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Load Balancing Algorithms - Round Robin, Least Connections, IP Hash, Weighted

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ArchitectureTraceChallenge
🎯
Load Balancing Algorithms - Round Robin, Least Connections, IP Hash, Weighted
45 min · senior
senior

You are designing a load balancing service for a 50 million daily active user (DAU) startup that handles HTTP and TCP traffic for multiple backend services. The system must support multiple load balancing algorithms: Round Robin, Least Connections, IP Hash, and Weighted. The service must maintain P99 latency under 200ms under peak load, support encrypted traffic (TLS termination), and provide sticky sessions when required. The system should be highly available and horizontally scalable to handle millions of concurrent connections and requests per second.

Constraints
  • P99 latency under 200ms for request routing decisions
  • TLS termination and encryption required at load balancer layer
  • Support sticky sessions for stateful backend services
  • Handle 10 million concurrent connections and 1 million requests per second
  • Dynamic weight updates without downtime
  • Graceful failover and zero downtime deployments
📋
Guided Steps
1/6
Step 1
Choosing the Load Balancer Architecture
How would you architect the load balancer to support multiple algorithms and scale to 10M concurrent connections?
💡 Hint
Consider separating control and data planes and using event-driven networking frameworks.
Key Decision
Use Envoy Proxy as the data plane for high-performance L4/L7 load balancing with xDS API for control plane configuration, enabling dynamic algorithm switching and TLS termination.
Interviewer Expectation
Mid: Names Envoy or NGINX. Senior: Explains Envoy’s xDS dynamic config, event-driven architecture for low latency, and TLS termination capabilities.
⚠️Common Wrong Answer
Using a simple TCP proxy without dynamic config - leads to downtime on config changes and inability to support multiple algorithms dynamically.
Step 2
Implementing Load Balancing Algorithms
How will you implement and switch between Round Robin, Least Connections, IP Hash, and Weighted algorithms efficiently?
💡 Hint
Think about algorithm implementations in Envoy and how weights and session affinity can be managed dynamically.
Key Decision
Leverage Envoy’s built-in load balancing policies: Round Robin, Least Request (Least Connections), Maglev (consistent hashing for IP Hash), and Weighted Round Robin with dynamic weight updates via control plane.
Interviewer Expectation
Mid: Lists algorithms and basic usage. Senior: Explains Maglev consistent hashing for IP Hash, dynamic weight updates via xDS without restart, and trade-offs in statefulness and stickiness.
⚠️Common Wrong Answer
Implementing custom algorithms in the data plane - increases complexity and risks performance degradation.
Step 3
Ensuring Sticky Sessions and Session Affinity
How will you support sticky sessions for stateful backend services while maintaining scalability?
💡 Hint
Consider IP Hash and cookie-based session affinity and how Envoy supports these mechanisms.
Key Decision
Use Envoy’s consistent hashing (Maglev) for IP Hash affinity and HTTP cookie-based session affinity for sticky sessions, configurable per service via control plane.
Interviewer Expectation
Mid: Mentions IP Hash or cookies for stickiness. Senior: Explains pros/cons of IP Hash (scales well but sensitive to backend changes) vs cookie affinity (more flexible but requires cookie management).
⚠️Common Wrong Answer
Using only IP Hash without fallback - causes uneven load and session loss on backend changes.
Step 4
Handling TLS Termination and Security
Where and how will you handle TLS termination to meet encryption requirements without impacting latency?
💡 Hint
Think about offloading TLS at the load balancer and certificate management automation.
Key Decision
Terminate TLS at Envoy proxies using automated certificate management (e.g., cert-manager with Let's Encrypt), enabling encrypted traffic handling with minimal latency overhead.
Interviewer Expectation
Mid: TLS termination at load balancer. Senior: Explains certificate rotation automation, hardware acceleration options, and impact on latency.
⚠️Common Wrong Answer
Terminating TLS at backend servers - increases latency and complexity, reduces ability to inspect traffic for routing.
Step 5
Designing the Control Plane APIs
What APIs will you expose to operators for managing backend pools, algorithms, and monitoring?
💡 Hint
Consider REST/gRPC APIs for configuration and metrics endpoints for observability.
Key Decision
Expose RESTful APIs for backend pool management (add/remove servers), algorithm configuration per service, and Prometheus-compatible metrics endpoints for real-time monitoring.
Interviewer Expectation
Mid: Basic CRUD APIs for backends. Senior: Includes dynamic algorithm switching, weight updates, health check config, and metrics with alerting hooks.
⚠️Common Wrong Answer
Hardcoding backend configs or requiring load balancer restarts - causes downtime and operational complexity.
Step 6
Ensuring High Availability and Zero Downtime
How will you design for failover and zero downtime deployments of the load balancer service?
💡 Hint
Think about active-active clusters, health checks, and rolling upgrades.
Key Decision
Deploy Envoy proxies in active-active clusters behind DNS or anycast with health checks and use control plane with versioned configs to enable rolling upgrades without traffic disruption.
Interviewer Expectation
Mid: Mentions load balancer redundancy. Senior: Details active-active design, health probes, config versioning, and graceful connection draining.
⚠️Common Wrong Answer
Single load balancer instance or manual failover - leads to downtime and poor fault tolerance.
🔌
API Design Challenge
Sketch 3 core API endpoints for the load balancer management plane to configure algorithms, update backend pools, and retrieve metrics.
Hints
  • How does an operator add or remove backend servers dynamically?
  • How can the operator switch or configure load balancing algorithms per service?
  • What endpoints provide real-time metrics and health status for backend pools?
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Use of Envoy Proxy with xDS API for dynamic configuration and TLS termination
  • Implementation of all four load balancing algorithms with dynamic switching
  • Support for sticky sessions via consistent hashing and cookie affinity
  • Control plane APIs for backend and algorithm management
  • Design for high availability and zero downtime deployments
Good to Have - 30%
  • Automated certificate management for TLS
  • Prometheus-compatible metrics and alerting integration
Bonus - 10%
  • Hardware acceleration for TLS termination
  • Use of Maglev consistent hashing for minimal disruption on backend changes
Anti-patterns to Avoid
✅
Model Answer
▼ Reveal

