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Array Performance

Array Performance

Introduction

Different array operations have different time complexities. Understanding these helps you write efficient code.

Performance Table

Operation	Time Complexity	Method
Access by index	O(1)	`arr[i]`
Add to end	O(1)	`push()`
Remove from end	O(1)	`pop()`
Add to start	O(n)	`unshift()`
Remove from start	O(n)	`shift()`
Insert/Remove	O(n)	`splice()`
Search (unsorted)	O(n)	`find()`, `indexOf()`
Sort	O(n log n)	`sort()`
Iteration	O(n)	`forEach()`, `map()`

Visual Explanation

flowchart LR
    A["push/pop: O(1)"]
    B["shift/unshift: O(n)"]
    C["Access: O(1)"]
    D["Search: O(n)"]

    subgraph Fast
        A
        C
    end

    subgraph Slower
        B
        D
    end

Best Practices

Prefer push/pop over shift/unshift for queue-like operations
Use typed arrays for large numeric datasets
Avoid frequent splice on large arrays

Summary

End operations (push/pop) are O(1) — fast
Start operations (shift/unshift) are O(n) — slower
Index access is O(1) — instant

Further Reading

V8: Array Performance