Hi DEV's,
Let's dive in the topic of Systems Design/ Architecture, what to expect and how to prepare yourself for an interview.
Introduction
Systems design interviews are a crucial part of the hiring process for senior software engineering roles, especially at large tech companies. These interviews assess your ability to design scalable, reliable, and efficient systems.
Now a days most of the companies(to not say all) required you to have this knowledge and is one of the most important fases on your hiring process.
Understanding the Purpose
Before diving into preparation, it's important to understand what interviewers are looking for:
- Problem-solving skills
- Knowledge of distributed systems
- Ability to make trade-offs
- Communication skills
- Experience with real-world systems
Preparation Strategy
1. Study Fundamental Concepts
Focus on these key areas:
- Scalability
- Load balancing
- Caching
- Database sharding and replication
- Microservices architecture
- API design
- Consistency models
- Messaging systems
Example: Scalability
Understand vertical scaling (adding more power to existing machines) vs. horizontal scaling (adding more machines). For instance, if you're designing a social media platform, horizontal scaling might be preferred for the ability to handle millions of concurrent users.
2. Learn System Components
Familiarize yourself with:
- Load Balancers (e.g., Nginx, HAProxy)
- Caching systems (e.g., Redis, Memcached)
- Databases (SQL vs. NoSQL, specific products like PostgreSQL, MongoDB)
- Message queues (e.g., RabbitMQ, Apache Kafka)
- Content Delivery Networks (CDNs)
Example: Caching
Understand different caching strategies:
- Cache-aside
- Write-through
- Write-back
- Read-through
For a news website, you might use a cache-aside strategy for article content, updating the cache when the article is modified in the database.
3. Study Existing System Designs
Analyze designs of popular systems:
- Twitter's timeline
- Facebook's news feed
- Google's search engine
- Netflix's video streaming service
4. Practice Estimations
Learn to estimate:
- Traffic (requests per second)
- Storage requirements
- Bandwidth usage
Example: Storage Estimation
For a photo-sharing app:
- Assume 1 million daily active users
- Each user uploads 2 photos per day
- Average photo size is 5 MB
Daily storage requirement:
1,000,000 * 2 * 5 MB = 10 TB per day
5. Understand Non-Functional Requirements
Be familiar with:
- Reliability
- Availability
- Scalability
- Performance
- Security
What to Expect in the Interview
- Problem statement
- Requirements clarification
- High-level design
- Detailed component design
- Bottleneck identification and mitigation
- Follow-up questions and trade-offs
Key Questions and Answers
Q1: Design a URL shortener (like bit.ly)
Functional Requirements:
- Shorten long URLs
- Redirect users to original URL
- Custom short URLs (optional)
- Analytics (optional)
Non-Functional Requirements:
- High availability
- Low latency for redirections
- Scalable to handle high traffic
High-Level Design:
[Client] <-> [Load Balancer] <-> [Application Servers]
|
[Database (URL mappings)]
Key Considerations:
- URL encoding: Use base62 encoding (a-z, A-Z, 0-9) for short URLs
- Database choice: NoSQL (e.g., Cassandra) for better scalability
- Caching: Use Redis to cache frequent URL mappings
Q2: Design a distributed cache
Functional Requirements:
- Get/Set operations
- TTL (Time to Live) for entries
- Eviction policy
Non-Functional Requirements:
- Low latency
- High availability
- Scalability
High-Level Design:
[Clients] <-> [Cache Servers (Sharded)] <-> [Consistent Hashing Ring]
Key Considerations:
- Sharding strategy: Use consistent hashing
- Replication: Implement master-slave replication for fault tolerance
- Eviction policy: LRU (Least Recently Used) or LFU (Least Frequently Used)
Best Practices
- Clarify requirements thoroughly
- Start with a high-level design
- Dive into details of important components
- Discuss trade-offs explicitly
- Consider scalability from the start
- Address potential bottlenecks
- Mention monitoring and logging
Key Considerations
- CAP Theorem: Understand the trade-offs between Consistency, Availability, and Partition Tolerance
- Eventual Consistency: Know when it's acceptable
- Data Partitioning: Understand strategies like range-based, hash-based, and directory-based partitioning
- Load Balancing: Round-robin, least connections, IP hash
- Caching: Cache invalidation strategies, cache eviction policies
- Database Indexing: Understand its impact on read/write performance
- API Design: RESTful principles, versioning strategies
Technical Deep Dive: Designing a Social Media Platform
Let's design a simplified version of a social media platform focusing on the news feed feature.
