A new Capgemini customer-facing application needs to be highly available and fault-tolerant. As an SRE, how would you design its system architecture to ensure these properties from the ground up?

Designing for a highly available and fault-tolerant system as an SRE at Capgemini begins with a holistic approach, prioritizing redundancy, fault isolation, and rapid recovery. We would start by distributing components across multiple availability zones or regions to eliminate single points of failure. Implementing stateless services wherever possible facilitates horizontal scaling and quick recovery from instance failures. Critical components like databases should utilize replication and failover mechanisms to ensure data durability and continuous access.

Reliability principles in practice

Proactive system design includes implementing robust load balancing, auto-scaling groups, and circuit breakers or bulkheads to prevent cascading failures between services. Graceful degradation mechanisms ensure core functionality remains available even under extreme load or partial system outages. We would also incorporate robust monitoring and alerting from the outset, focusing on golden signals (latency, traffic, errors, saturation) to quickly detect and diagnose issues before they impact users.

Best practice

An active-active architecture across multiple availability zones is paramount for continuous availability. This involves distributing incoming traffic simultaneously to all zones, ensuring immediate failover capabilities without manual intervention. Furthermore, adopting immutable infrastructure principles, where new instances are deployed rather than updating existing ones, reduces configuration drift and ensures predictable, repeatable deployments.

Edge case interviewers probe for

Interviewers often probe on challenges related to data consistency in a multi-region active-active setup, especially for write-heavy workloads, or how to manage complex stateful services across failure domains. They also look for an understanding of potential cascading failures that can arise even within distributed systems if dependencies are not properly isolated and throttled.

Common mistake

A frequent error is over-relying on a single cloud provider’s region or availability zone, creating a significant single point of failure. Another common mistake is neglecting thorough chaos engineering or disaster recovery drills, leading to untested recovery procedures that fail in real incidents. Underestimating the complexity of distributed data consistency and eventual consistency models is also a significant pitfall.

What the interviewer is checking

The interviewer is assessing your ability to think at a system level, apply core SRE principles to architectural choices, understand trade-offs between availability, consistency, and partition tolerance (CAP theorem), and demonstrate practical knowledge of designing resilient, observable, and maintainable systems under various failure scenarios.