In the realm of developement, there are instances when a generic programming language might not fully capture the specificity of a domain or the nuances of a particular problem. This is where Domain-Specific Languages or DSLs come into play. DSLs are specialized languages designed to address specific problem domains, offering a more expressive and tailored solution.
In this blog post, I will guide you through the process of building a custom DSL in Python, a versatile and widely-used programming language. Before delving into the intricacies of building a custom DSL, it's crucial to grasp the fundamental concepts. A DSL, in essence, is a language tailored to a specific problem domain. In our case, it will be created using Python, a language renowned for its readability and simplicity.
YAML with DSL ?
YAML-DSL proves to be a suitable choice for both individuals, with technical backgrounds and for their non-technical colleagues, particularly when dealing with simple syntax.
tasks:
- title: Buy groceries
priority: high
completed: false
due_date: 2022-02-28
tags:
- shopping
- title: Finish report
priority: medium
completed: true
due_date: 2022-03-10
tags:
- work
subtasks:
- title: Research
completed: true
- title: Write
completed: true
- title: Edit
completed: false
YAML exhibits high readability and appears quite natural, especially when one doesn't focus on distinct brackets and colons. The second version, being more concise, may appear more convenient to certain individuals, as we can see above. You can find the source code on GitHub.
Now, what?
We must take this YAML File and convert it into an JSON, to make it usable in Insight's dashboard based on how long does it take user to complete it's task and substacks for example. Let's create schema.py where we will define the yaml schema based on above: -
task_list_schema = {
'type': 'object',
'properties': {
'tasks': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'title': {'type': 'string'},
'priority': {'type': 'string'},
'completed': {'type': 'boolean'},
'due_date': {'type': ['string', 'null']},
'tags': {'type': 'array', 'items': {'type': 'string'}},
'subtasks': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'title': {'type': 'string'},
'completed': {'type': 'boolean'}
},
'required': ['title', 'completed']
}
}
},
'required': ['title', 'priority', 'completed']
}
}
},
'required': ['tasks']
}
Here we can see few fields that are in format of datetime, let's first convert them into string before parsing them.
from schema import task_list_schema
# Function to convert date objects to strings recursively
def convert_dates_to_strings(obj):
if isinstance(obj, dict):
return {key: convert_dates_to_strings(value) for key, value in obj.items()}
elif isinstance(obj, list):
return [convert_dates_to_strings(item) for item in obj]
elif isinstance(obj, date):
return obj.strftime('%Y-%m-%d')
else:
return obj
def parse_dates(obj):
if isinstance(obj, dict):
return {key: parse_dates(value) for key, value in obj.items()}
elif isinstance(obj, list):
return [parse_dates(item) for item in obj]
elif isinstance(obj, (date, datetime)):
return obj.strftime('%Y-%m-%d')
elif isinstance(obj, str):
return obj
else:
return obj
With both of functions ready to work along with dates mentioned in the schema.py, let's move to loading yaml to the program functionality
# existing code
def load_tasks_from_yaml(yaml_string):
loaded_tasks = yaml.safe_load(yaml_string)
return parse_dates(loaded_tasks)
The function load_tasks_from_yaml takes a YAML string as input, loads it using yaml.safe_load to convert it into a Python object (likely a dictionary or a list), and then passes the loaded data through the parse_dates function. The purpose of calling parse_dates is to handle date objects within the loaded YAML data.
We need tovalidate the tasks object against the specified schema (task_list_schema), before we can convert the YAML to JSON
def validate_tasks(tasks):
try:
validate(instance=tasks, schema=task_list_schema)
print("Validation successful.")
except ValidationError as e:
print(f"Validation error: {e}")
The validate_tasks function checks if the provided set of tasks conforms to a predefined JSON schema. If the tasks pass validation, it prints a success message; otherwise, it prints an error message indicating the validation error. This type of validation is useful for ensuring that the structure of the tasks adheres to a specified format or set of rules.
Let's have one last step before we convert the YAML to JSON, is to make changes in subtask so in the modified yaml or JSON file we can verfiy the code worked perfectly fine or not.
# Load tasks from YAML DSL
tasks = load_tasks_from_yaml(yaml_dsl)
# Validate tasks against the schema
validate_tasks(tasks)
tasks['tasks'].append({
'title': 'Plan vacation',
'priority': 'medium',
'completed': False,
'due_date': date(2022, 4, 15),
'tags': ['travel'],
'subtasks': [
{'title': 'Choose destination', 'completed': False},
{'title': 'Book flights', 'completed': False},
{'title': 'Pack bags', 'completed': False}
]
})
With the changes done, it's time to write our conversion function().
def convert_yaml_to_json(yaml_string):
loaded_data = yaml.safe_load(yaml_string)
loaded_data = convert_dates_to_strings(loaded_data)
json_data = json.dumps(loaded_data, indent=2)
return json_data
The convert_yaml_to_json function converts a YAML string to a JSON string, handling date objects during the conversion process. This function can be useful when you need to work with data interchangeably in YAML and JSON formats, ensuring that date representations are consistent between the two formats.
But we need JSON file, right? Let's add code to create the folder, and save the json each time the file is run. Modify the above function as : -
def convert_yaml_to_json(yaml_string, folder_path):
loaded_data = yaml.safe_load(yaml_string)
loaded_data = convert_dates_to_strings(loaded_data)
json_data = json.dumps(loaded_data, indent=2)
# Create a folder if it doesn't exist
if not os.path.exists(folder_path):
os.makedirs(folder_path)
# Determine the next available file number
file_number = 1
while os.path.exists(os.path.join(folder_path, f'tasks_{file_number}.json')):
file_number += 1
# Save to a new JSON file with a numbered filename
json_filename = os.path.join(folder_path, f'tasks_{file_number}.json')
with io.open(json_filename, 'w', encoding='utf8') as json_file:
json_file.write(json_data)
return json_filename
Now, the convert_yaml_to_json function takes a YAML string, converts it to a JSON string, and saves the JSON data to a file with a numbered filename in the specified folder. This allows for the creation of multiple JSON files with incremental filenames, making it suitable for scenarios where you want to store different versions of the JSON data over time.
Let's add couple of line to see how it will work at the end of main.py
json_filename = convert_yaml_to_json(yaml_dsl, 'modified_json_tasks')
print("Original YAML DSL:")
print(yaml_dsl)
print(f"\nConverted YAML to JSON saved to {json_filename}")
Now, When you run main.py file, we can see a folder modified_json_task with task_1.json, each time the python file is executed, we can see a new file with file_number increased signifying the number of time the code has been executed.
And, That's it!!! You have successfully created your first YAML-DSL in python! 🎉
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