System Design — Tips

Designing a robust and scalable system involves careful planning and consideration of various factors. Here are some essential tips to guide you through the process:

1. Understand the Requirements

• Functional Requirements: What the system should do (features and functionality).
• Non-Functional Requirements: Performance, scalability, security, and availability.

2. Choose the Right Architecture

• Monolithic vs. Microservices: Decide whether to build a monolithic application or break it down into microservices.
• Layered Architecture :Use a layered approach (presentation, application, business, and data layers) for better separation of concerns.

3. Scalability

• Horizontal Scaling: Add more servers to handle increased load.
• Vertical Scaling: Add more resources (CPU, RAM) to existing servers.
• Load Balancing: Distribute incoming traffic across multiple servers to ensure no single server is overwhelmed.

4. Database Design

• Normalization vs. Denormalization: Balance between normalized (reduces redundancy) and denormalized (improves read performance) data models.
• Choosing the Right Database: Use SQL for relational data and NoSQL for unstructured data.
• Sharding: Distribute data across multiple databases to handle large datasets and high traffic.
• Locking mechanism: Optimistic Concurrency Control, Pessimistic Concurrency Control.

5. Distributed Locking

• Avoid Deadlocks: Always set a TTL (Time to Live) for locks to prevent deadlocks in case a process crashes before releasing the lock.
• Renewal Mechanism: Implement a renewal mechanism if the process holding the lock needs more time.

Redis: Use Redis with its SETNX command for simple locking mechanisms.

Zookeeper: Provides more complex coordination with strong consistency guarantees.

Etcd: Another option for distributed locking with strong consistency and fault tolerance.

6. Distributed Caching

• Data Caching: Store frequently accessed data in a cache to reduce database load and speed up responses.
• Content Delivery Networks (CDNs): Use CDNs to cache and deliver static content closer to the users.
• Partitioning: Distribute data across multiple partitions or shards to handle high throughput.

7. Fault Tolerance and High Availability

• Redundancy: Duplicate critical components to prevent single points of failure.
• Failover Mechanisms: Automatically switch to a standby system in case of failure.
• Backup and Recovery: Regularly back up data and have a recovery plan in place.

8. Security

• Authentication and Authorization: Ensure users are who they say they are and have permission to access resources.
• Encryption: Encrypt data at rest and in transit to protect sensitive information.
• Firewalls and Intrusion Detection: Implement network security measures to detect and prevent unauthorized access.

9. Monitoring and Logging

• Real-time Monitoring: Use monitoring tools to track system performance and health in real time.
• Logging: Maintain logs for debugging and analyzing system behaviour.

10. APIs and Communication

• RESTful APIs: Use REST for web services to ensure scalability and stateless communication.
• Message Queues: Use message queues (e.g., RabbitMQ, Kafka) for asynchronous communication between services.

11. Development Best Practices

• Version Control: Use version control systems like Git for tracking changes and collaboration.
• Continuous Integration/Continuous Deployment (CI/CD): Automate testing and deployment to ensure rapid and reliable delivery of updates.
• Code Reviews: Regularly review code to maintain quality and consistency.

1. Low Latency Requirement: Make use of Cache and CDN.

Scenario: Video Streaming Platform

Challenge: Delivering high-quality video content with minimal latency to users worldwide.

Solution: Utilize a combination of cache and Content Delivery Network (CDN) to optimize content delivery. The platform caches frequently accessed video files at edge servers located close to users’ geographical locations. When users request video content, the CDN delivers it from the nearest edge server, reducing latency and improving streaming performance.

Benefits: By leveraging cache and CDN, the video streaming platform ensures low-latency delivery of video content, resulting in smoother streaming experiences for users. This approach reduces buffering, improves playback quality, and enhances overall user satisfaction, especially for viewers accessing content from diverse geographic regions.

By combining caching for static assets and a CDN for dynamic video delivery, the streaming service achieves low latency, enhancing the user experience.

