Airbnb Software Engineer Interview Questions

On June 3, 2025, Posted by CRS Info Solutions , In Interview Questions, With Comments Off

What is the difference between Array and LinkedList, and when would you use one over the other?
Can you explain how hash tables work and provide an example of their use?
Write a program to check if a string is a palindrome.
What is the Big-O notation for common sorting algorithms like QuickSort, MergeSort, and BubbleSort?
Explain the CAP theorem and its implications in distributed systems.
Discuss the differences between monolithic and event-driven architectures and their trade-offs.
Imagine you are tasked with developing a recommendation system for users. How would you approach the problem?
How would you design a feature to detect and prevent duplicate bookings in real time?

Preparing for an Airbnb Software Engineer interview can feel both exciting and challenging. From tackling complex data structures and algorithms to designing scalable systems and solving real-world problems, Airbnb’s interview process tests not just your technical skills but also your ability to innovate and collaborate. They look for engineers who can think critically, write clean code in languages like Python, Java, or JavaScript, and contribute meaningfully to a team. Alongside technical expertise, Airbnb values cultural alignment, so expect questions that explore your approach to teamwork, leadership, and problem-solving in dynamic environments.

In this guide, I’ve compiled key questions and answers that reflect the type of challenges you’ll encounter during your interview. Whether it’s coding exercises, system design scenarios, or behavioral questions, this content is crafted to help you feel confident and prepared. By diving into these examples, you’ll gain insights into Airbnb’s expectations and refine your skills to stand out as a strong candidate. Let’s get started and ensure you’re ready to excel in your next interview!

1. What is the difference between Array and LinkedList, and when would you use one over the other?

In my experience, arrays and linked lists serve different purposes based on the scenario. Arrays are fixed in size and store elements in contiguous memory, which makes them efficient for random access using an index. For example, if I need to access the third element in an array, I can do it directly with array[2], making it a great choice when frequent lookups are required. However, resizing arrays can be costly because they involve copying elements to a new memory block.
On the other hand, linked lists consist of nodes where each node holds the data and a reference to the next node. This makes them efficient for dynamic resizing and frequent insertions or deletions. If I had a situation where elements needed to be added or removed frequently, such as implementing a queue, I would use a linked list. The trade-off is slower random access because you have to traverse nodes sequentially.

# Example: Array vs LinkedList
# Array for random access
array = [10, 20, 30, 40]
print(array[2])  # Output: 30

# Linked List for dynamic insertion
class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

head = Node(10)
second = Node(20)
head.next = second
print(head.next.data)  # Output: 20

In this snippet, the array provides direct access using an index, showcasing its efficiency for lookups. The linked list example demonstrates how nodes are dynamically connected, allowing easy insertion or deletion. While arrays excel in accessing elements quickly, linked lists are ideal for dynamic data with frequent updates.

2. Can you explain how hash tables work and provide an example of their use?

I would describe hash tables as a data structure that maps keys to values for efficient lookups, insertions, and deletions. Hash tables use a hashing function to convert keys into indices, which determine where the corresponding value will be stored. In my experience, they’re particularly useful when working with dictionaries or managing large datasets with frequent lookups.
One practical example is when I needed to count the occurrences of words in a document. Using a hash table (like Python’s dict), I could quickly map each word to its frequency. The hashing function ensures constant-time complexity for most operations, although collisions can sometimes slow things down.

# Example: Hash Table for word count
word_count = {}
text = "airbnb interview questions are important"
for word in text.split():
    word_count[word] = word_count.get(word, 0) + 1
print(word_count)  # Output: {'airbnb': 1, 'interview': 1, 'questions': 1, 'are': 1, 'important': 1}

This snippet demonstrates the hash table’s efficiency in storing and retrieving word frequencies. It uses get to handle missing keys gracefully, showcasing a practical application for text analysis.

