Identifying content decay in SEO Fundamentals - Time & Space Complexity

Time Complexity: Identifying content decay

O(n)

Understanding Time Complexity

When checking for content decay, we want to know how much work it takes to find outdated or underperforming pages.

How does the effort grow as the website gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following SEO process.

// For each page in the website
for page in website_pages:
  // Check page metrics (traffic, rankings)
  analyze_metrics(page)
  // Compare with past data
  compare_with_history(page)
  // Flag if performance dropped
  if performance_dropped(page):
    add_to_decay_list(page)
    

This code checks every page's performance to find which ones have decayed over time.

Identify Repeating Operations

• Primary operation: Looping through each page in the website.
• How many times: Once for every page, so as many times as there are pages.

How Execution Grows With Input

As the number of pages grows, the checks grow at the same pace.

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

Pattern observation: The work grows directly with the number of pages.

Final Time Complexity

Time Complexity: O(n)

This means the time to identify content decay grows in a straight line with the number of pages.

Common Mistake

[X] Wrong: "Checking one page means the whole process is always fast regardless of site size."

[OK] Correct: Because you must check every page, the total time grows as the site grows, not stays the same.

Interview Connect

Understanding how work grows with site size helps you plan SEO tasks and explain your approach clearly in discussions.

Self-Check

"What if we only checked pages updated in the last 6 months? How would the time complexity change?"