slick-queue-py

Python implementation of SlickQueue - a lock-free multi-producer multi-consumer (MPMC) queue with C++ interoperability through shared memory.

This is the Python binding for the SlickQueue C++ library. The Python implementation maintains exact binary compatibility with the C++ version, enabling seamless interprocess communication between Python and C++ applications.

Features

• Dual Mode Operation:
  • Local Memory Mode: In-process queue using local memory (no shared memory overhead)
  • Shared Memory Mode: Inter-process queue for interprocess communication
• Lock-Free Multi-Producer Multi-Consumer: True MPMC support using atomic operations
• C++/Python Interoperability: Python and C++ processes can share the same queue
• Cross-Platform: Windows and Linux/macOS support (x86-64)
• Memory Layout Compatible: Exact binary compatibility with C++ slick::SlickQueue<T>
• High Performance: Hardware atomic operations for minimal overhead

Requirements

• Python 3.8+ (uses multiprocessing.shared_memory)
• 64-bit platform
• For true lock-free operation: x86-64 CPU with CMPXCHG16B support (most CPUs since 2006)

Installation

pip install -e .

Or just copy the Python files to your project.

Quick Start

Local Memory Mode (Single Process)

from slick_queue_py import SlickQueue

# Create a queue in local memory (no shared memory)
q = SlickQueue(size=1024, element_size=256)

# Producer: Reserve a slot, write data, and publish
idx = q.reserve()
buf = q[idx]
buf[:len(b'hello')] = b'hello'
q.publish(idx)

# Consumer: Read data
read_index = 0
data, size, read_index = q.read(read_index)
if data is not None:
    print(f"Received: {data[:size]}")

q.close()  # unlink() does nothing for local mode

Shared Memory Mode (Multi-Process)

from slick_queue_py import SlickQueue

# Create a new shared memory queue (size must be power of two)
q = SlickQueue(name='my_queue', size=1024, element_size=256)

# Producer: Reserve a slot, write data, and publish
idx = q.reserve()
buf = q[idx]
buf[:len(b'hello')] = b'hello'
q.publish(idx)

# Consumer: Read data
read_index = 0
data, size, read_index = q.read(read_index)
if data is not None:
    print(f"Received: {data[:size]}")

q.close()
q.unlink()  # Delete shared memory segment

Multi-Producer Usage

from multiprocessing import Process
from slick_queue_py import SlickQueue
import struct

def producer_worker(queue_name, worker_id, num_items):
    # Open existing queue
    q = SlickQueue(name=queue_name, element_size=32)

    for i in range(num_items):
        # Reserve slot (thread-safe with atomic CAS)
        idx = q.reserve(1)

        # Write unique data
        data = struct.pack("<I I", worker_id, i)
        slot = q[idx]
        slot[:len(data)] = data

        # Publish (makes data visible to consumers)
        q.publish(idx, 1)

    q.close()

# Create queue
q = SlickQueue(name='mpmc_queue', size=64, element_size=32)

# Start multiple producers
producers = []
for i in range(4):
    p = Process(target=producer_worker, args=('mpmc_queue', i, 100))
    p.start()
    producers.append(p)

# Wait for completion
for p in producers:
    p.join()

q.close()
q.unlink()

Multi-Consumer Work-Stealing

For multiple consumers sharing work from a single queue, use an AtomicCursor to enable work-stealing patterns where each item is consumed by exactly one consumer.

Local Mode (Multi-Threading)

from threading import Thread
from slick_queue_py import SlickQueue, AtomicCursor
import struct

def consumer_worker(q, cursor, worker_id, results):
    items_processed = 0
    while True:
        # Atomically claim next item (work-stealing)
        data, size, index = q.read(cursor)

        if data is None:
            break  # No more data

        # Process the claimed item
        worker, seq = struct.unpack("<I I", data[:8])
        items_processed += 1

    results[worker_id] = items_processed

# Create local queue and cursor
q = SlickQueue(size=64, element_size=32)
cursor_buf = bytearray(8)
cursor = AtomicCursor(cursor_buf, 0)
cursor.store(0)  # Initialize cursor to 0

# Producer writes items
for i in range(100):
    idx = q.reserve()
    data = struct.pack("<I I", 0, i)
    q[idx][:len(data)] = data
    q.publish(idx)

# Start multiple consumer threads that share the work
results = {}
threads = []
for i in range(4):
    t = Thread(target=consumer_worker, args=(q, cursor, i, results))
    t.start()
    threads.append(t)

