5 Best Ways to Utilize Memory Mapped File Support in Python mmap

💡 Problem Formulation: In situations where there is a need to efficiently read and write large files without loading the entire file into memory, Python’s mmap module offers a solution. For instance, applications that handle large log files, binary data in image or video processing, or multi-GB data sets for machine learning could benefit from having an input file >1000MB and a desired output of specific bytes being read or written instantly without the overhead of traditional file handling approaches.

Method 1: Basic Memory Mapping

This method involves creating a memory-mapped object that allows you to manipulate a file’s contents directly in memory. This is suitable for both reading and writing data. Using mmap.mmap(), one can create a memory-mapped object that behaves like both a bytearray and a file object.

Here’s an example:

import mmap

# Open file for reading and writing
with open('example.dat', 'r+b') as f:
    # Memory map the file
    mm = mmap.mmap(f.fileno(), 0)
    # Read content via standard file methods
    print(mm.readline()) 
    # Update content using slice notation
    mm[6:11] = b'world'
    # Close the map
    mm.close()

Output:

Hello world

This code snippet opens a file called ‘example.dat’ in read-write binary mode and then uses mmap.mmap() to create a memory-mapped object. It reads a line from the memory-mapped file and updates part of the file using slice notation. This technique is particularly efficient for files too large to fit in memory because it does not require loading the entire file.

Method 2: Accessing Memory Mapped Files with Context Managers

To ensure memory-mapped files are safely and efficiently closed after their operations, Python’s context manager can be used with mmap.mmap(). This method is beneficial when working with large files, providing an elegance similar to conventional file handling but with the advantage of memory-mapped efficiency.

Here’s an example:

import mmap

with open('example.dat', 'r+b') as f:
    with mmap.mmap(f.fileno(), 0) as mm:
        print(mm[:5])  # Read first 5 bytes
        mm[10:15] = b'abcde'  # Modify bytes

Output:

Hello

Using the with statement not only simplifies the code but also ensures that resources are promptly released. Upon exiting the block, mmap.mmap() is automatically closed, mimicking the behavior of file objects within context managers. This eliminates errors related to resource leakage and is a best practice for resource management.

Method 3: File Slicing with mmap

The memory mapped file object provided by mmap can also be sliced for reading and writing, similar to regular Python sequences such as strings and lists. This method provides an intuitive way to work with subsets of the data in the memory-mapped file.

Here’s an example:

import mmap

with open('example.dat', 'r+b') as f:
    mm = mmap.mmap(f.fileno(), 0)
    # Slice to get the first 10 bytes
    print(mm[:10])
    # Overwrite the first 5 bytes with 'ABCDE'
    mm[:5] = b'ABCDE'
    mm.close()

Output:

ABCDEworld

This snippet demonstrates slicing a memory-mapped object to read the first 10 bytes and then overwrite the first 5 bytes with new data. This makes it simple to navigate and mutate data within a file without the need for arbitrary seek and read/write operations, enhancing code readability and maintainability.

Method 4: Regexp Matching Within Memory Mapped Files

Python’s regular expressions can be used with memory-mapped files to find patterns. By applying the re module directly on the memory-mapped object, one can quickly search and possibly replace sequences within the file.

Here’s an example:

import mmap
import re

with open('example.dat', 'r+b') as f:
    mm = mmap.mmap(f.fileno(), 0)
    # Find and print all occurrences of the word 'world'
    for match in re.finditer(b'world', mm):
        print('Found at offset:', match.start())
    mm.close()

Output:

Found at offset: 6

This code uses the re.finditer() function to iterate over all matches of the byte string ‘world’ in the memory-mapped file. It demonstrates how regular expressions can be applied directly to memory-mapped data, providing a potent combination of pattern matching and the efficiency of memory mapping.

Bonus One-Liner Method 5: Quickly Changing File Size with mmap

Python’s mmap module also allows one to easily extend the size of a file by changing the size of the memory-mapped object. This can be useful when you need to append large amounts of data to a file.

Here’s an example:

import mmap

with open('example.dat', 'r+b') as f:
    mm = mmap.mmap(f.fileno(), 0)
    # Resize file to 1KB
    mm.resize(1024)
    mm.close()

This single line demonstrates the simplicity and power of mmap when dealing with file sizes. By allowing for seamless resizing of the file’s memory mapping, it provides flexibility and control over file data management in Python.

Summary/Discussion

• Method 1: Basic Memory Mapping. Strengths: Simple and direct approach to read and write files using memory mapping. Weaknesses: Manually managing the map’s closure can be error-prone.
• Method 2: Accessing Memory Mapped Files with Context Managers. Strengths: Clean-up is automatic, reducing potential resource leaks. Weaknesses: Developers must be familiar with context managers for maximum benefit.
• Method 3: File Slicing with mmap. Strengths: Intuitive file content manipulations. Weaknesses: Requires knowing the structure of the file to slice it correctly.
• Method 4: Regexp Matching Within Memory Mapped Files. Strengths: Integrates powerful pattern matching with efficient file handling. Weaknesses: It may be less efficient for very large data sets due to the complexity of regexp processing.
• Method 5: Quickly Changing File Size with mmap. Strengths: Quick and easy file resizing. Weaknesses: May require additional logic to handle the new file space properly.