Note: for scalability use multi-region CDN

2. Read-Heavy System: Use Cache for faster reads.

Scenario: News Website

Challenge: A news website with millions of daily visitors needs to ensure that articles load quickly. Most visitors are reading the latest news, making the system read-heavy.

Solution: Implement caching to speed up read operations.

Caching Strategy

Article Cache: Store the most frequently accessed articles in a cache.

TTL (Time to Live): Set a TTL to refresh the cache periodically, ensuring users receive the latest updates.

Workflow

Initial Request: A user in New York requests an article. The article is fetched from the database and stored in the cache.

Subsequent Requests: When another user requests the same article, it is served directly from the cache, significantly reducing read time.

Cache Implementation

Cache Layer: Use a caching solution like Redis or Memcached.

Integration: Modify the application logic to check the cache before querying the database.

Benefits:

Faster Read Times: Serving articles from the cache reduces latency.

Reduced Database Load: Lower read pressure on the database, improving overall system performance.

Scalability: Handles high traffic efficiently, providing a smooth experience for all users.

By using a cache to store and quickly serve frequently accessed articles, the news website ensures fast-read operations, enhancing the user experience during peak traffic times.

3. Write-Heavy System: Employ Message Queues for async writing.

Scenario: Social Media Platform

Challenge: A social media platform experiences a high volume of user-generated content, such as posts, comments, and likes. Handling these write-heavy operations synchronously can lead to performance bottlenecks and latency issues.

Solution: Implement message queues for asynchronous writing to handle the write-heavy workload efficiently.

Message Queue Setup

Choice of Message Queue: Select a reliable message queue system like Apache Kafka or RabbitMQ.

Topics and Queues: Set up separate topics or queues for different types of write operations (e.g., posts, comments).

Asynchronous Writing Workflow

User Action: A user creates a new post on the platform.

Publish to Queue: Instead of writing directly to the database, the platform publishes a message containing the post data to the appropriate queue.

Asynchronous Processing: Background workers consume messages from the queue and perform the necessary write operations to the database.

Scalability and Performance

Horizontal Scaling: Add more message brokers and worker instances to handle increased message throughput.

Load Balancing: Distribute messages evenly across multiple queue partitions or worker nodes for optimal performance.

Fault Tolerance and Durability

Message Persistence: Configure the message queue to store messages persistently to prevent data loss in case of failures.

Retry Mechanism: Implement retry logic for failed message processing to handle transient errors.

Example Implementation

In our social media platform example:

When a user creates a new post, the platform publishes a message containing the post data to the “post_creation” queue in RabbitMQ.

Background worker instances consume messages from the “post_creation” queue asynchronously and perform the necessary write operations to store the post in the database.

Benefits:

Improved Performance: Asynchronous writing reduces response times for user actions, enhancing the overall user experience.

Scalability: The system can handle bursts of write operations by scaling message brokers and worker instances as needed.

Fault Tolerance: Message queues provide durability and fault tolerance, ensuring data integrity and resilience against failures.

By employing message queues for asynchronous writing, the social media platform efficiently handles its write-heavy workload while maintaining high performance and scalability.

4. Unstructured Data: Use NoSQL Database.

Scenario: E-commerce Product Catalog

Challenge: Managing a diverse range of product data with varying attributes, descriptions, and images.

Solution: Utilize MongoDB as a NoSQL database to store product data in flexible JSON-like documents. Each document represents a product with its unique attributes, allowing for efficient storage and retrieval of unstructured data.

Benefits: MongoDB’s document-oriented approach simplifies the storage of unstructured product data, enabling dynamic updates and scalability as the catalogue expands.

5. ACID Compliant DB: Choose RDBMS/SQL Database.

Scenario: Online Reservation System

Challenge: Ensuring accurate and reliable booking transactions for hotel reservations.

Solution: Utilize MySQL as the RDBMS for its ACID compliance. MySQL guarantees Atomicity, Consistency, Isolation, and Durability for all booking transactions, providing a secure and robust database platform.

