate = ,[object Object],.success_count / total_requests
,[object Object], success_rate > ,[object Object], ,[object Object], ,[object Object],.current_rate < ,[object Object],.base_rate * ,[object Object],:
,[object Object],
,[object Object],.current_rate = ,[object Object],(,[object Object],.current_rate * ,[object Object],, ,[object Object],.base_rate * ,[object Object],)
,[object Object], success_rate < ,[object Object],:
,[object Object],
,[object Object],.current_rate = ,[object Object],(,[object Object],.current_rate * ,[object Object],, ,[object Object],.base_rate * ,[object Object],)
,[object Object],
,[object Object],.limiter = SlidingWindowLimiter(,[object Object],(,[object Object],.current_rate), ,[object Object],.window_size)
,[object Object],
,[object Object],.success_count = ,[object Object],
,[object Object],.failure_count = ,[object Object],
,[object Object],.last_adjustment = time.time()Advanced Queue Management
Effective queue management ensures that high-priority commands are processed promptly while maintaining system stability. Implementing sophisticated queuing strategies helps handle traffic bursts and provides quality of service guarantees.
Priority Queue Implementation
python
[object Object], heapq
,[object Object], asyncio
,[object Object], typing ,[object Object], ,[object Object],, ,[object Object],, ,[object Object],
,[object Object], dataclasses ,[object Object], dataclass, field
,[object Object], enum ,[object Object], IntEnum
,[object Object], ,[object Object],(,[object Object],):
EMERGENCY = ,[object Object],
HIGH = ,[object Object],
NORMAL = ,[object Object],
LOW = ,[object Object],
BACKGROUND = ,[object Object],
,[object Object],
,[object Object], ,[object Object],:
priority: Priority
timestamp: ,[object Object],
request_id: ,[object Object],
device_id: ,[object Object],
command: ,[object Object],
parameters: ,[object Object],[,[object Object],, ,[object Object],]
callback: ,[object Object],[,[object Object],] = ,[object Object],
timeout: ,[object Object], = ,[object Object],
retry_count: ,[object Object], = ,[object Object],
max_retries: ,[object Object], = ,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object],.priority != other.priority:
,[object Object], ,[object Object],.priority < other.priority
,[object Object], ,[object Object],.timestamp < other.timestamp
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.max_size = max_size
,[object Object],._queue = []
,[object Object],._index = ,[object Object],
,[object Object],.lock = asyncio.Lock()
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object], ,[object Object],(,[object Object],._queue) >= ,[object Object],.max_size:
,[object Object],
,[object Object], request.priority <= Priority.NORMAL:
,[object Object],
,[object Object],._queue = [item ,[object Object], item ,[object Object], ,[object Object],._queue
,[object Object], item[,[object Object],].priority <= Priority.NORMAL]
heapq.heapify(,[object Object],._queue)
,[object Object], ,[object Object],(,[object Object],._queue) >= ,[object Object],.max_size:
,[object Object], ,[object Object], ,[object Object],
,[object Object],:
,[object Object], ,[object Object], ,[object Object],
heapq.heappush(,[object Object],._queue, (,[object Object],._index, request))
,[object Object],._index += ,[object Object],
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[QueuedRequest]:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object], ,[object Object],._queue:
_, request = heapq.heappop(,[object Object],._queue)
,[object Object], request
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[QueuedRequest]:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object], ,[object Object],._queue:
,[object Object], ,[object Object],._queue[,[object Object],][,[object Object],]
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object], ,[object Object],(,[object Object],._queue)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object],._queue = [(idx, req) ,[object Object], idx, req ,[object Object], ,[object Object],._queue
,[object Object], req.device_id != device_id]
heapq.heapify(,[object Object],._queue)
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.max_concurrent_devices = max_concurrent_devices
,[object Object],.device_queues = {}
,[object Object],.active_processors = {}
,[object Object],.global_queue = PriorityQueue()
,[object Object],.processing = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
device_id = request.device_id
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.device_queues:
,[object Object],.device_queues[device_id] = PriorityQueue(max_size=,[object Object],)
,[object Object],
,[object Object], ,[object Object], ,[object Object],.device_queues[device_id].enqueue(request):
