aiocop 1.1.1

Non-intrusive monitoring for Python asyncio and uvloop. Detects, pinpoints, and logs blocking IO and CPU calls that freeze your event loop.

Non-intrusive monitoring for Python asyncio.
Detects, pinpoints, and logs blocking IO and CPU calls that freeze your event loop.

Features

• Production-Safe & Low Overhead: Leverages Python's sys.audit hooks for minimal runtime overhead, making it safe for production use
• Works with asyncio and uvloop: Compatible with both standard asyncio and uvloop event loops out of the box
• Blocking I/O Detection: Automatically detects blocking I/O calls (file operations, network calls, subprocess, etc.) in your async code
• Stack Trace Capture: Captures full stack traces to pinpoint exactly where blocking calls originate
• Severity Scoring: Assigns severity scores to blocking events to help prioritize fixes
• Callback-based Events: Register callbacks to handle slow task events however you need (logging, metrics, alerts)
• Dynamic Controls: Enable/disable monitoring at runtime, useful for gradual rollout or debugging sessions
• Exception Raising: Optionally raise exceptions on high-severity blocking I/O for strict enforcement during development

How It Works

aiocop wraps the event loop's scheduling methods (call_soon, call_later, etc.) and uses Python's sys.audit hooks to detect blocking calls. This approach works with both standard asyncio and uvloop. When your code calls a blocking function like open(), the audit event is captured along with the full stack trace—letting you know exactly where the problem is.

Why aiocop?

aiocop was built to solve specific production constraints that existing approaches didn't quite fit.

vs. Heavy Monkey-Patching (e.g., blockbuster): Many excellent tools rely on extensive monkey-patching of standard library logic to detect blocking calls. While effective, this approach can sometimes conflict with other libraries that instrument code (like APMs). aiocop prioritizes native sys.audit hooks, using minimal wrappers only where necessary to emit audit events. This significantly reduces the risk of conflicts with other instrumentation tools.

vs. asyncio Debug Mode: Python's built-in debug mode is invaluable during development. However, it can be heavy on logs and performance, making it impractical to leave on in high-traffic production environments. aiocop is designed to be "always-on" safe.

Feature	Heavy Monkey-Patching Tools	asyncio Debug Mode	aiocop
Detection Method	Extensive Wrappers	Event Loop Instrumentation	sys.audit Hooks + Minimal Wrappers
Interference Risk	Medium (can conflict with APMs)	None	None
Production Overhead	Low-Medium	High	Very Low (~13μs/task)
Stack Traces	Yes	No (timing only)	Yes
Runtime Control	Varies	Flag at startup	Dynamic on/off
uvloop Support	Varies	No	Yes

Performance

aiocop adds approximately 13 microseconds of overhead per async task:

Scenario	Overhead	Impact on 50ms Request
Pure async (no blocking I/O)	~1 us	0.002%
Light blocking (os.stat)	~14 us	0.03%
Moderate blocking (file read)	~12 us	0.02%
Realistic HTTP handler	~22 us	0.04%

For typical web applications, this means less than 0.05% overhead.

Run the benchmark yourself: python benchmarks/run_benchmark.py

Installation

pip install aiocop

Quick Start

Copy this into a file and run it - no dependencies needed besides aiocop:

# test_aiocop.py
import asyncio
import aiocop


def on_slow_task(event):
    print(f"SLOW TASK DETECTED: {event.elapsed_ms:.1f}ms")
    print(f"  Severity: {event.severity_level}")
    for evt in event.blocking_events:
        print(f"  - {evt['event']} at {evt['entry_point']}")


async def blocking_task():
    # This synchronous open() will block the loop - aiocop will catch it!
    with open("/dev/null", "w") as f:
        f.write("data")
    await asyncio.sleep(0.1)


async def main():
    aiocop.patch_audit_functions()
    aiocop.start_blocking_io_detection()
    aiocop.detect_slow_tasks(threshold_ms=10, on_slow_task=on_slow_task)
    aiocop.activate()

    await asyncio.gather(blocking_task(), blocking_task())


if __name__ == "__main__":
    asyncio.run(main())

python test_aiocop.py
# Output:
# SLOW TASK DETECTED: 102.3ms
#   Severity: medium
#   - open(/dev/null, w) at test_aiocop.py:14:blocking_task

Usage with ASGI (FastAPI, Starlette, etc.)

