My Solution for Design a Load Balancer with Score: 8/10

by iridescent_luminous693

System requirements


Functional Requirements

  1. Traffic Distribution:
    • Distribute incoming traffic across multiple backend servers using load-balancing algorithms such as:
      • Round-robin.
      • Least connections.
      • IP hash.
      • Weighted round-robin.
  2. Health Monitoring:
    • Continuously monitor the health of backend servers to reroute traffic from failed or unhealthy servers.
  3. Session Persistence:
    • Ensure session stickiness for stateful applications by mapping client requests to the same backend server during a session.
  4. SSL Termination:
    • Decrypt SSL traffic and forward plain HTTP requests to backend servers to offload SSL processing.
  5. Scalability:
    • Support dynamic scaling of backend servers by automatically adding or removing servers based on load.
  6. Failover:
    • Redirect traffic to healthy servers in case of server failures.
  7. Logging and Monitoring:
    • Log request details and provide real-time monitoring metrics (e.g., server load, request latency).

Non-Functional Requirements

  1. Performance:
    • Handle high throughput and low latency for traffic distribution.
  2. Scalability:
    • Scale horizontally to accommodate increasing traffic.
  3. Reliability:
    • Ensure high availability with failover and redundant load balancer instances.
  4. Security:
    • Support secure communication through SSL/TLS encryption.
  5. Ease of Integration:
    • Provide APIs for easy configuration and management of backend servers.
  6. Fault Tolerance:
    • Gracefully handle backend server failures and recover automatically.





Capacity estimation

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



Assumptions:

  1. Traffic:
    • Peak requests per second: 100,000.
    • Average request size: 2 KB.
  2. Backend Servers:
    • Each server can handle 5,000 RPS.
  3. Storage:
    • Logs for requests: 100,000 RPS×2 KB=200 MB/sec100,000 \, \text{RPS} \times 2 \, \text{KB} = 200 \, \text{MB/sec}100,000RPS×2KB=200MB/sec.
    • Daily log storage: 200 MB/sec×86,400 sec=16.2 TB/day200 \, \text{MB/sec} \times 86,400 \, \text{sec} = 16.2 \, \text{TB/day}200MB/sec×86,400sec=16.2TB/day.
  4. Load Balancer Instances:
    • Instances required to handle 100,000 RPS: ⌈100,000 RPS/5,000 RPS per instance⌉=20 instances\lceil 100,000 \, \text{RPS} / 5,000 \, \text{RPS per instance} \rceil = 20 \, \text{instances}⌈100,000RPS/5,000RPS per instance⌉=20instances.



API design

Define what APIs are expected from the system...


1. Traffic Distribution APIs

  • POST /api/server/register:
    • Input: { server_ip: string, weight: int (optional) }.
    • Output: { success: boolean }.
    • Registers a new backend server.
  • DELETE /api/server/unregister/{server_id}:
    • Output: { success: boolean }.
    • Removes a backend server from the pool.

2. Health Monitoring APIs

  • GET /api/health/status:
    • Output: { servers: [ { server_id, status, response_time } ] }.
    • Fetches the health status of all backend servers.

3. Configuration APIs

  • PUT /api/config/algorithm:
    • Input: { algorithm: string, options: { key: value } }.
    • Output: { success: boolean }.
    • Updates the load-balancing algorithm (e.g., round-robin, least connections).
  • GET /api/config/current:
    • Output: { algorithm: string, options: { key: value } }.
    • Retrieves the current configuration.

4. Monitoring and Logging APIs

  • GET /api/logs:
    • Input: { filters: { start_time, end_time } }.
    • Output: { logs: [ { timestamp, client_ip, server_ip, status_code } ] }.
    • Retrieves logs for a specified time range.




Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...


1. Server Configuration Database

  • Schema Details:
    • Table Name: BackendServers
      • server_id (Primary Key): Unique identifier for each server.
      • server_ip: IP address of the server.
      • weight: Weight for weighted algorithms.
      • status: Current health status (e.g., healthy, unhealthy).
      • last_checked: Timestamp of the last health check.
  • Purpose:
    • Store and manage information about registered backend servers.
  • Tech Used:
    • Relational Database (e.g., PostgreSQL).
  • Tradeoff:
    • Pros: Strong consistency for server updates and configuration.
    • Cons: Requires sharding for scalability with a large number of servers.

