String Scanning, Parsing, And Formatting

What's being tested

These exercises test deterministic string processing: scanning characters or tokens once, maintaining compact state, and enforcing boundary conditions precisely. They probe whether you can translate ambiguous formatting rules into explicit invariants, choose between direct scanning, sorting, and prefix data structures, and state time and space complexity. Hudson River Trading cares because production code often sits on hot paths where predictable O(n) behavior, low allocation, and correct handling of malformed or boundary inputs matter more than clever abstractions.

Core knowledge

Linear scanning processes each character or token once with a small state machine, giving O(n) time and usually O(1) auxiliary space. Track the current index, accumulated result, and only the state needed for future decisions.
For fixed-length substrings, a window beginning at index i is valid when i + k <= n; iterate i through 0..n-k. Avoid constructing every substring when a predicate can be checked directly, reducing allocations from O(nk) toward O(n).
A character predicate should be explicit and centralized: for example, c in "aeiou" or a boolean lookup table. Decide whether uppercase letters, accented characters, digits, and non-ASCII Unicode vowels are included before coding.
Prefix matching between decimal strings compares characters from index zero until the first mismatch. Across arrays, a direct all-pairs approach costs O(abL), where a and b are array sizes and L is maximum digit length; a trie can reduce repeated-prefix work.
A trie stores one edge per digit, giving insertion and lookup proportional to digit length, O(L). Building a trie for N values costs O(total_digits) time and space, but pointer-heavy nodes may consume substantially more memory than sorting.
Sorting strings lexicographically can expose adjacent-prefix structure: after sorting, the maximum common prefix among cross-array candidates can often be found through carefully chosen neighbors. Be precise about numeric versus lexicographic ordering and leading zeros.
Parsing should separate lexical recognition from semantic validation. First identify digits, separators, and suffixes; then validate ranges, empty fields, overflow, repeated delimiters, and whether signs or whitespace are legal.
For length-limited formatting, model each part as payload + suffix. If the suffix is "<part>/<total>", its overhead is len(str(part)) + len(str(total)) + 2; feasibility depends on payload capacity remaining after that overhead.
Formatting has a circular dependency when the total number of parts affects suffix length, while suffix length affects the number of parts. Try candidate totals or grow the numbering regime incrementally; never assume a fixed suffix width without proving it.
Invariants make implementation and review easier: every emitted part must satisfy len(part) <= limit, concatenating payloads must recover the original message in order, and numbering must be contiguous and complete.
In Wordle-style logic, represent repeated-letter constraints with frequency counts, not only sets. Process exact-position matches before misplaced matches so one target occurrence cannot satisfy multiple guess positions.
State complexity precisely: input size may be N characters, M words, or D total digits; output storage is often unavoidable and should be distinguished from auxiliary memory. Mention whether conversion to strings creates O(D) copies.

Worked example: Split a Message into Length-Limited Parts with Numbered Suffixes

A strong candidate first asks whether splitting may occur only at arbitrary character boundaries, whether spaces must be preserved, what happens when the limit cannot fit even one payload character plus suffix, and whether empty input is valid. They then declare assumptions, such as counting characters rather than bytes and preserving the message exactly after removing suffixes. The answer can be organized around four pillars: calculate suffix overhead, determine a feasible total-part count, allocate each part’s payload capacity, and validate numbering plus reconstruction. The key design decision is whether to find the total iteratively or test candidate totals, because the total changes the number of digits in every suffix. A simple implementation can repeatedly estimate the required number of parts and restart when the suffix width changes; a more formal approach computes feasibility for each candidate total and selects the smallest feasible one. The candidate should explicitly guard against a limit smaller than the shortest possible suffix and avoid silently dropping characters. They should also discuss whether Unicode “character length” means code points or user-visible grapheme clusters, since byte-length APIs can violate the stated limit. Testing should include one part, an exact boundary, a suffix digit transition such as 9 to 10, and an impossible limit. If I had more time, I’d add property-based tests asserting length bounds, ordered reconstruction, and complete numbering across randomly generated messages.

A second angle

Find the Longest Common Digit Prefix Across Two Arrays applies the same scanning discipline but changes the optimization question from formatting feasibility to repeated-prefix reuse. A direct pairwise scan is easiest and may be appropriate when both arrays are small, while a digit trie avoids rescanning common prefixes when the arrays contain many long values. The candidate should clarify whether inputs are integers or strings, because leading zeros disappear during integer conversion but remain meaningful in textual identifiers. They should compare O(abL) pairwise work with O(D) trie construction and explain the memory tradeoff before choosing. A useful correctness invariant is that every reported prefix is a prefix of at least one value from each array.

Common pitfalls

Pitfall: Treating a set of letters as sufficient for Wordle-style matching gives wrong results for repeated characters; use target frequency counts and consume matches in the correct order.

A tempting analytical mistake is claiming that converting every number to a string automatically makes the prefix problem O(n). Conversion itself costs the total number of digits, and comparing every cross-array pair can still be quadratic in array count; state the actual variables and cost.

A communication mistake is jumping directly into code without clarifying limits, indexing, leading zeros, or Unicode semantics. A stronger answer states assumptions first, then names the invariant that will make those assumptions visible in the implementation.

A depth mistake is ignoring impossible formatting cases or suffix-width transitions because the happy path works. Explicitly discuss minimum feasible limits, empty input, exact boundaries, and the point where part numbers gain another digit.

Connections

An interviewer may pivot to finite-state machines, tries, rolling hashes, or property-based testing. They may also ask about byte-oriented parsing, Unicode code points versus grapheme clusters, allocation behavior, or how to prove a single-pass algorithm’s invariant.