request_id = request.request_id
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.active_processors:
,[object Object], ,[object Object],._start_device_processor(device_id)
,[object Object], request_id
,[object Object],
,[object Object], request.priority == Priority.EMERGENCY:
,[object Object], ,[object Object], ,[object Object],.global_queue.enqueue(request):
,[object Object], request.request_id
,[object Object], Exception(,[object Object],)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object],.active_processors:
,[object Object],
,[object Object],.active_processors[device_id] = asyncio.create_task(
,[object Object],._process_device_queue(device_id)
)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
device_queue = ,[object Object],.device_queues[device_id]
,[object Object], ,[object Object],:
,[object Object],:
,[object Object],
emergency_request = ,[object Object], ,[object Object],.global_queue.peek()
,[object Object], (emergency_request ,[object Object],
emergency_request.priority == Priority.EMERGENCY ,[object Object],
emergency_request.device_id == device_id):
request = ,[object Object], ,[object Object],.global_queue.dequeue()
,[object Object],:
request = ,[object Object], device_queue.dequeue()
,[object Object], ,[object Object], request:
,[object Object],
,[object Object], asyncio.sleep(,[object Object],)
,[object Object],
,[object Object],
,[object Object], ,[object Object],._execute_request(request)
,[object Object], asyncio.CancelledError:
,[object Object],
,[object Object], Exception ,[object Object], e:
,[object Object],(,[object Object],)
,[object Object], asyncio.sleep(,[object Object],)
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object],.active_processors:
,[object Object], ,[object Object],.active_processors[device_id]
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],:
,[object Object],
,[object Object], time.time() - request.timestamp > request.timeout:
,[object Object], request.callback:
,[object Object], request.callback(,[object Object],, ,[object Object],)
,[object Object],
,[object Object],
result = ,[object Object], ,[object Object],._send_device_command(
request.device_id,
request.command,
request.parameters
)
,[object Object], request.callback:
,[object Object], request.callback(result, ,[object Object],)
,[object Object], Exception ,[object Object], e:
,[object Object],
,[object Object], request.retry_count < request.max_retries:
request.retry_count += ,[object Object],
request.timestamp = time.time() ,[object Object],
,[object Object],
new_priority = ,[object Object],(Priority.BACKGROUND, request.priority + ,[object Object],)
request.priority = new_priority
device_queue = ,[object Object],.device_queues.get(request.device_id)
,[object Object], device_queue:
,[object Object], device_queue.enqueue(request)
,[object Object],:
,[object Object], request.callback:
,[object Object], request.callback(,[object Object],, ,[object Object],(e))
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object],
,[object Object], asyncio.sleep(,[object Object],) ,[object Object],
,[object Object], {,[object Object],: ,[object Object],, ,[object Object],: device_id}Circuit Breaker Pattern
Implement circuit breaker pattern to prevent cascade failures:
python
[object Object], enum ,[object Object], Enum
,[object Object], asyncio
,[object Object], time
,[object Object], ,[object Object],(,[object Object],):
CLOSED = ,[object Object],
OPEN = ,[object Object],
HALF_OPEN = ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.failure_threshold = failure_threshold
,[object Object],.recovery_timeout = recovery_timeout
,[object Object],.test_timeout = test_timeout
,[object Object],.failure_count = ,[object Object],
,[object Object],.last_failure_time = ,[object Object],
,[object Object],.state = CircuitState.CLOSED
,[object Object],.lock = asyncio.Lock()
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object], ,[object Object],.lock:
,[object Object], ,[object Object],.state == CircuitState.OPEN:
,[object Object], time.time() - ,[object Object],.last_failure_time >= ,[object Object],.recovery_timeout:
,[object Object],.state = CircuitState.HALF_OPEN
,[object Object],:
,[object Object], Exception(,[object Object],)
,[object Object], ,[object Object],.state == CircuitState.HALF_OPEN:
,[object Object],
,[object Object],:
result = ,[object Object], asyncio.wait_for(func(*args, **kwargs), ,[object Object],.test_timeout)
,[object Object],.state = CircuitState.CLOSED
,[object Object],.failure_count = ,[object Object],
,[object Object], result
,[object Object], Exception ,[object Object], e:
,[object Object],.state = CircuitState.OPEN
,[object Object],.last_failure_time = time.time()
,[object Object], e
,[object Object],
,[object Object],:
result = ,[object Object], func(*args, **kwargs)
