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ANTLR
ANTLR
ANTLR
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ANTLR
Original authorsTerence Parr and others
Initial releaseApril 10, 1992; 33 years ago (1992-04-10)
Stable release
4.13.2 / 3 August 2024; 14 months ago (2024-08-03)
Repository
Written inJava
PlatformCross-platform
LicenseBSD License
Websitewww.antlr.org

In computer-based language recognition, ANTLR (pronounced antler), or ANother Tool for Language Recognition, is a parser generator that uses a LL(*) algorithm for parsing. ANTLR is the successor to the Purdue Compiler Construction Tool Set (PCCTS), first developed in 1989, and is under active development. Its maintainer is Professor Terence Parr of the University of San Francisco.[citation needed]

PCCTS 1.00 was announced April 10, 1992.[1][2]

Usage

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ANTLR takes as input a grammar that specifies a language and generates as output source code for a recognizer of that language. While Version 3 supported generating code in the programming languages Ada95, ActionScript, C, C#, Java, JavaScript, Objective-C, Perl, Python, Ruby, and Standard ML,[3] Version 4 at present targets C#, C++, Dart,[4][5] Java, JavaScript, Go, PHP, Python (2 and 3), and Swift.

A language is specified using a context-free grammar expressed using Extended Backus–Naur Form (EBNF).[citation needed][6] ANTLR can generate lexers, parsers, tree parsers, and combined lexer-parsers. Parsers can automatically generate parse trees or abstract syntax trees, which can be further processed with tree parsers. ANTLR provides a single consistent notation for specifying lexers, parsers, and tree parsers.

By default, ANTLR reads a grammar and generates a recognizer for the language defined by the grammar (i.e., a program that reads an input stream and generates an error if the input stream does not conform to the syntax specified by the grammar). If there are no syntax errors, the default action is to simply exit without printing any message. In order to do something useful with the language, actions can be attached to grammar elements in the grammar. These actions are written in the programming language in which the recognizer is being generated. When the recognizer is being generated, the actions are embedded in the source code of the recognizer at the appropriate points. Actions can be used to build and check symbol tables and to emit instructions in a target language, in the case of a compiler.[citation needed][6]

Other than lexers and parsers, ANTLR can be used to generate tree parsers. These are recognizers that process abstract syntax trees, which can be automatically generated by parsers. These tree parsers are unique to ANTLR and help processing abstract syntax trees.[citation needed][6]

Licensing

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ANTLR 3[citation needed] and ANTLR 4 are free software, published under a three-clause BSD License.[7] Prior versions were released as public domain software.[8] Documentation, derived from Parr's book The Definitive ANTLR 4 Reference, is included with the BSD-licensed ANTLR 4 source.[7][9]

Various plugins have been developed for the Eclipse development environment to support the ANTLR grammar, including ANTLR Studio, a proprietary product, as well as the "ANTLR 2"[10] and "ANTLR 3"[11] plugins for Eclipse hosted on SourceForge.[citation needed]

ANTLR 4

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ANTLR 4 deals with direct left recursion correctly, but not with left recursion in general, i.e., grammar rules x that refer to y that refer to x.[12]

Development

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As reported on the tools[13] page of the ANTLR project, plug-ins that enable features like syntax highlighting, syntax error checking and code completion are freely available for the most common IDEs (Intellij IDEA, NetBeans, Eclipse, Visual Studio[14] and Visual Studio Code).

Projects

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Software built using ANTLR includes:

Over 200 grammars implemented in ANTLR 4 are available on GitHub.[20] They range from grammars for a URL to grammars for entire languages like C, Java and Go.

Example

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In the following example, a parser in ANTLR describes the sum of expressions can be seen in the form of "1 + 2 + 3": 

// Common options, for example, the target language
options
{
  language = "CSharp";
}

// Followed by the parser 
class SumParser extends Parser;
options
{
  k = 1; // Parser Lookahead: 1 Token
}

// Definition of an expression
statement: INTEGER (PLUS^ INTEGER)*;

// Here is the Lexer
class SumLexer extends Lexer;
options
{
  k = 1; // Lexer Lookahead: 1 characters
}
PLUS: '+';
DIGIT: ('0'..'9');
INTEGER: (DIGIT)+;

The following listing demonstrates the call of the parser in a program: 

TextReader reader;
// (...) Fill TextReader with character
SumLexer lexer = new SumLexer(reader);
SumParser parser = new SumParser(lexer);

parser.statement();

See also

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References

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Bibliography

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

ANTLR

View on Grokipedia
from Grokipedia
ANTLR (ANother Tool for Language Recognition) is an open-source parser generator that enables developers to create parsers for reading, processing, executing, or translating structured text or binary files. It generates code for building and walking parse trees from grammar specifications, supporting rapid prototyping of languages, tools, and frameworks. Developed by Terence Parr starting in 1989 as part of his work on language tools at , ANTLR evolved from the earlier PCCTS project and has undergone multiple major revisions, with ANTLR 4 as the current stable version (4.13.2, released August 3, 2024). Parr, a former professor of computer science at the until 2022, now serves as a tech lead at and continues to maintain the project under a BSD license. The tool employs an LL(*) parsing algorithm, which allows for efficient, adaptive lookahead during without fixed limits, making it suitable for complex grammars. ANTLR supports code generation for over a dozen target languages, including , C#, Python, , and Go, facilitating its integration into diverse applications such as compilers, interpreters, and data processing pipelines. Notable uses include X (formerly )'s query , Apache and for SQL-like querying, and tools like and Hibernate's HQL processor. Praised by figures like Python creator for its power and ease of use, ANTLR remains a cornerstone in development and compiler engineering.

