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Data Structure Selection and Trade Offs Questions

Skill in selecting appropriate data structures and algorithmic approaches for practical problems and performance constraints. Candidates should demonstrate how to choose between arrays lists maps sets trees heaps and specialized structures based on access patterns memory and CPU requirements and concurrency considerations. Coverage includes case based selection for domain specific systems such as games inventory or spatial indexing where structures like quadtrees or spatial hashing are appropriate, and language specific considerations such as value versus reference types or object pooling. Emphasis is on explaining rationale trade offs and expected performance implications in concrete scenarios.

HardSystem Design
0 practiced
Design a compact, versioned metadata store for deployments that supports quick rollbacks and lookups by key and version. Compare using immutable persistent data structures (e.g., COW trees) versus a mutable index with append-only log and periodic snapshots. Include memory and lookup time trade-offs for SRE rollback operations.
EasyTechnical
0 practiced
Describe an LRU cache and why it's popular for SRE caching use-cases. Explain how to implement LRU with O(1) get/put, memory overhead, and where LRU may fail (e.g., bursty workloads) requiring alternative policies like LFU, TTL, or windowed-LRU.
HardTechnical
0 practiced
Service dependency graphs are used for impact analysis. For a large microservice mesh with millions of edges, compare adjacency list, adjacency matrix, and edge list representations. For common SRE operations (BFS/DFS for blast-radius, topological sort for deploy order), which representation scales best and why?
MediumTechnical
0 practiced
Compare Java's ConcurrentHashMap, a manually sharded map, and a lock-free hash map for a high-concurrency metric ingestion service. Discuss thread contention, resizing strategies, iterator consistency, and memory overhead from concurrency control structures.
MediumTechnical
0 practiced
Design a memory-efficient representation for sparse time-series across millions of hosts where most time-series are empty most of the time. Compare sparse vectors, inverted-index style storage, chunking with compression, and delta-encoding. Discuss query latency vs compression trade-offs for SRE dashboards.

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