3. Write a program to check if a string is a palindrome.

In my experience, a palindrome is a word or phrase that reads the same forwards and backwards, ignoring spaces and punctuation. To check if a string is a palindrome, I compare it with its reverse. This logic can be implemented using slicing, which is both simple and efficient.
For example, when working with user input validation, I often need to handle palindromes. By converting the string to lowercase and stripping non-alphanumeric characters, I ensure accuracy. This program is particularly helpful in applications like spell-checkers or pattern-matching algorithms.

# Example: Check palindrome
def is_palindrome(s):
    s = ''.join(filter(str.isalnum, s)).lower()
    return s == s[::-1]

print(is_palindrome("Madam"))  # Output: True
print(is_palindrome("Airbnb"))  # Output: False

The code filters out non-alphanumeric characters and uses slicing to reverse the string, making it easy to verify if it’s a palindrome. This approach ensures robustness for different types of input strings.

4. What is the Big-O notation for common sorting algorithms like QuickSort, MergeSort, and BubbleSort?

In my experience, understanding Big-O notation is crucial for evaluating algorithm efficiency. For example, QuickSort has an average time complexity of O(n log n) but can degrade to O(n²) in the worst case if the pivot selection is poor. MergeSort, in contrast, maintains a consistent time complexity of O(n log n) regardless of the input.
BubbleSort, on the other hand, is less efficient, with a time complexity of O(n²) due to its nested loops. I’ve rarely used BubbleSort in practice, except for educational purposes. QuickSort and MergeSort are far more practical for large datasets because of their scalability and divide-and-conquer approach.

# Example: QuickSort vs BubbleSort
# QuickSort (Python's sorted uses Timsort, based on QuickSort and MergeSort)
data = [3, 6, 8, 10, 1, 2, 1]
print(sorted(data))  # Output: [1, 1, 2, 3, 6, 8, 10]

# BubbleSort Implementation
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

print(bubble_sort(data))  # Output: [1, 1, 2, 3, 6, 8, 10]

This demonstrates BubbleSort’s inefficiency compared to Python’s optimized sorting. While BubbleSort sorts incrementally through pairwise comparisons, QuickSort uses a divide-and-conquer approach for better performance.

5. Explain the difference between synchronous and asynchronous programming.

In my experience, synchronous programming processes tasks sequentially, meaning one task must finish before the next starts. For example, in Python, a synchronous web scraper fetches a URL, processes the response, and then moves to the next URL. While simple, this can be slow if tasks involve waiting, such as network or I/O operations.
Asynchronous programming, on the other hand, allows tasks to run concurrently without waiting for one to finish before starting another. This is especially useful for handling multiple I/O operations simultaneously. For instance, when I work with APIs, asynchronous calls significantly reduce response time by leveraging event loops like those in Python’s asyncio.

# Example: Synchronous vs Asynchronous
import asyncio

# Synchronous example
def fetch_sync(url):
    # Simulated fetch
    print(f"Fetching {url}")
    return f"Data from {url}"

print(fetch_sync("https://example.com"))

# Asynchronous example
async def fetch_async(url):
    print(f"Fetching {url} asynchronously")
    await asyncio.sleep(1)
    return f"Data from {url}"

async def main():
    await fetch_async("https://example.com")

asyncio.run(main())

The synchronous code executes one task at a time, blocking the next until it completes. The asynchronous code leverages await to handle tasks concurrently, significantly improving efficiency in scenarios like API calls or database queries.

6. What is memoization, and how is it used in dynamic programming?

In my experience, memoization is a technique used in dynamic programming to improve performance by storing the results of expensive function calls and reusing them when the same inputs occur again. This avoids redundant calculations and reduces time complexity, making it particularly effective in recursive solutions like calculating Fibonacci numbers or solving knapsack problems.

I have often used memoization by implementing a dictionary or cache to store previously computed results. For instance, in Python, I use a dictionary to save results of function calls. This approach ensures that the function does not recompute the same values multiple times, saving computational resources.