# Wait for all consumers
for t in threads:
    t.join()

print(f"Total items processed: {sum(results.values())}")
q.close()

Shared Memory Mode (Multi-Process)

from multiprocessing import Process, shared_memory
from slick_queue_py import SlickQueue, AtomicCursor
import struct

def consumer_worker(queue_name, cursor_name, worker_id):
    # Open shared queue and cursor
    q = SlickQueue(name=queue_name, element_size=32)
    cursor_shm = shared_memory.SharedMemory(name=cursor_name)
    cursor = AtomicCursor(cursor_shm.buf, 0)

    items_processed = 0
    while True:
        # Atomically claim next item (work-stealing)
        data, size, index = q.read(cursor)

        if data is None:
            break  # No more data

        # Process the claimed item
        worker, seq = struct.unpack("<I I", data[:8])
        items_processed += 1

    print(f"Worker {worker_id} processed {items_processed} items")
    cursor_shm.close()
    q.close()

# Create queue and shared cursor
q = SlickQueue(name='work_queue', size=64, element_size=32)
cursor_shm = shared_memory.SharedMemory(name='work_cursor', create=True, size=8)
cursor = AtomicCursor(cursor_shm.buf, 0)
cursor.store(0)  # Initialize cursor to 0

# Producer writes items
for i in range(100):
    idx = q.reserve()
    data = struct.pack("<I I", 0, i)
    q[idx][:len(data)] = data
    q.publish(idx)

# Start multiple consumer processes that share the work
consumers = []
for i in range(4):
    p = Process(target=consumer_worker, args=('work_queue', 'work_cursor', i))
    p.start()
    consumers.append(p)

# Wait for all consumers
for p in consumers:
    p.join()

cursor_shm.close()
cursor_shm.unlink()
q.close()
q.unlink()

C++/Python Interoperability

The Python implementation is fully compatible with the C++ SlickQueue library. Python and C++ processes can produce and consume from the same queue with:

• Exact memory layout compatibility: Binary-compatible with slick::SlickQueue<T>
• Atomic operation compatibility: Same 16-byte and 8-byte CAS semantics
• Bidirectional communication: C++ ↔ Python in both directions
• Multi-producer support: Mix C++ and Python producers on the same queue

Platform Support for C++/Python Interop:

• ✅ Linux/macOS: Full interoperability (both use POSIX shm_open)
• ✅ Windows: Full interoperability
• ✅ Python-only: Works on all platforms (Windows/Linux/macOS)

Basic C++ → Python Example

C++ Producer:

#include "queue.h"

int main() {
    // Open existing queue created by Python
    slick::SlickQueue<uint8_t> q(32, "shared_queue");

    for (int i = 0; i < 100; i++) {
        auto idx = q.reserve();
        uint32_t value = i;
        std::memcpy(q[idx], &value, sizeof(value));
        q.publish(idx);
    }
}

Python Consumer:

from slick_queue_py import SlickQueue
import struct

# Create queue that C++ will write to
q = SlickQueue(name='shared_queue', size=64, element_size=32)

read_index = 0
for _ in range(100):
    data, size, read_index = q.read(read_index)
    if data is not None:
        value = struct.unpack("<I", data[:4])[0]
        print(f"Received from C++: {value}")

q.close()
q.unlink()

Building C++ Programs

To use the C++ SlickQueue library with your Python queues:

# Clone the C++ library
git clone https://github.com/SlickQuant/slick-queue.git

# Build your C++ program
g++ -std=c++17 -I slick-queue/include my_program.cpp -o my_program

Or use CMake (see CMakeLists.txt for reference):

include(FetchContent)
FetchContent_Declare(
    slick-queue
    GIT_REPOSITORY https://github.com/SlickQuant/slick-queue.git
    GIT_TAG main
)
FetchContent_MakeAvailable(slick-queue)

add_executable(my_program my_program.cpp)
target_link_libraries(my_program PRIVATE slick::queue)

See tests/test_interop.py and tests/cpp_*.cpp for comprehensive examples.

API Reference

SlickQueue

__init__(*, name=None, size=None, element_size=None)

Create a queue in local memory or shared memory mode.

