Detailed Explanation of Memory Mapping and the mmap Module in Python

1. Basic Concepts of Memory Mapping

Memory mapping is a technique that maps file or device data directly into a process's virtual memory space. Through memory mapping, file contents can be accessed as an in-memory array, avoiding frequent read/write system calls and improving I/O efficiency. Its core features include:

• Direct memory manipulation: Modifications to the mapped memory are automatically synchronized to the file (optional).
• Efficient sharing: Multiple processes can map the same file for efficient data sharing.
• Lazy loading: Data is loaded from disk to memory only when needed, saving physical memory.

2. Application Scenarios of Memory Mapping

• Large file processing: No need to load the entire content at once, reducing memory pressure.
• Inter-Process Communication (IPC): Achieve data sharing by mapping the same file.
• Real-time log file analysis: Dynamically read files being written by other processes.

3. The mmap Module in Python

Python's standard library mmap module provides memory mapping functionality, primarily through the mmap.mmap() function to create mapping objects.

3.1 Basic Steps to Create a Memory Map

1. Open a file: Use the open() function with an appropriate mode (e.g., r+b).
2. Create a mapping object: Call mmap.mmap(), passing the file descriptor, mapping length, and access mode.
3. Operate on the mapped memory: Read and write data as if operating on a byte array.
4. Close the map and file: Call close() to release resources.

Example code:

import mmap  

# 1. Open the file  
with open('example.txt', 'r+b') as f:  
    # 2. Create a memory map, 0 means map the entire file  
    with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_WRITE) as mm:  
        # 3. Read data (similar to a byte array)  
        print(mm[:10])  # Output the first 10 bytes  
        # Modify data  
        mm[0:5] = b'HELLO'  # Replace the first 5 bytes  
        # Data is automatically synchronized to the file  

4. Detailed Explanation of mmap Key Parameters

• length: Mapping length. Set to 0 to map the entire file.
• access: Access permissions:
  • ACCESS_READ: Read-only.
  • ACCESS_WRITE: Writable (modifications are synchronized to the file).
  • ACCESS_COPY: Copy-on-write (modifications are not synchronized to the file).

5. Advanced Features of Memory Mapping

5.1 Search and Replace

Mapping objects support search operations similar to strings:

with open('data.bin', 'r+b') as f:  
    with mmap.mmap(f.fileno(), 0) as mm:  
        pos = mm.find(b'old_data')  # Find the byte sequence  
        if pos != -1:  
            mm[pos:pos+8] = b'new_data'  # Replace the content  

5.2 Inter-Process Sharing

When multiple processes map the same file, modifications are immediately visible to each other (synchronization mechanisms are required to control race conditions):

# Process A  
import mmap  
with open('shared.bin', 'r+b') as f:  
    mm = mmap.mmap(f.fileno(), 0)  
    mm[0:4] = b'1234'  # Process B will see the changes immediately  

6. Precautions and Limitations

• File size limit: The mapping length cannot exceed the current file size (can be extended via truncate()).
• Platform differences: Behavior of the access parameter may differ between Windows and Unix systems.
• Synchronization issues: Locks or other synchronization mechanisms are needed to avoid data corruption during multi-process writing.

7. Performance Optimization Scenarios

• Random access to large files: After mapping, read and write directly by offset, which is more efficient than traditional seek()/read().
• Read-only mode: Use ACCESS_READ to avoid unnecessary write-back operations.

Through memory mapping, Python programs can handle file I/O at speeds close to native memory operations, making it particularly suitable for scenarios requiring high-frequency random access or inter-process data sharing.