The Problem

We were building a camera monitoring system to process multiple RTSP streams simultaneously using Python, GStreamer, and multiprocessing. Each camera stream ran in its own process for isolation and performance. However, we kept encountering mysterious segmentation faults during GStreamer pipeline creation.

The Stack Trace

Fatal Python error: Segmentation fault
Thread 0x00007fee8b7fe640 (most recent call first):
File "stream_manager.py", line 135 in test_pipeline_startup
File "stream_manager.py", line 168 in try_codec_fallback  
File "stream_manager.py", line 224 in start_stream
File "rtsp_processor.py", line 114 in run
File "camera_controller.py", line 115 in run_stream_processor
File "/usr/lib/python3.10/multiprocessing/process.py", line 108 in run

The crashes occurred when calling Gst.parse_launch() or pipeline.set_state(Gst.State.PLAYING) .

Our Original (Problematic) Code

# camera_controller.py - BROKEN VERSION
import multiprocessing
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GObject
# GStreamer initialized in main process
Gst.init(None)
def run_stream_processor(camera_id, rtsp_url):
from stream_manager import StreamManager
stream_manager = StreamManager(camera_id, rtsp_url)
stream_manager.start_stream()
class CameraController:
def start_camera(self, camera_id, rtsp_url):
# Uses fork() by default on Linux
process = multiprocessing.Process(
target=run_stream_processor,
args=(camera_id, rtsp_url)
)
process.start()
return process.pid

Root Cause: Fork vs GStreamer

The issue stems from how Python’s multiprocessing works on Unix systems and GStreamer’s internal state management.

What Happens with Fork

1. Main Process: Gst.init(None) initializes global state including:
   • Plugin registry at memory address (e.g., 0x1234)
   • Thread pools and mutexes
   • Memory allocators
2. Fork Creates Child: multiprocessing.Process uses fork() which:
   • Copies ALL parent memory including GStreamer state
   • Multiple processes share the same memory pointers
   • Each process thinks it owns the same resources
3. Memory Corruption: When child processes access GStreamer: # All processes have identical pointers gst_registry = 0x1234 # SAME in all processes! # Multiple processes modify same memory location pipeline = Gst.parse_launch("...") # SEGFAULT!

Why GStreamer Isn’t Fork-Safe

GStreamer maintains global state that becomes corrupted when shared across forked processes:

• Plugin registry: Shared registry pointers cause conflicts
• Thread pools: Invalid when accessed from different processes
• Mutexes: Become corrupted across process boundaries
• Memory allocators: Concurrent access causes corruption

The Solution: Use Spawn Instead of Fork

Python’s multiprocessing offers three start methods:

• fork : Copies parent memory ( causes GStreamer segfaults)
• spawn : Fresh Python interpreter ( safe for GStreamer)
• forkserver : Hybrid approach (complex, not needed)

Fixed Implementation

# camera_controller.py - FIXED VERSION
import multiprocessing
# 🔧 CRITICAL FIX: Set spawn method BEFORE any imports
if __name__ == "__main__":
multiprocessing.set_start_method('spawn', force=True)
def run_stream_processor(camera_id, rtsp_url):
# 🔧 Import GStreamer ONLY in spawned process
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GObject
# Initialize GStreamer in child process
Gst.init(None)
from stream_manager import StreamManager
stream_manager = StreamManager(camera_id, rtsp_url)
stream_manager.start_stream()
class CameraController:
def start_camera(self, camera_id, rtsp_url):
# 🔧 Use spawn context explicitly
ctx = multiprocessing.get_context('spawn')
process = ctx.Process(
target=run_stream_processor,
args=(camera_id, rtsp_url)
)
process.start()
return process.pid
def main():
controller = CameraController()
# Start cameras...
if __name__ == "__main__":
main()

Stream Manager Fix

# stream_manager.py - FIXED VERSION
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GObject
# 🔧 Initialize GStreamer in each process
if not Gst.is_initialized():
Gst.init(None)
class StreamManager:
def __init__(self, camera_id, rtsp_url):
self.camera_id = camera_id
self.rtsp_url = rtsp_url
# 🔧 Ensure GStreamer is initialized
if not Gst.is_initialized():
Gst.init(None)
def create_pipeline(self):
pipeline_str = f'''
rtspsrc location={self.rtsp_url} !
rtph264depay ! h264parse ! avdec_h264 !
videoconvert ! videoscale !
appsink name=sink
'''
try:
pipeline = Gst.parse_launch(pipeline_str)
return pipeline
except Exception as e:
logging.error(f"Pipeline creation failed: {e}")
return None

Key Differences: Fork vs Spawn

Aspect	Fork	Spawn
Memory	Shares parent state	Fresh interpreter
Startup	~50ms	~200ms
GStreamer	Segfaults	Safe
Isolation	Partial	Complete

Testing Results

Before Fix (Fork Method)

# Stress test: 20 concurrent camera streams
Fast startup
18/20 processes crashed within 2 minutes
Random segmentation faults
Memory corruption

After Fix (Spawn Method)

# Same test: 20 concurrent camera streams
Slower startup (+150ms per process)
20/20 processes stable for 10+ hours
Zero segmentation faults
Clean resource isolation

Best Practices

1. Always Set Spawn Method First

if __name__ == "__main__":
multiprocessing.set_start_method('spawn', force=True)

2. Initialize GStreamer Per Process

# In each subprocess
import gi
gi.require_version('Gst', '1.0') 
from gi.repository import Gst
if not Gst.is_initialized():
Gst.init(None)

3. Never Initialize GStreamer in Main Process

# DON'T: Will be copied to children with fork
Gst.init(None)  
# DO: Initialize in each subprocess  
def subprocess_main():
Gst.init(None)

4. Proper Cleanup

def cleanup():
if pipeline:
pipeline.set_state(Gst.State.NULL)
import gc
gc.collect()

Debugging Tips

Enable Fault Handler

PYTHONFAULTHANDLER=1 python3 camera_controller.py

GStreamer Debug

export GST_DEBUG=3
export GST_DEBUG_DUMP_DOT_DIR=/tmp/gst_debug

Process Monitoring

import psutil
def monitor_process(pid):
try:
proc = psutil.Process(pid)
memory_mb = proc.memory_info().rss / 1024 / 1024
print(f"Process {pid}: {memory_mb:.1f}MB")
except psutil.NoSuchProcess:
print(f"Process {pid} dead")

Production Results

Our camera monitoring system now runs with:

• 50+ concurrent RTSP streams
• Zero segmentation faults
• Stable 24/7 operation
• Predictable resource usage

The trade-off of slightly slower process startup (200ms vs 50ms) is insignificant compared to the stability gains.

Conclusion

Key Takeaways:

1. GStreamer is not fork-safe – shared state causes memory corruption
2. Use spawn method for multimedia applications with multiprocessing
3. Initialize GStreamer per process – never in the parent
4. Test under load – issues may not appear with single processes

The fix is simple once you understand the problem: when using GStreamer with Python multiprocessing, always use spawn method and initialize GStreamer in each subprocess.

This solution has been tested in production and eliminated all segmentation faults while maintaining high performance for concurrent video stream processing.