Parameters:

• name (str, optional): Shared memory segment name. If None, uses local memory mode (single process).
• size (int): Queue capacity (must be power of 2). Required for local mode or when creating shared memory.
• element_size (int, required): Size of each element in bytes

Examples:

# Local memory mode (single process)
q = SlickQueue(size=256, element_size=64)

# Create new shared memory queue
q = SlickQueue(name='my_queue', size=256, element_size=64)

# Open existing shared memory queue
q2 = SlickQueue(name='my_queue', element_size=64)

reserve(n=1) -> int

Reserve n elements for writing. Multi-producer safe using atomic CAS.

Parameters:

• n (int): Number of elements to reserve (default 1)

Returns:

• int: Starting index of reserved space

Example:

idx = q.reserve(1)  # Reserve 1 elements

publish(index, n=1)

Publish data written to reserved space. Uses atomic operations with release memory ordering.

Parameters:

• index (int): Index returned by reserve()
• n (int): Number of elements to publish (default 1)

Example:

idx = q.reserve()
q[idx][:data_len] = data
q.publish(idx)

read(read_index) -> Tuple[Optional[bytes], int, int] or read(atomic_cursor) -> Tuple[Optional[bytes], int]

Read from queue with two modes:

Single-Consumer Mode (when read_index is int): Uses a plain int cursor for single-consumer scenarios. Returns the new read_index.

Multi-Consumer Mode (when read_index is AtomicCursor): Uses an atomic cursor for work-stealing/load-balancing across multiple consumers. Each consumer atomically claims items, ensuring each item is consumed exactly once.

Parameters:

• read_index (int or AtomicCursor): Current read position or shared atomic cursor

Returns:

• Single-consumer: Tuple[Optional[bytes], int, int] - (data or None, size, new_read_index)
• Multi-consumer: Tuple[Optional[bytes], int] - (data or None, size)

API Difference from C++: Unlike C++ where read_index is updated by reference, the Python single-consumer version returns the new index. This is the Pythonic pattern since Python doesn't have true pass-by-reference.

# Python single-consumer (returns new index)
data, size, read_index = q.read(read_index)

# Python multi-consumer (atomic cursor)
from slick_queue_py import AtomicCursor
cursor = AtomicCursor(cursor_shm.buf, 0)
data, size, index = q.read(cursor)  # Atomically claim next item

# C++ (updates by reference for both)
auto [data, size] = queue.read(read_index);  // read_index modified in-place
auto [data, size] = queue.read(atomic_cursor);  // atomic_cursor modified in-place

Single-Consumer Example:

read_index = 0
while True:
    data, size, read_index = q.read(read_index)
    if data is not None:
        process(data)

Multi-Consumer Example (Local Mode - Threading):

from slick_queue_py import AtomicCursor

# Create local cursor for multi-threading
cursor_buf = bytearray(8)
cursor = AtomicCursor(cursor_buf, 0)
cursor.store(0)

# Multiple threads can share this cursor
while True:
    data, size, index = q.read(cursor)  # Each thread atomically claims items
    if data is not None:
        process(data)

Multi-Consumer Example (Shared Memory Mode - Multiprocess):

from multiprocessing import shared_memory
from slick_queue_py import AtomicCursor

# Create shared cursor for multi-process
cursor_shm = shared_memory.SharedMemory(name='cursor', create=True, size=8)
cursor = AtomicCursor(cursor_shm.buf, 0)
cursor.store(0)

# Multiple processes can share this cursor
while True:
    data, size, index = q.read(cursor)  # Each process atomically claims items
    if data is not None:
        process(data)

read_last() -> Tuple[Optional[bytes], int]

Read the most recently published item.

Returns:

• Tuple[Optional[bytes], int]: Tuple of (data, size)
  • data: Last published data or None if queue is empty
  • size: Number of slots the item occupies (0 if queue is empty)

Example:

data, size = q.read_last()
if data is not None:
    print(f"Last item: {data[:size * element_size]}")

__getitem__(index) -> memoryview

Get memoryview for writing to reserved slot.

Parameters:

• index (int): Index from reserve()

Returns:

• memoryview: View into the data array

close()

Close the shared memory connection. Always call this before unlinking.

unlink()

Delete the shared memory segment. Only call from the process that created it.

AtomicCursor

The AtomicCursor class enables multi-consumer work-stealing patterns by providing an atomic read cursor that multiple consumers can coordinate through. Works in both local mode (multi-threading) and shared memory mode (multi-process).

__init__(buffer, offset=0)

Create an atomic cursor wrapper around a memory buffer.

