System requirements

Functional:

Traffic Distribution:

• Distribute incoming network traffic across multiple web servers using algorithms like:
• Round Robin: Sequentially assign requests to servers, promoting fairness.
• Least Connections: Direct traffic to the server with the fewest active connections, optimizing resource utilization.
• IP Hash: Distribute traffic based on the client's IP address, promoting session persistence without explicit configuration.

Health Monitoring:

• Continuously monitor the health of web servers using techniques like:
• Health checks: Periodically send probes to servers to verify responsiveness.
• Active health checks: Simulate actual user requests to assess server functionality.
• Remove unhealthy servers from the pool and reroute traffic to healthy ones.

Session Persistence:

• Optionally maintain session persistence by directing requests from a client to the same server throughout a session.
• Implement techniques like:
• Sticky sessions: Use cookies or server-side session management to identify clients and direct them to the appropriate server.
• URL rewriting: Modify URLs to embed server information for routing.

SSL Termination:

• Decrypt incoming HTTPS traffic and establish secure connections to web servers using its own SSL certificate.
• Offload the processing burden of SSL encryption/decryption from web servers for improved performance.

Non-Functional:

Scalability:

• The load balancer must handle increasing traffic loads by automatically adding or removing servers from the pool.
• Horizontal scaling allows for efficient resource allocation based on demand.

High Availability:

• The load balancer itself should be highly available, with redundancy built-in to ensure minimal downtime in case of failures.
• Techniques like active-passive failover can be implemented for automatic failover to a backup load balancer.

Performance:

• The load balancer should introduce minimal latency when distributing traffic.
• Efficient algorithms and hardware optimization can minimize processing overhead.

Security:

• The load balancer should be secure against common network attacks like DDoS (Distributed Denial-of-Service) attacks.
• Secure communication protocols and intrusion detection systems can be implemented for added security.

Manageability:

• The load balancer should be easy to configure, monitor, and manage.
• An intuitive interface can be provided for configuration changes and health checks.

Integration:

• The load balancer should seamlessly integrate with existing network infrastructure and web servers.
• Standard protocols like HTTP/HTTPS and well-defined configuration options can facilitate easy integration.

Capacity estimation

Estimate the scale of the system you are going to design...

API design

The Load Balancer will expose a set of APIs to interact with it and manage the pool of web servers it distributes traffic to. Here's a breakdown of potential APIs categorized by functionality:

Traffic Management APIs:

• Add Server: Add a new web server to the pool of available servers.
• Remove Server: Remove a server from the pool (e.g., for maintenance).
• List Servers: Get a list of all servers currently in the pool and their health status.
• Set Distribution Algorithm: Specify the desired traffic distribution algorithm (round-robin, least connections, IP hash).
• Get Distribution Algorithm: Retrieve the currently used traffic distribution algorithm.

Health Monitoring APIs:

• Get Server Health: Get the health status of a specific server in the pool.
• List Unhealthy Servers: Get a list of all currently unhealthy servers.
• Set Health Check Configuration: Configure the health check parameters (interval, timeout, etc.).

Session Management APIs (Optional):

• Enable Session Persistence: Activate session persistence functionality.
• Disable Session Persistence: Deactivate session persistence functionality.
• Get Session Persistence Status: Retrieve the current session persistence state (enabled/disabled).

Scaling APIs:

• Register Autoscaler: Define rules and thresholds for automatic scaling based on traffic load.
• Deregister Autoscaler: Disable automatic scaling functionality.
• Get Scaling Configuration: Retrieve the current autoscaling configuration.

Monitoring and Analytics APIs:

• Get Traffic Statistics: Retrieve statistics on incoming traffic, server load distribution, and overall system performance.
• Get Health Check History: Access historical data on server health checks and failures.

Security APIs:

• Upload SSL Certificate: Upload the SSL certificate and private key for secure connections.
• Get SSL Certificate Info: Retrieve information about the currently used SSL certificate.

Database design

Database Choices:

Here's a breakdown of potential database choices for each entity group, considering CAP theorem implications:

Group 1: Server & HealthCheck Data (AP Focus)

• Entities: Server, HealthCheck
• Database Type: Relational Database (SQL) - Example: MySQL, PostgreSQL
• Reasoning:
• Normalized data structure for efficient querying and data integrity.
• ACID properties ensure data consistency (important for server health and configuration).
• SQL queries are well-suited for retrieving server and health check data.
• CAP Theorem Focus: Availability and Partition Tolerance (AP)

In this scenario, maintaining server health information and configuration data across potential network partitions is more critical than ensuring absolute consistency across all replicas. This makes a relational database with eventual consistency a suitable choice.

Group 2: Traffic Distribution & Session Data (Optional) (CP Focus)

• Entities: Traffic Distribution (Optional), Session (Optional)
• Database Type: Key-Value Store (NoSQL) - Example: Redis
• Reasoning:
• Fast data access for frequently changing traffic distribution configuration.
• Session data requires fast retrieval based on client ID (if session persistence is enabled).
• Key-value stores offer high performance and scalability for such data.
• CAP Theorem Focus: Consistency and Partition Tolerance (CP)

For traffic distribution and session data, ensuring consistent configuration across all replicas is crucial. Key-value stores provide strong consistency guarantees, making them a good choice for this data group.

Group 3: SSL Certificate (Optional)

• Entities: SSL Certificate (Optional)
• Database Type: File System or Secure Secret Store
• Reasoning:
• SSL certificates are large binary data pieces.
• File systems or dedicated secret stores offer a more secure and efficient way to store and manage them.

By grouping entities based on their access patterns and consistency requirements, we can select appropriate database technologies for each group. This design leverages the strengths of different databases to achieve optimal performance, scalability, and consistency guarantees for our Load Balancer system.

Partitioning Strategies:

While the previous section focused on database choices, partitioning strategies are crucial for efficient data management within those databases. Here's a breakdown of potential partitioning approaches for each entity group:

Server & HealthCheck Data

• Partitioning Strategy: Range Partitioning by Server ID
• Key Column: Server.id
• Reasoning:
• Range partitioning based on the server ID ensures data for each server (and its health checks) is stored together.
• This simplifies queries that retrieve information about a specific server and its health status.

Geographical Partitioning:

Geographical partitioning might be relevant if the Load Balancer serves users globally. In this scenario, we could consider partitioning servers based on their geographic location. This allows routing users to servers in the nearest location for optimal performance. However, due to the potentially dynamic nature of server health, implementing geographical partitioning for all server data might introduce complexity. It's crucial to evaluate the trade-offs between performance benefits and increased management overhead.

Scaling Strategies:

Scaling the Load Balancer system effectively is essential to handle increasing traffic demands. Here are potential scaling strategies for different components:

• Horizontal Scaling:
• Servers: Add new web servers to the pool to distribute the load.
• Database (Group 1): Scale the relational database by adding replica sets or sharding the data based on the chosen partitioning strategy.
• Database (Group 2): Scale the key-value store by adding additional nodes to the cluster for horizontal scaling.
• Vertical Scaling:
• Load Balancer: Increase the hardware resources (CPU, memory) of the Load Balancer itself if horizontal scaling isn't sufficient.
• Database (Group 1 & 2): Upgrade the database server hardware for improved processing power and storage capacity.

High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?