🔹 Requirement gathering
🔹 System architecture
🔹 Data design
🔹 Domain design
🔹 Scalability
🔹 Reliability
🔹 Availability
🔹 Performance
🔹 Security
🔹 Maintainability
🔹 Testing
🔹 User experience design
🔹 Cost estimation
🔹 Documentation
🔹 Migration plan
System design is a broad field that involves creating the architecture and structure of a software system. It encompasses high-level planning, scalability, reliability, and the technical requirements needed to build robust and efficient systems. Here are the **core concepts of system design**, explained with examples:
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### 1. **Scalability**
Scalability refers to the system's ability to handle growth in terms of users, data, or workload without compromising performance.
- **Horizontal Scaling (Scaling Out):**
- Adding more machines (servers) to distribute the load.
- Example: In a large e-commerce platform like **Amazon**, if the number of users increases, the company adds more servers to handle the additional traffic, rather than upgrading the existing ones.
- **Vertical Scaling (Scaling Up):**
- Increasing the resources (CPU, RAM) of a single machine.
- Example: A **database** server that runs out of memory can be upgraded to a larger machine with more memory to handle more queries.
- **Example of Scalability:**
- **Netflix** needs to scale horizontally by distributing its video content across servers worldwide to manage millions of users streaming simultaneously.
---
### 2. **Reliability**
Reliability ensures the system operates continuously without failure. A reliable system minimizes downtime and guarantees that services are available when needed.
- **Redundancy:**
- Duplicating critical components or functions of a system so that if one fails, the other can take over.
- Example: **AWS (Amazon Web Services)** has multiple availability zones, so if one data center goes down, others can keep serving requests.
- **Failover:**
- When one server fails, another standby server takes over.
- Example: A **primary and secondary database setup** where the secondary database is automatically promoted to primary if the original primary fails.
- **Example of Reliability:**
- **Google Cloud** uses redundant data storage and global load balancing to ensure services are always available to its customers.
---
### 3. **Latency**
Latency is the time it takes for a system to respond to a request. It’s critical to minimize latency for real-time applications or those requiring quick feedback.
- **Caching:**
- Storing frequently requested data in memory for faster access.
- Example: **Facebook** caches user profiles in a service like **Memcached** so that subsequent profile lookups are faster.
- **Content Delivery Network (CDN):**
- Distributing content to data centers closer to users to reduce the time it takes for data to travel.
- Example: **Akamai** and **Cloudflare** provide CDNs to deliver web pages, images, and videos with lower latency.
- **Example of Latency Optimization:**
- **YouTube** uses CDNs to cache and deliver video streams from the nearest edge location, reducing latency and improving playback speed for users.
---
### 4. **Availability**
Availability refers to the percentage of time the system is operational. High availability is crucial for mission-critical applications where downtime must be minimized.
- **Load Balancing:**
- Distributing incoming traffic across multiple servers to ensure no single server is overwhelmed.
- Example: **Google Search** uses load balancers to distribute search requests across thousands of servers, ensuring that it’s always available.
- **Replication:**
- Storing copies of the same data on different servers.
- Example: In **distributed databases** like **Cassandra**, data is replicated across different nodes so that if one node fails, the data is still available on others.
- **Example of High Availability:**
- **Amazon Web Services (AWS)** provides high-availability architectures by offering services across multiple regions and availability zones, ensuring that even if one region is down, the service is still available.
---
### 5. **Consistency**
Consistency ensures that all clients see the same data at the same time, even in a distributed system. Achieving strong consistency can be challenging in distributed systems.
- **Strong Consistency:**
- Ensures all nodes have the same data after an update.
- Example: **Relational databases** like **PostgreSQL** guarantee strong consistency by ensuring that transactions either complete entirely or not at all (ACID properties).
- **Eventual Consistency:**
- Allows for temporary inconsistency but ensures that all nodes will eventually converge to the same value.
- Example: **Amazon DynamoDB** provides eventual consistency, where changes may not be immediately reflected on all nodes but will eventually propagate.
- **Example of Consistency:**
- **Google Spanner** is a globally distributed database that guarantees strong consistency using synchronized clocks across its data centers.
---
### 6. **Partitioning (Sharding)**
Partitioning (also called **sharding**) is the practice of splitting data across different databases or servers to improve performance, scalability, and manageability.
- **Horizontal Partitioning (Sharding):**
- Dividing data across multiple databases based on some criteria (e.g., user ID).
- Example: In **Twitter**, user tweets can be partitioned based on user ID so that different partitions handle different sets of users, reducing the load on a single database.
- **Vertical Partitioning:**
- Splitting different types of data into different tables or databases.
- Example: In a web application, user profile data might be stored in one database, and user activity logs in another.
- **Example of Partitioning:**
- **MongoDB** allows horizontal partitioning through sharding, where large datasets are divided across multiple machines to ensure performance at scale.
---
### 7. **Load Balancing**
Load balancing distributes incoming network traffic across multiple servers to ensure no server gets overloaded and that the system can handle high traffic efficiently.
- **Round-Robin:**
- Each server gets an equal share of the requests in a cyclic order.
- Example: **Nginx** and **HAProxy** are commonly used load balancers that distribute HTTP requests in a round-robin fashion.
- **Health Checks:**
- Regularly checking the health of backend servers to ensure they can handle traffic.
- Example: A load balancer might remove a server from its pool if it becomes unhealthy and redirect traffic to the healthy servers.
- **Example of Load Balancing:**
- **Netflix** uses load balancing to manage traffic from millions of users by distributing streaming requests across multiple data centers and edge servers.
---
### 8. **Database Design**
Choosing the right database design is crucial in system design, and it depends on whether your system requires transactional support, scalability, or flexibility.
- **Relational Databases (SQL):**
- Enforces strong consistency and supports complex queries.
- Example: **MySQL** is used by many web applications (like **WordPress**) where transactional support and consistency are essential.
- **NoSQL Databases:**
- Focuses on scalability and flexibility, often at the cost of strong consistency.
- Example: **MongoDB** is used in high-traffic applications like **Uber** and **eBay**, which need flexible schemas and scalable storage.
- **Example of Database Design:**
- **Airbnb** uses **PostgreSQL** for its relational database needs (for bookings, transactions) and **Cassandra** for managing large-scale, high-availability systems.
---
### 9. **Caching**
Caching stores copies of frequently accessed data in memory to improve read performance and reduce load on the primary database or backend.
- **In-memory Cache:**
- Example: **Redis** is used by applications like **Twitter** to store user sessions and frequently accessed data in memory, reducing latency.
- **CDN Caching:**
- Example: **Cloudflare** provides CDN caching, allowing static assets (like images or CSS files) to be cached at edge locations closer to users, improving load times.
- **Example of Caching:**
- **Facebook** uses **Memcached** to cache user profile information, allowing for quick retrieval of frequently requested data.
---
### 10. **Security**
Security measures are essential to protect systems and data from unauthorized access and attacks.
- **Authentication and Authorization:**
- Example: **OAuth 2.0** is used by platforms like **Google** and **Facebook** for secure third-party authentication.
- **Encryption:**
- Example: **TLS (Transport Layer Security)** is used in **HTTPS** to ensure that communication between clients and servers is secure.
- **Example of Security:**
- **AWS IAM (Identity and Access Management)** ensures secure access control for cloud resources, allowing only authorized users to access them.
---
### Conclusion
Understanding and implementing these core system design concepts is crucial for creating high-performing, scalable, and reliable software systems. Depending on the system’s requirements (e.g., low latency, high availability, scalability), different techniques and architectural patterns can be applied to meet the desired goals.
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