How to Merge Large Files Without Loading Them into Memory?

What are the methods to merge large files without loading the entire file into memory?

# Pseudocode for merging large files step by step
file1 = 'large_file_1.txt'
file2 = 'large_file_2.txt'
merged_file = 'merged_output.txt'

# Open the output file in write mode
with open(merged_file, 'w') as outfile:
    # Open the first file in read mode
    with open(file1, 'r') as f1:
        for line in f1:
            outfile.write(line)  # Write each line to the merged file
    # Open the second file in read mode
    with open(file2, 'r') as f2:
        for line in f2:
            outfile.write(line)  # Write each line to the merged file

Merging large files without fully loading them into memory is a crucial technique in software engineering, especially when dealing with files that exceed system memory limits. This method ensures that your application avoids crashing due to memory overload and effectively handles file data in a stream-based manner.

# Example of merging two large files in Python using buffered reading

buffer_size = 1024 * 1024  # 1 MB

with open('large_file_1.txt', 'rb') as f1, open('large_file_2.txt', 'rb') as f2, open('merged_output.txt', 'wb') as outfile:
    while chunk := f1.read(buffer_size):
        outfile.write(chunk)  # Write first file chunks
    while chunk := f2.read(buffer_size):
        outfile.write(chunk)  # Write second file chunks

Causes

• Files are too large to fit in memory at once.
• Inefficient resource utilization leading to application crashes.
• Needs for processing large data files, such as logs or databases, without compromising performance.

Solutions

• Utilize file streaming to read and write files line by line or in chunks.
• Use `with open()` statements in Python for better resource management.
• Consider buffering techniques to optimise read/write speed.

Mistake: Not closing files properly after writing or reading.

Solution: Always use 'with' statements to ensure files are closed automatically.

Mistake: Trying to load the entire file into memory at once.

Solution: Read and process files in smaller chunks to prevent running out of memory.