Overview
Tokenization scans characters and emits units such as identifiers, numbers, operators, punctuation, or words.
FoundationSource checked
Tokenization
Breaks raw text into meaningful tokens before parsing or analysis.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Parsing
- Difficulty
- Beginner
- Family
- Lexical Processing
- Tier
- Foundation
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 0
- Relationships
- 6
- Code example
- Ready
- Visualization
- Ready
- Aliases
- Lexing, Lexical Analysis
Core Concepts
character scantoken rulelongest matchsource position
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(n)
Average
O(n)
Worst
O(n)
Space
O(tokens)
When to Use
- Use it when text must be split into meaningful units before parsing or analysis.
- Use it for programming languages, search indexing, natural-language preprocessing, or data import.
- Use it when downstream rules depend on token type and position.
When Not to Use
- Avoid ad hoc tokenization when quoting, escaping, or Unicode rules matter.
- Avoid losing position information if diagnostics or highlighting need it.
- Avoid assuming whitespace splitting is enough for structured languages.
What It Does
- Scans input characters
- Groups characters into tokens
- Tracks token type and position
Use Cases
- Programming language parsing
- Search indexing
- Text preprocessing
Inputs
- Raw text
- Token rules
Outputs
- Token stream
Pseudocode
- 1.Start at the first character
- 2.Skip or emit whitespace according to rules
- 3.Match the longest valid token pattern
- 4.Emit token type, text, and position
- 5.Repeat until input ends
Canonical Example
- Input
- text = "x = 42"
- Output
- [identifier(x), equals, number(42)]
- Explanation
- The tokenizer groups characters into typed units for later parsing.
Implementation Notes
- Define token classes before scanning.
- Emit token type, lexeme, and source position when diagnostics matter.
- Keep whitespace and comment handling explicit.
Common Pitfalls
- Dropping source positions needed for error messages.
- Handling Unicode, escaping, and comments inconsistently.
- Letting tokenizer and parser disagree about token boundaries.
Interactive Mechanism Walkthrough
Sequence Window & Automaton AlignmentShow a source string split into colored token spans.
Input:ABABABCABAB
Window:ABAB
Step 1 of 5: Start at the first character
parse-flowNotes
Tokenization quality strongly affects parser errors and user-facing diagnostics.
Sources (1)
What each reference supports for this entry.
Compilers: Principles, Techniques, and Tools
BookCompiler textbook support for Tokenization's grammar analysis, parser construction, and implementation constraints.
Explore Next
Good next jumps from the relationship graph, ranked for useful discovery.
UsesByte Pair Encoding
Compression · Core
Modern tokenizers often use byte-pair encoding to learn merge-based subword vocabularies.
Compare with thisOften paired withLexical Tie-In Parsing
Parsing · Specialized
Lexical tie-ins coordinate parser state with tokenizer behavior.
Compare with thisPrerequisite ofRecursive Descent Parsing
Parsing · Foundation
Recursive descent parsing typically operates on tokens rather than raw characters.
Compare with thisRelationships
View full relationship pagePrerequisite ofLR Parsing
LR parsers consume token streams from a lexer.
Prerequisite ofRecursive Descent Parsing
Recursive descent parsing typically operates on tokens rather than raw characters.
Related Algorithms
Tags
ParsingTokenizationStrings