Benefits: With MySQL, the reservation system maintains data integrity and consistency, ensuring that bookings are processed accurately and reliably, even during high-demand periods.

5. Complex Data (Videos, Images, Files): Prefer Blob/Object storage.

Scenario: Media Sharing Platform

Challenge: Storing and retrieving large volumes of videos, images, and files uploaded by users.

Solution: Opt for Amazon S3 as the object storage solution for its ability to store and serve multimedia files efficiently. S3 offers scalable, durable, and cost-effective storage for complex data types like videos, images, and files.

Benefits: By leveraging Amazon S3, the media-sharing platform efficiently stores and retrieves multimedia content, ensuring high availability and reliability for users accessing videos, images, and files.

7. High Availability: Use Load Balancer.

Scenario: E-commerce Website

Challenge: Ensuring high availability and distributing incoming traffic efficiently to multiple web servers.

Solution: Implement a load balancer (e.g., AWS Elastic Load Balancer) to evenly distribute incoming traffic across multiple web servers. The load balancer monitors server health and redirects requests to healthy servers, ensuring continuous availability and preventing overload on any single server.

Benefits: With the load balancer in place, the e-commerce website maintains high availability, minimizes downtime, and delivers a seamless browsing experience for users, even during peak traffic periods.

8. Scaling SQL Database: Implement Database Sharding.

Scenario: Social Media Platform

Challenge: Scaling an SQL database to accommodate the growing user base and increasing data volume.

Solution: Implement database sharding to horizontally partition the data across multiple database instances. Each shard contains a subset of the data, distributed based on a shard key (e.g., user ID or geographic region). Queries are routed to the appropriate shard, allowing for parallel processing and improved scalability.

Benefits: By implementing database sharding, the social media platform efficiently scales its SQL database, accommodating the growing user base and data volume while maintaining performance and availability.

11. Global Data Delivery: Consider CDN.

Scenario: Content Publishing Platform

Challenge: Delivering content to users worldwide with low latency and high performance.

Solution: Utilize a Content Delivery Network (CDN) such as Cloudflare or Akamai to cache and deliver content from edge servers located strategically around the globe. When a user requests content, the CDN serves it from the nearest edge server, reducing latency and improving load times.

Benefits: By leveraging a CDN, the content publishing platform ensures fast and reliable global data delivery, enhancing the user experience and accommodating users from diverse geographical locations.

12. Graph Data: Utilize Graph Database.

Scenario: Social Network

Challenge: Managing complex relationships and connections between users, posts, and interests.

Solution: Utilize a graph database like Neo4j to model and query the intricate network of relationships in the social network. Nodes represent users, posts, and interests, while edges represent connections such as friendships, likes, and follows.

Benefits: With Neo4j, the social network efficiently stores and navigates the graph of relationships, enabling features like personalized recommendations, friend suggestions, and social network analysis.

13. High-Performing Database Queries: Utilize Database Indexes.

Scenario: E-commerce Platform

Challenge: Retrieving product information quickly for search and display purposes.

Solution: Utilize database indexes on commonly queried fields such as product name, category, and price. By indexing these fields, the database can quickly locate relevant products without scanning the entire dataset, resulting in faster query performance.

Benefits: With database indexes in place, the e-commerce platform accelerates product searches and improves the overall user experience, leading to increased customer satisfaction and higher conversion rates.

14. Single Point of Failure: Introduce Redundancy.

Scenario: Payment Processing System

Challenge: Minimizing the risk of downtime and data loss due to a single point of failure in the payment processing infrastructure.

Solution: Introduce redundancy by deploying multiple instances of critical components such as payment gateways, databases, and servers. Implement load balancers and failover mechanisms to route traffic to healthy instances in case of failure.

Benefits: With redundancy measures in place, the payment processing system enhances resilience and ensures continuous operation, reducing the likelihood of downtime and mitigating the impact of potential failures on business operations.

