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Match alphanumeric patterns in a stream

Last updated: Mar 29, 2026

Quick Overview

This question evaluates skills in exact and approximate alphanumeric string matching, streaming pattern lookup, noise-tolerant comparison, and performance-oriented data structures and encodings.

  • Medium
  • Optiver
  • Coding & Algorithms
  • Data Scientist

Match alphanumeric patterns in a stream

Company: Optiver

Role: Data Scientist

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Take-home Project

Given a reference 7–8 character alphanumeric code and four candidate codes, select the exact match as quickly as possible. Design a system that: - Efficiently determines whether any candidate exactly matches the reference (consider case sensitivity and lookalike characters such as O/0 and I/ 1). - Supports a streaming setting where batches arrive at increasing rates and latency must remain low. - Handles input noise (e.g., OCR errors) by optionally allowing a configurable Hamming distance threshold without compromising throughput. Discuss your data structures (e.g., hashing, tries, SIMD-friendly encodings), algorithmic choices, complexity, and how you would benchmark and scale this for high QPS.

Quick Answer: This question evaluates skills in exact and approximate alphanumeric string matching, streaming pattern lookup, noise-tolerant comparison, and performance-oriented data structures and encodings.

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Optiver logo
Optiver
Sep 6, 2025, 12:00 AM
Data Scientist
Take-home Project
Coding & Algorithms
3
0

Given a reference 7–8 character alphanumeric code and four candidate codes, select the exact match as quickly as possible. Design a system that:

  • Efficiently determines whether any candidate exactly matches the reference (consider case sensitivity and lookalike characters such as O/0 and I/ 1).
  • Supports a streaming setting where batches arrive at increasing rates and latency must remain low.
  • Handles input noise (e.g., OCR errors) by optionally allowing a configurable Hamming distance threshold without compromising throughput. Discuss your data structures (e.g., hashing, tries, SIMD-friendly encodings), algorithmic choices, complexity, and how you would benchmark and scale this for high QPS.

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