LeetCode #1352 — MEDIUM

Product of the Last K Numbers

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

Design an algorithm that accepts a stream of integers and retrieves the product of the last k integers of the stream.

Implement the ProductOfNumbers class:

ProductOfNumbers() Initializes the object with an empty stream.
void add(int num) Appends the integer num to the stream.
int getProduct(int k) Returns the product of the last k numbers in the current list. You can assume that always the current list has at least k numbers.

The test cases are generated so that, at any time, the product of any contiguous sequence of numbers will fit into a single 32-bit integer without overflowing.

Example:

Input
["ProductOfNumbers","add","add","add","add","add","getProduct","getProduct","getProduct","add","getProduct"]
[[],[3],[0],[2],[5],[4],[2],[3],[4],[8],[2]]

Output
[null,null,null,null,null,null,20,40,0,null,32]

Explanation
ProductOfNumbers productOfNumbers = new ProductOfNumbers();
productOfNumbers.add(3);        // [3]
productOfNumbers.add(0);        // [3,0]
productOfNumbers.add(2);        // [3,0,2]
productOfNumbers.add(5);        // [3,0,2,5]
productOfNumbers.add(4);        // [3,0,2,5,4]
productOfNumbers.getProduct(2); // return 20. The product of the last 2 numbers is 5 * 4 = 20
productOfNumbers.getProduct(3); // return 40. The product of the last 3 numbers is 2 * 5 * 4 = 40
productOfNumbers.getProduct(4); // return 0. The product of the last 4 numbers is 0 * 2 * 5 * 4 = 0
productOfNumbers.add(8);        // [3,0,2,5,4,8]
productOfNumbers.getProduct(2); // return 32. The product of the last 2 numbers is 4 * 8 = 32

Constraints:

0 <= num <= 100
1 <= k <= 4 * 10⁴
At most 4 * 10⁴ calls will be made to add and getProduct.
The product of the stream at any point in time will fit in a 32-bit integer.

Follow-up: Can you implement both GetProduct and Add to work in O(1) time complexity instead of O(k) time complexity?

Step 01

Brute Force Baseline

Problem summary: Design an algorithm that accepts a stream of integers and retrieves the product of the last k integers of the stream. Implement the ProductOfNumbers class: ProductOfNumbers() Initializes the object with an empty stream. void add(int num) Appends the integer num to the stream. int getProduct(int k) Returns the product of the last k numbers in the current list. You can assume that always the current list has at least k numbers. The test cases are generated so that, at any time, the product of any contiguous sequence of numbers will fit into a single 32-bit integer without overflowing.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Math · Design

Example 1

["ProductOfNumbers","add","add","add","add","add","getProduct","getProduct","getProduct","add","getProduct"]
[[],[3],[0],[2],[5],[4],[2],[3],[4],[8],[2]]

Step 02

Core Insight

What unlocks the optimal approach

Keep all prefix products of numbers in an array, then calculate the product of last K elements in O(1) complexity.
When a zero number is added, clean the array of prefix products.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1352: Product of the Last K Numbers
class ProductOfNumbers {
    private List<Integer> s = new ArrayList<>();

    public ProductOfNumbers() {
        s.add(1);
    }

    public void add(int num) {
        if (num == 0) {
            s.clear();
            s.add(1);
            return;
        }
        s.add(s.get(s.size() - 1) * num);
    }

    public int getProduct(int k) {
        int n = s.size();
        return n <= k ? 0 : s.get(n - 1) / s.get(n - k - 1);
    }
}

/**
 * Your ProductOfNumbers object will be instantiated and called as such:
 * ProductOfNumbers obj = new ProductOfNumbers();
 * obj.add(num);
 * int param_2 = obj.getProduct(k);
 */

// Accepted solution for LeetCode #1352: Product of the Last K Numbers
type ProductOfNumbers struct {
	s []int
}

func Constructor() ProductOfNumbers {
	return ProductOfNumbers{[]int{1}}
}

func (this *ProductOfNumbers) Add(num int) {
	if num == 0 {
		this.s = []int{1}
		return
	}
	this.s = append(this.s, this.s[len(this.s)-1]*num)
}

func (this *ProductOfNumbers) GetProduct(k int) int {
	n := len(this.s)
	if n <= k {
		return 0
	}
	return this.s[len(this.s)-1] / this.s[len(this.s)-k-1]
}

/**
 * Your ProductOfNumbers object will be instantiated and called as such:
 * obj := Constructor();
 * obj.Add(num);
 * param_2 := obj.GetProduct(k);
 */

# Accepted solution for LeetCode #1352: Product of the Last K Numbers
class ProductOfNumbers:
    def __init__(self):
        self.s = [1]

    def add(self, num: int) -> None:
        if num == 0:
            self.s = [1]
            return
        self.s.append(self.s[-1] * num)

    def getProduct(self, k: int) -> int:
        return 0 if len(self.s) <= k else self.s[-1] // self.s[-k - 1]


# Your ProductOfNumbers object will be instantiated and called as such:
# obj = ProductOfNumbers()
# obj.add(num)
# param_2 = obj.getProduct(k)

// Accepted solution for LeetCode #1352: Product of the Last K Numbers
#[derive(Default)]
struct ProductOfNumbers {
    prefix: Vec<i32>,
}

impl ProductOfNumbers {
    fn new() -> Self {
        let prefix = vec![1];
        ProductOfNumbers { prefix }
    }

    fn add(&mut self, num: i32) {
        if num > 0 {
            let prev = self.prefix[self.prefix.len() - 1];
            self.prefix.push(prev * num);
        } else {
            self.prefix = vec![1];
        }
    }

    fn get_product(&self, k: i32) -> i32 {
        let k = k as usize;
        let n = self.prefix.len();
        if k >= n {
            0
        } else {
            self.prefix[n - 1] / self.prefix[n - 1 - k]
        }
    }
}

#[test]
fn test() {
    let mut obj = ProductOfNumbers::new();
    obj.add(3);
    obj.add(0);
    obj.add(2);
    obj.add(5);
    obj.add(4);
    assert_eq!(obj.get_product(2), 20);
    assert_eq!(obj.get_product(3), 40);
    assert_eq!(obj.get_product(4), 0);
    obj.add(8);
    assert_eq!(obj.get_product(2), 32);
}

// Accepted solution for LeetCode #1352: Product of the Last K Numbers
class ProductOfNumbers {
    s = [1];

    add(num: number): void {
        if (num === 0) {
            this.s = [1];
        } else {
            const i = this.s.length;
            this.s[i] = this.s[i - 1] * num;
        }
    }

    getProduct(k: number): number {
        const i = this.s.length;
        if (k > i - 1) return 0;
        return this.s[i - 1] / this.s[i - k - 1];
    }
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(1)

Space

O(n)

Approach Breakdown

NAIVE

O(n) per op time

O(n) space

Use a simple list or array for storage. Each operation (get, put, remove) requires a linear scan to find the target element — O(n) per operation. Space is O(n) to store the data. The linear search makes this impractical for frequent operations.

OPTIMIZED DESIGN

O(1) per op time

O(n) space

Design problems target O(1) amortized per operation by combining data structures (hash map + doubly-linked list for LRU, stack + min-tracking for MinStack). Space is always at least O(n) to store the data. The challenge is achieving constant-time operations through clever structure composition.

Shortcut: Combine two data structures to get O(1) for each operation type. Space is always O(n).

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.