Data Structures
Overview
Use this skill to choose data structures with explicit tradeoffs, then justify the choice with workload assumptions and failure modes.
Scope Boundaries
- Use this skill when the task matches the trigger condition described in
description. - Do not use this skill when the primary task falls outside this skill's domain.
Inputs To Gather
- Required operations (
lookup,insert,delete,scan,range,top-k). - Read/write ratio and mutation frequency.
- Data volume now and forecast at peak.
- Latency/memory constraints and concurrency model.
Deliverables
- Candidate structure comparison with tradeoffs.
- Selected structure and rationale tied to workload.
- Risk list (memory blowup, contention, degeneration cases).
- Verification plan (microbenchmarks and edge-case tests).
Quick Decision Examples
- Frequent key lookup + updates:
hash map(+ collision strategy). - Sorted range queries:
balanced treeor ordered index. - Top-N / priority scheduling:
heap. - FIFO work pipeline:
queue(bounded if backpressure is needed). - Membership checks with low memory:
bitset/Bloom filter (with false-positive caveat).
Quality Standard
- Choice is tied to actual operation mix, not preference.
- Complexity claims include worst/average behavior assumptions.
- Memory growth and peak usage are estimated.
- Concurrency implications are explicit (lock scope, contention hotspots).
Workflow
- Define workload model and required guarantees.
- Enumerate feasible structures at the same abstraction level.
- Compare complexity, memory, and concurrency behavior.
- Select one and document rejection reasons for others.
- Define benchmark and edge-case validation plan.
Failure Conditions
- Stop when workload assumptions are missing or contradictory.
- Stop when chosen structure cannot satisfy mandatory operations.
- Escalate when memory or contention risk is unbounded at target scale.