2. Session Persistence Database

  • Schema Details:
    • Table Name: SessionMap
      • session_id (Primary Key): Unique identifier for each client session.
      • client_ip: IP address of the client.
      • server_id: ID of the server handling the session.
      • created_at: Timestamp when the session was created.
      • last_accessed: Timestamp of the last request.
  • Purpose:
    • Map client sessions to backend servers for session stickiness.
  • Tech Used:
    • In-Memory Database (e.g., Redis).
  • Tradeoff:
    • Pros: Low-latency lookups for session mapping.
    • Cons: Limited storage capacity compared to disk-based databases.

3. Logging Database

  • Schema Details:
    • Table Name: RequestLogs
      • log_id (Primary Key): Unique identifier for each log entry.
      • timestamp: Time of the request.
      • client_ip: IP address of the client.
      • server_ip: IP address of the backend server.
      • response_time: Time taken to process the request.
      • status_code: HTTP status code returned.
  • Purpose:
    • Store request logs for monitoring and debugging.
  • Tech Used:
    • Columnar Database (e.g., ClickHouse).
  • Tradeoff:
    • Pros: Optimized for analytical queries on large datasets.
    • Cons: Higher write latency compared to row-based databases.




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...


1. Client Request Handler

Overview:

Acts as the entry point for all incoming client traffic. It handles connection requests, decrypts SSL/TLS traffic (if enabled), and forwards requests to the appropriate backend server.

Responsibilities:

  • Accept client connections and validate the request format.
  • Perform SSL termination to offload decryption overhead from backend servers.
  • Forward requests to the Load Balancing Engine for backend server selection.

2. Load Balancing Engine

Overview:

Implements the core logic of traffic distribution. Based on the configured load-balancing algorithm, it decides which backend server will process a client request.

Responsibilities:

  • Distribute requests using algorithms such as round-robin, least connections, IP hash, or weighted round-robin.
  • Maintain fairness in traffic distribution while optimizing server utilization.
  • Collaborate with the Session Persistence Manager to ensure sticky sessions for stateful applications.

3. Health Monitoring Service

Overview:

Continuously monitors the health of backend servers by performing periodic health checks. It ensures that traffic is only routed to healthy servers.

Responsibilities:

  • Send health check requests to backend servers (e.g., HTTP GET or TCP ping).
  • Mark servers as healthy or unhealthy based on responses and update the Load Balancing Engine.
  • Automatically reintegrate healthy servers into the pool after recovery.

4. Session Persistence Manager

Overview:

Maintains client session mappings to backend servers, ensuring that stateful applications can consistently serve clients from the same server.

Responsibilities:

  • Map clients to backend servers based on IP addresses, cookies, or custom session IDs.
  • Store and retrieve session mappings from an in-memory store (e.g., Redis).
  • Ensure session stickiness for the duration of a session or until explicitly cleared.

5. SSL Termination Service

Overview:

Decrypts incoming SSL/TLS traffic from clients and forwards plain HTTP requests to backend servers. This offloads the computational cost of SSL decryption from backend servers.

Responsibilities:

  • Terminate SSL/TLS connections and decrypt client traffic.
  • Optionally re-encrypt traffic when forwarding to secure backend servers.
  • Manage SSL certificates and keys securely.

6. Configuration and Management API

Overview:

Provides an interface for administrators to configure the load balancer, register/unregister backend servers, and modify traffic distribution algorithms.

Responsibilities:

  • Register or remove backend servers and update their weights.
  • Configure load-balancing algorithms and session persistence settings.
  • Expose health status and performance metrics for monitoring.

7. Logging and Monitoring Service

Overview:

Tracks system performance and logs request details for debugging, auditing, and real-time monitoring.

Responsibilities:

  • Log request details, such as client IP, backend server, response time, and HTTP status codes.
  • Provide real-time metrics on server health, request distribution, and system performance.
  • Trigger alerts for anomalies, such as server downtime or high response times.




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...