Parameters:

• buffer (memoryview or bytearray): Memory buffer
  • For local mode (threading): use bytearray(8)
  • For shared memory mode (multiprocess): use SharedMemory.buf
• offset (int, optional): Byte offset in buffer (default 0)

Local Mode Example (Multi-Threading):

from slick_queue_py import AtomicCursor

# Create local cursor for multi-threading
cursor_buf = bytearray(8)
cursor = AtomicCursor(cursor_buf, 0)
cursor.store(0)  # Initialize to 0

Shared Memory Mode Example (Multi-Process):

from multiprocessing import shared_memory
from slick_queue_py import AtomicCursor

# Create shared cursor for multi-process
cursor_shm = shared_memory.SharedMemory(name='cursor', create=True, size=8)
cursor = AtomicCursor(cursor_shm.buf, 0)
cursor.store(0)  # Initialize to 0

load() -> int

Load the cursor value with atomic acquire semantics.

Returns:

• int: Current cursor value

store(value)

Store a new cursor value with atomic release semantics.

Parameters:

• value (int): New cursor value

compare_exchange_weak(expected, desired) -> Tuple[bool, int]

Atomically compare and swap the cursor value.

Parameters:

• expected (int): Expected cursor value
• desired (int): Desired cursor value

Returns:

• Tuple[bool, int]: (success, actual_value)

Note: This is used internally by read(atomic_cursor) and typically doesn't need to be called directly.

Memory Layout

The queue uses the same memory layout as C++ slick::SlickQueue<T>:

Offset | Size          | Content
-------|---------------|------------------
0      | 16 bytes      | reserved_info (atomic)
       |   0-7         |   uint64_t index_
       |   8-11        |   uint32_t size_
       |   12-15       |   padding
16     | 4 bytes       | uint32_t size_ (queue capacity)
20     | 44 bytes      | padding (to 64 bytes)
64     | 16*size bytes | slot array
       | per slot:     |
       |   0-7         |   uint64_t data_index (atomic)
       |   8-11        |   uint32_t size
       |   12-15       |   padding
64+... | elem*size     | data array

Platform Support

Fully Supported (Lock-Free)

• Windows x86-64: Uses C++ extension (atomic_ops_ext.pyd) with std::atomic
• Linux x86-64: Uses C++ extension (atomic_ops_ext.so) with std::atomic, fallback to libatomic
• macOS x86-64: Uses C++ extension (atomic_ops_ext.so) with std::atomic, fallback to compiler builtins

Platform-specific atomic operation implementations:

• All platforms: The atomic_ops_ext C++ extension is now used on all platforms for the most reliable cross-process atomic operations
• Fallback support: Linux/macOS can fall back to libatomic or compiler builtins if the extension isn't available

Building and Installation

The C++ extension is built automatically during installation:

# Install with automatic extension build
pip install -e .

# Or build manually first
python setup.py build_ext --inplace
pip install -e .

Build requirements:

• Windows: Visual Studio 2017+ or MSVC build tools
• Linux: GCC 5+ or Clang 3.8+
• macOS: Xcode command line tools (clang)
• All platforms: Python development headers (included with standard Python installation)

The extension will be built as:

• Windows: atomic_ops_ext.cp3XX-win_amd64.pyd
• Linux: atomic_ops_ext.cpython-3XX-x86_64-linux-gnu.so
• macOS: atomic_ops_ext.cpython-3XX-darwin.so

(where XX is your Python version, e.g., 312 for Python 3.12)

Requirements for Lock-Free Operation

All platforms require hardware support for lock-free atomic operations:

• x86-64 CPU with CMPXCHG16B instruction (Intel since ~2006, AMD since ~2007)
• For C++/Python interoperability, both must use the same atomic hardware instructions
• No fallback implementation exists - lock-free atomics are mandatory for multi-producer queues

Why no fallback? The queue requires true atomic CAS operations for correctness in multi-producer scenarios. A lock-based fallback would:

• Break binary compatibility with C++ SlickQueue
• Fail to work correctly in multi-process scenarios (Python ↔ C++)
• Not provide the performance guarantees of a lock-free queue

Not Supported

• 32-bit platforms (no 16-byte atomic CAS)
• ARM64 (requires ARMv8.1+ CASP instruction - future support planned)
• CPUs without CMPXCHG16B support (very old x86-64 CPUs from before 2006)