# Example: Fibonacci with memoization
def fibonacci(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 2:
        return 1
    memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo)
    return memo[n]

print(fibonacci(10))  # Output: 55

Here, the memo dictionary stores the results of previously computed Fibonacci values. When the function is called with a value already in the dictionary, it returns the cached result instead of recalculating, improving efficiency.

7. How does garbage collection work in languages like Java or Python?

In my experience, garbage collection is an automated memory management process that reclaims memory occupied by objects no longer in use. In Java, garbage collection is handled by the JVM, which uses techniques like reference counting and generational garbage collection to identify and remove unused objects.

Similarly, Python’s garbage collector uses reference counting and a cyclic garbage collector to clean up objects involved in circular references. For example, when an object’s reference count drops to zero, it is marked for collection. This process ensures efficient memory utilization and prevents memory leaks.

# Example: Python garbage collection
import gc

class Test:
    def __del__(self):
        print("Object deleted")

a = Test()
b = a
del a
gc.collect()  # Forces garbage collection

In this snippet, Python’s garbage collector frees memory when objects go out of scope or their reference count reaches zero. The gc.collect() method triggers garbage collection manually, showcasing how unused memory is reclaimed.

8. Explain the RESTful API design principles and their importance.

From my experience, RESTful APIs are built on principles like statelessness, resource representation, and HTTP methods. Statelessness ensures that each request from a client contains all necessary information for processing, which simplifies server management. Common HTTP methods like GET, POST, PUT, and DELETE map to operations on resources.

These principles are crucial for building scalable and maintainable APIs. For instance, when designing a resource like users, the API should respond with JSON representations of the data, and operations like adding or deleting a user are performed with corresponding HTTP methods.

# Example: RESTful API design
from flask import Flask, jsonify, request

app = Flask(__name__)

users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]

@app.route('/users', methods=['GET'])
def get_users():
    return jsonify(users)

@app.route('/users', methods=['POST'])
def add_user():
    new_user = request.json
    users.append(new_user)
    return jsonify(new_user), 201

if __name__ == "__main__":
    app.run(debug=True)

This code demonstrates a RESTful API with two endpoints: one for retrieving users and another for adding users. It adheres to REST principles by using JSON for resource representation and HTTP methods for operations.

9. What are closures in JavaScript, and how do they work?

In my experience, closures in JavaScript are functions that “remember” the scope in which they were created, even after that scope has exited. This allows functions to access variables from their outer scope, making closures particularly useful for creating private variables or callbacks.

For instance, I use closures to encapsulate state in modules or to create function factories. By retaining access to variables in their scope, closures provide a powerful way to manage state without polluting the global namespace.

// Example: Closure
function counter() {
    let count = 0;
    return function () {
        count++;
        return count;
    };
}

const increment = counter();
console.log(increment()); // Output: 1
console.log(increment()); // Output: 2

Here, the inner function retains access to the count variable from the outer function’s scope. Even after counter has executed, the inner function can modify and use count, showcasing the power of closures.

10. Can you explain the purpose of unit testing and how it contributes to software quality?

In my opinion, unit testing is vital for ensuring that individual components of a program work as expected. By isolating and testing each unit of code, developers can identify bugs early and ensure that changes to one part of the codebase don’t break others. For example, testing a login function separately guarantees its reliability before integrating it into the larger system.

Unit testing also serves as documentation and facilitates refactoring. Tools like Python’s unittest or Java’s JUnit automate this process, enabling quick feedback. Writing tests improves confidence in the code, especially in large projects with multiple contributors.

# Example: Unit testing in Python
import unittest

def add(a, b):
    return a + b

class TestAddFunction(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(2, 3), 5)
        self.assertEqual(add(-1, 1), 0)

if __name__ == '__main__':
    unittest.main()

This code defines a unit test for the add function. The test_add method verifies that the function produces the correct results for various inputs, ensuring correctness and stability.

11. How would you design a distributed system for high availability and scalability?

In my experience, designing a distributed system requires balancing high availability and scalability. For high availability, I would implement redundancy by replicating data across multiple nodes, ensuring that if one node fails, others can continue serving requests. Load balancers can distribute requests evenly among nodes, avoiding overload on any single resource.