Overview

Definition and Purpose

ANTLR (ANother Tool for Language Recognition) is an open-source parser generator that produces lexers, parsers, and tree walkers from declarative grammar files written in a . It enables developers to define the structure of languages or data formats in a concise, human-readable manner, automatically generating the corresponding recognition and processing code in target programming languages such as , C#, Python, or . The primary purpose of ANTLR is to facilitate the construction of language processors, including compilers, interpreters, query engines, and data translators, by allowing users to specify syntactic and lexical rules declaratively rather than implementing them imperatively. This approach is particularly valuable for handling structured text or binary files, such as programming languages, domain-specific languages, configuration formats, or network protocols, where accurate and efficient recognition of input is essential. For instance, it powers query parsing in systems like Twitter's search engine, processing billions of queries daily, and supports data processing tools like Apache Hive and Pig. ANTLR was developed to simplify the creation of recognizers for complex structured data, addressing the challenges of manually coding parsers that are prone to errors and difficult to maintain. Originating from efforts dating back to 1989 by its creator, Terence Parr, it has evolved into a widely adopted tool in both academia and industry. A key benefit lies in its use of LL(*) parsing, an adaptive predictive parsing strategy that achieves efficiency by lookahead without requiring in the majority of cases, making it suitable for real-time and large-scale applications.

Core Components

The core components of ANTLR form a modular pipeline for processing input text into structured representations, beginning with and extending to and . The lexer tokenizes the input character into discrete tokens based on predefined rules, serving as the initial stage that breaks down raw text into meaningful units like keywords, identifiers, and literals. Tokens are the fundamental vocabulary symbols produced by the lexer, each encapsulating attributes such as type, text content, , and position, which are managed within a token for subsequent processing. The parser operates on this token stream to validate syntax and construct parse trees according to context-free grammar rules, employing an adaptive LL(*) parsing algorithm that dynamically determines lookahead needs for efficient prediction of alternatives without fixed k-value limitations. ANTLR distinguishes lexer rules, named in uppercase and defining token patterns using regular expressions, from parser rules, named in lowercase and specifying higher-level . Rules in both lexer and parser grammars support alternatives, denoted by the | operator, allowing multiple production options within a single rule to model syntactic choices like expressions or statements. Semantic predicates, embedded as boolean expressions like {condition}?, integrate application-specific logic into the grammar to resolve ambiguities or constrain parsing decisions at runtime, such as disambiguating overloaded operators based on context. Following parsing, tree walkers facilitate traversal of the resulting parse trees, which in ANTLR 4 double as abstract syntax trees (ASTs) without requiring separate tree grammars. The ParseTreeWalker class performs depth-first traversal, invoking methods on listener or visitor objects to process nodes; listeners use callback patterns for enter/exit events on rules, while visitors enable explicit recursive descent for custom computations like symbol resolution or code generation.

History

Origins and Early Versions

ANTLR was developed by Terence Parr during his graduate studies at Purdue University, beginning in 1988 as a student project under advisor Henry G. Dietz for a compiler construction course, and evolving into his master's thesis work. Initially named YUCC, the tool was renamed ANTLR (ANother Tool for Language Recognition) and became a core component of the Purdue Compiler Construction Tool Set (PCCTS), a suite aimed at simplifying compiler development through integrated lexical and syntactic analysis. Motivated by the limitations of existing parser generators like YACC, which struggled with efficient lookahead for k > 1 in LL(k) and LR(k) grammars, Parr's research focused on practical variants using techniques such as grammar lookahead automata and linear approximations to manage complexity. This PhD project, defended in 1993, laid the foundation for ANTLR's emphasis on flexible, high-performance parsing. The first official release, ANTLR 1.00B, arrived in February 1990 and primarily generated C++ code for LL(1) parsers, merging lexical and syntactic analysis while supporting basic (AST) construction. Subsequent updates, such as version 1.06 in December 1992, introduced semantic predicates for disambiguating grammar rules and enhanced error recovery mechanisms, including better diagnostic messages. By version 1.10 in August 1993, ANTLR incorporated arbitrary lookahead operators, enabling more robust handling of complex grammars without exponential computational overhead. These early iterations were distributed as part of PCCTS, with sample grammars for languages like and Pascal, fostering initial adoption among compiler developers. In the mid-1990s, following Parr's PhD and a period of refinement, ANTLR underwent a major rewrite leading to version 2.0.0 in May 1997, which added Java code generation as a primary target alongside C++, reflecting the growing popularity of in . This version improved overall performance through optimized lexer strategies and introduced tree parsers, allowing users to process ASTs with grammar-like specifications for tasks such as code transformation and analysis. Error handling was further enhanced with exception-based reporting derived from ANTLRException, providing finer control over recovery from syntax errors. Later releases in the 2.x series, up to 2.7.5 in 2005, expanded support to additional targets like Python and incorporated features such as inheritance for . ANTLR 3, released on May 17, 2007, marked a significant under Parr's continued , including his work through the he founded in 1997. It shifted to an adaptive LL(*) parsing strategy, which dynamically determines lookahead needs using deterministic finite automata (DFA) for efficiency, outperforming fixed-k approaches in handling ambiguous grammars. Performance gains were achieved through a redesigned recursive-descent lexer, and code generation became more flexible via the StringTemplate engine, supporting multiple targets including , C#, Python, and C. Released under a clean BSD license with contributor agreements to , ANTLR 3 transitioned fully to an open-source model, broadening its adoption beyond academic circles.

