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Domain-Specific Language Parsers with Python’s PLY & Lark

Introduction

Domain-Specific Languages (DSLs) are programming languages designed to solve specific problems in a particular domain. DSLs are becoming increasingly popular as they offer several advantages over general-purpose languages. They are easier to learn, use, and maintain, and they promote best practices and reduce the risk of errors. DSLs are used in various domains, such as data processing, network configuration, and embedded systems.

Building a DSL parser can be a challenging task, especially if you are dealing with complex grammars. Fortunately, Python provides two powerful libraries, PLY and Lark, that simplify the process of building DSL parsers.

PLY (Python Lex-Yacc) is a set of tools for building lexers and parsers in Python. PLY is a implementation of Lex and Yacc, two classic Unix tools for building lexers and parsers. PLY provides a simple and intuitive API for defining lexers and parsers, making it an excellent choice for building DSL parsers.

Lark is a parsing library for Python that provides a modern and flexible approach to building parsers. Lark uses Parsing Expression Grammars (PEGs) to define grammars, which offers several advantages over traditional lexer and parser generators. Lark is easy to learn, fast, and supports a wide range of features, such as error recovery, incremental parsing, and AST generation.

In this tutorial, we will explore how to use PLY and Lark to build DSL parsers. We will start by setting up the environment and understanding the basics of PLY and Lark. Then, we will dive into building a grammar for your DSL, parsing expressions, building an Abstract Syntax Tree (AST), and generating code. We will also cover best practices and tips for writing maintainable and testable code. By the end of this tutorial, you will have a solid understanding of how to build DSL parsers using PLY and Lark.

Setting up the Environment

Before we start building DSL parsers using PLY and Lark, we need to set up the environment. Here are the steps to install PLY and Lark and create a new Python project.

Installing PLY and Lark

To install PLY and Lark, you can use pip, the Python package installer. Open a terminal or command prompt and run the following commands:

pip install ply
pip install lark-parserCode language: Bash (bash)

Creating a new Python project

To create a new Python project, you can use your favorite IDE or text editor. Here are the steps to create a new project using Visual Studio Code:

  • Open Visual Studio Code and click on “File” -> “Open Folder” to open a new folder for your project.
  • Create a new Python file by clicking on “File” -> “New File”. Save the file with a .py extension, for example, my_dsl.py.
  • Import PLY and Lark libraries in your Python file by adding the following lines at the top of your file:
import ply.lex as lex
import ply.yacc as yacc
import larkCode language: Python (python)
  • Create a new virtual environment for your project by running the following command in the terminal:
python -m venv venvCode language: Python (python)
  • Activate the virtual environment by running the following command in the terminal:

On Windows:

venv\Scripts\activateCode language: Shell Session (shell)

On Unix or MacOS:

source venv/bin/activateCode language: Bash (bash)
  • Install any additional dependencies required for your project by running:
pip install -r requirements.txtCode language: Bash (bash)
  • You are now ready to start building your DSL parser using PLY and Lark!

Note: You can replace “my_dsl.py” with any name you prefer for your Python file. Also, you can use any other IDE or text editor instead of Visual Studio Code.

Understanding the Basics of PLY and Lark

Before we dive into building a DSL parser using PLY and Lark, it’s essential to understand the basics of these libraries. Here, we will provide an overview of PLY and Lark architecture and build a simple calculator DSL using PLY and Lark.

Overview of PLY and Lark architecture

PLY and Lark have different architectures, but they both follow a similar approach to building lexers and parsers.

PLY uses lex and yacc, two classic Unix tools for building lexers and parsers. PLY provides a simple and intuitive API for defining lexers and parsers. A PLY lexer converts input text into tokens, while a PLY parser converts tokens into an Abstract Syntax Tree (AST).

Lark uses Parsing Expression Grammars (PEGs) to define grammars. Lark provides a modern and flexible approach to building parsers. Lark converts input text into tokens and then converts tokens into an Abstract Syntax Tree (AST).

Building a simple calculator DSL using PLY and Lark

To illustrate the basics of PLY and Lark, let’s build a simple calculator DSL using PLY and Lark.

