Skip to content

Latest commit

 

History

History
117 lines (86 loc) · 3.51 KB

server.js_documentation.md

File metadata and controls

117 lines (86 loc) · 3.51 KB

Parse JSON Data

Description

The approach to parsing and validating JSON data depends heavily on the programming language you're using. Below are examples in Python and JavaScript, demonstrating different validation strategies. These examples assume you have a predefined JSON schema representing the expected structure.

Python

Python offers excellent JSON handling with its built-in json library and robust schema validation with libraries like jsonschema.

import json
from jsonschema import validate, ValidationError

# Sample JSON data
json_data = """
{
  "name": "John Doe",
  "age": 30,
  "city": "New York"
}
"""

# JSON Schema defining the expected structure
schema = {
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "age": {"type": "integer", "minimum": 0},
    "city": {"type": "string"}
  },
  "required": ["name", "age", "city"]
}

try:
  # Load JSON data
  data = json.loads(json_data)

  # Validate against the schema
  validate(instance=data, schema=schema)
  print("JSON data is valid.")

except json.JSONDecodeError as e:
  print(f"Invalid JSON: {e}")

except ValidationError as e:
  print(f"JSON schema validation error: {e}")

This Python code first attempts to parse the JSON string using json.loads(). If successful, it uses jsonschema.validate() to check if the data conforms to the predefined schema. Error handling catches both JSON parsing errors and schema validation failures, providing informative messages.

JavaScript

JavaScript uses JSON.parse() for parsing and often leverages libraries like ajv (Another JSON Schema Validator) for schema validation.

const jsonData = `{
  "name": "John Doe",
  "age": 30,
  "city": "New York"
}`;

const schema = {
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "age": {"type": "integer", "minimum": 0},
    "city": {"type": "string"}
  },
  "required": ["name", "age", "city"]
};

try {
  const data = JSON.parse(jsonData);

  //Using ajv (requires installation: npm install ajv)
  const Ajv = require('ajv');
  const ajv = new Ajv();
  const validate = ajv.compile(schema);
  const valid = validate(data);

  if (valid) {
    console.log("JSON data is valid.");
  } else {
    console.error("JSON schema validation error:", validate.errors);
  }

} catch (e) {
  if (e instanceof SyntaxError) {
    console.error("Invalid JSON:", e);
  } else {
    console.error("An error occurred:", e);
  }
}

This JavaScript code similarly handles JSON parsing and validation. It uses ajv for schema validation, providing detailed error messages if validation fails. Remember to install ajv using npm install ajv.

Key Considerations:

  • Schema Definition: A well-defined JSON schema is crucial for effective validation. Consider using tools like online schema generators or editors to create and manage your schemas.
  • Error Handling: Robust error handling is essential to gracefully manage invalid JSON or schema violations.
  • Schema Complexity: For very complex schemas, consider using more advanced validation libraries that offer features like custom validation functions and support for different schema versions.
  • Performance: For high-volume data processing, optimize your validation process to avoid performance bottlenecks.

Remember to replace the sample JSON data and schema with your actual data and schema. Choose the language and approach that best suits your project's requirements.

Developed by

Hacı Mert Gökhan

Last updated: 2025-01-11 17:44:33