,[object Object],.failure_count = ,[object Object],
,[object Object], result
,[object Object], Exception ,[object Object], e:
,[object Object],.failure_count += ,[object Object],
,[object Object],.last_failure_time = time.time()
,[object Object], ,[object Object],.failure_count >= ,[object Object],.failure_threshold:
,[object Object],.state = CircuitState.OPEN
,[object Object], e
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.breakers = {}
,[object Object],.device_stats = {}
,[object Object], ,[object Object],(,[object Object],) -> CircuitBreaker:
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.breakers:
,[object Object],.breakers[device_id] = CircuitBreaker()
,[object Object],.device_stats[device_id] = {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
}
,[object Object], ,[object Object],.breakers[device_id]
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
breaker = ,[object Object],.get_breaker(device_id)
stats = ,[object Object],.device_stats[device_id]
stats[,[object Object],] += ,[object Object],
,[object Object],:
result = ,[object Object], breaker.call(func, *args, **kwargs)
stats[,[object Object],] += ,[object Object],
,[object Object], result
,[object Object], Exception ,[object Object], e:
stats[,[object Object],] += ,[object Object],
,[object Object], breaker.state == CircuitState.OPEN:
stats[,[object Object],] += ,[object Object],
,[object Object], e
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[,[object Object],, ,[object Object],]:
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.device_stats:
,[object Object], {}
stats = ,[object Object],.device_stats[device_id]
breaker = ,[object Object],.breakers[device_id]
success_rate = ,[object Object],
,[object Object], stats[,[object Object],] > ,[object Object],:
success_rate = stats[,[object Object],] / stats[,[object Object],]
,[object Object], {
,[object Object],: device_id,
,[object Object],: breaker.state.value,
,[object Object],: success_rate,
,[object Object],: stats[,[object Object],],
,[object Object],: stats[,[object Object],],
,[object Object],: breaker.failure_count
}Retry Strategies and Backoff Algorithms
Implementing intelligent retry strategies helps handle temporary failures while avoiding excessive load on struggling devices.
Exponential Backoff with Jitter
python
[object Object], random
,[object Object], asyncio
,[object Object], typing ,[object Object], ,[object Object],, ,[object Object],, ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.base_delay = base_delay
,[object Object],.max_delay = max_delay
,[object Object],.exponential_base = exponential_base
,[object Object],.jitter = jitter
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], attempt == ,[object Object],:
,[object Object], ,[object Object],
delay = ,[object Object],.base_delay * (,[object Object],.exponential_base ** (attempt - ,[object Object],))
delay = ,[object Object],(delay, ,[object Object],.max_delay)
,[object Object], ,[object Object],.jitter:
,[object Object],
jitter_range = delay * ,[object Object],
delay += random.uniform(-jitter_range, jitter_range)
,[object Object], ,[object Object],(,[object Object],, delay)
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
last_exception = ,[object Object],
,[object Object], attempt ,[object Object], ,[object Object],(max_attempts):
,[object Object],:
,[object Object], ,[object Object], func()
,[object Object], exceptions ,[object Object], e:
last_exception = e
,[object Object], attempt == max_attempts - ,[object Object],:
,[object Object],
,[object Object],
delay = ,[object Object],.calculate_delay(attempt + ,[object Object],)
,[object Object], asyncio.sleep(delay)
,[object Object], last_exception
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.device_id = device_id
,[object Object],.success_history = []
,[object Object],.failure_patterns = {}
,[object Object],.base_backoff = ExponentialBackoff()
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
max_attempts = ,[object Object],._calculate_max_attempts(context)
backoff_multiplier = ,[object Object],._calculate_backoff_multiplier(context)
,[object Object],
custom_backoff = ExponentialBackoff(
base_delay=,[object Object],.base_backoff.base_delay * backoff_multiplier,
max_delay=,[object Object],.base_backoff.max_delay,
exponential_base=,[object Object],.base_backoff.exponential_base
)
start_time = time.time()
,[object Object],:
result = ,[object Object], custom_backoff.retry_with_backoff(func, max_attempts)
,[object Object],._record_success(context, time.time() - start_time)
,[object Object], result
,[object Object], Exception ,[object Object], e:
,[object Object],._record_failure(context, ,[object Object],(e), time.time() - start_time)
,[object Object],
