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Implements invoice total calculation with quantity, unit price, percentage discount, and rounding.
Chooses a sequential collection from size, mutation, access pattern, memory locality, and the behavior callers actually need.
Avoids hidden quadratic work by indexing repeated matches while preserving duplicate, missing, ordering, and memory semantics.
Separates ordinal identity comparison from culture-aware display ordering and keeps hash-based collection semantics consistent.
Implements chunking for batch processing without dropping final partial batches.
Uses complexity as a first filter while accounting for constants, locality, allocations, data size, and the complete operation sequence.
Implements comparison for simple semantic version strings with missing patch parts treated as zero.
Implements a word-frequency counter with normalization, punctuation handling, and stable ordering.
Implements duplicate suppression while preserving the first event for each idempotency key.
Explains how hash-based collections use hash codes and equality, then chooses Dictionary or HashSet from the value the program needs to store.
Implements a stable first-unique-character search using counts and original order.
Flattens a tree into display paths while preserving order and preventing simple cycles.
Builds customer summaries from order rows using grouping, aggregation, and deterministic ordering.
Implements a small LRU cache using a dictionary plus linked list for O(1) get and put.
Balances declarative transformations with explicit control flow when allocation, early exit, mutation, diagnostics, or complex state matters.
Chooses when to keep a sequence lazy and when to create a stable collection with ToList, ToArray, or another deliberate materialization.
Implements interval merging for scheduling-style time ranges with sorted output.
Implements top-K frequency selection with deterministic tie-breaking.
Explains the output shape of core LINQ transformations and uses them without hiding empty groups, duplicate keys, or excessive buffering.
Distinguishes operators that can yield incrementally from operators that must consume substantial input before producing results.
Uses async streams for incremental asynchronous production while preserving cancellation, disposal, backpressure expectations, and API boundaries.