Redundancy and fault tolerance in HLD - Scalability & System Analysis

Scalability Analysis - Redundancy and fault tolerance

Growth Table: Redundancy and Fault Tolerance at Different Scales

Scale	System Setup	Redundancy Level	Fault Tolerance Features	Monitoring & Recovery
100 users	Single server with backup	Basic: one backup server or snapshot	Simple failover, manual recovery	Basic alerts, manual checks
10,000 users	Multiple servers with load balancer	Active-passive redundancy	Automatic failover, health checks	Automated monitoring, alerting
1,000,000 users	Distributed servers across regions	Active-active redundancy, data replication	Automatic failover, self-healing, data consistency checks	Advanced monitoring, auto-scaling, incident response
100,000,000 users	Global multi-region clusters with microservices	Multi-level redundancy (network, compute, storage)	Geo-redundancy, disaster recovery, chaos engineering	AI-driven monitoring, predictive failure detection

First Bottleneck

At small scale, the first bottleneck is often the single point of failure in the server or storage. Without redundancy, any hardware or software failure causes downtime.

As users grow, network and data replication delays become bottlenecks. Ensuring data consistency across redundant systems can slow down operations.

At very large scale, complexity in managing failover and recovery across regions can cause delays and partial outages.

Scaling Solutions

• Horizontal scaling: Add more servers to distribute load and provide redundancy.
• Active-active redundancy: Run multiple instances simultaneously to avoid downtime.
• Data replication: Use synchronous or asynchronous replication to keep copies of data.
• Load balancers: Detect failures and route traffic to healthy servers.
• Health checks and monitoring: Automatically detect failures and trigger failover.
• Disaster recovery plans: Backup data and systems in different geographic locations.
• Chaos engineering: Test system resilience by simulating failures.

Back-of-Envelope Cost Analysis

Requests per second (RPS):

• 100 users: ~10-50 RPS
• 10,000 users: ~1,000-5,000 RPS
• 1,000,000 users: ~100,000-500,000 RPS
• 100,000,000 users: ~10,000,000+ RPS

Storage needs increase with data replication and backups. For example, 1 TB primary data may require 2-3 TB total with redundancy.

Network bandwidth must support replication traffic and failover synchronization, which grows with scale.

Interview Tip

When discussing redundancy and fault tolerance, start by identifying single points of failure. Then explain how to add redundancy at each layer: compute, storage, network.

Describe trade-offs between synchronous and asynchronous replication, and how failover is detected and handled.

Use real-world analogies like backup generators or multiple cashiers to explain concepts simply.

Self Check Question

Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Add read replicas and implement caching to reduce load on the primary database before scaling vertically or sharding.

Key Result

Redundancy and fault tolerance start by removing single points of failure and adding backups. As scale grows, data replication, automatic failover, and multi-region setups become critical to maintain availability and performance.