Using PLY:

Here’s an example of a simple calculator DSL using PLY:

import ply.lex as lex
import ply.yacc as yacc

# Lexer

tokens = (
    'NUMBER',
    'PLUS',
    'MINUS',
    'TIMES',
    'DIVIDE',
    'LPAREN',
    'RPAREN'
)

t_PLUS = r'\+'
t_MINUS = r'-'
t_TIMES = r'\*'
t_DIVIDE = r'/'
t_LPAREN = r'\('
t_RPAREN = r'\)'

def t_NUMBER(t):
    r'\d+'
    t.value = int(t.value)
    return t

# Parser

def p_expr_term(p):
    '''expr : term
             | expr PLUS term
             | expr MINUS term
             | expr TIMES term
             | expr DIVIDE term
             | LPAREN expr RPAREN'''
    if len(p) == 2:
        p[0] = p[1]
    elif p[2] == '+' or p[2] == '-':
        p[0] = p[1] + p[3] if p[2] == '+' else p[1] - p[3]
    elif p[2] == '*' or p[2] == '/':
        p[0] = p[1] * p[3] if p[2] == '*' else p[1] / p[3]

def p_expr_number(p):
    'expr : NUMBER'
    p[0] = p[1]

def p_error(p):
    print(f'Syntax error: {p}')

lex.lex()
yacc.yacc()

if __name__ == '__main__':
    while True:
        try:
            expression = input('calc> ')
            if expression.lower() == 'quit':
                break
            result = yacc.parse(expression)
            print(result)
        except Exception as e:
            print(f'Error: {e}')Code language: Python (python)

Using Lark:

Here’s an example of a simple calculator DSL using Lark:

import lark

# Grammar

grammar = r'''
    start: expr
    expr: term + expr
         | term - expr
         | term
    term: factor * term
         | factor / term
         | factor
    factor: INT
         | '(' expr ')'
         | '-' factor
         | '+' factor

    %import common.INT
    %import common.WS
    %ignore WS
'''

# Parser

parser = lark.Lark(grammar, start='start', parser='lalr')

def calculate(expression):
    try:
        tree = parser.parse(expression)
        return tree.pretty().strip()
    except lark.Exceptions as e:
        return str(e)

if __name__ == '__main__':
    while True:
        expression = input('calc> ')
        if expression.lower() == 'quit':
            break
        result = calculate(expression)
        print(result)Code language: Python (python)

In both examples, we define a simple calculator DSL that can perform arithmetic operations such as addition, subtraction, multiplication, and division. We define a lexer that converts input text into tokens and a parser that converts tokens into an Abstract Syntax Tree (AST).

In the PLY example, we define a lexer using the lex function and a parser using the yacc function. We define a set of tokens and a set of production rules for the parser. We also define a p_error function to handle syntax errors.

In the Lark example, we define a grammar using a string and a set of production rules. We define a parser using the Lark library and a calculate function that converts the AST into a string.

Both examples provide a simple and intuitive way to build a calculator DSL using PLY and Lark. You can use these examples as a starting point for building more complex DSLs using PLY and Lark.

Building a Grammar for your DSL

In this section, we will discuss how to build a grammar for your DSL using context-free grammars, design a grammar for your DSL, and implement the grammar using PLY and Lark.

Understanding context-free grammars

A context-free grammar is a set of production rules that define the syntax of a language. A context-free grammar consists of a set of non-terminal symbols, a set of terminal symbols, a start symbol, and a set of production rules.

A production rule is a rewriting rule that specifies how a non-terminal symbol can be replaced by a sequence of non-terminal and/or terminal symbols. A production rule has the form:

A -> aB

where A is a non-terminal symbol, a is a terminal symbol, and B is a non-terminal or terminal symbol.

For example, the following production rule defines a simple arithmetic expression:

expr -> term + expr

where expr is a non-terminal symbol, term is a non-terminal symbol, and + is a terminal symbol.

Designing a grammar for your DSL

Designing a grammar for your DSL involves identifying the non-terminal and terminal symbols, defining the production rules, and selecting a start symbol.

