LTRIM for list capping in Redis - Time & Space Complexity

Time Complexity: LTRIM for list capping

O(m)

Understanding Time Complexity

We want to understand how the time it takes to trim a Redis list changes as the list gets bigger.

Specifically, how does the LTRIM command behave when keeping a list capped at a certain size?

Scenario Under Consideration

Analyze the time complexity of the following Redis commands used to cap a list size.

LPUSH mylist "new_item"
LTRIM mylist 0 99
    

This code adds a new item to the start of the list and then trims the list to keep only the first 100 items.

Identify Repeating Operations

Look for operations that repeat or scale with input size.

• Primary operation: The LTRIM command removes elements outside the specified range.
• How many times: It processes elements outside the kept range, which depends on how many items exceed the cap.

How Execution Grows With Input

The time to trim depends on how many extra items are removed beyond the cap.

Input Size (n)	Approx. Operations
100	Few or no removals if list is at cap
1,000	Removes about 900 items if trimming to 100
10,000	Removes about 9,900 items if trimming to 100

Pattern observation: The more items beyond the cap, the more work LTRIM does, growing roughly linearly with the number of removed items.

Final Time Complexity

Time Complexity: O(m)

This means the time grows linearly with the number of elements removed from the list during trimming.

Common Mistake

[X] Wrong: "LTRIM always runs in constant time regardless of list size."

[OK] Correct: LTRIM must remove all elements outside the range, so if many elements are removed, it takes more time.

Interview Connect

Understanding how commands like LTRIM scale helps you reason about performance in real systems and shows you can think about resource use clearly.

Self-Check

"What if we changed the trim range to keep more items, say 0 to 999 instead of 0 to 99? How would the time complexity change?"