zero-copy-ipc 1.0.1

A pure Python zero-copy shared memory dictionary for multi-process IPC with O(1) performance

Zero-Copy IPC

A pure Python zero-copy shared memory dictionary for multi-process IPC, 3x faster than multiprocessing.Manager

中文文档 (Chinese Documentation)

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Key Features

⚡ Zero-Copy - True Shared Memory

• Multiple processes access the same shared memory directly
• No serialization/deserialization overhead
• Nanosecond-level inter-process synchronization

🚀 High Performance - O(1) Operations

• Segregated Lists memory allocator (O(1))
• In-place modification: update pointers only, no data movement
• Benchmarked: 3x faster than Manager().dict()

🔒 Process-Safe - Cross-Platform Locking

• Windows/Linux/macOS fully supported
• File-based locking for inter-process mutual exclusion
• Automatic timeout mechanism (10 seconds)

🎯 Simple API - Dictionary-like Interface

d["key"] = value  # Write
value = d["key"]  # Read
del d["key"]      # Delete

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Performance Comparison

vs multiprocessing.Manager().dict():

Operation	ZeroCopyDict	Manager	Improvement
Write	46,161 ops/s	33,345 ops/s	1.38x
Read	149,459 ops/s	27,268 ops/s	5.48x
Update	74,965 ops/s	33,340 ops/s	2.25x
Average	-	-	3.04x ✅

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Installation

From PyPI (Recommended)

pip install zero-copy-ipc

From Source

git clone https://github.com/senyangcai/zero-copy-ipc.git
cd zero-copy-ipc
pip install -e .

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Quick Start

Basic Usage

from zero_copy_ipc import ZeroCopyDict

# Create shared dictionary
d = ZeroCopyDict.create(
    "my_dict",
    max_items=1000,
    heap_size=10*1024*1024  # 10MB
)

# Write data
d["name"] = "Alice"
d["age"] = 30
d["data"] = {"nested": "dict"}

# Read data
print(d["name"])  # "Alice"

# Update data (O(1) operation)
d["age"] = 31

# Close
d.close()

Multi-Process Sharing

# Process 1: Create and write
from zero_copy_ipc import ZeroCopyDict

d1 = ZeroCopyDict.create("shared_dict")
d1["counter"] = 0

# Process 2: Attach and read
d2 = ZeroCopyDict.attach("shared_dict")
print(d2["counter"])  # Instantly sees 0

# Process 3: Update
d3 = ZeroCopyDict.attach("shared_dict")
d3["counter"] = 100  # d1 and d2 immediately see the update!

Context Manager (Recommended)

with ZeroCopyDict.create("temp_dict") as d:
    d["key"] = "value"
    # Auto cleanup

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Use Cases

✅ Suitable For

• Multi-process high-frequency read/write shared data
• Web application real-time statistics (visit count, response time)
• Distributed crawler task queue
• Real-time log aggregation system
• API rate limiting counter
• ML training progress sharing

❌ Not Suitable For

• Cross-machine distributed communication (use Redis)
• Persistent data storage (use database)
• Very large datasets (TB-scale)

---

Technical Architecture

Memory Layout

Shared Memory Structure:
┌────────────────────────┐
│ Header (56 bytes)       │
│  - Magic: 0x5A45524F    │
│  - Version: 3           │
│  - Slot count           │
│  - Heap size            │
└────────────────────────┘
┌────────────────────────┐
│ Slot Table (24B each)   │
│  - key_offset (8B)      │
│  - key_size (4B)        │
│  - value_offset (8B)    │
│  - value_size (4B)      │
└────────────────────────┘
┌────────────────────────┐
│ Segregated Lists (40B)  │
│  - 5 size class heads   │
└────────────────────────┘
┌────────────────────────┐
│ Heap Area (variable)    │
│  - Keys data            │
│  - Values data          │
└────────────────────────┘

Core Components

• MemoryLayout: mmap shared memory management
• HashTable: Linear probing hash table
• HeapManager: Segregated Lists allocator
• ProcessLock: Cross-platform file lock
• Serializer: pickle serializer

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Supported Data Types

All pickle-able Python objects:

# ✅ Basic types
d["int"] = 42
d["float"] = 3.14
d["str"] = "hello"
d["bool"] = True
d["bytes"] = b"data"

# ✅ Container types
d["list"] = [1, 2, 3]
d["dict"] = {"nested": "value"}
d["tuple"] = (1, 2, 3)
d["set"] = {1, 2, 3}

# ✅ Custom objects
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

d["point"] = Point(10, 20)