Evolution to ANTLR 4

ANTLR 4 was developed as a complete rewrite of its predecessor to address key limitations in ANTLR 3, particularly the inability to directly handle left-recursive rules, which are common in grammars for expressions and other language constructs. This redesign enabled ANTLR 4 to automatically convert left-recursive productions into equivalent non-left-recursive forms, simplifying grammar authoring for complex structures without manual refactoring. The primary motivations for ANTLR 4 included enhancing support for ambiguous grammars and applications in , where traditional LL parsers often struggle with nondeterminism and context sensitivity. By adopting an adaptive LL(*) parsing strategy, ANTLR 4 improved error recovery and prediction, making it more robust for real-world languages that exhibit , while also easing maintenance through cleaner semantics and reduced boilerplate in generated code. These changes aimed to demystify for developers building tools like query processors and configuration parsers. Development began in earnest around 2012, with Terence Parr leading the effort alongside contributors like Sam Harwell, through open-source collaboration on GitHub. Beta and release candidate versions, such as 4.0-rc-1, were made available in December 2012 to gather community feedback. The stable 4.0 release launched on January 21, 2013, coinciding with the publication of Parr's book, The Definitive ANTLR 4 Reference, which served as a comprehensive guide to the new version's features and usage. Since its stable debut, ANTLR 4 has seen continuous updates, with ongoing improvements to runtimes for targets including , Python, and , focusing on performance optimizations, bug fixes, and expanded language support. Key milestones include the addition of new targets like in version 4.12.0 and general stability enhancements up to the latest release, 4.13.2, on August 3, 2024, ensuring compatibility and efficiency across diverse environments.

Technical Architecture

Grammar Syntax

ANTLR grammars are defined using a declarative syntax in files with a .g4 extension, where the grammar name must match the filename without the extension. The structure begins with a header declaration such as grammar Name;, followed by optional sections for options, imports, tokens, channels (for lexers), and named actions, before the main rules section containing parser and lexer rules. This format allows for combined, parser-only (parser grammar Name;), or lexer-only (lexer grammar Name;) grammars, providing flexibility in defining lexical and syntactic structures. Parser rules, which define the syntactic , start with a lowercase letter (e.g., expr), while lexer rules, which handle tokenization, begin with an uppercase letter (e.g., ID). Each rule consists of one or more alternatives separated by the pipe operator |, terminated by a , enabling the specification of multiple matching patterns for a given rule. Subrules within alternatives can be labeled using the syntax label: subrule to facilitate access to nodes during traversal. Semantic predicates, denoted by { predicate }?, allow for conditional matching based on arbitrary code evaluation during parsing, enhancing the expressiveness of rules for context-sensitive grammars. Actions enclosed in curly braces {...} embed arbitrary code that executes at specific points, such as during rule entry or exit, to perform side effects like variable assignments. Lexer modes, introduced with mode ModeName;, enable switching between different lexical states, useful for handling embedded languages or varying tokenization contexts within the same grammar. For token definitions, ANTLR supports wildcards with the dot . to match any single character, character ranges like [a-z] for sets of characters, and string literals enclosed in double quotes "literal" for exact matches. Grammar-level actions include directives like @header { ... } to inject code into generated files, @members { ... } for shared class members (target-specific, e.g., ), and scoped actions such as @parser::init { ... } for rule-specific initialization. Options, set via options { language=[Java](/page/Java); }, configure aspects like the target language for code generation.