Check platform support:

from atomic_ops import check_platform_support

supported, message = check_platform_support()
print(f"Platform: {message}")

Performance

Typical throughput on modern hardware (x86-64):

• Single producer/consumer: ~5-10M items/sec
• 4 producers/1 consumer: ~3-8M items/sec
• High contention (8+ producers): ~1-5M items/sec

Performance depends on:

• CPU cache topology
• Queue size (smaller = more contention)
• Item size
• Memory bandwidth

Advanced Usage

Batch Operations

Reserve and publish multiple elements at once:

# Reserve 10 elements
idx = q.reserve(10)

# Write data to each slot
for i in range(10):
    element = q[idx + i]
    element[:data_len] = data[i]

# Publish all 10 elements at once
q.publish(idx, 10)

Wrap-Around Handling

The queue automatically handles ring buffer wrap-around:

# Queue with size=8
q = SlickQueue(name='wrap_test', size=8, element_size=32)

# Reserve more items than queue size - wraps automatically
for i in range(100):
    idx = q.reserve()
    q[idx][:4] = struct.pack("<I", i)
    q.publish(idx)

Testing

Python Tests

Run the Python test suite:

# Atomic operations tests (clean output)
python tests/run_test.py tests/test_atomic_ops.py

# Basic queue tests (clean output)
python tests/run_test.py tests/test_queue.py

# Local mode tests
python tests/test_local_mode.py

# Multi-producer/consumer tests
# Note: If tests fail with "File exists" errors, run cleanup first:
python tests/cleanup_shm.py
python tests/test_multi_producer.py

C++/Python Interoperability Tests

Build and run comprehensive interop tests:

# 1. Build C++ test programs with CMake
mkdir build && cd build
cmake ..
cmake --build .

# 2. Run interoperability test suite
cd ..
python tests/test_interop.py

# Or run specific tests:
python tests/test_interop.py --test python_producer_cpp_consumer
python tests/test_interop.py --test cpp_producer_python_consumer
python tests/test_interop.py --test multi_producer_interop
python tests/test_interop.py --test stress_interop
python tests/test_interop.py --test cpp_shm_creation

The interop tests verify:

• Python → C++: Python producers write data that C++ consumers read
• C++ → Python: C++ producers write data that Python consumers read
• Mixed Multi-Producer: Multiple C++ and Python producers writing to same queue
• Stress Test: High-volume bidirectional communication
• SHM created by C++: C++ producers create the SHM and write data that Python consumers read

Note on Windows: If child processes from previous test runs don't terminate properly, you may need to manually kill orphaned python.exe processes before running tests again.

Known Issues

1. Buffer Cleanup Warning: You may see a BufferError: cannot close exported pointers exist warning during garbage collection. This is a harmless warning caused by Python's ctypes creating internal buffer references that persist beyond explicit cleanup. It occurs during program exit and does not affect functionality, performance, or correctness. The queue works perfectly despite this warning.

2. UserWarning: On Linux you may see UserWarning: resource_tracker: There appear to be 4 leaked shared_memory objects to clean up at shutdown. This is a harmless warning caused by Python's ctypes creating internal buffer references that persist beyond explicit cleanup. It occurs during program exit and does not affect functionality, performance, or correctness. The queue works perfectly despite this warning.

Architecture

Atomic Operations

The queue uses platform-specific atomic operations:

• 8-byte CAS: For reserved_info structure (multi-producer coordination)
• 8-byte CAS: For slot data_index fields (publish/read synchronization)
• Memory barriers: Acquire/release semantics for proper ordering

Memory Ordering

• reserve(): Uses memory_order_release on successful CAS
• publish(): Uses memory_order_release for data_index store
• read(): Uses memory_order_acquire for data_index load

This ensures:

• All writes to data are visible before publishing
• All reads of data happen after acquiring the index
• No reordering that could cause data races

Comparison with C++

Feature	C++	Python
Multi-producer	✅	✅
Multi-consumer (work-stealing)	✅	✅ (with AtomicCursor)
Lock-free (x86-64)	✅	✅
Memory layout	Reference	Matches exactly
Performance	Baseline	~50-80% of C++
Ease of use	Medium	High
read(int) single-consumer	✅	✅
read(atomic cursor) multi-consumer	✅	✅

Contributing

Issues and pull requests welcome at SlickQuant/slick-queue-py.

License

MIT License - see LICENSE file for details.

Made with ⚡ by SlickQuant