The load balancing system uses Envoy Proxy as the data plane with xDS API for dynamic configuration, supporting Round Robin, Least Connections, IP Hash (via Maglev consistent hashing), and Weighted algorithms. TLS termination is handled at Envoy with automated certificate management. Sticky sessions are supported via consistent hashing and HTTP cookie affinity. The control plane exposes RESTful APIs for backend pool management, algorithm configuration, and Prometheus-compatible metrics. The system is deployed in active-active clusters with health checks and rolling upgrades to ensure zero downtime and high availability.

Key Decisions
  • Use Envoy Proxy for L4/L7 load balancing with built-in algorithms and dynamic config via xDS
  • Implement IP Hash using Maglev consistent hashing for session affinity
  • Support sticky sessions with HTTP cookie-based affinity configurable per service
  • Terminate TLS at Envoy with automated certificate issuance and rotation
  • Expose RESTful APIs for backend management, algorithm switching, and metrics retrieval
  • Deploy Envoy in active-active clusters behind DNS/anycast with health checks and rolling upgrades
  • Use Prometheus metrics endpoints for real-time monitoring and alerting
  • Enable dynamic weight updates via xDS without requiring load balancer restarts
  • Envoy Proxy as data plane for high-performance L4/L7 load balancing with xDS dynamic config
  • Envoy’s built-in algorithms: Round Robin, Least Request, Maglev consistent hashing, Weighted Round Robin
  • Consistent hashing (Maglev) for IP Hash affinity and HTTP cookie-based session affinity for sticky sessions
  • TLS termination at Envoy with automated certificate management (cert-manager + Let's Encrypt)
  • RESTful control plane APIs for backend pool management, algorithm config, and Prometheus metrics endpoints
  • Active-active Envoy clusters with health checks and rolling upgrades for zero downtime
  • Use of Prometheus and Grafana for real-time monitoring and alerting on load balancer health and performance
  • Dynamic weight updates via xDS API without load balancer restarts

Practice

(1/5)
1. If the API gateway loses the logic to parse the X-API-Version header, what is the exact failure observed by clients using header-based versioning?
medium
A. Clients' requests are routed to the latest version regardless of header, breaking older clients
B. Clients get 404 Not Found errors because the gateway cannot route requests
C. Clients receive responses from the default version, causing silent compatibility issues
D. Clients experience increased latency as the gateway retries version parsing repeatedly

Solution

  1. Step 1: Understand gateway header parsing role

    The gateway uses X-API-Version header to route requests; losing parsing logic means it cannot determine version.