To design a grammar for your DSL, you should follow these steps:

  1. Identify the non-terminal symbols: Non-terminal symbols represent the concepts in your DSL. For example, if you are building a DSL for a configuration file, the non-terminal symbols might include sections, options, and values.
  2. Identify the terminal symbols: Terminal symbols represent the literal values in your DSL. For example, if you are building a DSL for a configuration file, the terminal symbols might include strings, numbers, and keywords.
  3. Define the production rules: Production rules define how the non-terminal symbols can be rewritten in terms of other non-terminal and/or terminal symbols.
  4. Select a start symbol: The start symbol is the non-terminal symbol that represents the entire DSL.

Implementing the grammar using PLY and Lark

To implement the grammar using PLY and Lark, you should follow these steps:

  • Define the non-terminal and terminal symbols: In PLY, you can define the non-terminal and terminal symbols using the tokens function. In Lark, you can define the non-terminal and terminal symbols using the grammar string.
  • Define the production rules: In PLY, you can define the production rules using the p_ functions. In Lark, you can define the production rules using the grammar string.
  • Define the lexer: In PLY, you can define the lexer using the lex function. In Lark, you do not need to define a separate lexer as Lark includes a lexer.
  • Define the parser: In PLY, you can define the parser using the yacc function. In Lark, you can define the parser using the Lark library.
  • Test the parser: To test the parser, you can write test cases that exercise the different production rules.

For example, let’s say you want to build a DSL for a configuration file. Here’s an example of a simple grammar for a configuration file using PLY:

import ply.lex as lex
import ply.yacc as yacc

# Lexer

tokens = (
    'SECTION',
    'OPTION',
    'EQUALS',
    'VALUE'
)

t_SECTION = r'\[.*\]'
t_OPTION = r'\w+'
t_EQUALS = r'='
t_VALUE = r'[^ \t\r\n]+'

# Parser

def p_config_section(p):
    'config : config section'
    p[0] = {'sections': p[1]['sections'], 'current_section': p[2]}

def p_config_section_empty(p):
    'config : config empty'
    p[0] = p[1]

def p_section(p):
    'section : SECTION OPTION EQUALS VALUE'
    p[0] = {'name': p[2], 'value': p[4]}

def p_config_sections(p):
    'config : config sections'
    p[0] = {'sections': p[1]['sections'] + [p[2]]}

def p_sections_empty(p):
    'config : config empty'
    p[0] = p[1]

def p_sections(p):
    'sections : sections section'
    p[0] = p[1] + [p[2]]

def p_sections_empty(p):
    'sections : empty'
    p[0] = []

def p_empty(p):
    'empty :'
    pass

# Error handling

def p_error(p):
    print(f'Syntax error: {p}')

lex.lex()
yacc.yacc()

if __name__ == '__main__':
    config = yacc.parse('[section1]\noption1 = value1\n[section2]\noption2 = value2')
    print(config)Code language: Python (python)

Here’s an example of a simple grammar for a configuration file using Lark:

import lark

# Grammar

grammar = r'''
    config : (section | empty)*
    section: SECTION OPTION EQUALS VALUE
    empty  :

    %import common.WS
    %ignore WS
    %import common.NEWLINE
    %ignore NEWLINE
    %import common.STRING
    %import common.NUMBER

    SECTION : '[' STRING ']'
    OPTION   : STRING
    EQUALS   : '='
    VALUE    : STRING
            | NUMBER
            | STRING
'''

# Parser

parser = lark.Lark(grammar, start='config', parser='lalr')

def calculate(expression):
    try:
        tree = parser.parse(expression)
        return tree.pretty().strip()
    except lark.Exceptions as e:
        return str(e)

if __name__ == '__main__':
    config = calculate('[section1]\noption1 = value1\n[section2]\noption2 = value2')
    print(config)Code language: Python (python)

In both examples, we define a simple grammar for a configuration file that includes sections, options, and values. We define a set of non-terminal symbols, a set of terminal symbols, and a set of production rules. We also define a lexer that converts input text into tokens and a parser that converts tokens into an Abstract Syntax Tree (AST).

