ANTLR vs. Other Parser Generators: When to Choose It

Building a Domain-Specific Language with ANTLR — Step-by-StepCreating a domain-specific language (DSL) is a powerful way to give domain experts an expressive, concise, and safe way to describe problems and solutions. ANTLR (Another Tool for Language Recognition) is a mature parser generator that simplifies building lexers, parsers, parse trees, and visitor/listener-based processing for custom languages. This guide walks through designing and implementing a simple but practical DSL using ANTLR, from initial design to implementation, testing, and embedding in a host application.

Why build a DSL?

A DSL focuses on a specific problem domain and offers higher productivity, improved readability, and fewer errors than general-purpose languages. Examples include SQL for databases, CSS for styling, and Makefiles for builds. DSLs can be external (their own syntax) or internal (embedded in a host language). This guide focuses on an external DSL implemented with ANTLR.

Overview of the example DSL

We’ll build a small external DSL named TaskScript for describing task workflows. TaskScript goals:

Define named tasks with inputs, outputs, and commands.
Specify dependencies between tasks.
Support variables, simple expressions, and conditional execution.
Be easy to read and script by non-programmers.

Example TaskScript:

task build { inputs: [ “src/.java” ] outputs: [ “build/app.jar” ] run: “javac -d build src/.java && jar cf build/app.jar -C build .” }

task test { depends_on: [ build ] run: if (env == “ci”) { “mvn -DskipTests=false test” } else { “mvn -DskipTests=true test” } }

This example shows tasks, lists, dependencies, and a conditional expression for the run command.

Design the language grammar

Before writing ANTLR grammar, design the language constructs and tokens. For TaskScript we need:

Identifiers (task names, variable names)
String literals
Numbers (if needed)
Punctuation: braces, brackets, commas, colons
Keywords: task, inputs, outputs, run, depends_on, if, else
Expressions: equality comparisons, variable lookups, string concatenation
Comments and whitespace

Decide on operator precedence and expression constructs. Keep the syntax simple to lower grammar complexity.

Create the ANTLR grammar (TaskScript.g4)

Here is a workable ANTLR v4 grammar for TaskScript. Place it in TaskScript.g4.

grammar TaskScript; script: statement* EOF ; statement     : taskDecl     ; taskDecl     : 'task' ID '{' taskBody '}'      ; taskBody     : (taskField)*     ; taskField     : 'inputs' ':' list     | 'outputs' ':' list     | 'depends_on' ':' list     | 'run' ':' expr     ; list     : '[' (expr (',' expr)*)? ']'     ; expr     : conditionalExpr     ; conditionalExpr     : 'if' '(' comparison ')' '{' expr '}' 'else' '{' expr '}'      # IfExpr     | comparison                                                    # ToComparison     ; comparison     : additive (('==' | '!=' | '<' | '>' | '<=' | '>=') additive)*     ; additive     : primary (('+' | '-') primary)*     ; primary     : STRING     | NUMBER     | ID     | '(' expr ')'     ; ID  : [a-zA-Z_][a-zA-Z_0-9]* ; NUMBER : [0-9]+ ('.' [0-9]+)? ; STRING : '"' (~["\] | '\' .)* '"' ; WS  : [ 	 ]+ -> skip ; LINE_COMMENT : '//' ~[ ]* -> skip ; BLOCK_COMMENT : '/*' .*? '*/' -> skip ;

Notes:

The grammar keeps expressions relatively simple. Expand as needed.
String literal rule supports escaped characters.
Comments are supported.

Generate parser and lexer

Install ANTLR 4 (jar) and the runtime for your target language (Java, Python, C#, JavaScript, etc.). For Java, a typical workflow:

Download antlr-4.x-complete.jar and put it in your tools folder.
Generate code:

   java -jar antlr-4.x-complete.jar -Dlanguage=Java TaskScript.g4

Compile generated sources along with your runtime dependency.

For Python:

java -jar antlr-4.x-complete.jar -Dlanguage=Python3 TaskScript.g4 pip install antlr4-python3-runtime

Adjust commands and runtime library for your language.

Parse a script and build an AST or use parse tree

ANTLR produces a parse tree. For processing you can either:

Walk the parse tree with a listener (good for simple, event-driven processing).
Use a visitor to build an AST or evaluate expressions (better control and returns values).

