Online PySpark to Raku Converter
Click to select or drop your input code file here.
You can also type the input code below.
How to use this tool?
This free online converter lets you convert code from PySpark to Raku in a click of a button. To use this converter, take the following steps -
- Type or paste your PySpark code in the input box.
- Click the convert button.
- The resulting Raku code from the conversion will be displayed in the output box.
Key differences between PySpark and Raku
| Characteristic | PySpark | Raku |
|---|---|---|
| Syntax | Python-based, uses familiar Python syntax with additional Spark-specific APIs. | Unique syntax, evolved from Perl, supports multiple paradigms and flexible constructs. |
| Paradigm | Primarily functional and data-parallel, supports object-oriented features via Python. | Multi-paradigm: procedural, object-oriented, functional, concurrent, and more. |
| Typing | Dynamically typed (inherits Python's dynamic typing). | Gradually typed: supports both dynamic and static typing. |
| Performance | Optimized for distributed data processing; performance depends on Spark cluster. | Generally slower than mainstream languages; not optimized for big data or distributed computing. |
| Libraries and frameworks | Rich ecosystem for big data, machine learning, and analytics via Spark and Python libraries. | Smaller ecosystem; fewer libraries, mostly general-purpose and some niche domains. |
| Community and support | Large, active community; strong support from Apache, extensive documentation. | Smaller, passionate community; limited resources and support compared to mainstream languages. |
| Learning curve | Moderate if familiar with Python; Spark concepts may require additional learning. | Steep, due to unique syntax and advanced features. |
