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Infrastructure and Database Systems Questions

Fundamental infrastructure and database engineering concepts relevant to analytics platforms and general backend systems. Topics include relational and non relational database architecture indexing strategies query optimization replication and consistency trade offs sharding and partitioning approaches caching systems design message queues and event streaming systems and how these components integrate to meet performance reliability and cost objectives. Candidates should be able to reason about capacity planning high availability disaster recovery backup strategies and operational concerns such as monitoring alerting and graceful degradation under load.

HardTechnical
24 practiced
Provide PostgreSQL PL/pgSQL pseudo-code or function that performs an online backfill to populate a new computed column without locking the entire table. The approach should update rows in batches, persist progress to a migration control table, support resume after failures, and minimize read/write contention.
HardTechnical
29 practiced
Implement a simple Python coordinator (pseudocode is fine) that moves a contiguous key range from source shard A to target shard B in a distributed key-value store. Focus on steps ensuring atomic handoff: lock range, copy data in chunks with checksums, switch routing atomically, drain in-flight writes, and provide idempotent retry semantics.
HardSystem Design
49 practiced
Design a monitoring, alerting, and runbook system for a fleet of thousands of database instances to proactively detect performance regressions. Include anomaly detection approaches (statistical baselines vs ML), alert deduplication and prioritization, automated remediation playbooks, integration with runbooks, and human escalation flow for oncall teams.
MediumSystem Design
31 practiced
Design a graceful degradation strategy for a web service that depends on several backend databases and caches when one or more downstream systems become slow or unavailable. Prioritize user-facing functionality, detail circuit-breaker behavior, fallback caches, degraded UX, and how to communicate status to users and internal stakeholders.
HardTechnical
24 practiced
Heavy ad-hoc analytics queries from data teams cause cascade failures in production by saturating the primary OLTP database. Design isolation mechanisms to prevent this class of failure: options include separate analytic cluster with CDC, resource groups/QoS, query governors, admission control, and incentives/policy. Choose an approach and detail rollout steps.

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