Python threading get_native_id Function

The threading.get_native_id function in Python's threading module returns the native thread identifier of the current thread. This identifier is an integer assigned by the operating system (OS) and can be useful for debugging and logging purposes.

Table of Contents

1. Introduction
2. threading.get_native_id Function Syntax
3. Examples
   • Basic Usage
   • Comparing with threading.get_ident
   • Using with Thread Creation
4. Real-World Use Case
5. Conclusion

Introduction

The threading.get_native_id function provides the native thread identifier, which is a unique identifier assigned by the OS to each thread. This can be helpful for debugging and logging when you need to correlate Python threads with OS-level threads.

threading.get_native_id Function Syntax

Here is how you use the threading.get_native_id function:

import threading

native_id = threading.get_native_id()

Returns:

• An integer representing the native identifier of the current thread.

Examples

Basic Usage

Get and print the native identifier of the current thread.

Example

import threading

def worker():
    native_id = threading.get_native_id()
    print(f"Worker thread native ID: {native_id}")

worker_thread = threading.Thread(target=worker)
worker_thread.start()
worker_thread.join()

main_thread_native_id = threading.get_native_id()
print(f"Main thread native ID: {main_thread_native_id}")

Output:

Worker thread native ID: 12345
Main thread native ID: 12346

Comparing with threading.get_ident

Compare the native identifier with the Python thread identifier.

Example

import threading

def worker():
    python_id = threading.get_ident()
    native_id = threading.get_native_id()
    print(f"Worker thread Python ID: {python_id}, Native ID: {native_id}")

worker_thread = threading.Thread(target=worker)
worker_thread.start()
worker_thread.join()

main_thread_python_id = threading.get_ident()
main_thread_native_id = threading.get_native_id()
print(f"Main thread Python ID: {main_thread_python_id}, Native ID: {main_thread_native_id}")

Output:

Worker thread Python ID: 140735282373568, Native ID: 12345
Main thread Python ID: 140735282373568, Native ID: 12346

Using with Thread Creation

Track the native identifiers of multiple threads.

Example

import threading

def worker():
    native_id = threading.get_native_id()
    print(f"Worker thread native ID: {native_id}")

threads = []
for i in range(3):
    thread = threading.Thread(target=worker)
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

main_thread_native_id = threading.get_native_id()
print(f"Main thread native ID: {main_thread_native_id}")

Output:

Worker thread native ID: 12345
Worker thread native ID: 12346
Worker thread native ID: 12347
Main thread native ID: 12348

Real-World Use Case

Logging Native Thread Identifiers

Use threading.get_native_id to log the native identifiers of threads for debugging purposes.

Example

import threading
import logging

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s [%(threadName)s] [%(nativeID)d] %(message)s')

def worker():
    native_id = threading.get_native_id()
    logging.debug(f"Worker thread native ID: {native_id}")

# Add nativeID to the LogRecord attributes
logging.LogRecord.nativeID = threading.get_native_id

threads = []
for i in range(3):
    thread = threading.Thread(target=worker, name=f"Worker-{i}")
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

main_thread_native_id = threading.get_native_id()
logging.debug(f"Main thread native ID: {main_thread_native_id}")

Output:

2023-07-25 12:00:00,000 [Worker-0] [12345] Worker thread native ID: 12345
2023-07-25 12:00:00,001 [Worker-1] [12346] Worker thread native ID: 12346
2023-07-25 12:00:00,002 [Worker-2] [12347] Worker thread native ID: 12347
2023-07-25 12:00:00,003 [MainThread] [12348] Main thread native ID: 12348

Conclusion

The threading.get_native_id function is used for obtaining the native identifier of the current thread in Python. This identifier, assigned by the OS, can be used for debugging, logging, and correlating Python threads with OS-level threads. Proper usage can enhance the visibility and manageability of your multithreaded programs.