# ❌ Not supported
# File objects, sockets, lambdas, threading.Lock

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Capacity Planning

Calculation Formula

# Estimate required space
avg_key_size = 10    # bytes
avg_value_size = 100 # bytes
num_items = 1000

heap_size = num_items * (avg_key_size + avg_value_size) * 2  # 2x safety margin
# = 1000 * 110 * 2 = 220KB

d = ZeroCopyDict.create(
    "my_dict",
    max_items=1000,
    heap_size=220*1024
)

Memory Overhead

• Fixed overhead: Header(56B) + Slot table(24B × max_items)
• Per record: 24 bytes + serialized size

---

Platform Support

Platform	Shared Memory Location	Lock Mechanism	Performance
Linux	/dev/shm	fcntl.flock	⭐⭐⭐⭐⭐
Windows	temp directory	msvcrt.locking	⭐⭐⭐⭐
macOS	/tmp	fcntl.flock	⭐⭐⭐⭐

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API Reference

Creation

ZeroCopyDict.create(
    name: str,              # Shared memory name
    max_items: int = 10000, # Maximum number of items
    heap_size: int = 100MB  # Heap area size
)

Attachment

ZeroCopyDict.attach(name: str)  # Attach to existing dict

Operations

d[key] = value       # Write
d[key]               # Read
del d[key]           # Delete
key in d             # Check existence
len(d)               # Get length
d.get(key, default)  # Safe read
d.keys()             # Get all keys
d.values()           # Get all values
d.items()            # Get all key-value pairs
d.stats()            # Get statistics
d.close()            # Close and cleanup

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Monitoring & Statistics

stats = d.stats()
print(f"Used slots: {stats['used_slots']}")
print(f"Load factor: {stats['load_factor']:.2%}")
print(f"Heap used: {stats['heap_used']} bytes")

gc_stats = d.gc_stats()
print(f"Free blocks: {gc_stats['total_free_blocks']}")
print(f"Free space: {gc_stats['total_free_space']} bytes")

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Documentation

• README.md - Project introduction (this file)
• 中文文档 - Chinese documentation
• docs/QUICKSTART.md - Quick start guide
• docs/ARCHITECTURE.md - Technical architecture
• docs/USAGE.md - Detailed usage guide
• CHANGELOG.md - Version history

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Examples

# Basic usage
python examples/basic_usage.py

# Multi-process communication
python examples/multiprocess.py

# Performance benchmark
python examples/benchmark.py

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Testing

# Quick test
python tests/quick_test.py

# Performance comparison
python tests/comparison_final.py

# Full test suite
pytest tests/test_dict.py -v

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FAQ

Q: How to cleanup shared memory?

# Normal cleanup
d.close()

# Manual cleanup (Linux)
os.remove("/dev/shm/zero_copy_ipc_my_dict")

# Manual cleanup (Windows)
import tempfile
os.remove(os.path.join(tempfile.gettempdir(), "zero_copy_ipc_my_dict.shm"))

Q: Lock timeout issue?

try:
    d["key"] = "value"
except TimeoutError:
    print("Lock timeout, possible zombie process")
    # Cleanup lock file
    # Retry operation

Q: How to improve concurrent performance?

# 1. Batch operations to reduce lock frequency
d.update({"k1": "v1", "k2": "v2", "k3": "v3"})

# 2. Use sufficient heap_size to avoid frequent allocation
heap_size = estimated_items * avg_size * 3

# 3. Different key operations can be parallel (hash to different slots)

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Version History

v1.0.0 (2025-06-01)

• ✅ Segregated Lists O(1) memory allocator
• ✅ 3x faster than Manager().dict()
• ✅ Cross-platform support (Windows/Linux/macOS)
• ✅ Complete documentation and testing

See CHANGELOG.md for details.

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License

MIT License - See LICENSE

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Contributing

Issues and Pull Requests are welcome!

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Acknowledgments

Inspired by Apache Arrow and other IPC solutions, but this library focuses on local multi-process zero-copy scenarios, providing a lighter and more efficient solution.

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Get Started:

pip install zero-copy-ipc
python -c "from zero_copy_ipc import ZeroCopyDict; print('✅ Installation successful')"