Scenario: Client Makes a Request to the Load Balancer

Step 1: Accept the Client Request

  1. Component Involved: Client Request Handler
  2. The client sends a request to the load balancer.
  3. The handler validates the request format to ensure it meets protocol standards.
  4. If SSL/TLS is enabled, the SSL Termination Service decrypts the client traffic.

Step 2: Determine the Backend Server

  1. Component Involved: Load Balancing Engine
  2. The load balancer applies the configured load-balancing algorithm (e.g., Round-Robin or Least Connections) to select a backend server.
  3. If session persistence is enabled, the Session Persistence Manager retrieves the client-to-server mapping and directs the request to the same server used in previous interactions.

Step 3: Verify the Backend Server Health

  1. Component Involved: Health Monitoring Service
  2. The Health Monitoring Service checks the health status of the selected backend server based on recent health check results.
  3. If the server is unhealthy, the Load Balancing Engine reselects another healthy server from the pool.

Step 4: Forward the Request to the Backend Server

  1. Component Involved: Client Request Handler
  2. The Client Request Handler forwards the validated request to the selected backend server over HTTP/HTTPS.
  3. It tracks response times and records the HTTP status code received from the backend server.

Step 5: Log and Monitor the Request

  1. Component Involved: Logging and Monitoring Service
  2. The Logging and Monitoring Service logs details of the request, including:
    • Client IP Address.
    • Selected Backend Server.
    • Response Time.
    • HTTP Status Code.
    • It also updates real-time performance metrics for administrators to monitor system health.

Step 6: Respond to the Client

  1. Component Involved: Client Request Handler
  2. The Client Request Handler receives the backend server’s response and processes it for client delivery.
  3. If SSL termination was performed earlier, the response is re-encrypted before being sent back to the client.
  4. Finally, the response is returned to the client, completing the request lifecycle.

Scenario: Administrator Configures the Load Balancer

Step 1: Register a Backend Server

  1. Component Involved: Configuration and Management API
  2. The administrator sends a request (e.g., POST /api/server/register) to register a new backend server.
  3. The request includes the server’s IP address and optional parameters such as weight for weighted load balancing.

Step 2: Update Server Pool

  1. Component Involved: Load Balancing Engine
  2. The Load Balancing Engine adds the new server to the pool of available servers.
  3. It updates its internal configuration to distribute traffic appropriately across the updated server list.

Step 3: Start Health Monitoring

  1. Component Involved: Health Monitoring Service
  2. The Health Monitoring Service initiates periodic health checks for the newly registered server.
  3. The server is marked as active only if it passes the initial health checks successfully.

Step 4: Update Configuration

  1. Component Involved: Configuration and Management API
  2. The system confirms the server registration, updates the configuration, and notifies the administrator of the successful operation.
  3. The new server is now ready to receive and handle client traffic.




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...


1. Client Request Handler

End-to-End Working:

The Client Request Handler is the entry point for all incoming requests from clients. It is responsible for accepting client connections, decrypting SSL/TLS traffic if enabled, and validating the request format. After validation, the request is forwarded to the Load Balancing Engine for backend server selection. Once the backend server processes the request, the handler packages and sends the response back to the client, performing re-encryption if SSL termination was used.

Communication with Other Components:

  • Protocols Used:
    • HTTP/HTTPS: To handle client communication.
    • REST/gRPC: To forward validated requests to the Load Balancing Engine and retrieve responses.
  • Inter-Service Communication:
    • Forwards incoming requests to the Load Balancing Engine.
    • Receives backend server responses for client delivery.

Data Structures and Algorithms:

  • Connection Pooling:
    • Maintains a pool of reusable connections to reduce overhead during high-frequency request handling.
  • Request Queue:
    • Implements a FIFO queue to buffer incoming requests during peak traffic.
  • SSL Optimization:
    • Utilizes session resumption (e.g., TLS session tickets) to minimize SSL handshake overhead.

Handling Peak Traffic:

  • Horizontal Scaling:
    • Deploys multiple instances of the Client Request Handler behind an upstream load balancer.
  • Connection Multiplexing:
    • Uses HTTP/2 or HTTP/3 protocols to handle multiple requests over a single connection.

Edge Cases:

  • Malformed Requests:
    • Responds with HTTP 400 for invalid request formats.
  • Connection Timeouts:
    • Implements timeouts to terminate stale or incomplete client connections.
  • SSL Handshake Failures:
    • Logs errors and gracefully retries the handshake.