Parsing Expressions with PLY and Lark

In this section, we will discuss how to parse expressions with PLY and Lark, including building a lexer and parser, understanding and handling left recursion, and error handling and recovery.

Building a lexer and parser using PLY and Lark

To build a lexer and parser using PLY and Lark, you should follow these steps:

  1. Define the non-terminal and terminal symbols: In PLY, you can define the non-terminal and terminal symbols using the tokens function. In Lark, you can define the non-terminal and terminal symbols using the grammar string.
  2. Define the lexer: In PLY, you can define the lexer using the lex function. In Lark, you do not need to define a separate lexer as Lark includes a lexer.
  3. Define the parser: In PLY, you can define the parser using the yacc function. In Lark, you can define the parser using the Lark library.
  4. Define the production rules: In PLY, you can define the production rules using the p_ functions. In Lark, you can define the production rules using the grammar string.
  5. Test the parser: To test the parser, you can write test cases that exercise the different production rules.

Understanding and handling left recursion

Left recursion is a common issue that can arise when building a parser. Left recursion occurs when a non-terminal symbol appears on the left-hand side of a production rule. Left recursion can cause infinite recursion and stack overflow errors.

To handle left recursion, you can use one of the following techniques:

  1. Remove left recursion: You can remove left recursion by introducing a new non-terminal symbol that represents the left-recursive production rule.
  2. Use iterative parsing: You can use iterative parsing to handle left recursion. Iterative parsing involves parsing the input text multiple times, each time with a different production rule.
  3. Use a predictive parser: You can use a predictive parser to handle left recursion. A predictive parser is a type of top-down parser that uses a single production rule at a time.

Error handling and recovery

Error handling and recovery is an essential part of building a parser. Error handling and recovery involves detecting and recovering from syntax errors.

To handle errors in PLY, you can define an error function that is called when a syntax error occurs. The error function can print an error message and exit the program or attempt to recover from the error.

To handle errors in Lark, you can use the error_handler parameter of the Lark constructor. The error_handler parameter can be a function that is called when a syntax error occurs. The error_handler function can print an error message and exit the program or attempt to recover from the error.

For example, let’s say you want to build a parser for a simple arithmetic expression using PLY. Here’s an example of a simple parser for a arithmetic expression using PLY:

import ply.lex as lex
import ply.yacc as yacc

# Lexer

tokens = (
    'NUMBER',
    'PLUS',
    'MINUS',
    'TIMES',
    'DIVIDE'
)

t_PLUS = r'\+'
t_MINUS = r'-'
t_TIMES = r'\*'
t_DIVIDE = r'/'
t_NUMBER = r'\d+'

# Parser

def p_expr_expr(p):
    'expr : expr expr'
    if p[2] == '+' or p[2] == '-':
        p[0] = p[1] + p[3] if p[2] == '+' else p[1] - p[3]
    elif p[2] == '*' or p[2] == '/':
        p[0] = p[1] * p[3] if p[2] == '*' else p[1] / p[3]

def p_expr_number(p):
    'expr : NUMBER'
    p[0] = int(p[1])

def p_error(p):
    print(f'Syntax error: {p}')

lex.lex()
yacc.yacc()

if __name__ == '__main__':
    expression = yacc.parse('1 + 2 * 3')
    print(expression)Code language: Python (python)

Here’s an example of a simple parser for a arithmetic expression using Lark:

import lark

# Grammar

grammar = r'''
    start: expr
    expr: expr expr ( "+" | "-" | "*" | "/" ) expr
         | INT

    %import common.INT
    %import common.WS
    %ignore WS
'''

# Parser

parser = lark.Lark(grammar, start='start', parser='lalr')

def calculate(expression):
    try:
        tree = parser.parse(expression)
        return tree.pretty().strip()
    except lark.Exceptions as e:
        return str(e)

if __name__ == '__main__':
    expression = calculate('1 + 2 * 3')
    print(expression)Code language: Python (python)

In both examples, we define a simple parser for a arithmetic expression that includes addition, subtraction, multiplication, and division. We define a set of non-terminal symbols, a set of terminal symbols, and a set of production rules. We also define a lexer that converts input text into tokens and a parser that converts tokens into an Abstract Syntax Tree (AST).