Parsing Process

The parsing process in ANTLR involves three primary phases: lexing, , and tree walking. During lexing, the lexer scans the input character and applies lexical rules defined in the to produce a sequential token , which serves as the input for subsequent phases. This phase handles tokenization, including context-sensitive rules in ANTLR 4, where the adaptive (*) strategy enables efficient processing of ambiguous lexemes like nested comments. In the parsing phase, the generated parser consumes the token stream using an adaptive to construct a concrete representing the syntactic structure of the input. The LL() approach is a predictive top-down strategy that employs an to model decisions, allowing arbitrary lookahead while minimizing through static construction of lookahead Deterministic Finite Automata (DFAs) for each decision point. In ANTLR 4, this evolves into the ALL() variant, which dynamically analyzes the at runtime to resolve nondeterminism, supporting direct left-recursion via integrated subparsers and graph-structured stacks without exponential complexity in practice. The parser traverses the , predicting alternatives based on input lookahead and semantic predicates, ensuring decisions are made with sufficient tokens (typically 1-2 on average) to disambiguate paths. Tree walking constitutes the final phase, where generated or visitors recursively traverse the to perform semantic analysis, code generation, or other post-processing tasks. This separation enables multiple walks over the same tree for efficiency, avoiding re-parsing the input. ANTLR incorporates robust handling during lexing and parsing to maintain progress through faulty input. The default error strategy implements automatic recovery by attempting single-token insertion for missing elements or deletion for extraneous tokens, followed by via consumption until a valid follow-set token is encountered. Custom error can be implemented to override reporting behaviors, such as suppressing duplicates or providing tailored diagnostics, while the parser notifies of events like input mismatches or failed predicates. For unambiguous grammars, the parsing process achieves O(n) time complexity, processing input linearly by consuming each token once, with empirical speeds reaching thousands of lines per second on large corpora like Java source code. This efficiency stems from the adaptive lookahead's ability to cache DFAs and throttle analysis to essential decisions, outperforming general parsing methods like GLR by orders of magnitude in practical scenarios.

ANTLR 4 Features

Key Improvements

ANTLR 4 introduced direct support for left-recursive rules, allowing grammars to define productions like expr : expr '+' term | term; without risking infinite loops during parsing. This advancement eliminates the need for manual refactoring into right-recursive forms, which was required in ANTLR 3 to avoid recursion issues. The parser achieves this through an adaptive LL(*) strategy that rewrites left-recursive rules into equivalent non-recursive forms using precedence parameters and semantic predicates, ensuring unambiguous resolution while preserving the original structure. A key enhancement in ambiguity resolution comes from ANTLR 4's ordered alternatives mechanism, where the parser prioritizes alternatives based on their order in the file for cases where multiple rules could match the input. This predictable behavior simplifies design for ambiguous languages, as the first matching alternative (with the lowest production number) is selected, akin to how PEG parsers operate. For instance, in expressions with operators of varying precedence, ordered alternatives ensure left-associativity without additional annotations, improving both usability and performance over ANTLR 3's less deterministic handling. ANTLR 4 promotes a cleaner separation between lexing and phases compared to ANTLR 3, where developers often relied on ad-hoc lexer hacks like island grammars or embedded actions to handle context-sensitive tokens. The introduction of lexical modes enables the lexer to switch between distinct rule sets dynamically—for example, recognizing different tokens inside strings versus code—without contaminating parser logic. This reduces complexity and errors in , as tokenization occurs independently while still integrating seamlessly with the parser. Tooling in ANTLR 4 has been significantly upgraded with integrated IDE support and visualization capabilities, enhancing developer productivity beyond ANTLR 3's basic features. Plugins for environments like , , and provide , real-time error detection, for rules and tokens, and live grammar interpretation. Grammar visualization tools, such as parse tree viewers and (Augmented Transition Network) diagrams, allow users to inspect generated structures interactively. Additionally, runtime APIs like BaseErrorListener offer customizable error reporting, including detailed messages for ambiguities and recovery suggestions, facilitating robust application integration.

Listener and Visitor Patterns

In ANTLR 4, the listener and visitor patterns provide mechanisms for traversing and processing parse trees generated by the parser, enabling users to perform actions such as semantic analysis, code generation, or tree transformations without modifying the core logic. These patterns are implemented through generated interfaces and base classes tailored to the grammar rules, allowing for modular extension of parser functionality. The listener pattern follows an event-driven approach, where a ParseTreeWalker traverses the in a depth-first manner and invokes callback methods on a listener object at key points during the traversal. ANTLR generates a ParseTreeListener interface specific to the grammar (e.g., MyGrammarListener), which declares enterRule and exitRule methods for each rule in the grammar, along with methods for terminal nodes, error nodes, and every rule context. Users typically extend the generated abstract base class MyGrammarBaseListener, which provides no-op implementations, and override only the desired methods to perform side-effect operations, such as printing node information or updating global state. For instance, to process a rule named expression, one might override enterExpression to initialize a and exitExpression to finalize it based on child results. The traversal is initiated by calling ParseTreeWalker.DEFAULT.walk(listener, parseTree), ensuring automatic, non-recursive invocation of methods without user control over the order or depth. This pattern is particularly suited for tasks involving side effects, like or building data structures incrementally, as it decouples the processing logic from the itself. In contrast, the employs a recursive traversal model inspired by the classic , where the object explicitly calls visit methods on each node and aggregates results from children. ANTLR generates a ParseTreeVisitor<T> interface (e.g., MyGrammarVisitor<T>), where T is the return type for computations (using Void if no value is needed), including a generic visit method and specialized visitRule methods for each grammar rule, plus handlers for terminals and errors. Users implement or extend the abstract MyGrammarBaseVisitor<T>, overriding visitRule methods to recurse into children via visitChildren and combine results—for example, in an expression evaluator, visitAddExpr might return the sum of visit(left) and visit(right). Unlike , visitors allow returning values up the call stack, enabling functional-style computations, and provide control to skip subtrees by returning early or overriding visitChildren to customize aggregation. Traversal starts with a top-level call like visitor.visit(parseTree), making it ideal for tasks requiring computed outputs, such as evaluating expressions or translating to another representation. The primary differences between the patterns lie in their traversal control and result handling: listeners rely on the walker's fixed depth-first order for side effects without returns, promoting simplicity for event-like processing, while visitors offer flexibility for recursive aggregation and early termination, better for value-oriented operations like symbol table construction. Both support customization by selectively overriding methods in the base classes, minimizing boilerplate, and can be generated via command-line flags (-listener for listeners, -visitor for visitors, or both by default). For listeners, exception handling during traversal can be managed by overriding the walker's triggerExitRuleEvent to avoid halting the parse. These patterns integrate seamlessly with ANTLR's parse tree generation, allowing post-parse processing without altering the parser itself.