15. Bulk Job Processing: Use Batch Processing and Message Queues.

Scenario: Email Marketing Campaign

Challenge: Sending bulk emails to thousands of subscribers efficiently and reliably.

Solution: Implement batch processing combined with message queues. Divide the email list into batches and enqueue them in a message queue. Background workers consume messages from the queue, processing each batch asynchronously and sending emails in parallel.

Benefits: By using batch processing and message queues, the email marketing system can handle large volumes of emails efficiently, ensuring timely delivery and preventing overload on the email server.

16. Server Load Management: Apply Rate Limiter.

Scenario: API Rate Limiting

Challenge: Preventing server overload and ensuring fair usage of API resources.

Solution: Implement a rate limiter to restrict the number of requests a client can make to the API within a specific time window. Set limits based on factors like user roles, subscription tiers, or API endpoints to control access and distribute server load evenly.

Benefits: With rate limiting in place, the API server effectively manages server load, preventing abuse and ensuring optimal performance for all users while protecting against denial-of-service attacks.

17. Microservices Architecture: Employ API Gateway.

Scenario: E-commerce Platform

Challenge: Managing multiple microservices and providing a unified entry point for client applications.

Solution: Implement an API Gateway to act as a single entry point for client applications. The API Gateway routes incoming requests to the appropriate microservices, handling authentication, rate limiting, and request transformation along the way.

Benefits: With an API Gateway in place, the e-commerce platform simplifies client interactions, improves security, and enables efficient communication between microservices, enhancing scalability and flexibility.

18. Data Integrity: Ensure Checksum Algorithm.

Scenario: File Transfer System

Challenge: Ensuring data integrity during file transfers to detect any corruption or tampering.

Solution: Implement a checksum algorithm such as MD5 or SHA-256 to calculate a unique checksum value for each file before and after transfer. Compare the checksum values to verify the integrity of the transferred file.

Benefits: By using a checksum algorithm, the file transfer system can detect any data corruption or tampering during transit, ensuring data integrity and reliability for transferred files.

19. Analytics and Audit Trails: Consider data lakes or append-only databases.

Scenario: Healthcare Records Management System

Challenge: Storing and analyzing large volumes of healthcare data while maintaining comprehensive audit trails for compliance purposes.

Solution: Implement a data lake or append-only database to store healthcare records. Data lakes allow for the storage of structured and unstructured data in its raw form, enabling advanced analytics and machine learning. Append-only databases ensure that data is never overwritten or deleted, maintaining a complete audit trail of all changes.

Benefits: With a data lake or append-only database, the healthcare records management system can efficiently store and analyze vast amounts of data while ensuring data integrity and compliance with regulatory requirements. Additionally, the system maintains a detailed audit trail of all data changes for accountability and transparency.

20. Fault-Tolerance: Implement Data Replication.

Scenario: Financial Trading Platform

Challenge: Ensuring continuous operation and data availability in the event of server failures or network outages.

Solution: Implement data replication across geographically distributed servers. Each transaction and order is replicated in real-time to multiple data centers or cloud regions, ensuring redundancy and fault tolerance. In case of a failure in one location, the system automatically fails over to the replicated data in another location.

Benefits: With data replication in place, the financial trading platform maintains high availability and fault tolerance, minimizing downtime and ensuring continuous operation for traders and clients, even in the face of unexpected failures or disasters.

21. User-to-User Fast Communication: Use Websockets.

Scenario: Real-time Chat Application

Challenge: Facilitating fast and responsive communication between users in a chat application.

Solution: Implement WebSockets to enable bidirectional, low-latency communication between the chat clients and the server. When a user sends a message, it is immediately transmitted over a WebSocket connection to the server, which then broadcasts the message to the recipient’s WebSocket connection, allowing for real-time message delivery.

Benefits: By using WebSockets, the chat application provides fast and responsive communication between users, enabling instant message delivery and a seamless chatting experience with minimal latency.