# In your ASGI application setup (e.g., main.py or asgi.py)
from contextlib import asynccontextmanager

import aiocop

def setup_monitoring() -> None:
    aiocop.patch_audit_functions()
    aiocop.start_blocking_io_detection(trace_depth=20)
    aiocop.detect_slow_tasks(threshold_ms=30, on_slow_task=log_to_monitoring)

def log_to_monitoring(event: aiocop.SlowTaskEvent) -> None:
    # Send to your monitoring system (Datadog, Prometheus, etc.)
    if event.exceeded_threshold:
        metrics.increment("async.slow_task", tags={
            "severity": event.severity_level,
            "reason": event.reason,
        })
        metrics.gauge("async.slow_task.elapsed_ms", event.elapsed_ms)

# Call setup early in your application lifecycle
setup_monitoring()

# Activate after startup (e.g., in a lifespan handler)
@asynccontextmanager
async def lifespan(app):
    aiocop.activate()  # Start monitoring after startup
    yield
    aiocop.deactivate()

Dynamic Controls

Enable/Disable Monitoring at Runtime

# Pause monitoring
aiocop.deactivate()

# Resume monitoring
aiocop.activate()

# Check if monitoring is active
if aiocop.is_monitoring_active():
    print("Monitoring is running")

Raise Exceptions on High Severity Blocking I/O

Useful during development and testing to catch blocking calls immediately:

# Enable globally for current context
aiocop.enable_raise_on_violations()

# Disable
aiocop.disable_raise_on_violations()

# Or use as a context manager
with aiocop.raise_on_violations():
    await some_operation()  # Will raise HighSeverityBlockingIoException if blocking

CI/CD Integration - Fail Tests on Blocking I/O

Use aiocop in your integration tests to prevent blocking code from being merged:

# conftest.py
import pytest
import aiocop

@pytest.fixture(scope="session", autouse=True)
def setup_aiocop():
    aiocop.patch_audit_functions()
    aiocop.start_blocking_io_detection()
    aiocop.detect_slow_tasks(threshold_ms=50)
    aiocop.activate()

# test_views.py
@pytest.mark.asyncio
async def test_my_async_endpoint(client):
    # Setup code can have blocking I/O (fixtures, test data, etc.)
    
    # Only the view execution is wrapped - this is what we care about
    with aiocop.raise_on_violations():
        response = await client.get("/api/endpoint")
    
    # Assertions can have blocking I/O too (DB checks, etc.)
    assert response.status_code == 200

We wrap only the async view (not the entire test) because test setup/teardown often has legitimate blocking code. See Integrations for complete examples.

Context Providers

Context providers allow you to capture external context (like tracing spans, request IDs, etc.) that will be passed to your callbacks. The context is captured within the asyncio task's context, ensuring proper propagation of contextvars.

Basic Usage

from typing import Any

def my_context_provider() -> dict[str, Any]:
    return {
        "request_id": get_current_request_id(),
        "user_id": get_current_user_id(),
    }

aiocop.register_context_provider(my_context_provider)

def on_slow_task(event: aiocop.SlowTaskEvent) -> None:
    request_id = event.context.get("request_id")
    print(f"Slow task in request {request_id}: {event.elapsed_ms}ms")

Integration with Datadog

from ddtrace import tracer
from typing import Any

def datadog_context_provider() -> dict[str, Any]:
    return {"datadog_span": tracer.current_span()}

aiocop.register_context_provider(datadog_context_provider)

def log_to_datadog(event: aiocop.SlowTaskEvent) -> None:
    if event.exceeded_threshold is False:
        return

    span = event.context.get("datadog_span")
    if span is None:
        return

    span.set_tag("slow_task.detected", True)
    span.set_metric("slow_task.elapsed_ms", event.elapsed_ms)
    span.set_metric("slow_task.severity_score", event.severity_score)
    span.set_tag("slow_task.severity_level", event.severity_level)
    span.set_tag("slow_task.reason", event.reason)

aiocop.detect_slow_tasks(threshold_ms=30, on_slow_task=log_to_datadog)

Why Context Providers?