2. Load Balancing Engine

End-to-End Working:

The Load Balancing Engine is the core component responsible for distributing client requests across available backend servers. It implements algorithms like round-robin, least connections, or IP hash to determine the most suitable server. After selection, the engine forwards the request to the backend server and updates server metrics for load tracking.

Communication with Other Components:

  • Protocols Used:
    • REST/gRPC: Communicates with the Health Monitoring Service for real-time updates on server health.
    • TCP/HTTP: Sends requests to backend servers.
  • Inter-Service Communication:
    • Queries the Session Persistence Manager for session stickiness.
    • Sends usage metrics to the Logging and Monitoring Service.

Data Structures and Algorithms:

  • Round-Robin Algorithm:
    • Selects servers cyclically for even distribution.
  • Least Connections Algorithm:
    • Maintains a dictionary of active connections and selects the server with the least.
  • Weighted Round-Robin:
    • Uses weights to distribute traffic proportionally based on server capacity.

Example Implementation for Round-Robin:

python Copy code class RoundRobinBalancer: def __init__(self, servers): self.servers = servers self.index = 0 def get_next_server(self): server = self.servers[self.index] self.index = (self.index + 1) % len(self.servers) return server

Handling Peak Traffic:

  • Dynamic Scaling:
    • Dynamically adjusts the number of backend servers based on load metrics.
  • Regional Load Balancers:
    • Distributes traffic geographically to reduce latency and prevent bottlenecks in specific regions.

Edge Cases:

  • Server Downtime:
    • Removes unhealthy servers from the pool based on health checks.
  • High Latency Servers:
    • Avoids routing to servers with consistently high response times.
  • Dynamic Server Addition:
    • Updates the server pool without restarting the load balancer.

3. Health Monitoring Service

End-to-End Working:

The Health Monitoring Service ensures that the load balancer routes traffic only to healthy servers. It periodically performs health checks (e.g., HTTP GET, TCP ping) on all backend servers. Based on the server's responses, it updates the status (healthy/unhealthy) and communicates these updates to the Load Balancing Engine.

Communication with Other Components:

  • Protocols Used:
    • gRPC/REST: Provides health status updates to the Load Balancing Engine.
    • HTTP/TCP: Sends health check requests to backend servers.
  • Inter-Service Communication:
    • Updates the status of each server in the Server Pool Database.
    • Publishes health metrics to the Monitoring Service.

Data Structures and Algorithms:

  • Server Health Map:
    • Maintains a hash map of server IDs to their health statuses.
  • Health Check Scheduler:
    • Uses a priority queue to schedule health checks based on server criticality.
  • Threshold-Based Algorithm:
    • Marks a server as unhealthy if it fails a predefined number of consecutive health checks.

Handling Peak Traffic:

  • Batch Health Checks:
    • Aggregates multiple checks in a batch to reduce the overhead during high server counts.
  • Adaptive Frequency:
    • Reduces health check frequency during low traffic to conserve resources.

Edge Cases:

  • Intermittent Failures:
    • Uses thresholds and retries before marking a server unhealthy.
  • Network Partitioning:
    • Differentiates between server and network failures using multi-path checks.

4. Session Persistence Manager

End-to-End Working:

The Session Persistence Manager maintains mappings of client sessions to backend servers for stateful applications. It ensures that subsequent requests from a client are routed to the same server for consistency. Mappings are stored in an in-memory database (e.g., Redis) for low-latency access.

Communication with Other Components:

  • Protocols Used:
    • gRPC/REST: Receives client session details from the Load Balancing Engine.
    • In-Memory Protocols: Manages session data storage and retrieval in Redis.
  • Inter-Service Communication:
    • Sends and receives session mappings from the Load Balancing Engine.

Data Structures and Algorithms:

  • Session Map:
    • A hash map where keys are client session IDs or IPs, and values are backend server IDs.
  • Session TTL:
    • Uses time-to-live (TTL) values to automatically expire old session mappings.

Handling Peak Traffic:

  • Sharding:
    • Distributes session data across multiple Redis nodes for horizontal scaling.
  • Caching:
    • Caches frequently accessed session mappings to reduce database queries.

