Algorithm Data Structure Selection

algorithm-data-structure-selection

Registry Trigger

Use when

• algorithm data structure Big O input size worst case memory budget map set list heap queue deque tree trie graph bloom filter LRU LFU
• interval tree segment tree top K dedupe grouping sorting streaming chunking pagination nested scan load all hot key skew

Do not use when

• no task-local algorithm data structure selection decision is required

Skill Role

Choose algorithms and data structures from problem shape, scale distribution, memory budget, identity and ordering semantics, access pattern, and operational constraints.

High-Value Rules

• Select algorithms from current input scale, distribution, access pattern, worst-case behavior, and failure consequence.
• Set an explicit memory bound for unbounded or oversized input.
• Use streaming, chunking, spill, or pagination when load-all processing exceeds that bound.
• Reject nested scans without a bounded cardinality or explicit complexity acceptance from the affected owner.
• Define memory and identity bounds for sorting, grouping, deduplication, top-K, graph, or probabilistic structures.
• Validate performance-sensitive choices with a representative test or benchmark and state where production scale remains unverified.
• Require a rejected simpler alternative and validation plan before adding indexes, caches, concurrency, dependencies, or probabilistic structures as a performance fix.

Anti-Patterns

• Choosing a familiar structure without naming the optimized access pattern substitutes habit for evidence.
• Fully sorting Top-K when K is much smaller than N wastes avoidable work.
• Hash deduplication without identity and collision semantics can corrupt results.
• Grouping unbounded keys without spill or chunking makes memory follow uncontrolled cardinality.
• Pagination without stable ordering and cursor semantics loses or duplicates work.
• Average-case selection that ignores skew or adversarial input hides the dominating case.
• Graph traversal without cycle handling and a frontier bound can loop or exhaust memory.

Stop Conditions

Escalate SLO, batch-window, queue-lag, memory, or cost impact to reliability-observability-gate. Route distributed processing to bigdata-product-extension and adversarial complexity to security-privacy-gate.

Output Contract

• Algorithm Decision: problem shape, input scale, selected structure, time/space bounds, memory budget, streaming/chunking choice, alternatives, benchmarks, and scale risk

Targeted References