| Name | zeroq JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | High-performance shared memory queue for Python (Rust-powered) |
| upload_time | 2025-02-03 14:02:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
queue
shared memory
ipc
rust
pyo3
fast
mpmc
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# zeroq
[zeroq](https://github.com/idkosilov/zeroq) is a high-performance, shared-memory,
multi-producer/multi-consumer (MPMC) queue written in Rust with Python bindings.
Designed for concurrent and inter-process communication, zeroq delivers
low latency and strict FIFO (First-In, First-Out) behavior even under heavy load.
## Features
- **Speed:** Up to 50× faster data transfer compared to `multiprocessing.Queue`.
- **Lock-Free Concurrency:** Utilizes atomic operations for efficient, lock-free synchronization across threads and processes.
- **Shared Memory Communication:** Enables fast inter-process messaging without the overhead of kernel-based IPC.
- **Flexible API:** Supports both blocking (`put`/`get`) and non-blocking (`put_nowait`/`get_nowait`) operations.
- **Predictable FIFO Ordering:** Guarantees that elements are dequeued in the exact order they were enqueued.
- **Python Bindings:** Easily integrate with Python projects while leveraging Rust's performance and safety.
## Installation
If pre-built wheels are available on [PyPI](https://pypi.org), installation is as simple as:
```bash
pip install zeroq
```
## Benchmarks
The following benchmarks compare `zeroq` with Python's standard
`multiprocessing.Queue` across different payload sizes. Tests were conducted
on an **Apple M1 Pro** CPU with Python 3.11.10. Each test measures
the time for a complete `put`+`get` operation cycle (1000 iterations).
| Payload size | Queue Type | Mean Time per Op | Ops/sec | Speedup |
|--------------|------------|------------------|---------|---------|
| 8 B | `zeroq` | 235.6 ns | 4.25M | 50x |
| | `mp.Queue` | 11.84 μs | 84.4K | |
| 128 B | `zeroq` | 252.8 ns | 3.96M | 47x |
| | `mp.Queue` | 11.86 μs | 84.3K | |
| 1 KiB | `zeroq` | 345.4 ns | 2.90M | 37x |
| | `mp.Queue` | 12.89 μs | 77.6K | |
| 256 KiB | `zeroq` | 23.5 μs | 42.5K | 6x |
| | `mp.Queue` | 142.0 μs | 7.04K | |
| 8 MiB | `zeroq` | 1.07 ms | 936 | 13.7x |
| | `mp.Queue` | 14.60 ms | 68.5 | |
| 32 MiB | `zeroq` | 6.03 ms | 166 | 7.7x |
| | `mp.Queue` | 46.31 ms | 22 | |
zeroq significantly accelerates interprocess communication,
delivering up to 50× faster data transfer compared to `multiprocessing.Queue`.
This performance gain is achieved through shared memory and lock-free
synchronization, eliminating the overhead of serialization and dynamic memory
allocation.
### Optimal Use Cases
zeroq is particularly well-suited for tasks that require fast,
low-latency data exchange between processes, including:
- **ML/AI Pipelines:** Efficient data transfer between processes for image and video processing, such as inference in computer vision models.
- **Multimedia Processing:** Passing video frames or audio streams between processes in real-time applications.
- **Real-Time Systems:** Handling events, logs, telemetry, and signals where minimal latency and high throughput are critical.
- **IoT and Embedded Systems:** Fast transmission of fixed-size data blocks between processes on a single device.
By leveraging shared memory and avoiding unnecessary memory operations,
zeroq provides a significant advantage in applications that demand
high-speed, low-latency communication.
## Usage
Once installed, you can easily integrate zeroq into your Python projects. Below is an example of video player.
To run this example, install OpenCV and FFmpeg:
```bash
pip install opencv-python
```
Make sure FFmpeg is installed and accessible via the command line. You can download it from ffmpeg.org and add it to your system’s PATH.
```python
import multiprocessing
import subprocess
import cv2
import numpy as np
from zeroq import Empty, Full, Queue
def producer(video_path: str) -> None:
"""Reads video frames via FFmpeg and pushes them to the shared queue."""
queue = Queue(
name='video-queue',
element_size=1920 * 1080 * 3,
capacity=16,
create=True,
)
process = subprocess.Popen(
[
'ffmpeg', '-re', '-i', video_path,
'-f', 'rawvideo', '-pix_fmt', 'rgb24', '-vf', 'scale=1920:1080',
'-vcodec', 'rawvideo', '-an', '-nostdin', 'pipe:1',
],
stdout=subprocess.PIPE,
stderr=subprocess.DEVNULL,
bufsize=1920 * 1080 * 3,
)
while True:
raw_frame = process.stdout.read(1920 * 1080 * 3)
if not raw_frame:
break
try:
queue.put(raw_frame, timeout=1.0)
except Full:
break
def consumer():
"""Retrieves frames from the queue and displays them using OpenCV."""
queue = Queue(name='video-queue', create=False)
while True:
try:
frame_data = queue.get(timeout=1.0)
except Empty:
break
frame = (
np.frombuffer(frame_data, dtype=np.uint8)
.reshape((1080, 1920, 3))
)
cv2.imshow('Video Stream', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
if __name__ == '__main__':
video_path = 'your_video.mp4'
multiprocessing.Process(target=producer, args=(video_path,)).start()
multiprocessing.Process(target=consumer).start()
```
## License
zeroq is distributed under the terms of the MIT License.