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], context ,[object Object], ,[object Object], ,[object Object],.failure_patterns:
,[object Object], ,[object Object], ,[object Object],
pattern = ,[object Object],.failure_patterns[context]
success_rate = pattern.get(,[object Object],, ,[object Object],)
,[object Object], success_rate > ,[object Object],:
,[object Object], ,[object Object], ,[object Object],
,[object Object], success_rate > ,[object Object],:
,[object Object], ,[object Object],
,[object Object],:
,[object Object], ,[object Object], ,[object Object],
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], context ,[object Object], ,[object Object], ,[object Object],.failure_patterns:
,[object Object], ,[object Object],
pattern = ,[object Object],.failure_patterns[context]
avg_response_time = pattern.get(,[object Object],, ,[object Object],)
,[object Object],
,[object Object], avg_response_time > ,[object Object],:
,[object Object], ,[object Object],
,[object Object], avg_response_time > ,[object Object],:
,[object Object], ,[object Object],
,[object Object],:
,[object Object], ,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],.success_history.append({
,[object Object],: context,
,[object Object],: duration,
,[object Object],: time.time()
})
,[object Object],
cutoff_time = time.time() - ,[object Object], ,[object Object],
,[object Object],.success_history = [h ,[object Object], h ,[object Object], ,[object Object],.success_history
,[object Object], h[,[object Object],] > cutoff_time]
,[object Object],._update_pattern_stats(context)
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], context ,[object Object], ,[object Object], ,[object Object],.failure_patterns:
,[object Object],.failure_patterns[context] = {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: [],
,[object Object],: {}
}
pattern = ,[object Object],.failure_patterns[context]
pattern[,[object Object],] += ,[object Object],
pattern[,[object Object],] += ,[object Object],
pattern[,[object Object],].append(duration)
,[object Object],
,[object Object], error ,[object Object], ,[object Object], pattern[,[object Object],]:
pattern[,[object Object],][error] = ,[object Object],
pattern[,[object Object],][error] += ,[object Object],
,[object Object],._update_pattern_stats(context)
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], context ,[object Object], ,[object Object], ,[object Object],.failure_patterns:
,[object Object],
pattern = ,[object Object],.failure_patterns[context]
successful_ops = ,[object Object],([h ,[object Object], h ,[object Object], ,[object Object],.success_history
,[object Object], h[,[object Object],] == context])
total_ops = pattern[,[object Object],] + successful_ops
,[object Object], total_ops > ,[object Object],:
pattern[,[object Object],] = successful_ops / total_ops
,[object Object], pattern[,[object Object],]:
pattern[,[object Object],] = ,[object Object],(pattern[,[object Object],]) / ,[object Object],(pattern[,[object Object],])Backpressure Handling
Backpressure handling prevents system overload by intelligently managing resource consumption and providing feedback to upstream components.
Flow Control Implementation
python
[object Object], asyncio
,[object Object], typing ,[object Object], ,[object Object],, ,[object Object],, ,[object Object],
,[object Object], dataclasses ,[object Object], dataclass
,[object Object], logging
,[object Object],
,[object Object], ,[object Object],:
queue_depth: ,[object Object],
processing_rate: ,[object Object],
error_rate: ,[object Object],
resource_utilization: ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.max_queue_depth = max_queue_depth
,[object Object],.target_processing_rate = target_processing_rate
,[object Object],.current_load = ,[object Object],
,[object Object],.request_queue = asyncio.Queue(maxsize=max_queue_depth)
,[object Object],.processing_semaphore = asyncio.Semaphore(,[object Object],) ,[object Object],
,[object Object],.metrics = BackpressureMetrics(,[object Object],, ,[object Object],, ,[object Object],, ,[object Object],)
,[object Object],.load_shedding_enabled = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
current_depth = ,[object Object],.request_queue.qsize()
,[object Object],
,[object Object],.metrics.queue_depth = current_depth
,[object Object],.metrics.resource_utilization = current_depth / ,[object Object],.max_queue_depth
,[object Object],
,[object Object], ,[object Object], ,[object Object],._should_reject_request(request, priority):
,[object Object], ,[object Object],
,[object Object],:
,[object Object],
timeout = ,[object Object], ,[object Object], priority >= Priority.HIGH ,[object Object], ,[object Object],
,[object Object], asyncio.wait_for(
,[object Object],.request_queue.put(request),