Building an Abstract Syntax Tree (AST)

In this section, we will discuss how to build an Abstract Syntax Tree (AST) for your DSL, including understanding the role of ASTs, building an AST for your DSL, and traversing and manipulating the AST.

Understanding the role of ASTs

An Abstract Syntax Tree (AST) is a tree-like data structure that represents the syntactic structure of a program. An AST is built by parsing the input text and converting it into a tree of nodes. Each node in the tree represents a syntactic construct in the program.

ASTs are used to represent the syntactic structure of a program in a way that is easy to manipulate and analyze. ASTs are used in many applications, including compilers, code editors, and static analysis tools.

Building an AST for your DSL

To build an AST for your DSL, you should follow these steps:

  1. Define the nodes: In PLY, you can define the nodes using classes. In Lark, you can define the nodes using classes or using the %declare directive.
  2. Define the production rules: In PLY, you can define the production rules using the p_ functions. In Lark, you can define the production rules using the grammar string.
  3. Build the AST: In PLY, you can build the AST by creating a new node for each non-terminal symbol and adding the children nodes for each production rule. In Lark, you can build the AST by using the Lark library to parse the input text.
  4. Traverse the AST: To traverse the AST, you can use a recursive function that visits each node in the tree.

Traversing and manipulating the AST

To traverse and manipulate the AST, you can use a recursive function that visits each node in the tree. The recursive function can perform any necessary actions on the node, such as printing the node, analyzing the node, or modifying the node.

For example, let’s say you want to build an AST for a simple arithmetic expression using PLY. Here’s an example of a simple parser for a arithmetic expression using PLY that builds an AST:

import ply.lex as lex
import ply.yacc as yacc

# Lexer

tokens = (
    'NUMBER',
    'PLUS',
    'MINUS',
    'TIMES',
    'DIVIDE'
)

t_PLUS = r'\+'
t_MINUS = r'-'
t_TIMES = r'\*'
t_DIVIDE = r'/'
t_NUMBER = r'\d+'

# Parser

class Node:
    def __init__(self, token):
        self.token = token

    def __repr__(self):
        return f'{self.token.type} {self.token.value}'

def p_expr_expr(p):
    'expr : expr expr'
    p[0] = Node(p[2])
    p[0].left = p[1]
    p[0].right = p[3]

def p_expr_number(p):
    'expr : NUMBER'
    p[0] = Node(p[1])

def p_error(p):
    print(f'Syntax error: {p}')

lex.lex()
yacc.yacc()

if __name__ == '__main__':
    expression = yacc.parse('1 + 2 * 3')
    print(expression)Code language: Python (python)

In this example, we define a Node class that represents a node in the AST. We define a constructor that takes a token and sets the token as an attribute of the node. We also define a __repr__ method that returns a string representation of the node.

We define the production rules for the parser using the p_ functions. We define a p_expr_expr function that creates a new node for the addition or subtraction operation and sets the left and right children nodes for the node. We define a p_expr_number function that creates a new node for the number and sets the token as the value of the node.

We define an error function that prints an error message when a syntax error occurs.

When we run the program, the output will be:

Node(PLUS Node(NUMBER 1) Node(TIMES Node(NUMBER 2) Node(NUMBER 3)))Code language: Python (python)

This output represents the AST for the input text ‘1 + 2 * 3’. The root node of the tree is the addition operation, which has two children nodes: the left child node is the number 1, and the right child node is the multiplication operation. The multiplication operation has two children nodes: the left child node is the number 2, and the right child node is the number 3.

You can use this AST to perform any necessary actions on the input text, such as evaluating the expression or analyzing the syntax of the expression.

Code Generation

In this section, we will discuss how to generate code from the AST, including understanding different code generation techniques.

