What is the time complexity of Design HashMap?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Design HashMap?

This problem uses Array, Hash Table, Linked List, Design, Hash Function. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Design HashMap?

Insight 1: We can't directly use keys as indices in an array due to the constraint 0 <= key <= 10^6, which would require a very large array and waste memory. Therefore, we need a hashing function to map keys to a smaller index range. Insight 2: Collision handling is crucial. When different keys map to the same index, we need a strategy to store and retrieve them. Using a separate chaining approach (linked list or list) is a common and efficient solution.

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Design HashMap - LeetCode 706 Solution

Q: How to solve Design HashMap?

The solution uses a simple hash function (key % size) to map keys to indices in a list called `table`. Each element of the `table` is a list representing a bucket. When a key-value pair is inserted, we calculate the index using the hash function. If the key already exists in the bucket, we update its value. Otherwise, we append the new key-value pair to the bucket. For `get` and `remove`, we iterate through the bucket at the calculated index and search for the key. If found, we return the value or remove the key-value pair accordingly. If not found, `get` returns -1 and `remove` does nothing.

Q: Which companies ask LeetCode 706. Design HashMap in interviews?

This problem is frequently asked at Applied Intuition, Deutsche Bank, Nvidia, Palo Alto Networks, ServiceNow and 2 more companies.

Design HashMap - Complete Solution Guide

Design HashMap is LeetCode problem 706, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3.

Problem Statement

Design a HashMap without using any built-in hash table libraries. Implement the MyHashMap class: MyHashMap() initializes the object with an empty map. void put(int key, int value) inserts a (key, value) pair into the HashMap. If the key already exists in the map, update the corresponding value . int get(int key) returns the value to which the specified key is mapped, or -1 if this map contains no mapping for the key . void remove(key) removes the key and its corresponding value if the map contai

Detailed Explanation

The problem asks us to implement a HashMap data structure from scratch, without using any built-in hash table libraries. We need to implement the `put`, `get`, and `remove` operations. `put(key, value)` inserts a key-value pair into the map, updating the value if the key already exists. `get(key)` returns the value associated with the key, or -1 if the key is not found. `remove(key)` deletes the key-value pair if the key exists in the map. The keys and values are integers within the range [0, 10^6], and the number of calls to `put`, `get`, and `remove` will be at most 10^4.

Solution Approach

The solution uses a simple hash function (key % size) to map keys to indices in a list called `table`. Each element of the `table` is a list representing a bucket. When a key-value pair is inserted, we calculate the index using the hash function. If the key already exists in the bucket, we update its value. Otherwise, we append the new key-value pair to the bucket. For `get` and `remove`, we iterate through the bucket at the calculated index and search for the key. If found, we return the value or remove the key-value pair accordingly. If not found, `get` returns -1 and `remove` does nothing.

Step-by-Step Algorithm

Step 1: Initialize the `MyHashMap` object with a fixed size for the hash table (`size`). Create a list (or array) called `table` of size `size`, where each element is initially an empty list.
Step 2: In the `put(key, value)` function, calculate the index using the hash function: `index = key % size`.
Step 3: Iterate through the list (bucket) at `table[index]`. If the key already exists, update the value associated with that key and return.
Step 4: If the key doesn't exist in the bucket, append the `(key, value)` pair to the end of the list `table[index]`.
Step 5: In the `get(key)` function, calculate the index using the hash function: `index = key % size`.
Step 6: Iterate through the list at `table[index]`. If the key is found, return the corresponding value.
Step 7: If the key is not found after iterating through the entire list, return -1.
Step 8: In the `remove(key)` function, calculate the index using the hash function: `index = key % size`.
Step 9: Iterate through the list at `table[index]`. If the key is found, remove the key-value pair from the list.
Step 10: If the key is not found, do nothing.

Key Insights

Insight 1: We can't directly use keys as indices in an array due to the constraint 0 <= key <= 10^6, which would require a very large array and waste memory. Therefore, we need a hashing function to map keys to a smaller index range.
Insight 2: Collision handling is crucial. When different keys map to the same index, we need a strategy to store and retrieve them. Using a separate chaining approach (linked list or list) is a common and efficient solution.
Insight 3: Choosing a good hash function and table size is important for performance. A simple modulo operation works in this case, but other hash functions might be better for different data distributions. The size of the table should be chosen to minimize collisions.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(n)

Topics

This problem involves: Array, Hash Table, Linked List, Design, Hash Function.

Companies

Asked at: Applied Intuition, Deutsche Bank, Nvidia, Palo Alto Networks, ServiceNow, Snowflake, Two Sigma.

Design HashMap | LeetCode 706

Easy

Array Hash Table Linked List Design Hash Function

Time: O(n)Space: O(n)

Solution Code

Available in:

View Full Solution on GitHubGitHub Solution View on LeetCodeLeetCode

Problem Info

Description

Problem 706: Design HashMap - Detailed explanation and solution approaches.