Edge Cases:

  • Session Expiry:
    • Clears expired sessions and reroutes requests to available servers.
  • Database Unavailability:
    • Implements a fallback mechanism using in-memory caching during outages.

5. Logging and Monitoring Service

End-to-End Working:

The Logging and Monitoring Service tracks system performance, request distribution, and backend server health. It logs request details (e.g., client IP, response time) and provides real-time dashboards for administrators to monitor system performance and detect anomalies.

Communication with Other Components:

  • Protocols Used:
    • HTTP/REST: Receives logs and metrics from the Client Request Handler and Health Monitoring Service.
    • WebSocket: Streams real-time metrics to the monitoring dashboard.
  • Inter-Service Communication:
    • Collects logs from all system components and aggregates them for analysis.

Data Structures and Algorithms:

  • Time-Series Database:
    • Stores metrics with timestamps for efficient time-series analysis.
  • Log Aggregation:
    • Consolidates logs from distributed services into a centralized repository.
  • Anomaly Detection:
    • Uses machine learning models to identify performance anomalies in metrics.

Handling Peak Traffic:

  • Asynchronous Logging:
    • Buffers logs in batches and writes them asynchronously to reduce write latency.
  • Distributed Logging:
    • Uses a cluster of log servers to handle high log volumes.

Edge Cases:

  • Log Overflow:
    • Implements rate-limiting for log generation to prevent resource exhaustion.
  • Metric Gaps:
    • Uses redundant monitoring agents to avoid data loss during outages.




Trade offs/Tech choices

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


Session Persistence Using In-Memory Store (e.g., Redis):

  • Trade-Off: Limited capacity compared to disk-based solutions.
  • Reason: Chosen for low-latency access to session data, critical for stateful applications.

Round-Robin vs. Weighted Algorithms:

  • Trade-Off: Simplicity of round-robin can lead to uneven load distribution.
  • Reason: Round-robin provides simplicity and fair distribution in uniform setups, with weighted algorithms used for heterogeneous environments.

Health Checks with Reduced Frequency During Low Traffic:

  • Trade-Off: Slightly delayed detection of server failures.
  • Reason: Reduces unnecessary resource usage during low-traffic periods.

Time-Series Database for Metrics:

  • Trade-Off: Higher write complexity compared to relational databases.
  • Reason: Optimized for real-time analytics and efficient time-series queries.




Failure scenarios/bottlenecks

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



  1. Overloaded Backend Servers:
    • Issue: Servers may fail under high traffic.
    • Mitigation: Use the least connections algorithm and enable dynamic scaling to add/remove servers based on load.
  2. Session Persistence Store Failure:
    • Issue: Loss of session mappings disrupts sticky sessions.
    • Mitigation: Implement multi-node Redis clusters with replication and fallback to a temporary in-memory cache.
  3. Health Check Service Downtime:
    • Issue: Unhealthy servers remain in rotation, leading to failed requests.
    • Mitigation: Use redundant health-check agents and fallback to historical health data.
  4. Logging Service Overload:
    • Issue: Excessive logs during peak traffic can overwhelm the logging system.
    • Mitigation: Batch log writes asynchronously and implement log rate limiting.
  5. SSL Termination Latency:
    • Issue: High SSL handshake latency affects request processing time.
    • Mitigation: Use session resumption techniques like TLS tickets and offload SSL termination to dedicated hardware.



Future improvements

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


  1. Enhanced Anomaly Detection:
    • Improvement: Integrate AI/ML models to identify anomalies in traffic patterns.
    • Mitigation: Automate responses like dynamic scaling or traffic rerouting for early anomaly detection.
  2. Geo-Distributed Load Balancing:
    • Improvement: Deploy regional load balancers for improved latency and fault isolation.
    • Mitigation: Synchronize server pools across regions with eventual consistency.
  3. Serverless Load Balancing:
    • Improvement: Implement serverless architecture for scaling the load balancer itself.
    • Mitigation: Use auto-scaling and container orchestration tools like Kubernetes to manage dynamic workloads.
  4. Improved Logging Storage:
    • Improvement: Transition to tiered logging storage to optimize costs.
    • Mitigation: Store hot logs in high-performance databases and archive cold logs in cheaper object storage.