timeout=timeout
)
,[object Object], ,[object Object],
,[object Object], asyncio.TimeoutError:
logging.warning(,[object Object],)
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.load_shedding_enabled:
,[object Object], ,[object Object],
,[object Object],
,[object Object], priority == Priority.EMERGENCY:
,[object Object], ,[object Object],
,[object Object],
,[object Object], priority >= Priority.LOW ,[object Object], ,[object Object],.metrics.resource_utilization > ,[object Object],:
,[object Object], ,[object Object],
,[object Object],
,[object Object], priority >= Priority.NORMAL ,[object Object], ,[object Object],.metrics.resource_utilization > ,[object Object],:
,[object Object], ,[object Object],
,[object Object],
,[object Object], ,[object Object],.metrics.resource_utilization >= ,[object Object],:
,[object Object], ,[object Object],
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object],:
,[object Object],:
request = ,[object Object], ,[object Object],.request_queue.get()
,[object Object],
,[object Object], ,[object Object], ,[object Object],.processing_semaphore:
,[object Object], ,[object Object],._handle_request(request)
,[object Object],.request_queue.task_done()
,[object Object], Exception ,[object Object], e:
logging.error(,[object Object],)
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.controllers = {}
,[object Object],.system_metrics = {}
,[object Object],.adjustment_interval = ,[object Object], ,[object Object],
,[object Object],.last_adjustment = time.time()
,[object Object], ,[object Object],(,[object Object],) -> BackpressureController:
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.controllers:
,[object Object],.controllers[device_id] = BackpressureController()
,[object Object],.system_metrics[device_id] = {
,[object Object],: [],
,[object Object],: [],
,[object Object],: []
}
,[object Object], ,[object Object],.controllers[device_id]
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object],:
,[object Object],:
current_time = time.time()
,[object Object], current_time - ,[object Object],.last_adjustment >= ,[object Object],.adjustment_interval:
,[object Object], ,[object Object],._adjust_backpressure_settings()
,[object Object],.last_adjustment = current_time
,[object Object], asyncio.sleep(,[object Object],) ,[object Object],
,[object Object], Exception ,[object Object], e:
logging.error(,[object Object],)
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], device_id, controller ,[object Object], ,[object Object],.controllers.items():
metrics = ,[object Object],.system_metrics[device_id]
,[object Object],
recent_throughput = ,[object Object],._calculate_recent_average(
metrics[,[object Object],], window_minutes=,[object Object],
)
recent_error_rate = ,[object Object],._calculate_recent_average(
metrics[,[object Object],], window_minutes=,[object Object],
)
,[object Object],
,[object Object], recent_error_rate < ,[object Object], ,[object Object], recent_throughput > controller.target_processing_rate:
,[object Object],
new_max_depth = ,[object Object],(controller.max_queue_depth * ,[object Object],, ,[object Object],)
controller.max_queue_depth = ,[object Object],(new_max_depth)
,[object Object], recent_error_rate > ,[object Object], ,[object Object], recent_throughput < controller.target_processing_rate * ,[object Object],:
,[object Object],
new_max_depth = ,[object Object],(controller.max_queue_depth * ,[object Object],, ,[object Object],)
controller.max_queue_depth = ,[object Object],(new_max_depth)
logging.info(,[object Object],
,[object Object],
,[object Object],
,[object Object],)
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
cutoff_time = time.time() - (window_minutes * ,[object Object],)
recent_values = [value ,[object Object], timestamp, value ,[object Object], history ,[object Object], timestamp > cutoff_time]
,[object Object], recent_values:
,[object Object], ,[object Object],(recent_values) / ,[object Object],(recent_values)
,[object Object], ,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object], ,[object Object],.system_metrics:
,[object Object],
timestamp = time.time()
metrics = ,[object Object],.system_metrics[device_id]
metrics[,[object Object],].append((timestamp, throughput))
metrics[,[object Object],].append((timestamp, error_rate))
metrics[,[object Object],].append((timestamp, response_time))
,[object Object],
cutoff_time = timestamp - ,[object Object],
,[object Object], history_list ,[object Object], metrics.values():
history_list[:] = [(t, v) ,[object Object], t, v ,[object Object], history_list ,[object Object], t > cutoff_time]Device-Specific Rate Limiting
Different AV devices require specialized rate limiting approaches based on their unique characteristics and limitations.