22. Failure Detection: Implement Heartbeat.

Scenario: Distributed System

Challenge: Detecting failures and maintaining system reliability across multiple nodes.

Solution: Implement a heartbeat mechanism where each node periodically sends a signal (heartbeat) to a centralized monitoring system. The monitoring system monitors the heartbeat signals and detects any nodes that stop sending them, indicating a potential failure.

Benefits: With a heartbeat mechanism in place, the distributed system can quickly detect failures and take appropriate action, such as rerouting traffic or initiating failover procedures, to maintain system reliability and availability.

23. Efficient Server Scaling: Apply Consistent Hashing.

Scenario: Online Retail Platform

Challenge: Scaling a Cassandra database cluster to efficiently handle data distribution and access patterns.

Solution: Implement consistent hashing in Cassandra to evenly distribute data partitions across nodes in the cluster. Each data partition is assigned a token, and consistent hashing determines which node is responsible for storing and serving data for a given partition. As the cluster scales horizontally by adding more nodes, consistent hashing ensures that data distribution remains balanced and optimized.

Benefits: With consistent hashing in Cassandra, the online retail platform achieves efficient server scaling by evenly distributing data across nodes in the database cluster. This results in improved performance, scalability, and reliability for handling high-volume data transactions and queries.

24. Decentralized Data Transfer: Consider Gossip Protocol.

Scenario: Decentralized File Sharing Network

Challenge: Efficiently distributing file updates and metadata across nodes in a decentralized network.

Solution: Implement the Gossip Protocol to disseminate file updates and metadata. Each node periodically shares information with a random subset of other nodes in the network. Through a series of gossip exchanges, updates propagate across the network, ensuring eventual consistency and minimizing network overhead.

Benefits: With the Gossip Protocol, the decentralized file sharing network efficiently distributes updates and metadata, enabling rapid dissemination of information across nodes without relying on central coordination. This results in a resilient and scalable system capable of handling dynamic changes and node failures.

25. High Availability Trade-Off: Embrace Eventual Consistency.

Scenario: Twitter Feed

Challenge: Balancing real-time updates with system availability in a distributed environment.

Solution: Embrace eventual consistency by allowing tweets to appear in users’ feeds with a slight delay. Instead of waiting for all servers to synchronize before displaying tweets, Twitter allows users to see recent tweets immediately, even if they haven’t propagated to all servers yet.

Benefits: By embracing eventual consistency, Twitter ensures that users can access their feeds without delays, even during periods of high traffic or network issues. This trade-off prioritizes user experience and system availability while allowing for temporary inconsistencies in tweet visibility across servers.

26. Handling Large Data: Implement Pagination.

Scenario: News Website

Challenge: Displaying a large number of articles while maintaining performance and user experience.

Solution: Implement pagination to divide the list of articles into manageable chunks, displaying only a subset of articles per page. When users navigate through pages, the website retrieves and displays the next set of articles dynamically, reducing load times and improving responsiveness.

Benefits: With pagination, the news website efficiently handles large amounts of data, ensuring fast loading times and a smooth browsing experience for users. This approach optimizes server resources and network bandwidth while accommodating varying user preferences and browsing behaviours.

27. Handling Traffic Spikes: Use Autoscaling.

Scenario: E-commerce Website

Challenge: Managing sudden spikes in traffic during peak shopping seasons or promotions.

Solution: Implement autoscaling to automatically adjust the number of web server instances based on traffic demand. When traffic increases, the autoscaling system dynamically provisions additional server instances to handle the load. Conversely, when traffic decreases, it scales down the number of instances to minimize costs.

Benefits: With autoscaling, the e-commerce website can seamlessly handle sudden increases in traffic without manual intervention, ensuring optimal performance and availability during peak periods. This approach improves scalability, reduces downtime, and optimizes resource utilization, leading to a better overall user experience.

References used in this blog:

• Medium blogs on System Design
• From my Experience

Always open for comment and improvement.

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