For scalability, I would use horizontal scaling by adding more servers to handle increased traffic. Using distributed databases like Cassandra or MongoDB can provide fault tolerance and scalability. To handle communication efficiently, I would employ messaging queues like RabbitMQ or Kafka to decouple services and ensure asynchronous, reliable messaging.

12. Explain the CAP theorem and its implications in distributed systems.

In my experience, the CAP theorem states that in a distributed system, you can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance (CAP).

Consistency: Every read receives the most recent write.
Availability: Every request receives a response, even if some nodes fail.
Partition Tolerance: The system continues to operate despite network partitioning.

For example, in a global e-commerce platform, prioritizing Availability and Partition Tolerance over strict Consistency can result in eventual consistency, where data across regions might take time to sync but ensures uninterrupted service. This decision depends on the application’s requirements.

13. How would you optimize a database query that is running slowly on a large dataset?

In my experience, optimizing slow database queries involves several strategies:

Indexing: I would create indexes on frequently queried columns to reduce lookup times.
Query Optimization: Analyze the query execution plan to identify inefficiencies and rewrite the query for better performance.
Limiting Data Retrieval: Use SELECT to fetch only necessary columns and avoid heavy operations like SELECT *.
Partitioning: Break large datasets into smaller, manageable chunks to improve performance.

For instance, I optimized a slow JOIN query by adding indexes on the joining columns, which reduced execution time from several minutes to seconds.

14. What are microservices, and how do you ensure communication between them is reliable?

From my experience, microservices architecture breaks down an application into smaller, independent services that each handle a specific business function. This enables faster development, easier maintenance, and scalability.

To ensure reliable communication between microservices:

Use message brokers like Kafka for asynchronous messaging and to decouple services.
Implement retry mechanisms and circuit breakers to handle transient failures gracefully.
Use service discovery tools like Consul or Eureka for locating services dynamically.
Employ API gateways to centralize and secure communication between services.

15. Discuss the differences between monolithic and event-driven architectures and their trade-offs.

In my experience, monolithic architectures consist of a single, tightly coupled application where all components are interconnected. This simplifies initial development but becomes challenging to scale and maintain as the application grows. Event-driven architectures decouple components through events, allowing independent scaling and flexibility.

Trade-offs of Monolithic Architecture:

Pros: Easier debugging, fewer communication issues.
Cons: Difficult to scale, changes affect the entire system.

Trade-offs of Event-Driven Architecture:

Pros: Scalability, resilience to failure.
Cons: Higher complexity, potential for event management challenges.

For example, in a high-traffic e-commerce platform, I found event-driven architecture ideal for handling inventory updates and order processing independently, ensuring smooth operation under heavy loads.

16. Airbnb has millions of users accessing its platform worldwide. How would you design a caching strategy to improve response times?

In my experience, when designing a caching strategy for a platform like Airbnb with millions of users, I would focus on data locality and cache expiration. For frequently accessed data, such as listings, search results, and user profiles, I would use a distributed cache like Redis or Memcached to store the data in-memory, which can significantly improve response times by reducing the load on the database.

I would also implement cache invalidation strategies, such as time-based expiration and event-driven invalidation, to ensure that the cache does not serve stale data. For example, I would set a TTL (Time-To-Live) for listing data, but when a user books or modifies their reservation, I would invalidate the cached data associated with that user or listing. Additionally, CDN caching could be leveraged for static assets like images and promotional content, improving scalability globally.

import redis

# Create a Redis client
cache = redis.StrictRedis(host='localhost', port=6379, db=0)

# Caching user data for 1 hour
cache.setex("user:123", 3600, '{"name": "John Doe", "bookings": 5}')

# Retrieving cached user data
user_data = cache.get("user:123")
print(user_data)

Code Explanation: The Redis client is used to cache user data. The setex method sets a cache key (user:123) with a TTL of 3600 seconds (1 hour). The data stored is a JSON string representing the user. The get method retrieves the cached data for that key. If the key exists and hasn’t expired, it returns the cached user data.

