Report generation script in Bash Scripting - Time & Space Complexity

Time Complexity: Report generation script

O(n)

Understanding Time Complexity

When running a report generation script, it is important to know how the time it takes grows as the data size grows.

We want to understand how the script's work increases when there are more items to process.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

#!/bin/bash

files=/var/log/*.log

for file in $files; do
  echo "Processing $file"
  lines=$(wc -l < "$file")
  echo "Lines: $lines"
  # Simulate report generation
  sleep 0.1
  echo "Report generated for $file"
done

This script lists all log files in a folder, then for each file it counts lines and simulates generating a report.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping over each log file to process it.
• How many times: Once for each file found in the directory.

How Execution Grows With Input

As the number of log files increases, the script runs the loop more times, doing similar work each time.

Input Size (n)	Approx. Operations
10	About 10 file processes
100	About 100 file processes
1000	About 1000 file processes

Pattern observation: The work grows directly with the number of files; doubling files doubles work.

Final Time Complexity

Time Complexity: O(n)

This means the time to finish grows in a straight line with the number of files to process.

Common Mistake

[X] Wrong: "The script runs in constant time because it just loops over files once."

[OK] Correct: Even though the loop runs once, it runs once per file, so more files mean more work and more time.

Interview Connect

Understanding how loops affect time helps you explain script performance clearly and shows you can reason about scaling work.

Self-Check

"What if the script also read every line inside each file? How would the time complexity change?"