Staying ahead of the curve in the dynamic field of system design demands a thorough comprehension of challenging problems with design. The capacity to design systems that are scalable, dependable, and efficient is essential as technology develops. In remote technical interviews, system design questions are an essential component that assesses your ability to develop reliable solutions that can manage difficult requirements.
A few fundamental system design concerns have come to light in 2024, serving as benchmarks for proficiency in this area. These questions include a variety of scenarios that evaluate your abilities to manage distributed systems, ensure performance, and solve scalability issues. Therefore, this blog explores the most important system design questions for 2024 and provides in-depth analysis and tips to help you perform well in coding interviews and real-world situations.
Top 5 System Design Questions
Mastering system design questions is crucial for achieving technical interview assessment and understanding complex architectures. Here are some top system design questions to focus on in 2024:
1. Design a Scalable Chat Application
Question: How would you create a chat application that can accommodate millions of users and offer group conversations, multimedia sharing, and real-time messaging?
Overview:
For possibly millions of users, real-time communications is supported by a scalable chat platform. Ensuring low latency, high availability, and scalability in response to growing user numbers are crucial aspects of system design. Moreover, scalability, security, data consistency, and real-time communication all need to be handled carefully in this situation.
Main Features:
- Real-Time Communication: WebSockets are the recommended option for real-time messaging since they offer a full-duplex communication channel over a single TCP connection. For chat applications, this makes it possible for messages to be transmitted and received instantly. By keeping the client and server connected persistently, WebSockets eliminate the need to create new connections for every communication. Similarly, you can utilise a message broker like Apache Kafka if you expect very high message volumes. Kafka delivers great throughput and dependability by efficiently managing and distributing messages across multiple consumers.
- Scalability: Horizontal scaling is necessary to manage growing user loads. Therefore, to equally divide incoming messages and user connections, deploy numerous chat server instances and employ a load balancer. With distinct services for media handling, message processing, and user administration, a microservices architecture may be useful in this situation. This allows each service to scale according to its load independently. Kubernetes and other container orchestration solutions facilitate the dynamic management and scaling of these services.
- Data Consistency: Maintaining data consistency in a distributed chat application can be difficult. Therefore, it might be appropriate to use an eventual consistency model, in which updates spread throughout the system gradually. Moreover, utilise replication to guarantee data availability and fault tolerance, and implement data partitioning (sharding) to distribute data over several nodes. Programs such as Apache Zookeeper can assist in managing dispersed coordination and preserving consistency among various system components.
- Security: For user data and communications to be protected in the given system design Question, chat applications must be secure. Similarly, use end-to-end encryption to make sure that messages are only viewed by the intended receivers. Additionally, it is imperative to use encryption methods like Advanced Encryption Standard (AES) for data at rest and Transport Layer Security (TLS) for data in transit. To control user sessions and stop unwanted access, employ safe authentication methods. Moreover, to further guard against potential threats, conduct frequent vulnerability assessments and security audits.
Key Components:
- Backend: Media processing, message handling, and user authentication are handled via microservices. It is possible to use technologies like Node.js, Python (Flask or Django), or Java (Spring Boot).
- Frontend: Using frameworks like React, Angular, or Vue.js, real-time chat interfaces are created.
- Database: NoSQL databases that work well for managing chat messages and user profiles are MongoDB and Cassandra.
Architecture:
- WebSocket Server: Oversees the exchange of messages in real-time and keeps enduring connections with clients.
- Message Queue (Kafka): Manages reliable, high-throughput message dissemination.
- User Service: Oversees session management, profiles, and user authentication.
- Media Service: Manages media files, including pictures and videos, for storage and retrieval.
- Database: Uses a NoSQL database to store user information, chat logs, and metadata.
2. Design an Online Video Streaming Platform
Question: How would you create a video streaming service that can accommodate millions of customers worldwide and offer both live and on-demand content?
Overview:
A platform for online video streaming needs to effectively provide consumers with high-quality video material, enabling both live and on-demand streaming.Additionally, managing massive amounts of video data, guaranteeing fluid playback, and offering scalable infrastructure are important components.
Main Features:
- Content Delivery Network (CDN): A CDN is required for optimal video transmission. Through the use of global server caching, content delivery networks (CDNs) lower latency and speed up load times. Additionally, CDNs reduce buffering and improve user experience by serving content from the closest edge server.
- Encoding and Transcoding: In order to accommodate a range of devices and network configurations, videos must be encoded and transcoded into many formats and resolutions. This process is automated by programs like FFmpeg, which converts videos into high-efficiency HEVC or regular H.264 codecs.
- Scalability: Use load balancers to equally distribute traffic and apply horizontal scaling for video servers to accommodate fluctuating workloads. Auto-scaling techniques are capable of modifying server capacity in response to current demand, guaranteeing optimal resource utilisation and upholding system performance even during surges.
- Storage: Use scalable cloud storage options, such as Amazon S3, for storing videos beacuse it provides great availability and durability.
Key Components:
- Backend: Microservices for managing users, delivering content, and processing videos. Moreover, for containerisation, use Docker, and for orchestration, Kubernetes.
- Frontend: You can use frameworks such as React, Vue.js, or Angular to create video players and user interfaces.
- Database: SQL or NoSQL databases for analytics, user data, and video metadata storage.
Architecture:
- Video Processing Service: This service converts and encodes video files into a range of file formats and resolutions.
- Content Delivery Network (CDN): This technology lowers latency and boosts performance by caching and delivering video material to end consumers.
- Streaming Server: Controls protocols for video playback and provides viewers with content.