17. Imagine you are tasked with developing a recommendation system for users. How would you approach the problem?

In my experience, developing a recommendation system requires a solid understanding of both the user’s preferences and the content being recommended. I would first gather user interaction data (such as booking history, clicks, and searches) and product data (like listing attributes) to build a profile for each user. One common approach is to use Collaborative Filtering, where I would recommend listings based on the behavior of similar users.

I would also explore Content-Based Filtering, where I suggest listings similar to those the user has already interacted with, using features like location, price range, and property type. For better accuracy, I would combine these methods into a Hybrid Model. To continuously improve the recommendations, I would implement A/B testing to measure the effectiveness of different algorithms and refine the model based on user feedback.

from sklearn.neighbors import NearestNeighbors
import numpy as np

# Example data: user ratings for listings
user_ratings = np.array([[5, 4, 0, 2],  # User 1
                         [4, 0, 5, 1],  # User 2
                         [1, 2, 4, 5],  # User 3
                         [2, 4, 3, 5]]) # User 4

# Building a Nearest Neighbors model for collaborative filtering
model = NearestNeighbors(n_neighbors=2, metric='cosine')
model.fit(user_ratings)

# Finding similar users to User 1
distances, indices = model.kneighbors([user_ratings[0]])
print(f"Recommended users for User 1: {indices[0]}")

Code Explanation: The code uses the Nearest Neighbors algorithm from sklearn to find similar users based on their ratings of listings. The data consists of ratings from multiple users, and the model identifies the two most similar users (neighbors) to User 1 using cosine similarity. This approach helps in collaborative filtering to recommend listings based on user behavior.

18. How would you handle a situation where a critical service in your microservices architecture fails?

In my experience, resilience is key when handling failures in a microservices architecture. First, I would use a circuit breaker pattern to prevent cascading failures. If a critical service is failing, the circuit breaker would open, and requests to the service would be temporarily stopped, preventing the system from overloading. Instead, I’d route traffic to fallback services or a cached version of the data.

Next, I would implement automatic retries with exponential backoff to give the service time to recover before retrying the request. I would also monitor the service health in real-time using tools like Prometheus and Grafana, which would alert the team about the issue immediately. Finally, I would ensure redundancy by having replicas of the critical service in multiple availability zones to increase availability during failures.

// Example of using a Circuit Breaker pattern in Java (Resilience4J)
import io.github.resilience4j.circuitbreaker.CircuitBreaker;
import io.github.resilience4j.circuitbreaker.CircuitBreakerConfig;
import java.time.Duration;

public class CircuitBreakerExample {
    public static void main(String[] args) {
        CircuitBreakerConfig config = CircuitBreakerConfig.custom()
            .failureRateThreshold(50)  // 50% failure rate to trigger the breaker
            .waitDurationInOpenState(Duration.ofMillis(1000))  // Time before retrying
            .build();
        
        CircuitBreaker circuitBreaker = CircuitBreaker.of("backendService", config);
        
        // Calling the backend service using the circuit breaker
        try {
            String response = circuitBreaker.executeSupplier(() -> callBackendService());
            System.out.println(response);
        } catch (Exception e) {
            System.out.println("Service is unavailable. Please try again later.");
        }
    }

    public static String callBackendService() {
        // Simulate a backend service call that may fail
        return "Service Response";
    }
}

Code Explanation: This Java code uses Resilience4J to implement a circuit breaker pattern. If the failure rate of the service exceeds 50%, the circuit breaker will open, preventing further requests to the service. The executeSupplier method tries to call the backend service, and if the service fails, it handles the exception by printing an error message. This helps manage failures in a microservices architecture.

19. A significant number of users report inconsistent booking statuses on the platform. What steps would you take to investigate and resolve the issue?

In my experience, inconsistent booking statuses often arise from race conditions, data synchronization issues, or service failures. First, I would check the logs to identify patterns of failures or errors when booking statuses are updated. If the problem is related to concurrent updates, I would investigate the locking mechanism or consider implementing optimistic concurrency control to prevent conflicts.