Display Device Rate Limiting
python
[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.display_type = display_type
,[object Object],.command_intervals = ,[object Object],._get_display_intervals()
,[object Object],.last_command_times = {}
,[object Object],.warming_up = ,[object Object],
,[object Object],.warmup_start_time = time.time()
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[,[object Object],, ,[object Object],]:
,[object Object],
intervals = {
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
},
,[object Object],: {
,[object Object],: ,[object Object],, ,[object Object],
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
}
}
,[object Object], intervals.get(,[object Object],.display_type, intervals[,[object Object],])
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object],
,[object Object], ,[object Object],.warming_up:
warmup_duration = time.time() - ,[object Object],.warmup_start_time
,[object Object], warmup_duration < ,[object Object],: ,[object Object],
,[object Object], command_type == ,[object Object],:
,[object Object], warmup_duration > ,[object Object], ,[object Object],
,[object Object],:
,[object Object], warmup_duration > ,[object Object], ,[object Object],
,[object Object],:
,[object Object],.warming_up = ,[object Object],
,[object Object],
interval = ,[object Object],.command_intervals.get(command_type, ,[object Object],.command_intervals[,[object Object],])
last_time = ,[object Object],.last_command_times.get(command_type, ,[object Object],)
,[object Object], time.time() - last_time >= interval:
,[object Object],.last_command_times[command_type] = time.time()
,[object Object], ,[object Object],
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
start_time = time.time()
,[object Object], time.time() - start_time < timeout:
,[object Object], ,[object Object], ,[object Object],.can_send_command(command_type):
,[object Object], ,[object Object],
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.processor_type = processor_type
,[object Object],.channel_limits = {}
,[object Object],.global_limit = TokenBucket(,[object Object],, ,[object Object],) ,[object Object],
,[object Object],.batch_commands = []
,[object Object],.batch_timer = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object],
,[object Object], ,[object Object], ,[object Object],.global_limit.consume():
,[object Object],
,[object Object], command ,[object Object], [,[object Object],, ,[object Object],, ,[object Object],]:
,[object Object], ,[object Object], ,[object Object],._add_to_batch(channel, command, value)
,[object Object], ,[object Object],
,[object Object],
,[object Object], channel ,[object Object], ,[object Object], ,[object Object],:
,[object Object], ,[object Object], ,[object Object], ,[object Object],._check_channel_limit(channel):
,[object Object], ,[object Object], ,[object Object],._add_to_batch(channel, command, value)
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], channel ,[object Object], ,[object Object], ,[object Object],.channel_limits:
,[object Object],.channel_limits[channel] = TokenBucket(,[object Object],, ,[object Object],) ,[object Object],
,[object Object], ,[object Object],.channel_limits[channel].consume()
,[object Object], ,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object],.batch_commands.append({
,[object Object],: channel,
,[object Object],: command,
,[object Object],: value,
,[object Object],: time.time()
})
,[object Object],
,[object Object], ,[object Object],.batch_timer:
,[object Object],.batch_timer.cancel()
,[object Object],.batch_timer = asyncio.get_event_loop().call_later(
,[object Object],, ,[object Object],: asyncio.create_task(,[object Object],._flush_batch())
)
,[object Object], ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object], ,[object Object],.batch_commands:
,[object Object],
,[object Object],
grouped_commands = {}
,[object Object], cmd ,[object Object], ,[object Object],.batch_commands:
cmd_type = cmd[,[object Object],]
,[object Object], cmd_type ,[object Object], ,[object Object], grouped_commands:
grouped_commands[cmd_type] = []
grouped_commands[cmd_type].append(cmd)
,[object Object],
,[object Object], cmd_type, commands ,[object Object], grouped_commands.items():
,[object Object], ,[object Object],._process_command_group(cmd_type, commands)
,[object Object],.batch_commands.clear()
,[object Object],.batch_timer = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], command_type == ,[object Object],:
,[object Object],
volume_changes = {}
,[object Object], cmd ,[object Object], commands:
volume_changes[cmd[,[object Object],]] = cmd[,[object Object],]
,[object Object], ,[object Object],._send_batch_volume_changes(volume_changes)
,[object Object], command_type == ,[object Object],:
,[object Object],
mute_changes = {}
,[object Object], cmd ,[object Object], commands:
mute_changes[cmd[,[object Object],]] = cmd[,[object Object],]
,[object Object], ,[object Object],._send_batch_mute_changes(mute_changes)
,[object Object],:
,[object Object],
,[object Object], cmd ,[object Object], commands:
,[object Object], ,[object Object],._send_individual_command(cmd)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],
,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],
,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],
,[object Object],Monitoring and Alerting
Comprehensive monitoring helps identify rate limiting issues before they impact system performance.