Usage and Implementation

Basic Workflow

The basic workflow for using ANTLR involves defining a , generating parser , integrating it into an application, and testing the . This process enables developers to create for custom languages or data formats without manually coding the parsing logic. ANTLR automates the generation of lexical analyzers, , and tree walkers from a declarative grammar specification, streamlining the development of language processors. The first step is to write a grammar file with the .g4 extension, specifying lexer rules for tokenization and parser rules for structure. For instance, a simple arithmetic expression grammar might define rules for numbers, operators, and expressions, but the focus remains on the overall structure rather than specific details. Once the is defined, the ANTLR tool is invoked to generate in the target language, such as or Python. The (CLI) tool, antlr4, is run from the directory containing the .g4 file, for example: antlr4 MyGrammar.g4. This generates files including the lexer, parser, and base listener classes. Options like -Dlanguage=Python3 specify the target runtime, while -o outputdir directs the output location. After generation, the produced code must be compiled alongside the application's source files using the appropriate for the target language. For Java, this involves javac on the generated .java files. The application then implements custom logic by extending the generated base listener or visitor classes to traverse the and perform actions, such as building an or evaluating expressions. Finally, input text is fed to the parser instance, which tokenizes it, builds the , and invokes the listener or visitor methods to process the output. For larger projects, ANTLR integrates with build tools like Maven and to automate grammar processing. In Maven, the antlr4-maven-plugin is configured in the pom.xml file under the build plugins section, specifying the grammar source directory (default: src/main/antlr4) and running during the generate-sources phase to produce code before compilation. Similarly, 's built-in ANTLR plugin applies the antlr extension to tasks, processing grammars from src/antlr and generating sources integrated into the Java or other plugin tasks. These integrations ensure consistent code generation across builds. Testing forms a crucial part of the workflow, with ANTLR providing the TestRig tool (also known as grun in some distributions) for validating grammars. Invoked as grun MyGrammar startRule -gui, it input text, displays the visually, or outputs it in text form with -tree. Unit tests can validate specific inputs against expected parse results, helping identify issues early. For example, piping input like 10 + 20 to the tool confirms correct of the start rule. Common pitfalls in this workflow include ambiguities, where multiple parse paths exist for the same input, leading to nondeterministic behavior or performance issues. These are often resolved through refactoring the structure, such as defining hierarchical rules for operator precedence (e.g., expr : addExpr; addExpr : multExpr (('+'|'-') multExpr)*;) or reordering rules to favor the intended interpretation during predictive . Ensuring the is LL(*) compatible by avoiding ambiguous lexer rules and leveraging ANTLR 4's support for prevents such problems during code generation.

Code Generation Targets

ANTLR generates parser and lexer code in various programming languages, known as code generation targets, allowing developers to integrate the resulting parsers into applications written in those languages. The primary targets include , which serves as the default and most full-featured option with comprehensive support for features like XPath-based tree navigation; C#; and ; Python; Go; and C++. These targets are selected using the -Dlanguage option during code generation with the ANTLR tool, for example, java -jar antlr-4.x-complete.jar -Dlanguage=Python3 MyGrammar.g4. Each target relies on a dedicated to handle core functionality such as token streams, parse trees, error reporting, and listener/ implementations. For , the runtime is included in the org.antlr.v4.runtime package within the ANTLR complete JAR, providing classes like CommonTokenStream for token management and ParseTreeWalker for . Similarly, C# uses the Antlr4.Runtime.Standard package for equivalent features; Python employs the antlr4-python3-runtime pip package with modules like InputStream and ParseTreeListener; and share the antlr4 package; Go utilizes the github.com/antlr/antlr4/runtime/Go/antlr module; and C++ draws from runtime sources built via tools like Conan. These libraries ensure consistent behavior across targets while adapting to language-specific idioms, such as async support in . In to the primary targets, ANTLR provides support for , PHP, and Dart. Developers should consult the official documentation for compatibility details, as some advanced features like ambiguous tree construction may not be fully available in these targets.