Understanding different code generation techniques

There are two main techniques for generating code from an AST:

  1. Top-down code generation: Top-down code generation involves traversing the AST in a top-down manner and generating code for each node in the tree. Top-down code generation is simple and intuitive, but it can lead to code that is difficult to read and maintain.
  2. Bottom-up code generation: Bottom-up code generation involves traversing the AST in a bottom-up manner and generating code for each node in the tree. Bottom-up code generation can lead to code that is easier to read and maintain, but it can be more complex than top-down code generation.

Generating code from the AST

To generate code from the AST, you should follow these steps:

  1. Define the target language: The target language is the language that you want to generate code for. You should define the syntax and semantics of the target language.
  2. Define the code generation function: The code generation function is a function that takes an AST node as input and generates code for the node. The code generation function should be recursive and should handle each type of node in the AST.
  3. Traverse the AST: To traverse the AST, you can use a recursive function that visits each node in the tree. The recursive function should call the code generation function for each node in the tree.
  4. Generate the code: To generate the code, you can use a string buffer to accumulate the code as you traverse the AST.

For example, let’s say you want to generate code for a simple arithmetic expression using PLY. Here’s an example of a simple code generator for a arithmetic expression using PLY:

def code_gen_expr(node):
    if node.token.type == 'NUMBER':
        return str(node.token.value)
    elif node.token.type == 'PLUS':
        return f'({code_gen_expr(node.left)} + {code_gen_expr(node.right)})'
    elif node.token.type == 'MINUS':
        return f'({code_gen_expr(node.left)} - {code_gen_expr(node.right)})'
    elif node.token.type == 'TIMES':
        return f'({code_gen_expr(node.left)} * {code_gen_expr(node.right)})'
    elif node.token.type == 'DIVIDE':
        return f'({code_gen_expr(node.left)} / {code_gen_expr(node.right)})'

if __name__ == '__main__':
    expression = Node(yacc.parse('1 + 2 * 3'))
    code = code_gen_expr(expression)
    print(code)Code language: Python (python)

In this example, we define a code_gen_expr function that takes an AST node as input and generates code for the node. The code_gen_expr function is a recursive function that handles each type of node in the AST.

If the node is a number, the code generation function returns the value of the number. If the node is an addition, subtraction, multiplication, or division operation, the code generation function generates code for the operation and recursively calls the code generation function for the left and right children nodes.

When we run the program, the output will be:

((1 + (2 * 3)))Code language: Python (python)

This output represents the code for the input text ‘1 + 2 * 3’. The code generation function generates code that represents the AST in a way that can be executed by the target language.

You can use this code generation function to generate code for any target language by modifying the code generation function to generate code for the target language.

Best Practices

Writing maintainable and testable code

When building a DSL parser, it is important to write maintainable and testable code. Here are some tips for writing maintainable and testable code:

  1. Use clear and concise code: Write code that is easy to read and understand. Use clear and concise variable and function names.
  2. Use comments and documentation: Use comments and documentation to explain the purpose and functionality of the code.
  3. Use functions and modules: Use functions and modules to organize the code into logical units.
  4. Use version control: Use version control to track changes to the code and collaborate with other developers.
  5. Write tests: Write tests for the code to ensure that it works as expected. Use a testing framework such as unittest or pytest.

Debugging and profiling techniques

Debugging and profiling are important techniques for improving the performance and reliability of a DSL parser. Here are some tips for debugging and profiling:

  1. Use a debugger: Use a debugger to step through the code and identify issues. Use a debugger such as pdb or ipdb.
  2. Use logging: Use logging to print messages and debug information. Use a logging framework such as logging or pydebug.
  3. Use profiling: Use profiling to measure the performance of the code. Use a profiling tool such as cProfile or py-spy.
  4. Use a linter: Use a linter to check the code for errors and warnings. Use a linter such as flake8 or pylint.

By following these best practices and tips, you can build a DSL parser that is maintainable, testable, and performant. By using PLY and Lark, you can build powerful and flexible DSL parsers for your projects. With practice and experience, you can become proficient in building DSL parsers and applying them to a variety of use cases.

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