Start by explaining the fundamental concurrency control mechanisms (locking vs. MVCC) and then map each isolation level to the anomalies it prevents. Use concrete examples to illustrate dirty reads, non-repeatable reads, and phantom reads, and discuss the trade-offs between consistency and performance.
Pro tip: Mention that most databases default to Read Committed (e.g., PostgreSQL, Oracle) or Repeatable Read (MySQL InnoDB), and that Snapshot Isolation prevents phantoms in practice but is not serializable. This shows awareness of real-world implementations beyond theory.
Clearly define dirty read, non-repeatable read, and phantom read with simple examples (e.g., reading uncommitted data, re-reading a row that changed, re-running a query that returns new rows).
Describe how databases prevent anomalies using locking (shared/exclusive locks, two-phase locking) and multiversion concurrency control (MVCC) with snapshots.
List the SQL standard isolation levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable) and state which anomalies each prevents (e.g., Read Committed prevents dirty reads but allows non-repeatable reads).
Highlight that actual databases may differ (e.g., MySQL InnoDB's Repeatable Read prevents phantoms via next-key locks; PostgreSQL's Serializable uses SSI). Discuss performance vs. consistency trade-offs.
Explain how to choose an isolation level based on application needs (e.g., financial transactions may require Serializable, while analytics can tolerate Read Committed) and mention optimistic vs. pessimistic concurrency control.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Started with latency and error rate signals, mentioned checking recent deploys first because that's usually it.
Start by clarifying the scope and impact of the slowdown, then systematically narrow down the bottleneck using metrics, logs, and tracing from the user-facing layer down to the infrastructure. Emphasize a data-driven, hypothesis-testing approach while considering recent changes and dependencies.
Pro tip: Always check for recent deployments or configuration changes first—most slowdowns are caused by something that changed. Also, mention that you'd validate your hypothesis with a quick experiment or rollback before diving deep.
Ask clarifying questions to understand when the slowdown started, which endpoints or services are affected, and the impact on users. Check if it's isolated to a specific region, customer segment, or time window.
Review recent deployments, config changes, and feature flags. Look at service-level dashboards (latency, error rates, throughput) to identify patterns and correlate with the timeline.
Use distributed tracing to identify which service or component is the bottleneck. If needed, profile the suspect service to find slow functions, database queries, or external calls.
Check CPU, memory, disk I/O, and network on the suspect service and its dependencies (databases, caches, third-party APIs). Look for saturation, throttling, or connection pool exhaustion.
Form a hypothesis about the root cause and validate it with a targeted experiment (e.g., rollback, scaling, query optimization). If confirmed, apply a fix and monitor to ensure resolution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked a little on the CQRS pitfalls part.
Define DDD and CQRS clearly, then explain when each is appropriate based on complexity and read/write patterns. Emphasize that they are not silver bullets and discuss common pitfalls like over-engineering and misapplying CQRS without event sourcing.
Pro tip: At Revolut, where high scalability and clear domain boundaries are crucial, highlight that DDD helps model complex financial domains, while CQRS can optimize read/write performance—but only when justified by actual needs, not hype.
Briefly explain Domain-Driven Design (focus on ubiquitous language, bounded contexts, aggregates) and Command Query Responsibility Segregation (separate read/write models).
Discuss scenarios like complex business domains with evolving rules, collaboration with domain experts, and when a shared language is critical. Avoid for simple CRUD apps.
Explain use cases: high read/write disparity, complex queries, scalability needs, and when eventual consistency is acceptable. Often paired with event sourcing.
Highlight over-engineering, applying DDD/CQRS everywhere, ignoring eventual consistency challenges, and not having the right team expertise.
Connect to Revolut's domain: e.g., DDD for modeling payments, CQRS for separating transaction writes from reporting reads, ensuring scalability and auditability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.