Example: Use a visitor to construct an in-memory representation of tasks.

Define model classes (pseudocode in Java):

class Task {   String name;   List<Expr> inputs;   List<Expr> outputs;   List<String> dependsOn;   Expr runExpr; }

Implement a visitor (TaskScriptBaseVisitor) that visits taskDecl, taskField, list, and expr nodes to populate Task instances. For conditional expressions return an AST node type IfExpr with condition, thenExpr, elseExpr.

Semantic analysis and validation

After building the AST, perform semantic checks:

No duplicate task names.
Dependencies reference existing tasks.
Inputs/outputs are valid patterns or paths.
Type checking for expressions (e.g., comparing strings to strings).
Detect cycles in dependencies (topological sort).

For dependency cycle detection, run a simple DFS-based cycle finder or attempt a topological sort.

Execution model

Decide how TaskScript will be executed:

Interpret: Evaluate run expressions at runtime, execute commands using a shell.
Compile: Translate tasks into a Makefile, a shell script, or another build system.
Hybrid: Generate an executable plan that can be inspected, then executed.

For our example, an interpreter that:

Topologically sorts tasks by dependency.
For each task, evaluates run expression (resolving env variables or config).
Executes command(s) in a subprocess, checks exit codes, and logs output.

Be careful with security: don’t execute untrusted scripts without sandboxing.

Example: Visitor snippets (Java)

Visitor methods for taskDecl and taskField (simplified):

@Override public Task visitTaskDecl(TaskScriptParser.TaskDeclContext ctx) {   String name = ctx.ID().getText();   Task task = new Task(name);   for (TaskScriptParser.TaskFieldContext fctx : ctx.taskBody().taskField()) {     visitTaskField(fctx, task);   }   return task; } public void visitTaskField(TaskScriptParser.TaskFieldContext ctx, Task task) {   if (ctx.getText().startsWith("inputs")) {     task.inputs = visitList(ctx.list());   } else if (ctx.getText().startsWith("outputs")) {     task.outputs = visitList(ctx.list());   } else if (ctx.getText().startsWith("depends_on")) {     task.dependsOn = visitListOfIds(ctx.list());   } else if (ctx.getText().startsWith("run")) {     task.runExpr = visitExpr(ctx.expr());   } }

Testing and debugging grammars

Use ANTLR’s TestRig (grun) to quickly parse test files and inspect parse trees.
Add unit tests for the parser: test valid scripts, invalid scripts, and edge cases.
When grammar conflicts arise, enable parser debug or inspect DOT output for parse trees.
Use lexer modes or more specific token rules if ambiguities appear.

Tooling and editor support

Provide syntax highlighting (TextMate/VSCode) using the grammar tokens.
Create snippets and language server (LSP) for autocompletion and diagnostics.
Provide formatting tools (pretty-printer) and linters to improve user experience.

Packaging and distribution

Package the runtime and CLI so users can run TaskScript files (jar, pip package, npm, etc.).
Provide a CLI with commands: validate, plan, run, dry-run, and graph (visualize dependencies).
Offer example scripts and templates for common tasks.

Extending the language

Common extensions:

Variables and parameterization: allow tasks to accept parameters.
Templates and includes: compose scripts from multiple files.
Advanced expressions: functions, regex matching, collections.
Hooks and event triggers: run tasks on file change or schedule.

Keep backward compatibility in mind and version the grammar.

Security considerations

Sanitize any evaluated strings that will be passed to a shell.
Consider a dry-run mode that shows commands without executing them.
Use sandboxing or containerization when running untrusted TaskScript files.
Validate external inputs used in expressions.

Example end-to-end: Parse, validate, and run (high-level)

Parse with ANTLR-generated parser.
Visit parse tree to build AST (Task objects).
Run semantic validation: uniqueness, dependency existence, cycle check.
Topologically sort tasks.
For each task in order:
- Evaluate run expression in a controlled environment.
- Execute command(s) using ProcessBuilder (Java) or subprocess (Python).
- Capture logs and enforce timeouts/retries.

Conclusion

ANTLR accelerates development of DSLs by generating robust lexers and parsers, letting you focus on semantics, tooling, and execution. Start small with a focused grammar, iterate with tests and users, and add features (editor support, packaging, security) as adoption grows. With the steps above you can go from language idea to a working DSL that improves productivity in your domain.