Raw data
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"description": "# zeroq\n\n[zeroq](https://github.com/idkosilov/zeroq) is a high-performance, shared-memory, \nmulti-producer/multi-consumer (MPMC) queue written in Rust with Python bindings. \nDesigned for concurrent and inter-process communication, zeroq delivers \nlow latency and strict FIFO (First-In, First-Out) behavior even under heavy load.\n\n## Features\n\n- **Speed:** Up to 50\u00d7 faster data transfer compared to `multiprocessing.Queue`.\n- **Lock-Free Concurrency:** Utilizes atomic operations for efficient, lock-free synchronization across threads and processes.\n- **Shared Memory Communication:** Enables fast inter-process messaging without the overhead of kernel-based IPC.\n- **Flexible API:** Supports both blocking (`put`/`get`) and non-blocking (`put_nowait`/`get_nowait`) operations.\n- **Predictable FIFO Ordering:** Guarantees that elements are dequeued in the exact order they were enqueued.\n- **Python Bindings:** Easily integrate with Python projects while leveraging Rust's performance and safety.\n\n## Installation\n\nIf pre-built wheels are available on [PyPI](https://pypi.org), installation is as simple as:\n\n```bash\npip install zeroq\n```\n\n## Benchmarks\n\n\n\nThe following benchmarks compare `zeroq` with Python's standard \n`multiprocessing.Queue` across different payload sizes. Tests were conducted \non an **Apple M1 Pro** CPU with Python 3.11.10. Each test measures \nthe time for a complete `put`+`get` operation cycle (1000 iterations). \n\n| Payload size | Queue Type | Mean Time per Op | Ops/sec | Speedup |\n|--------------|------------|------------------|---------|---------|\n| 8 B | `zeroq` | 235.6 ns | 4.25M | 50x |\n| | `mp.Queue` | 11.84 \u03bcs | 84.4K | |\n| 128 B | `zeroq` | 252.8 ns | 3.96M | 47x |\n| | `mp.Queue` | 11.86 \u03bcs | 84.3K | |\n| 1 KiB | `zeroq` | 345.4 ns | 2.90M | 37x |\n| | `mp.Queue` | 12.89 \u03bcs | 77.6K | |\n| 256 KiB | `zeroq` | 23.5 \u03bcs | 42.5K | 6x |\n| | `mp.Queue` | 142.0 \u03bcs | 7.04K | |\n| 8 MiB | `zeroq` | 1.07 ms | 936 | 13.7x |\n| | `mp.Queue` | 14.60 ms | 68.5 | |\n| 32 MiB | `zeroq` | 6.03 ms | 166 | 7.7x |\n| | `mp.Queue` | 46.31 ms | 22 | |\n\nzeroq significantly accelerates interprocess communication, \ndelivering up to 50\u00d7 faster data transfer compared to `multiprocessing.Queue`. \nThis performance gain is achieved through shared memory and lock-free \nsynchronization, eliminating the overhead of serialization and dynamic memory \nallocation.\n\n### Optimal Use Cases\n\nzeroq is particularly well-suited for tasks that require fast, \nlow-latency data exchange between processes, including:\n\n- **ML/AI Pipelines:** Efficient data transfer between processes for image and video processing, such as inference in computer vision models.\n- **Multimedia Processing:** Passing video frames or audio streams between processes in real-time applications.\n- **Real-Time Systems:** Handling events, logs, telemetry, and signals where minimal latency and high throughput are critical.\n- **IoT and Embedded Systems:** Fast transmission of fixed-size data blocks between processes on a single device.\n\nBy leveraging shared memory and avoiding unnecessary memory operations, \nzeroq provides a significant advantage in applications that demand \nhigh-speed, low-latency communication.\n\n## Usage\n\nOnce installed, you can easily integrate zeroq into your Python projects. Below is an example of video player.\n\nTo run this example, install OpenCV and FFmpeg:\n\n```bash\npip install opencv-python\n```\n\nMake sure FFmpeg is installed and accessible via the command line. You can download it from ffmpeg.org and add it to your system\u2019s PATH.\n\n\n```python\nimport multiprocessing\nimport subprocess\n\nimport cv2\nimport numpy as np\n\nfrom zeroq import Empty, Full, Queue\n\n\ndef producer(video_path: str) -> None:\n \"\"\"Reads video frames via FFmpeg and pushes them to the shared queue.\"\"\"\n queue = Queue(\n name='video-queue',\n element_size=1920 * 1080 * 3,\n capacity=16,\n create=True,\n )\n\n process = subprocess.Popen(\n [\n 'ffmpeg', '-re', '-i', video_path,\n '-f', 'rawvideo', '-pix_fmt', 'rgb24', '-vf', 'scale=1920:1080',\n '-vcodec', 'rawvideo', '-an', '-nostdin', 'pipe:1',\n ],\n stdout=subprocess.PIPE,\n stderr=subprocess.DEVNULL,\n bufsize=1920 * 1080 * 3,\n )\n\n while True:\n raw_frame = process.stdout.read(1920 * 1080 * 3)\n if not raw_frame:\n break\n\n try:\n queue.put(raw_frame, timeout=1.0)\n except Full:\n break\n\n\ndef consumer():\n \"\"\"Retrieves frames from the queue and displays them using OpenCV.\"\"\"\n queue = Queue(name='video-queue', create=False)\n\n while True:\n try:\n frame_data = queue.get(timeout=1.0)\n except Empty:\n break\n\n frame = (\n np.frombuffer(frame_data, dtype=np.uint8)\n .reshape((1080, 1920, 3))\n )\n cv2.imshow('Video Stream', frame)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n video_path = 'your_video.mp4'\n\n multiprocessing.Process(target=producer, args=(video_path,)).start()\n multiprocessing.Process(target=consumer).start()\n```\n\n\n## License\n\nzeroq is distributed under the terms of the MIT License.\n\n",
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