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Implement XML tokenizer and parser with operations

Last updated: Mar 29, 2026

Quick Overview

This question evaluates XML tokenization, hierarchical tree construction and validation, stack-based parsing, iterative DFS traversal, and API-level manipulation of tree nodes, assessing data-structure design, parsing correctness, and algorithmic complexity reasoning.

  • Medium
  • Snapchat
  • Coding & Algorithms
  • Software Engineer

Implement XML tokenizer and parser with operations

Company: Snapchat

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: Medium

Interview Round: Technical Screen

You are given either (a) a raw XML-like string such as <catalog><book><author>Gambardella, Matthew</author></book></catalog> or (b) its tokenized form as a list of dictionaries like [{'text': 'catalog', 'token_type': 'open_tag'}, {'text': 'book', 'token_type': 'open_tag'}, {'text': 'author', 'token_type': 'open_tag'}, {'text': 'Gambardella, Matthew', 'token_type': 'raw_text'}, {'text': 'author', 'token_type': 'close_tag'}, {'text': 'book', 'token_type': 'close_tag'}, {'text': 'catalog', 'token_type': 'close_tag'}]. Implement: 1) tokenize(xml_str) -> list[dict] that emits tokens where token_type ∈ {open_tag, close_tag, raw_text}; 2) class XMLParser with __init__(tokens: list[dict]) that validates the structure and raises an exception for malformed input; 3) to_string()/__str__() that reconstructs the original XML; 4) add_element(path: list[str], tag: str, text: str|null, index: int|null) to insert a new element under the node identified by path; 5) remove_element(path: list[str]) to delete a node; 6) traverse_iterative() that performs an iterative DFS (no recursion) and yields nodes in preorder. Constraints and requirements: use a linear scan with a stack for validation; overall validation should be O (n) time and O (h) space where h is tree height; tags have no attributes and text may contain any characters except '<' and '>'; handle edge cases such as mismatched or out-of-order closing tags, unclosed tags at EOF, empty token lists, and extraneous raw_text between sibling tags. Describe your data structures, algorithms, and time/space complexity for each method.

Quick Answer: This question evaluates XML tokenization, hierarchical tree construction and validation, stack-based parsing, iterative DFS traversal, and API-level manipulation of tree nodes, assessing data-structure design, parsing correctness, and algorithmic complexity reasoning.

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Snapchat
Aug 10, 2025, 12:00 AM
Software Engineer
Technical Screen
Coding & Algorithms
2
0

You are given either (a) a raw XML-like string such as <catalog><book><author>Gambardella, Matthew</author></book></catalog> or (b) its tokenized form as a list of dictionaries like [{'text': 'catalog', 'token_type': 'open_tag'}, {'text': 'book', 'token_type': 'open_tag'}, {'text': 'author', 'token_type': 'open_tag'}, {'text': 'Gambardella, Matthew', 'token_type': 'raw_text'}, {'text': 'author', 'token_type': 'close_tag'}, {'text': 'book', 'token_type': 'close_tag'}, {'text': 'catalog', 'token_type': 'close_tag'}]. Implement:

  1. tokenize(xml_str) -> list[dict] that emits tokens where token_type ∈ {open_tag, close_tag, raw_text};
  2. class XMLParser with init (tokens: list[dict]) that validates the structure and raises an exception for malformed input;
  3. to_string()/ str () that reconstructs the original XML;
  4. add_element(path: list[str], tag: str, text: str|null, index: int|null) to insert a new element under the node identified by path;
  5. remove_element(path: list[str]) to delete a node;
  6. traverse_iterative() that performs an iterative DFS (no recursion) and yields nodes in preorder. Constraints and requirements: use a linear scan with a stack for validation; overall validation should be O (n) time and O (h) space where h is tree height; tags have no attributes and text may contain any characters except '<' and '>'; handle edge cases such as mismatched or out-of-order closing tags, unclosed tags at EOF, empty token lists, and extraneous raw_text between sibling tags. Describe your data structures, algorithms, and time/space complexity for each method.

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