- User Service: Manages subscriptions, profile data, and user authentication.
- Database: Uses a scalable database system to store analytics, user data, and video metadata.
3. Design a Distributed File Storage System
Question: How would you create a distributed file storage system similar to Google Drive or Dropbox while maintaining data consistency and availability?
Overview:
A distributed file storage system is designed to store and manage large amounts of data across multiple nodes, since it ensures high availability, fault tolerance, and efficient access. Moreover, this involves handling data replication, consistency, load balancing, and recovery.
Main Features:
- Data Replication: Implement data replication to improve fault tolerance and data availability. Several nodes can copy data using replication schemes such as primary replica (master-slave) or peer-to-peer architectures. This ensures that data is available even if a few nodes fail.
- Consistency Models: Depending on the needs of the application, select between eventual consistency and strong consistency. Moreover, nodes with strong consistency, using algorithms like Paxos or Raft, ensure that they all have the same data at all times.
- Load Balancing: To uniformly distribute file requests among storage nodes, use load balancers. Moreover, this guarantees effective data access and keeps any one node from acting as a bottleneck.
- Data Recovery: To address node failures or data corruption, put data recovery procedures in place. Methods include backups, data reconstruction methods, and recurring snapshots.
Key Components:
- Backend: Distributed file systems like Ceph or Hadoop HDFS, storage nodes, and metadata servers.
- Frontend: File management and access interfaces created using frameworks such as React or Angular.
- Databases: NoSQL databases for high-performance data storage and SQL databases for metadata.
Architecture:
- Storage Nodes: Manage the replication, retrieval, and storage of data while maintaining fault tolerance and high availability.
- Metadata Server: Provides effective data management by managing directory services, access control, and file metadata.
- Load Balancer: Prevents bottlenecks and optimises performance by distributing file requests among storage nodes.
- Recovery Service: Controls data availability by managing backups, snapshots, and restoration procedures. Hence, providing on-demand recovery services.
4. Design a Real-Time Analytics Platform
Question: To deliver useful insights, how would you develop a system that processes and analyses real-time data streams?
Overview:
With the least amount of latency, a real-time analytics platform interprets and processes streaming data to deliver useful insights. Additionally, to facilitate prompt decision-making, this entails gathering data, analysing it in real-time, and providing insights as soon as possible.
Main Features:
- Data Ingestion: To capture and stream data, use data ingestion systems such as Apache Kafka or Apache Pulsar.
- Data processing: To analyse and transform data in real-time, use stream processing frameworks like Apache Flink or Spark Streaming. Additionally, real-time insights and reporting are made possible by these frameworks’ support for sophisticated event processing, aggregations, and analytics on streaming data.
- Storage: To manage data, use both batch and real-time storage options. Similarly, batch storage solutions like Amazon Redshift or Google BigQuery enable more thorough data analysis and reporting, while real-time databases like Apache HBase or Cassandra manage quick read-and-write operations.
- Visualisation: To display analytics data, use a real-time virtual whiteboard and visualisation tools.
Key Components:
- Backend: Services for ingesting and processing data using Flink, Spark, Kafka, and other technologies.
- Frontend: React or Angular frameworks are used to build dashboards and visualisation tools.
- Database: Batch storage systems for thorough analysis. Furthermore, it also manages real-time databases for instantaneous data access.
Architecture:
- Data Ingestion Service: Provides high throughput and dependability by capturing and streaming data from several sources.
- Stream Processing Engine: Performs real-time transformations and aggregations while processing and analysing data.
- Real-Time Database: Supports real-time queries and changes by storing processed data and enabling quick access to it.
- Illustration Dashboard: Allows users to engage with data and create reports by displaying real-time insights and analytics.
5. Design a Search Engine
Question: How would you create a web page indexing search engine that returns quick, relevant results?
Overview:
A search engine swiftly returns relevant outcomes based on user queries by indexing and retrieving data from a sizable dataset. Hence, indexing, search algorithms, ranking, and performance optimisation are important factors to take into account.
Main Features:
- Indexing: To handle searchable content, create an effective indexing system.
- Search Algorithms: Utilise search algorithms to rank and filter results according to relevancy.
- Caching: Caching results lessens the search engine’s burden and speeds up response times. Additionally, it improves search efficiency.
- Load Balancing: To guarantee high availability, distribute search queries among several search nodes using load balancers.
Key Components:
- Backend: Elasticsearch or Solr-powered search engines and indexing services.
- Frontend: With frameworks like React or Vue.js, we create search interfaces and result displays.
- Database: NoSQL databases hold the search results and indexed data.
Architecture:
- Indexing Service: Oversees the indexing and updating of documents. Moreover, it guarantees the accuracy and currentness of the search index.
- Search Engine: Manages user-inputted search queries, giving pertinent information and ranking results.
- Caching Layer: By keeping frequently requested search results, this layer speeds up query response times.
- User Interface: Provides an easy-to-use search experience by managing user interactions and displaying search results.
Conclusion
In conclusion, to succeed in coding technical interviews and overcome challenging architectural problems in 2024, one must become proficient in answering questions about system design. Developing scalable applications such as chat platforms and video streaming services, establishing distributed file storage systems, developing real-time analytics platforms, and generating effective search engines are among the top online technical interview questions. Moreover, key components including fault tolerance, scalability, real-time processing, and effective data management are highlighted in each scenario. Therefore, understanding these ideas and putting them into practice with modern technologies and architectural patterns will allow you to prove that you are capable of creating scalable and reliable systems.