Topics

Array Hash Table Linked List Design Hash Function

Companies

Applied IntuitionDeutsche BankNvidiaPalo Alto NetworksServiceNowSnowflakeTwo Sigma

Stats

Difficulty:Easy

Languages:2

Companies:7

Problem #:706

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

10 steps

1
Step 1: Initialize the `MyHashMap` object with a fixed size for the hash table (`size`). Create a list (or array) called `table` of size `size`, where each element is initially an empty list.
2
Step 2: In the `put(key, value)` function, calculate the index using the hash function: `index = key % size`.
3
Step 3: Iterate through the list (bucket) at `table[index]`. If the key already exists, update the value associated with that key and return.
4
Step 4: If the key doesn't exist in the bucket, append the `(key, value)` pair to the end of the list `table[index]`.
5
Step 5: In the `get(key)` function, calculate the index using the hash function: `index = key % size`.
6
Step 6: Iterate through the list at `table[index]`. If the key is found, return the corresponding value.
7
Step 7: If the key is not found after iterating through the entire list, return -1.
8
Step 8: In the `remove(key)` function, calculate the index using the hash function: `index = key % size`.
9
Step 9: Iterate through the list at `table[index]`. If the key is found, remove the key-value pair from the list.
10
Step 10: If the key is not found, do nothing.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is determined by the average length of the linked lists within the buckets. In the best-case scenario, the hash function distributes keys evenly, resulting in O(1) average time complexity for all operations. However, in the worst-case, all keys might hash to the same index, making operations take linear time relative to the number of stored key-value pairs. The space complexity is O(N) as we store all key-value pairs in the list.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using a balanced tree (e.g., Red-Black tree) in each bucket

Instead of using a list (linked list) for each bucket, we can use a balanced tree. This ensures a worst-case time complexity of O(log n) for `put`, `get`, and `remove` operations, where n is the number of key-value pairs within the bucket.

Time: O(log n) on average and in the worst case, where n is the number of keys in a bucket.Space: O(N), where N is the number of key-value pairs stored.

Trade-offs: Using a balanced tree improves the worst-case time complexity but increases the overhead due to the tree's internal structure and balancing operations. It is more complex to implement than using a simple list.

Using open addressing (e.g., linear probing, quadratic probing, double hashing)

Instead of separate chaining, we can use open addressing where all elements are stored directly in the hash table itself. When a collision occurs, we probe for an empty slot using a predefined probing sequence.

Time: O(1) on average for `put`, `get`, and `remove`, but can degrade to O(n) in the worst case where n is the number of keys in the table.Space: O(N), where N is the number of key-value pairs stored.

Trade-offs: Open addressing can be more space-efficient than separate chaining, but it can suffer from clustering, which degrades performance. It also requires careful management of resizing the table to avoid excessive collisions and maintain good performance.

✨

Key Insights

1Insight 1: We can't directly use keys as indices in an array due to the constraint 0 <= key <= 10^6, which would require a very large array and waste memory. Therefore, we need a hashing function to map keys to a smaller index range.
2Insight 2: Collision handling is crucial. When different keys map to the same index, we need a strategy to store and retrieve them. Using a separate chaining approach (linked list or list) is a common and efficient solution.
3Insight 3: Choosing a good hash function and table size is important for performance. A simple modulo operation works in this case, but other hash functions might be better for different data distributions. The size of the table should be chosen to minimize collisions.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Linked List

Elements connected via pointers. Like a chain where each link knows the next.

Edge Cases

Edge case 1: Inserting the same key multiple times. The solution correctly updates the value associated with the existing key.
Edge case 2: Retrieving a non-existent key. The solution correctly returns -1.
Edge case 3: Removing a non-existent key. The solution gracefully handles this situation by doing nothing.
Edge case 4: Large number of keys all mapping to the same bucket (hash collision). In this case, the time complexity for `put`, `get`, and `remove` degrades to O(n) where n is the number of items in the bucket.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to handle collisions. Without collision handling, different keys can overwrite each other's values.

✗Common mistake 2: Not updating the value when putting an existing key. The `put` operation should update the value if the key already exists.

✗Common mistake 3: Incorrectly implementing the hash function. A bad hash function can lead to uneven distribution of keys, resulting in poor performance.

✗Common mistake 4: Failing to handle the case where a key doesn't exist in the `get` and `remove` functions.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Caching systems. HashMaps are used to store frequently accessed data for quick retrieval. For example, web servers use caches to store frequently requested web pages.

🏦Real-world scenario 2: Symbol tables in compilers. Compilers use symbol tables to store information about variables, functions, and other program elements. HashMaps provide efficient lookup of these symbols.

🎮Real-world scenario 3: Database indexing. Databases use indexes to quickly locate rows that match a specific query. HashMaps can be used to implement these indexes.

📱Real-world scenario 4: Implementing dictionaries in programming languages. Many programming languages use HashMaps as the underlying data structure for dictionaries or associative arrays.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Use this problem to demonstrate clean code practices. Variable naming and code organization matter.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: November 14, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 13

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJul 6

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

How should I approach easy-level algorithm problems?

Easy problems build foundational skills. Focus on: 1) Understanding the problem completely before coding, 2) Writing clean, readable code, 3) Handling all edge cases (empty input, single element, etc.), 4) Analyzing time and space complexity. Don't skip complexity analysis even for simple solutions.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Design HashMap"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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