Functional Requirements:
- Users can post status updates
- Users can follow other users
- Users can view a personalized news feed
Non-Functional Requirements:
- High availability (99.99% uptime)
- Low latency for feed generation (<100ms)
- Scalable to millions of users
High-Level Design:
[Clients] <-> [Load Balancer]
|
+---------------+---------------+
| | |
[Web Servers] [App Servers] [Feed Generation Service]
| | |
+-------+-------+-------+-------+
| |
[User Service DB] [Post Service DB]
(Sharded PostgreSQL) (Sharded PostgreSQL)
| |
[Follow Graph Cache] [Post Cache]
(Redis Cluster) (Redis Cluster)
Detailed Component Design:
-
User Service:
- Handles user registration, authentication
- Database: Sharded PostgreSQL
- Sharding key: user_id
-
Post Service:
- Handles creating and retrieving posts
- Database: Sharded PostgreSQL
- Sharding key: post_id
- Use a distributed ID generator (e.g., Twitter's Snowflake) for post_id
-
Follow Graph Service:
- Manages user follow relationships
- Store in Redis for fast lookups
- Format: SET for each user's followers
-
Feed Generation Service:
- Generates personalized news feeds
- Uses a pull-based model for inactive users and push-based for active users
- Algorithm:
- Get user's followees
- Fetch recent posts from these followees
- Sort posts by time
- Apply content ranking algorithm (optional)
-
Caching Layer:
- Use Redis clusters for caching
- Cache user data, post data, and pre-generated feeds
Scalability Considerations:
-
Database Sharding:
- Shard user and post data across multiple PostgreSQL instances
- Use consistent hashing for shard allocation
-
Caching:
- Implement multi-level caching (application server cache, distributed cache)
- Use write-through caching for user and post data
-
Feed Generation:
- Pre-generate feeds for active users and store in cache
- Use a message queue (e.g., Apache Kafka) to handle feed updates asynchronously
-
CDN:
- Use a CDN to serve static content (images, videos)
Performance Optimizations:
-
News Feed:
- Implement cursor-based pagination for feed retrieval
- Use a separate service for feed ranking and personalization
-
Database:
- Use database connection pooling
- Implement read replicas for read-heavy operations
-
Caching:
- Implement cache warming for predicted high-traffic events
Monitoring and Logging:
- Use distributed tracing (e.g., Jaeger) for request flow tracking
- Implement centralized logging (e.g., ELK stack)
- Set up alerts for system metrics (CPU, memory, disk I/O)
- Monitor application-specific metrics (e.g., feed generation time, cache hit ratio)
Now let's see some examples:
- High-Level Architecture with Communication Flow
+----------------+
| Client Apps |
| (Web, Mobile) |
+--------+-------+
|
| HTTPS
v
+-------------+--------------+
| API Gateway |
| (Kong, AWS API GW) |
+-------------+--------------+
|
+--------+--------+--------+--------+
| | | | |
v v v v v
+-------+--+ +---+----+ +-+-----+ ++------+ +-----+--+
| Auth | | User | | Post | | Feed | | Search |
| Service | | Service| |Service| |Service| | Service|
+----+----+ +---+----+ +-+-----+ ++------+ +-----+--+
| | | | |
| | | | |
+----v----------v--------v--------v--------------v----+
| Message Queue |
| (Kafka, RabbitMQ) |
+----+----------+--------+--------+--------+----------+
| | | | |
v v v v v
+----+----+ +---+----+ +-+-----+ ++------+ +--------+
| User DB | |Post DB | |Feed DB| |Search | |Analytics|
| (Shard) | |(Shard) | | | |Engine | | Service |
+---------+ +--------+ +-------+ +-------+ +--------+
Arrows indicate the direction of communication. Most interactions are bidirectional, but some (like event publishing to the message queue) are unidirectional.
- Detailed User Service Flow
+----------------+
| API Gateway |
+--------+-------+
|
+-------v-------+
| User Service |
+-------+-------+
|
+------v------+ +----------------+
| User Cache |<-->| User DB |
| (Redis) | | (PostgreSQL) |
+------+------+ +----------------+
|
+------v------+
|Message Queue|
|(Kafka Topic)|
+-------------+
- API Gateway to User Service: Bidirectional (HTTP/gRPC)
- User Service to Cache: Bidirectional (Read/Write)
- User Service to DB: Bidirectional (CRUD operations)
- User Service to Message Queue: Unidirectional (Publish events)
- Post Creation and Feed Update Flow
+----------------+
| API Gateway |
+--------+-------+
|
+-------v-------+
| Post Service |
+-------+-------+
|
+------v------+ +----------------+
| Post Cache |<-->| Post DB |
| (Redis) | | (PostgreSQL) |
+------+------+ +----------------+
|
+------v------+
|Message Queue|
|(Kafka Topic)|
+------+------+
|
+-------v-------+
| Feed Service |
+-------+-------+
|
+------v------+
| Feed Cache |
| (Redis) |
+-------------+
- Post Service to Message Queue: Unidirectional (Publish new post event)
- Message Queue to Feed Service: Unidirectional (Consume new post event)
- Feed Service to Feed Cache: Bidirectional (Update feeds)
- Search Service Interaction
+----------------+
| API Gateway |
+--------+-------+
|
+-------v-------+
|Search Service |
+-------+-------+
|
+------v------+
|Search Engine|
| (Elastic- |
| search) |
+------+------+
|
+------v------+
|Message Queue|
|(Kafka Topic)|
+-------------+
- Search Service to Search Engine: Bidirectional (Index and search operations)
- Search Service to Message Queue: Bidirectional (Consume index updates, publish search events)
- Authentication Flow
+----------------+
| API Gateway |
+--------+-------+
|
+-------v-------+
| Auth Service |
+-------+-------+
|
+------v------+ +----------------+
| Token Cache |<-->| User DB |
| (Redis) | | (PostgreSQL) |
+-------------+ +----------------+
- API Gateway to Auth Service: Bidirectional (Authenticate requests)
- Auth Service to Token Cache: Bidirectional (Store/retrieve tokens)
- Auth Service to User DB: Bidirectional (Verify credentials)
- Analytics Data Flow
+----------------+
| API Gateway |
+--------+-------+
|
+------v------+
|Message Queue|
|(Kafka Topic)|
+------+------+
|
+-------v-------+
|Analytics Svc |
+-------+-------+
|
+------v------+
| Time-series|
| DB |
| (InfluxDB) |
+-------------+
- All Services to Message Queue: Unidirectional (Publish events)
- Message Queue to Analytics Service: Unidirectional (Consume events)
- Analytics Service to Time-series DB: Bidirectional (Store and query analytics data)
These diagrams illustrate the complex interactions between different components in a microservices architecture. Key points to note:
The API Gateway acts as the single entry point for all client requests, providing a unified interface to the microservices.
