blocksnoop 0.3.0

Detect blocking calls in asyncio event loops using eBPF + Austin

blocksnoop

Detect blocking calls in Python asyncio event loops using eBPF + Austin.

blocksnoop attaches to a running Python process (or launches one) and reports every time the event loop is blocked longer than a configurable threshold — with the Python stack trace that caused it.

How it works

eBPF (kernel)          Austin (userspace)
  │ monitors               │ samples Python
  │ epoll gaps              │ stacks continuously
  └──────────┐   ┌─────────┘
             ▼   ▼
          Correlator
             │
             ▼
       Reporter → sinks (console, JSON, file)

1. An eBPF probe hooks epoll_wait syscalls and measures the time between returns (callback start) and the next entry (callback end). If the gap exceeds the threshold, it emits an event.
2. A stack sampler (Austin) runs as a long-lived subprocess, continuously streaming Python stack traces into a ring buffer. Austin's pipe mode avoids per-sample subprocess overhead, enabling sub-10ms threshold detection.
3. The correlator enriches each blocking event with the closest matching Python stack.
4. The reporter fans out events to one or more output sinks.

Requirements

• Linux with eBPF support (kernel 4.15+)
• Root privileges (for eBPF and Austin)
• BCC (BPF Compiler Collection)
• Austin
• austin-python (installed automatically as a dependency)
• Python 3.12+

Installation

pip install blocksnoop

Or for development:

git clone [e-mail]:PaulM5406/blocksnoop.git
cd blocksnoop
uv sync --all-extras --dev

Usage

Attach to a running process

sudo blocksnoop <PID>
sudo blocksnoop -t 50 <PID>          # 50ms threshold (default: 100ms)
sudo blocksnoop --tid 1234 <PID>     # monitor specific thread
sudo blocksnoop -v <PID>             # enable debug logging

Launch and monitor a process

sudo blocksnoop -- python app.py
sudo blocksnoop -t 50 -- python app.py

Output modes

# Human-readable to stderr (default)
sudo blocksnoop -- python app.py

# JSON lines to stdout (for piping to jq, etc.)
sudo blocksnoop --json -- python app.py

# Structured JSON to file (for Datadog/Fluentd/CloudWatch)
sudo blocksnoop --log-file /var/log/blocksnoop/events.json --service my-api --env production -- python app.py

# Combine: console to terminal + JSON to file
sudo blocksnoop --log-file /var/log/blocksnoop/events.json --service my-api -- python app.py

Example output

Human-readable:

[   1.23s] #1   BLOCKED     302.1ms  tid=1234
  Python stack (most recent call last):
    app.py:7 in blocking_io
    app.py:13 in main

[   2.05s] #2   BLOCKED     298.5ms  tid=1234
  Python stack (most recent call last):
    app.py:7 in blocking_io
    app.py:13 in main

--- blocksnoop session ---
Duration: 8.0s
Blocking events detected: 2

JSON (--json):

{"event_number": 1, "timestamp_s": 1.23, "duration_ms": 302.1, "pid": 5678, "tid": 1234, "python_stacks": [[{"function": "blocking_io", "file": "app.py", "line": 7}, {"function": "main", "file": "app.py", "line": 13}]], "level": "warning"}

CLI reference

blocksnoop [OPTIONS] [PID] [-- COMMAND ...]

Options:
  -t, --threshold FLOAT        Blocking threshold in ms (default: 100)
  --tid INT                    Thread ID to monitor (default: main thread)
  --json                       JSON lines output to stdout
  --log-file PATH              Write structured JSON to file for log aggregators
  --service NAME               Service name for structured logs (default: blocksnoop)
  --env ENV                    Environment tag for structured logs
  --no-color                   Disable ANSI colors in terminal output
  -v, --verbose                Enable debug logging to stderr
  --error-threshold MS         Duration in ms above which events are errors (default: 500)
  --correlation-padding MS     Correlation time window padding in ms (default: 200)

Docker

blocksnoop requires kernel access, so Docker containers need --privileged and --pid=host:

# Pull from Docker Hub
docker pull oloapm/blocksnoop

# Attach to a process on the host
docker run --rm --privileged --pid=host oloapm/blocksnoop blocksnoop -t 100 <PID>

# Launch and monitor a process
docker run --rm --privileged --pid=host oloapm/blocksnoop blocksnoop -t 100 -- python app.py

For local development:

# docker-compose.yml
services:
  blocksnoop:
    build: .
    privileged: true
    pid: host

docker compose run --rm blocksnoop blocksnoop -t 100 -- python app.py

Kubernetes

blocksnoop uses eBPF which operates at the kernel level, so you run it on the node, not inside the application container. The target process just needs to be visible from the host PID namespace.

Ephemeral debug container (recommended)

Attach directly to a running pod with an ephemeral container. --profile=sysadmin (K8s 1.28+) grants the privileged access required for eBPF:

# Find the pod
kubectl get pods -l app=my-api

# Attach an ephemeral debug container with eBPF privileges
kubectl debug -it my-api-pod-7b8c9d \
  --image=oloapm/blocksnoop:latest \
  --target=my-api \
  --profile=sysadmin \
  -- sh -c "mount -t debugfs debugfs /sys/kernel/debug 2>/dev/null; exec sh"

--target shares the process namespace with the app container, so you can see its PIDs. --profile=sysadmin enables privileged mode for eBPF and debugfs access.

Inside the debug container, find the Python process and attach:

# Find the Python PID
ps aux | grep python

# Attach blocksnoop
blocksnoop -t 50 <PID>

# Or with structured logging
blocksnoop --json -t 50 <PID>

blocksnoop automatically detects and symlinks available kernel headers when the running kernel differs from the installed headers package (common in containers).

DaemonSet sidecar

For continuous monitoring, deploy blocksnoop as a DaemonSet that monitors processes on each node:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: blocksnoop
spec:
  selector:
    matchLabels:
      app: blocksnoop
  template:
    metadata:
      labels:
        app: blocksnoop
    spec:
      hostPID: true
      containers:
        - name: blocksnoop
          image: oloapm/blocksnoop:latest
          command: ["blocksnoop", "--json", "--log-file", "/var/log/blocksnoop/events.json", "--service", "my-api", "--env", "production", "-t", "100"]
          securityContext:
            privileged: true
          volumeMounts:
            - name: logs
              mountPath: /var/log/blocksnoop
            - name: debugfs
              mountPath: /sys/kernel/debug
      volumes:
        - name: logs
          hostPath:
            path: /var/log/blocksnoop
        - name: debugfs
          hostPath:
            path: /sys/kernel/debug

The log file at /var/log/blocksnoop/events.json can be tailed by Datadog Agent, Fluentd, or any log collector running on the node.

Node shell (quick one-off)

For a quick check without building images:

# SSH into the node (or use a node shell tool)
kubectl node-shell <node-name>

# Install blocksnoop
pip install blocksnoop

# Find the Python process (hostPID shows all processes)
ps aux | grep python

# Attach
blocksnoop -t 50 <PID>

Development

# Install dependencies
uv sync --all-extras --dev

# Run unit tests
uv run --extra dev pytest tests/ -v --ignore=tests/integration

# Run integration tests (requires Docker)
uv run --extra dev pytest -m docker tests/integration/ -v

# Lint and format
ruff check blocksnoop/ tests/
ruff format blocksnoop/ tests/

# Type check
ty check blocksnoop/

License

GPL-3.0-or-later (due to the austin-python dependency)