Chunked reading for large files in Data Analysis Python - Time & Space Complexity

Time Complexity: Chunked reading for large files

O(n)

Understanding Time Complexity

When reading very large files, we often read them in smaller parts called chunks.

We want to know how the time to read grows as the file size grows.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

chunk_size = 1024  # bytes
with open('large_file.csv', 'r') as file:
    while chunk := file.read(chunk_size):
        process(chunk)  # some processing on the chunk

This code reads a large file piece by piece and processes each piece.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Reading chunks of the file repeatedly in a loop.
• How many times: Number of chunks equals total file size divided by chunk size.

How Execution Grows With Input

As the file size grows, the number of chunks grows proportionally.

Input Size (n bytes)	Approx. Number of Reads
10,240 (10 KB)	10 (if chunk size is 1024 bytes)
102,400 (100 KB)	100
1,024,000 (1 MB)	1000

Pattern observation: The number of read operations grows linearly with file size.

Final Time Complexity

Time Complexity: O(n)

This means the time to read and process the file grows directly in proportion to the file size.

Common Mistake

[X] Wrong: "Reading in chunks makes the time constant no matter the file size."

[OK] Correct: Even with chunks, you still read every byte once, so time grows with file size.

Interview Connect

Understanding how chunked reading scales helps you handle big data files efficiently in real projects.

Self-Check

"What if we doubled the chunk size? How would the time complexity change?"