When aiocop detects a slow task, the callback is invoked after the task completes. By that time, the original context (like the active tracing span) might no longer be accessible via standard context lookups.

Context providers solve this by capturing the context at the start of each task execution, within the task's own contextvars context. This ensures that:

1. Tracing spans are captured before they're closed
2. Request-scoped data is available to callbacks
3. Any contextvar-based state is properly preserved

Managing Context Providers

# Register a provider
aiocop.register_context_provider(my_provider)

# Unregister a specific provider
aiocop.unregister_context_provider(my_provider)

# Clear all providers
aiocop.clear_context_providers()

Context providers are completely optional. If none are registered, event.context will simply be an empty dict.

Event Types

SlowTaskEvent

Emitted when either:

• Blocking I/O is detected (reason="io_blocking") - regardless of whether the task exceeded the threshold
• Task exceeds threshold but no blocking I/O detected (reason="cpu_blocking") - indicates CPU-bound blocking

@dataclass(frozen=True)
class SlowTaskEvent:
    elapsed_ms: float        # How long the task took
    threshold_ms: float      # Configured threshold
    exceeded_threshold: bool # True if elapsed > threshold
    severity_score: int      # Aggregate severity (sum of event weights), 0 for cpu_blocking
    severity_level: str      # "low", "medium", or "high"
    reason: str              # "io_blocking" or "cpu_blocking"
    blocking_events: list[BlockingEventInfo]  # List of detected events (empty for cpu_blocking)
    context: dict[str, Any]  # Custom context from context providers (default: {})

BlockingEventInfo

Information about each blocking event:

class BlockingEventInfo(TypedDict):
    event: str        # e.g., "open(/path/to/file)"
    trace: str        # Stack trace
    entry_point: str  # First frame in the trace
    severity: int     # Weight of this event

Severity Weights

Events are classified by severity:

Weight	Value	Examples
WEIGHT_HEAVY	50	socket.connect, subprocess.Popen, time.sleep, DNS lookups
WEIGHT_MODERATE	10	open(), file mutations, os.listdir
WEIGHT_LIGHT	1	os.stat, fcntl.flock, os.kill
WEIGHT_TRIVIAL	0	os.getcwd, os.path.abspath

Severity levels are determined by aggregate score:

• high: score >= 50
• medium: score >= 10
• low: score < 10

API Reference

Setup Functions

• patch_audit_functions() - Patches stdlib functions to emit audit events
• start_blocking_io_detection(trace_depth=20) - Registers the audit hook
• detect_slow_tasks(threshold_ms=30, on_slow_task=None) - Patches the event loop
• activate() / deactivate() - Control monitoring at runtime

Callback Management

• register_slow_task_callback(callback) - Add a callback
• unregister_slow_task_callback(callback) - Remove a callback
• clear_slow_task_callbacks() - Remove all callbacks

Context Provider Management

• register_context_provider(provider) - Add a context provider
• unregister_context_provider(provider) - Remove a context provider
• clear_context_providers() - Remove all context providers

Raise-on-Violations Controls

• enable_raise_on_violations() - Enable for current context
• disable_raise_on_violations() - Disable for current context
• is_raise_on_violations_enabled() - Check current state
• raise_on_violations() - Context manager

Utility Functions

• calculate_io_severity_score(events) - Calculate severity from events
• get_severity_level_from_score(score) - Get "low"/"medium"/"high"
• format_blocking_event(raw_event) - Format a raw event
• get_blocking_events_dict() - Get all monitored events with weights
• get_patched_functions() - Get list of patched functions