About Spark Schema Generator Tool

Simply paste your JSON array/object or CSV data, select input and output formats, then click "Generate Schema". The tool will instantly create the appropriate Spark schema definition.

Input Options

Choose between JSON or CSV input formats. For best results, provide a complete sample of your data structure.

Output Options

Generate schemas as PySpark StructType code, Spark SQL DDL or JSON Schema. The "Treat all strings as StringType" option prevents automatic type inference for strings.

Key Features

• Multiple Input Formats: Support for both JSON and CSV data structures
• Multi Output Options: Generate PySpark StructType code, JSON Schema or Spark SQL DDL
• Automatic Type Detection: Intelligently infers data types from sample data
• Complex Structure Support: Handles nested objects and arrays
• Strict String Mode: Option to treat all values as strings when needed

Frequently Asked Questions

What is a Spark Schema?

A Spark schema defines the structure of your data, including field names and data types. It's crucial for processing structured data efficiently in Apache Spark applications.

Why use a Schema Generator instead of manual schema definition?

Manual schema definition is time-consuming and error-prone, especially with complex data structures. This tool automatically generates accurate schemas in seconds, saving development time.

How does the automatic type detection work?

The tool analyzes your sample data to determine appropriate Spark data types. For example, it detects integers, doubles, booleans, timestamps, and strings based on the value patterns.

Can it handle nested structures?

Yes, the generator automatically detects nested objects and arrays and creates the appropriate StructType and ArrayType definitions in the resulting schema.