Examples

Simple Grammar Example

A simple in ANTLR defines rules for recognizing basic arithmetic expressions involving and . Consider the following grammar file, named Expr.g4, which specifies a left-recursive parser rule for expressions and lexer rules for integers and whitespace:

grammar Expr; expr: expr ('+' | '-') expr | INT ; INT : [0-9]+ ; WS : [ \t\r\n]+ -> skip ;

grammar Expr; expr: expr ('+' | '-') expr | INT ; INT : [0-9]+ ; WS : [ \t\r\n]+ -> skip ;

This grammar treats expressions as left-associative, allowing inputs like 3+4 to be parsed unambiguously. Upon processing Expr.g4 with the ANTLR tool (version 4), it generates Java source files including ExprLexer.java for tokenization and ExprParser.java for parsing the token stream into a concrete syntax tree. To use the generated parser in a application, load input from a , create the lexer and parser instances, invoke the entry rule, and visualize the . The following snippet demonstrates this for the input "3 + 4" (assuming necessary imports and ANTLR runtime on the classpath):

java

import org.antlr.v4.runtime.*; import org.antlr.v4.runtime.tree.*; public class ExprTest { public static void main([String](/page/String)[] args) throws Exception { [String](/page/String) input = "3 + 4"; CharStream stream = CharStreams.fromString(input); ExprLexer lexer = new ExprLexer(stream); CommonTokenStream tokens = new CommonTokenStream(lexer); ExprParser parser = new ExprParser(tokens); ParseTree tree = parser.expr(); System.out.println(tree.toStringTree(parser)); } }

import org.antlr.v4.runtime.*; import org.antlr.v4.runtime.tree.*; public class ExprTest { public static void main([String](/page/String)[] args) throws Exception { [String](/page/String) input = "3 + 4"; CharStream stream = CharStreams.fromString(input); ExprLexer lexer = new ExprLexer(stream); CommonTokenStream tokens = new CommonTokenStream(lexer); ExprParser parser = new ExprParser(tokens); ParseTree tree = parser.expr(); System.out.println(tree.toStringTree(parser)); } }

Executing this code tokenizes the input into INT tokens for 3 and 4, plus a '+' token, while skipping whitespace. The resulting parse tree output is (expr (expr 3) + (expr 4)), illustrating the hierarchical structure: the top-level expr rule matches the addition, with each operand as a subexpression matching INT.

Real-World Application

ANTLR has been employed in production environments to develop sophisticated parsers for query languages, exemplified by its use in Twitter's search infrastructure where it parses complex user queries to enable efficient retrieval from vast datasets. In a detailed case study, Bytebase integrated ANTLR4 to build a SQL autocomplete framework supporting multiple dialects, starting with a grammar that defines core structures like SELECT and FROM clauses to handle real-world query variations. A representative SQL-like query parser grammar, drawn from the official ANTLR grammars repository, illustrates this application. The specifies rules for SELECT statements, including column selections and table sources, while addressing ambiguities such as keyword-identifier overlaps through mode switching in the lexer. For instance:

query: selectStatement EOF ; selectStatement : SELECT selectItem (',' selectItem)* FROM tableSource ; selectItem: qualifiedName ; tableSource: qualifiedName ;

query: selectStatement EOF ; selectStatement : SELECT selectItem (',' selectItem)* FROM tableSource ; selectItem: qualifiedName ; tableSource: qualifiedName ;

This setup resolves potential conflicts, like distinguishing reserved words in different contexts, by leveraging ANTLR's adaptive lookahead (LL(*)) parsing strategy, which efficiently predicts alternatives without . Implementation extends beyond syntax with custom s for semantic analysis, such as type checking expressions in query clauses. In Bytebase's framework, a traverses the to validate types—ensuring, for example, that numeric columns are not mismatched with string filters—and extracts metadata like table references for further processing. A simplified visitor snippet demonstrates this:

java

public class QueryVisitor extends PostgreSQLParserBaseVisitor<Object> { @Override public Object visitSelectStatement(PostgreSQLParser.SelectStatementContext ctx) { // Type check select items for (var item : ctx.selectItem()) { visitSelectItem(item); // Validate expression types } return visitChildren(ctx); } private void visitSelectItem(PostgreSQLParser.SelectItemContext ctx) { // Implement [type inference](/page/Type_inference) and checking logic here // e.g., resolve column types from [schema](/page/Schema) } }

public class QueryVisitor extends PostgreSQLParserBaseVisitor<Object> { @Override public Object visitSelectStatement(PostgreSQLParser.SelectStatementContext ctx) { // Type check select items for (var item : ctx.selectItem()) { visitSelectItem(item); // Validate expression types } return visitChildren(ctx); } private void visitSelectItem(PostgreSQLParser.SelectItemContext ctx) { // Implement [type inference](/page/Type_inference) and checking logic here // e.g., resolve column types from [schema](/page/Schema) } }