Rate Limiting Metrics Collection
python
[object Object], asyncio
,[object Object], json
,[object Object], typing ,[object Object], ,[object Object],, ,[object Object],, ,[object Object],
,[object Object], dataclasses ,[object Object], dataclass, asdict
,[object Object], datetime ,[object Object], datetime, timedelta
,[object Object],
,[object Object], ,[object Object],:
timestamp: ,[object Object],
device_id: ,[object Object],
metric_type: ,[object Object],
value: ,[object Object],
tags: ,[object Object],[,[object Object],, ,[object Object],] = ,[object Object],
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.retention_hours = retention_hours
,[object Object],.metrics = []
,[object Object],.alerts = []
,[object Object],.thresholds = {
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],,
,[object Object],: ,[object Object],
}
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
metric = RateLimitMetric(
timestamp=time.time(),
device_id=device_id,
metric_type=metric_type,
value=value,
tags=tags ,[object Object], {}
)
,[object Object],.metrics.append(metric)
,[object Object],._check_thresholds(metric)
,[object Object],._cleanup_old_metrics()
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
metric_key = ,[object Object],
warning_threshold = ,[object Object],.thresholds.get(,[object Object],)
critical_threshold = ,[object Object],.thresholds.get(,[object Object],)
,[object Object], critical_threshold ,[object Object], metric.value >= critical_threshold:
,[object Object],._create_alert(,[object Object],, metric)
,[object Object], warning_threshold ,[object Object], metric.value >= warning_threshold:
,[object Object],._create_alert(,[object Object],, metric)
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
alert = {
,[object Object],: metric.timestamp,
,[object Object],: severity,
,[object Object],: metric.device_id,
,[object Object],: metric.metric_type,
,[object Object],: metric.value,
,[object Object],: ,[object Object],
}
,[object Object],.alerts.append(alert)
,[object Object],(,[object Object],)
,[object Object], ,[object Object],(,[object Object],):
,[object Object],
cutoff_time = time.time() - (,[object Object],.retention_hours * ,[object Object],)
,[object Object],.metrics = [m ,[object Object], m ,[object Object], ,[object Object],.metrics ,[object Object], m.timestamp > cutoff_time]
,[object Object],.alerts = [a ,[object Object], a ,[object Object], ,[object Object],.alerts ,[object Object], a[,[object Object],] > cutoff_time]
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],[,[object Object],, ,[object Object],]:
,[object Object],
cutoff_time = time.time() - (hours * ,[object Object],)
device_metrics = [m ,[object Object], m ,[object Object], ,[object Object],.metrics
,[object Object], m.device_id == device_id ,[object Object], m.timestamp > cutoff_time]
,[object Object], ,[object Object], device_metrics:
,[object Object], {}
,[object Object],
by_type = {}
,[object Object], metric ,[object Object], device_metrics:
,[object Object], metric.metric_type ,[object Object], ,[object Object], by_type:
by_type[metric.metric_type] = []
by_type[metric.metric_type].append(metric.value)
,[object Object],
summary = {
,[object Object],: device_id,
,[object Object],: hours,
,[object Object],: {}
}
,[object Object], metric_type, values ,[object Object], by_type.items():
summary[,[object Object],][metric_type] = {
,[object Object],: ,[object Object],(values),
,[object Object],: ,[object Object],(values) / ,[object Object],(values),
,[object Object],: ,[object Object],(values),
,[object Object],: ,[object Object],(values),
,[object Object],: values[-,[object Object],] ,[object Object], values ,[object Object], ,[object Object],
}
,[object Object], summary
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], == ,[object Object],:
,[object Object], json.dumps([asdict(m) ,[object Object], m ,[object Object], ,[object Object],.metrics], indent=,[object Object],)
,[object Object], ,[object Object], == ,[object Object],:
,[object Object], ,[object Object],._export_csv()
,[object Object],:
,[object Object], ValueError(,[object Object],)
,[object Object], ,[object Object],(,[object Object],) -> ,[object Object],:
,[object Object],
,[object Object], ,[object Object], ,[object Object],.metrics:
,[object Object], ,[object Object],
lines = [,[object Object],]
,[object Object], metric ,[object Object], ,[object Object],.metrics:
tags_str = json.dumps(metric.tags) ,[object Object], metric.tags ,[object Object], ,[object Object],
