LeetCode #2349 — MEDIUM

Design a Number Container System

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

Solve on LeetCode
The Problem

Problem Statement

Design a number container system that can do the following:

  • Insert or Replace a number at the given index in the system.
  • Return the smallest index for the given number in the system.

Implement the NumberContainers class:

  • NumberContainers() Initializes the number container system.
  • void change(int index, int number) Fills the container at index with the number. If there is already a number at that index, replace it.
  • int find(int number) Returns the smallest index for the given number, or -1 if there is no index that is filled by number in the system.

Example 1:

Input
["NumberContainers", "find", "change", "change", "change", "change", "find", "change", "find"]
[[], [10], [2, 10], [1, 10], [3, 10], [5, 10], [10], [1, 20], [10]]
Output
[null, -1, null, null, null, null, 1, null, 2]

Explanation
NumberContainers nc = new NumberContainers();
nc.find(10); // There is no index that is filled with number 10. Therefore, we return -1.
nc.change(2, 10); // Your container at index 2 will be filled with number 10.
nc.change(1, 10); // Your container at index 1 will be filled with number 10.
nc.change(3, 10); // Your container at index 3 will be filled with number 10.
nc.change(5, 10); // Your container at index 5 will be filled with number 10.
nc.find(10); // Number 10 is at the indices 1, 2, 3, and 5. Since the smallest index that is filled with 10 is 1, we return 1.
nc.change(1, 20); // Your container at index 1 will be filled with number 20. Note that index 1 was filled with 10 and then replaced with 20. 
nc.find(10); // Number 10 is at the indices 2, 3, and 5. The smallest index that is filled with 10 is 2. Therefore, we return 2.

Constraints:

  • 1 <= index, number <= 109
  • At most 105 calls will be made in total to change and find.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: Design a number container system that can do the following: Insert or Replace a number at the given index in the system. Return the smallest index for the given number in the system. Implement the NumberContainers class: NumberContainers() Initializes the number container system. void change(int index, int number) Fills the container at index with the number. If there is already a number at that index, replace it. int find(int number) Returns the smallest index for the given number, or -1 if there is no index that is filled by number in the system.

Baseline thinking

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

Pattern signal: Hash Map · Design · Segment Tree

Example 1

["NumberContainers","find","change","change","change","change","find","change","find"]
[[],[10],[2,10],[1,10],[3,10],[5,10],[10],[1,20],[10]]

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Step 02

Core Insight

What unlocks the optimal approach

  • Use a hash table to efficiently map each number to all of its indices in the container and to map each index to their current number.
  • In addition, you can use ordered set to store all of the indices for each number to solve the find method. Do not forget to update the ordered set according to the change method.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. 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 #2349: Design a Number Container System
class NumberContainers {
    private Map<Integer, Integer> d = new HashMap<>();
    private Map<Integer, TreeSet<Integer>> g = new HashMap<>();

    public NumberContainers() {
    }

    public void change(int index, int number) {
        if (d.containsKey(index)) {
            int oldNumber = d.get(index);
            g.get(oldNumber).remove(index);
        }
        d.put(index, number);
        g.computeIfAbsent(number, k -> new TreeSet<>()).add(index);
    }

    public int find(int number) {
        var ids = g.get(number);
        return ids == null || ids.isEmpty() ? -1 : ids.first();
    }
}

/**
 * Your NumberContainers object will be instantiated and called as such:
 * NumberContainers obj = new NumberContainers();
 * obj.change(index,number);
 * int param_2 = obj.find(number);
 */
Step 06

Interactive Study Demo

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

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(1) per op
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.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.