Most service-to-service communication is done asynchronously through the message queue, promoting loose coupling.
Each service has its own database, following the database-per-service pattern.
Caching is used extensively to reduce database load and improve response times.
The search service uses a specialized search engine (Elasticsearch) for efficient text search operations.
The analytics service consumes events from all other services to provide system-wide insights.
Authentication is centralized in the Auth Service, which other services rely on for user verification.
Remember that in a real-world scenario, you might need to add more components like circuit breakers, service discovery, and a configuration server to enhance reliability and manageability.
Conclusion
Designing and implementing a microservices-based architecture for large-scale web applications is a complex but rewarding. It requires a deep understanding of distributed systems, careful consideration of various components, and a strategic approach to system design.
Key takeaways from our discussion include:
Importance of Preparation: Success in system design interviews and real-world implementations hinges on thorough preparation. This includes studying fundamental concepts, understanding system components, and practicing with real-world scenarios.
Holistic Approach: Effective system design goes beyond just functional requirements. It necessitates careful consideration of non-functional requirements such as scalability, reliability, and performance.
Component Interaction: The diagrams and flows we've examined highlight the intricate interactions between various components in a microservices architecture. Understanding these interactions is crucial for designing robust, scalable systems.
Communication Patterns: Both unidirectional and bidirectional communication patterns play vital roles in microservices architectures. Asynchronous communication through message queues promotes loose coupling, while synchronous communication is sometimes necessary for immediate consistency.
Data Management: Proper data management, including strategic use of caching, database sharding, and specialized data stores (like search engines and time-series databases), is fundamental to system performance and scalability.
Scalability and Performance: These are not afterthoughts but core considerations that should inform every aspect of the design, from the high-level architecture to the choice of specific technologies.
Continuous Evolution: System design is not a one-time activity. As requirements change and systems grow, architects must be prepared to evolve their designs, always considering trade-offs and potential impacts.
Best Practices: Adhering to best practices such as the use of API gateways, implementation of proper authentication and authorization, and employing monitoring and logging, ensures the creation of robust and maintainable systems.
In conclusion, mastering system design for microservices-based web applications is a journey that requires continuous learning and adaptation. It demands a balance between theoretical knowledge and practical experience. As technology evolves and new challenges emerge, the principles of good system design - scalability, reliability, and maintainability - remain constant guideposts.
Whether preparing for a system design interview or architecting real-world systems, the key is to approach each problem methodically, communicate clearly, and always be ready to dive into the details while maintaining a view of the bigger picture. By doing so, you'll be well-equipped to design systems that can stand up to the demands of modern web applications and provide value to users and businesses alike.
Important tip:
- If you don't know in-depth some topic, scope the question or the use case to a high-level solution. Don't be afraid to express your lack of knowledge, that you're not sure or it's not your domain, and let the interviewer help you getting there.
- have in mind that this interview it's kinda bidirectional between you and the interviewer. Keep an open dialogue and check regularly if you're being clear and if the interviewer is understanding your point of view.
- There is no right or wrong(most of the time), it's more like how is your approach for each requirement and how effective and efficient it is.
Hope this was insightful, helpful and you got an overview of what you have to learn, where and how. Also what you should prioritize and focus.
It's also a good idea to practice on a whiteboard kind of software like:
- Whiteboard (by HackerRank)
- CoderPad
- Repl.it
- Diagrams.net
- Figma
Happy coding, and good luck for your interviews.
Have a look on my website at https://www.ricardogesteves.com
Follow me @ricardogesteves
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RicardoGEsteves (Ricardo Esteves) · GitHub
Full-Stack Developer | Passionate about creating intuitive and impactful user experiences | Based in Lisbon, Portugal 🇵🇹 - RicardoGEsteves
github.com
Top comments (1)
Thank you for these awsome tips!!