Sometimes, a standard CLI tool isn’t enough, but a full-blown programming language is overkill. This is where a Domain Specific Language (DSL) shines. A DSL is a mini-language designed to solve a very specific problem—think SQL for databases or HTML for web structure.
In this tutorial, we’ll build a simple DSL to calculate the total cost of a real estate portfolio. This will combine concepts from our Lexer and Indentation Analyser guides into a practical application.
The Vision: Our Custom Syntax
We want a language that looks like this:
# Real Estate Calculator
house num 100
house price 250,000
calculate price
It’s readable, human-friendly, and perfectly tailored to our task.
Implementation: The Parser
A simple way to build a DSL in Python is to read the source code line-by-line and split each line into “tokens.”
import locale
# Sample source code in our custom DSL
source_code = '''
# Property Analysis
house num 12
house price 350,000
calculate price
'''
def parse_dsl(source):
num_houses = 0
unit_price = 0
lines = source.strip().split('\n')
for line in lines:
tokens = line.split()
# 1. Ignore comments and empty lines
if not tokens or tokens[0] == '#':
continue
# 2. Handle 'house' commands
if tokens[0] == 'house':
if tokens[1] == 'num':
num_houses = int(tokens[2])
elif tokens[1] == 'price':
# Remove commas and convert to int
unit_price = int(tokens[2].replace(',', ''))
# 3. Handle 'calculate' commands
elif tokens[0] == 'calculate':
if tokens[1] == 'price':
total = num_houses * unit_price
print(f"Total Portfolio Value: {format_currency(total)}")
def format_currency(amount):
# Standard Python formatting for currency
return "${:,.2f}".format(amount)
# Run our DSL
parse_dsl(source_code)
Why go through the trouble?
1. Empowerment
DSLs allow non-programmers (like accountants or researchers) to write complex logic using a syntax that makes sense to them.
2. Validation
Because your DSL is specialized, your parser can catch errors that a general language wouldn’t. For example, your parser can throw an error if someone tries to set a negative number of houses.
3. Maintainability
If the logic for calculating “total price” changes (e.g., adding a tax rate), you only need to change it in one place (the Python function), and all your DSL scripts will automatically use the new logic.
Conclusion
Building a DSL is the ultimate form of automation. You aren’t just writing a script; you’re writing a tool that allows others to write their own scripts.
If you want to take this further, check out the ply or lark libraries in Python—they are designed to help you build much more complex and powerful languages!
Further Reading:
Written by
Abdur-Rahmaan Janhangeer
Chef
Python author of 7+ years having worked for Python companies around the world
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