This approach allows detection of semantic errors during traversal, enhancing query reliability. For integration, such parsers embed seamlessly into web applications like Bytebase's SQL Editor, where user input triggers real-time and suggestions, or CLI tools for queries with structured output. reporting is customized via ANTLR's , overriding methods like syntaxError to provide user-friendly messages, such as "Expected column name after SELECT" with line and position details, improving in interactive environments. The benefits manifest in scalability, as seen in ANTLR's application to validators—where grammars parse and validate nested structures efficiently—and domain-specific languages (DSLs), supporting high-throughput processing in tools like Twitter's query system without performance degradation for large inputs.

Ecosystem

Supported Languages

ANTLR grammars, defined using the .g4 file format, are inherently language-agnostic, allowing the same grammar to generate parser code for various host programming languages without modification. The ANTLR 4 runtime provides full support for over 10 target languages, enabling the execution of generated parsers in diverse environments; these include C++, C#, Dart, (version 8 and later), , Go, , Python 3, Swift, and . Experimental or third-party runtimes exist for additional languages such as , which offers a community-maintained implementation via the antlr4rust crate but lacks official integration as of 2025. All official runtimes maintain consistent versioning and API compatibility across targets to facilitate cross-language development. Runtime compatibility emphasizes robust handling of international text and concurrent operations tailored to each language's ecosystem. For Unicode support, ANTLR runtimes incorporate mechanisms like code-point buffering in and equivalent string handling in other targets, ensuring accurate parsing of and other encodings without locale-specific byte limitations. Threading models vary by language but generally require dedicated parser instances per thread for safety, as the core runtime structures like the DFA cache are shared and thread-safe, while mutable components such as lexers and parsers are not. To enhance development, ANTLR offers extensions in the form of IDE plugins for grammar editing and integration. Notable examples include the official ANTLR v4 plugin for and other IDEs, which provides syntax highlighting, grammar visualization, and code generation, and the ANTLR Language Support extension for , supporting both ANTLR 3 and 4 grammars with features like error detection and preview.

Notable Projects and Tools

ANTLR has been integral to several prominent open-source compilers and data processing systems. , a system built on Hadoop, employs ANTLR for HiveQL, its SQL-like , enabling complex data querying and analysis. Similarly, Apache Pig, another Hadoop ecosystem component for data processing, utilizes ANTLR to parse scripts, facilitating large-scale data transformations. Presto, a query engine (now known as Trino), relies on an ANTLR-based parser to convert SQL statements into syntax trees, supporting high-performance queries across diverse data sources. Among development tools, ANTLRWorks served as a dedicated graphical (IDE) for ANTLR v3 grammars, offering features like , grammar interpretation, and to streamline parser development; though legacy, it influenced subsequent tools. StringTemplate, a templating engine developed by ANTLR's creator Terence Parr, integrates closely with ANTLR for generating formatted output such as or web pages from parse trees, powering ANTLR's code generation targets. The GrammarsV4 repository on hosts over 100 community-contributed ANTLR v4 grammars for languages and formats ranging from programming languages to protocols, fostering reuse and collaboration in parser development. Extensions enhance ANTLR's usability in integrated environments. The ANTLR 4 IDE plugin for provides advanced , automatic code generation, and live visualization, aiding grammar authoring within the Eclipse workbench. For mobile applications, ANTLR's runtimes, particularly the JavaScript and C# , support in resource-constrained settings, with optimizations like DFA caching improving for on-device processing. ANTLR's adoption extends to industry and academia, demonstrating its versatility. Netflix leverages ANTLR to parse custom Query DSLs in its federated graph search system, enabling efficient content metadata querying across . In academia, ANTLR supports (NLP) tools, such as syntax structure analysis in frameworks like I-SOAS, where it generates lexers and parsers for handling English sentence grammars and semantic representations. As of , these applications underscore ANTLR's enduring impact in building robust processors for both production and research contexts.

Community and Licensing

Development Community

ANTLR's development is primarily led by Terence Parr, a former professor of computer science at the University of San Francisco (until 2022), who serves as the project's supreme leader and main maintainer, with significant contributions from co-author Sam Harwell and a broader group of target language specialists. The project emphasizes collaborative input, where community members contribute through bug fixes, enhancements to existing code generation targets, and proposals for new ones via pull requests on the official repository. The central hub for development is the GitHub repository at antlr/antlr4, which has garnered over 15,000 stars, reflecting its widespread adoption and active engagement as of 2025. Discussions and support occur through the project's , antlr-interest, and GitHub issues and discussions, fostering an environment for users to report problems, suggest improvements, and collaborate on releases. While no official server exists, community members have proposed such channels for real-time chats, though GitHub remains the primary platform for formal contributions. Releases follow a schedule incorporating community feedback, with updates typically issued annually or as major features stabilize, such as the 4.12.0 release in February 2023 that introduced a new target based on contributor input. Terence Parr actively engages the community through conference talks, including presentations on techniques at events like and QCon, and maintains educational resources via tutorials. This ongoing involvement ensures ANTLR's evolution aligns with user needs while maintaining its core focus on robust parser generation.

