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Updated on 2025-03-04

Summary of how to use Python to process reading of large files

introduction

In daily development work, we often encounter the need to process large files. Whether it is reading log files, processing data sets, or analyzing super-large text files, large file operations are a very common challenge. Especially in memory-limited environments, directly loading the entire file into memory may lead to memory exhaustion, so we need to adopt a more efficient strategy.

This article will introduce in detail how to use Python to handle the reading of large files, and introduce several commonly used technologies, including line-by-line reading, block reading, using generators, and precautions when processing binary files. Through these methods, we can efficiently process files exceeding the memory capacity.

1. Common file reading methods

Python provides a variety of ways to read files. When processing smaller files, we can useread()Read the entire file into memory at one time. But when the file is very large, this method is obviously not feasible. To deal with large files, there are usually the following ways to read the file content:

  1. Read line by line: Reading files line by line can save memory because only the current line will be loaded into memory.
  2. Block reading: Read file contents in blocks, and only fixed-size data is read at a time.
  3. Generator: Lazy loading of data through the generator, only generate data when needed, avoiding loading all data at once.

Next we will introduce these methods in detail.

2. Read the file line by line

Reading files line by line is one of the most common methods for handling large files and is suitable for text files. When we only need to process data on each row, reading line by line not only saves memory, but also is very intuitive and easy to understand.

2.1 Use for loop to read line by line

Python provides a simple and efficient way to read file content line by line, that is, use it directlyforLoop:

with open('large_file.txt', 'r') as file:
    for line in file:
        # Process each row of data        print(())

In this example, the for loop automatically reads the file line by line, and the strip() method is used to remove line breaks at the end of each line. If the file is very large, using this method can avoid loading the entire file into memory, as only the data of the current line will be processed at a time.

2.2 Using the readline() method

If we want to have more explicit control over line-by-line reading, we can use the readline() method:

with open('large_file.txt', 'r') as file:
    line = ()
    while line:
        # Process the current row        print(())
        line = ()  # Read the next line

This method is called manuallyreadline()To read each line of data, when the file is read,readline()Return empty string'', so we can passwhileLoop to read the file content line by line.

2.3 Use the readlines() method (not recommended)

Althoughreadlines()Methods can read each line in a file into a list at once, but this is not suitable for handling large files.readlines()Each line of the file will be loaded into memory. If the file is very large, it will easily lead to insufficient memory. This method is not recommended to handle large files unless the files are small.

3. Read files in chunks

In addition to line-by-line reading, another commonly used method is to read files by block, that is, read a fixed-size block of data each time. This approach is useful when you need to process binary files or read data of fixed byte lengths.

3.1 Use the read(size) method to read by block

read(size)The method allows us to specify the number of bytes per read. This method is especially suitable for scenarios where binary files are processed or processed at a fixed size.

chunk_size = 1024  # 1 KB of data is read each timewith open('large_file.txt', 'r') as file:
    chunk = (chunk_size)
    while chunk:
        # Processing data blocks        print(chunk)
        chunk = (chunk_size)  # Continue to read the next piece of data

In this example,chunk_sizeDefines the number of bytes read each time. For text files, 1 KB is a suitable block size, but you can adjust this value according to your needs. If you are dealing with binary files, you can userbMode to open the file.

3.2 Use iter() for chunked reading

Python's built-initer()Functions can convert file objects into an iterator. We can implement chunked reading by specifying a fixed-size read function:

def read_in_chunks(file_object, chunk_size=1024):
    """Generator function, read files by block"""
    while True:
        data = file_object.read(chunk_size)
        if not data:
            break
        yield data

with open('large_file.txt', 'r') as file:
    for chunk in read_in_chunks(file):
        # Process each piece of data        print(chunk)

This method implements lazy loading by block read through the generator, and can be easily processed when the file is very large.

4. Use generator to process large files

Generators are a very powerful tool for use when dealing with large files or large amounts of data. The generator can traverse like a list, but unlike the list, the generator only generates data when needed, thus saving a lot of memory.

4.1 Basic generator example

We can define a generator function and use it to read large files line by line:

def file_line_generator(file_name):
    with open(file_name, 'r') as file:
        for line in file:
            yield ()

# Use the generator to process files line by linefor line in file_line_generator('large_file.txt'):
    print(line)

This generator will read the file line by line and passyieldReturn each line to the caller. When working with large files, the advantage of the generator is that it does not load the entire file into memory, but generates data on demand.

5. Read binary files

When processing non-text files such as images and audio, we need to open the file in binary mode. Python providesrbMode (read binary) to process binary files.

5.1 Reading binary files

When reading binary files, you can read them in blocks, which can effectively avoid excessive memory usage.

chunk_size = 1024  # Read 1 KB block sizewith open('large_image.jpg', 'rb') as file:
    chunk = (chunk_size)
    while chunk:
        # Process binary data blocks        print(chunk)
        chunk = (chunk_size)

In this example, we use rb mode to open the file and read the file contents at a 1 KB block size. This method is suitable for processing any type of binary files, such as image, audio files, etc.

6. Use memory mapped files (mmap)

For particularly large files, you can use Python's mmap module, which allows us to map a portion of the file into memory, so that the entire file is not loaded at once.

6.1 Using the mmap module

Memory-mapping files are an efficient file processing method, suitable for scenarios where frequent random access to large files is required.

import mmap

with open('large_file.txt', 'r+b') as f:
    # Map files to memory    with ((), 0) as mm:
        # Read the first 100 bytes        print(mm[:100].decode('utf-8'))
        # Find the location of a string in the file        print((b'Python'))

In this example, we map the entire file to memory and can manipulate the file contents like a sequence of bytes in memory.mmapIdeal for handling scenarios where large files need to be read or written randomly.

7. Things to note when handling large documents

When working with large files, in addition to choosing the right reading method, there are some additional things to note:

  1. Select the right block size: When reading by block, the selection of block size is very important. Too large blocks may lead to excessive memory usage, and too small blocks may increase the frequency of I/O operations, resulting in performance degradation. Adjust the appropriate block size according to file type and system resources.

  2. Avoid reading large files at once: Whether reading text or binary files, avoid reading the entire file into memory at one time, especially when the file is very large. You can choose to read line by line or chunk reading.

  3. Using the generator: The generator is ideal for handling large files or data that needs to be loaded late, because it does not load all data at once, but generates data on demand, reducing memory consumption.

  4. Optimize I/O performance: File I/O operations may become a bottleneck when processing large files. Performance can be improved by reasonable caching and reducing the number of I/O operations. For example, block reading can effectively reduce disk I/O frequency.

8. Summary

This article introduces a variety of tips and methods for handling large files in Python, including line-by-line reading, block reading, using generators, and

Methods for processing binary files. By reasonably choosing the right file reading method, we can efficiently process large files that exceed memory limits.

The core idea of ​​processing large files is to avoid loading the entire file into memory at one time, but to reduce memory consumption through technologies such as gradual reading and chunking processing. These methods are useful when dealing with large-scale datasets, log files, or binary files.

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