line = ,[object Object],
lines.append(line)
,[object Object], ,[object Object],.join(lines)
,[object Object], ,[object Object],:
,[object Object],
,[object Object], ,[object Object],(,[object Object],):
,[object Object],.collector = metrics_collector
,[object Object],.update_interval = ,[object Object],
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object], ,[object Object],:
,[object Object],:
,[object Object], ,[object Object],._update_dashboard()
,[object Object], asyncio.sleep(,[object Object],.update_interval)
,[object Object], Exception ,[object Object], e:
,[object Object],(,[object Object],)
,[object Object], asyncio.sleep(,[object Object],)
,[object Object], ,[object Object], ,[object Object],(,[object Object],):
,[object Object],
,[object Object],
,[object Object],(,[object Object],)
,[object Object],(,[object Object],)
,[object Object],(,[object Object],)
,[object Object],()
,[object Object],
recent_time = time.time() - ,[object Object], ,[object Object],
recent_metrics = [m ,[object Object], m ,[object Object], ,[object Object],.collector.metrics ,[object Object], m.timestamp > recent_time]
devices = ,[object Object],(m.device_id ,[object Object], m ,[object Object], recent_metrics)
,[object Object], ,[object Object], devices:
,[object Object],(,[object Object],)
,[object Object],
,[object Object],
,[object Object], device_id ,[object Object], ,[object Object],(devices):
summary = ,[object Object],.collector.get_device_summary(device_id, hours=,[object Object],)
,[object Object], summary:
,[object Object],(,[object Object],)
,[object Object], metric_type, stats ,[object Object], summary[,[object Object],].items():
,[object Object],(,[object Object],
,[object Object],)
,[object Object],()
,[object Object],
recent_alerts = [a ,[object Object], a ,[object Object], ,[object Object],.collector.alerts ,[object Object], a[,[object Object],] > recent_time]
,[object Object], recent_alerts:
,[object Object],(,[object Object],)
,[object Object], alert ,[object Object], recent_alerts[-,[object Object],:]: ,[object Object],
timestamp = datetime.fromtimestamp(alert[,[object Object],])
,[object Object],(,[object Object],)Best Practices
Following established best practices ensures reliable and maintainable rate limiting implementations:
Implementation Guidelines
- Start Conservative: Begin with conservative rate limits and gradually increase based on observed performance
- Monitor Everything: Implement comprehensive monitoring and alerting for all rate limiting components
- Plan for Failures: Design systems to gracefully handle rate limiting failures and device unavailability
- Test Thoroughly: Test rate limiting under various load conditions and failure scenarios
- Document Limits: Maintain clear documentation of all rate limits and their rationale
Common Pitfalls to Avoid
- Over-Aggressive Limiting: Don't set limits so low that normal operations are impacted
- Ignoring Burst Traffic: Account for legitimate burst traffic patterns in your design
- Poor Error Messages: Provide clear, actionable error messages when rate limits are exceeded
- Lack of Observability: Always implement logging and metrics for rate limiting decisions
- Hardcoded Values: Use configuration files for rate limits to enable runtime adjustments
Troubleshooting Common Issues
Queue Backup Issues
When queues consistently back up, investigate:
- Device response times and availability
- Rate limit configurations
- Request prioritization logic
- Circuit breaker settings
High Error Rates
For persistent error rates:
- Check device health and connectivity
- Verify rate limits match device capabilities
- Review retry logic and backoff strategies
- Monitor network conditions
Performance Degradation
When system performance degrades:
- Analyze request patterns and timing
- Check for resource contention
- Review queue depths and processing rates
- Validate monitoring accuracy
This comprehensive guide to API rate limiting in AV systems provides the foundation for building robust, scalable control systems that can handle the demands of modern professional installations. By implementing these strategies, you'll ensure reliable operation even under challenging conditions while maintaining optimal system performance.
For additional resources on AV system programming and network optimization, explore our TCP/IP Control Systems Guide and AV Network Design documentation.
End of documentation