License Details

ANTLR 4 is licensed under the three-clause BSD License, a permissive that allows redistribution and use in source and binary forms with or without modification, provided that the , this list of conditions, and the are retained in all copies. This license permits commercial use, modification, and distribution without requirements, but it mandates attribution to the original authors, Terence Parr and Sam Harwell, and prohibits the use of their names for endorsement without permission. Historically, ANTLR versions up to 2.7 were released in the public domain, with no legal rights reserved by the developers, allowing unrestricted use and modification. Starting with version 3.0 in 2007, ANTLR adopted the BSD License, which has remained consistent through ANTLR 4, released in 2013. The BSD License's permissive nature means it imposes no obligations to share modifications or source code, making it compatible with proprietary software and projects under copyleft licenses like the GPL, though distributions incorporating ANTLR must include the BSD copyright notice and disclaimer. This flexibility has facilitated ANTLR's widespread adoption in both open-source and commercial applications without licensing conflicts. Since its adoption in 2007, the BSD License for ANTLR has seen no major changes, remaining stable across releases up to version 4.13.2 in 2024 and into 2025.

References

  1. https://www.antlr.org/
  2. https://www.antlr.org/about.html
  3. https://www.antlr2.org/history.html
  4. https://www.antlr.org/download.html
  5. https://www.antlr.org/api/Java/org/antlr/v4/runtime/Lexer.html
  6. https://www.antlr.org/papers/allstar-techreport.pdf
  7. https://www.antlr.org/papers/parr.phd.thesis.pdf
  8. https://www.researchgate.net/publication/220177492_PCCTS_reference_manual_version_100
  9. https://www.antlr2.org/doc/sor.html
  10. https://www.antlr2.org/doc/err.html
  11. https://www.artima.com/articles/terence-parr-introduces-antlr-30
  12. https://media.pragprog.com/titles/tpantlr2/preface.pdf
  13. https://github.com/antlr/antlr4
  14. https://repo1.maven.org/maven2/org/antlr/antlr4-runtime/4.0-rc-1/
  15. https://repositorio.uniandes.edu.co/bitstreams/7312198a-8cfe-44c8-b692-1d9139c35d13/download
  16. https://github.com/antlr/antlr4/releases
  17. https://github.com/antlr/antlr4/blob/master/doc/grammars.md
  18. https://www.antlr.org/papers/predicated-parsing.pdf
  19. https://www.antlr.org/papers/LL-star-PLDI11.pdf
  20. https://www.antlr.org/papers/antlrworks-draft.pdf
  21. https://www.antlr.org/api/Java/org/antlr/v4/runtime/DefaultErrorStrategy.html
  22. https://www.antlr.org/api/Java/org/antlr/v4/runtime/ANTLRErrorListener.html
  23. https://tomassetti.me/best-practices-for-antlr-parsers/
  24. https://www.antlr.org/tools.html
  25. https://www.antlr.org/api/Java/org/antlr/v4/runtime/tree/ParseTreeListener.html
  26. https://www.antlr.org/api/Java/org/antlr/v4/runtime/tree/ParseTreeVisitor.html
  27. https://raw.githubusercontent.com/antlr/antlr4/master/doc/listeners.md
  28. https://github.com/antlr/antlr4/blob/master/doc/getting-started.md
  29. https://www.antlr.org/api/maven-plugin/latest/usage.html
  30. https://docs.gradle.org/current/userguide/antlr_plugin.html
  31. https://github.com/antlr/antlr4/blob/master/doc/targets.md
  32. https://pypi.org/project/antlr4-python3-runtime/
  33. https://www.bytebase.com/blog/sql-auto-complete/
  34. https://github.com/antlr/grammars-v4/tree/master/sql
  35. http://pragprog.com/titles/tpantlr2/the-definitive-antlr-4-reference
  36. https://www.antlr.org/api/Java/org/antlr/v4/runtime/ANTLRErrorStrategy.html
  37. https://github.com/rrevenantt/antlr4rust
  38. https://www.antlr.org/api/Java/org/antlr/v4/runtime/atn/ParserATNSimulator.html
  39. https://github.com/antlr/antlr4/issues/3332
  40. https://plugins.jetbrains.com/plugin/7358-antlr-v4
  41. https://marketplace.visualstudio.com/items?itemName=mike-lischke.vscode-antlr4
  42. https://www.antlr.org/support.html
  43. https://github.com/antlr/antlr4/discussions/4036
  44. https://qconsf.com/sf2014/users/terence-parr.html
  45. https://www.youtube.com/watch?v=q8p1voEiu8Q
  46. https://www.antlr.org/license.html
  47. https://spdx.org/licenses/ANTLR-PD.html
  48. https://www.antlr3.org/license.html
  49. https://github.com/antlr/antlr4/blob/master/LICENSE.txt
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