Next, I would review the transaction boundaries to ensure that booking updates are atomic and that any failure during the process triggers a rollback. I would also check if there are issues with eventual consistency in the system, particularly if the system relies on asynchronous communication between services. To resolve the issue, I might implement more stringent data consistency checks or improve the synchronization mechanisms between services. Additionally, I would consider introducing more robust monitoring and alerting to catch such issues in real-time.

# Optimistic Concurrency Control: Checking if the data is modified before committing changes
def update_booking_status(current_status, new_status):
    if current_status == get_booking_status_from_db():
        update_booking_status_in_db(new_status)
        return "Booking status updated successfully"
    else:
        return "Booking status conflict detected"

Code Explanation: This Python snippet demonstrates optimistic concurrency control by comparing the current status of a booking from the database with the status being sent for update. If the status matches, the update is made; otherwise, it indicates a conflict. This ensures that updates only occur if no changes have been made to the booking status in between the time it was read and the time the update is attempted.

20. How would you design a feature to detect and prevent duplicate bookings in real time?

In my experience, detecting and preventing duplicate bookings in real-time requires careful synchronization between the application, database, and cache. I would start by implementing a unique transaction ID for each booking request, which can be used to check if a booking is being processed concurrently for the same listing or user. This can be done at the API level by using idempotent operations to ensure that duplicate requests with the same transaction ID do not result in multiple bookings.

Additionally, I would use a distributed lock (such as Redis’ SETNX command) to ensure that only one booking request is processed at a time for a given listing. If multiple requests attempt to book the same property, only the first request would be processed, and subsequent requests would be rejected or queued. Real-time notifications could also be used to inform users immediately if they attempt to duplicate their booking. By ensuring the system can handle such cases without any race conditions, the feature can effectively prevent duplicate bookings.

import redis

# Using Redis SETNX to create a distributed lock to prevent duplicate bookings
def prevent_duplicate_booking(listing_id, transaction_id):
    lock_key = f"lock:{listing_id}"
    lock_acquired = redis.StrictRedis().setnx(lock_key, transaction_id)
    
    if lock_acquired:
        # Proceed with booking process
        book_listing(listing_id, transaction_id)
        print("Booking successful")
    else:
        print("Duplicate booking attempt detected")

# Simulated booking function
def book_listing(listing_id, transaction_id):
    print(f"Booking {listing_id} for transaction {transaction_id}...")

Code Explanation: The code uses Redis’ SETNX to create a distributed lock. If the lock is acquired (i.e., no other process is currently booking the listing), the booking proceeds. If the lock is already held, it means another booking process is ongoing, and the duplicate attempt is detected. This method ensures that only one booking can occur for a specific listing at a time, preventing duplicates.

Conclusion

When preparing for Airbnb Software Engineer Interview Questions, it’s essential to go beyond just theoretical knowledge and focus on real-world problem-solving skills. Airbnb is known for its complex, high-traffic platform, and interviewers want to see how well you can design scalable systems, handle data inconsistencies, and ensure smooth user experiences. It’s not just about knowing algorithms and system design principles; it’s about applying them in scenarios that mirror the challenges you’ll face in the role. Demonstrating your ability to tackle issues like optimizing response times, handling service failures, or building resilient architectures will set you apart as a candidate ready to thrive in Airbnb’s fast-paced environment.

Moreover, Airbnb’s commitment to a seamless user experience means your ability to think critically and solve practical challenges will be under the microscope. From implementing caching strategies to preventing duplicate bookings, showcasing how you can optimize processes and prevent bottlenecks will prove your value. If you can confidently discuss how to approach problems that are directly relevant to Airbnb’s needs, you’ll prove you have what it takes to succeed in this highly competitive role. With the right preparation and a solid understanding of these core concepts, you’ll not only be ready for the interview but also prepared to make a lasting impact at Airbnb.

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