  2. Step 2: Trace failure consequences

    Without parsing, the gateway may retry or fallback inefficiently, causing increased latency rather than immediate errors or silent fallback.

  3. Final Answer:

    Option D -> Option D

  4. Quick Check:

    Missing header parsing causes retries and latency spikes [check]
Hint: Lost header parsing -> gateway retries, causing latency [check]
Common Mistakes:
2. For a CDN serving frequently updated dynamic content, which origin content delivery strategy is best to ensure freshness without overloading the origin server?
medium
A. Pull model where edge nodes fetch content from origin on cache miss with TTL-based caching
B. Push model where origin proactively sends all content updates to edge nodes
C. Push model with edge nodes polling origin periodically for updates
D. Pull model with edge nodes never caching content and always fetching from origin

Solution

  1. Step 1: Analyze push vs pull for dynamic content freshness

    Push model can overload origin with frequent updates; polling adds unnecessary load.

  2. Step 2: Evaluate pull with TTL caching

    Pull with TTL balances freshness and origin load by caching briefly and refreshing on expiry.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Pull with TTL caching balances freshness and origin load [check]
Hint: Pull with TTL balances freshness and load [check]
Common Mistakes:
3. If the commit metadata database becomes unavailable, what is the immediate failure observed when a user tries to create a new branch?
medium
A. The system creates the branch but marks it as unverified until metadata is restored
B. The branch creation request fails immediately with an error indicating metadata service unavailability
C. The system queues the branch creation request and retries later without user notification
D. The branch is created locally on the client but not persisted to the server

Solution

  1. Step 1: Understand role of commit metadata DB

    Commit metadata DB stores branch pointers; without it, server cannot persist new branches.

  2. Step 2: Trace branch creation failure

    Client may create branch locally, but server cannot persist it, so branch is effectively lost or inconsistent.

  3. Step 3: Eliminate other options

    Queuing without notification hides failure; immediate error is possible but not typical if client allows local ops; marking unverified branches requires metadata DB access.

  4. Final Answer:

    Option D -> Option D

  5. Quick Check:

    Metadata DB down -> branch created locally but not persisted [check]
Hint: Metadata DB down -> local branch only, no server persistence [check]
Common Mistakes:
4. At 10x scale, which component is most likely to fail first and why?
hard
A. The delivery workers, due to Kafka consumer lag and processing bottlenecks
B. The chat servers maintaining WebSocket connections, due to stateful connection limits
C. The relational database storing tweets, due to write amplification from fan-out
D. The timeline cache servers, due to memory pressure from large follower counts

Solution

  1. Step 1: Analyze timeline cache scaling challenges

    Timeline caches hold per-user timelines; large follower counts cause exponential memory growth.

  2. Step 2: Compare other components

    Relational DB (A) is mostly append-only and sharded; chat servers (B) irrelevant here; delivery workers (D) scale with Kafka partitions.

  3. Step 3: Identify first failure point

    Memory pressure on timeline caches causes eviction and stale timelines first at 10x scale.

  4. Final Answer:

    Option D -> Option D

  5. Quick Check:

    Timeline caches hit memory limits first due to follower fan-out [check]
Hint: Timeline caches hit memory bottleneck first at scale [check]
Common Mistakes:
5. At 10x scale, which component in WhatsApp's architecture is most likely to fail first and why?
hard
A. Chat Servers because WebSocket connections are stateful and cannot be resharded easily
B. Kafka brokers because partition count cannot be increased dynamically
C. The database because it cannot handle 10x more writes without sharding
D. Presence service because TTL cache invalidations become too frequent

Solution

  1. Step 1: Analyze stateful WebSocket connections on Chat Servers

    Chat Servers maintain in-memory state per WebSocket; scaling requires connection migration which is complex.

  2. Step 2: Compare other components

    Databases can shard; Kafka partitions can be increased with rebalancing; presence TTL cache scales horizontally.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Stateful WebSocket connections -> Chat Servers bottleneck at scale [check]
Hint: Stateful WebSocket -> Chat Servers bottleneck at 10x scale [check]
Common Mistakes:
Recommended

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