Online PySpark to Gleam 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 Gleam 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 Gleam code from the conversion will be displayed in the output box.
Key differences between PySpark and Gleam
| Characteristic | PySpark | Gleam |
|---|---|---|
| Syntax | Pythonic syntax with DataFrame and RDD APIs, similar to pandas but adapted for distributed computing. | Statically typed, functional syntax inspired by Erlang and Elm, with pattern matching and immutable data structures. |
| Paradigm | Distributed data processing using functional programming concepts (map, reduce, filter) on large datasets. | Purely functional programming with strong emphasis on immutability and concurrency, targeting the BEAM VM. |
| Typing | Dynamically typed (Python), with optional type hints in recent versions. | Statically typed with a strong, inferred type system. |
| Performance | High performance for big data workloads due to distributed execution on Spark clusters. | Efficient for concurrent and fault-tolerant applications on the BEAM VM, but not designed for big data processing. |
| Libraries and frameworks | Rich ecosystem for data processing, machine learning (MLlib), and integration with Hadoop, Hive, etc. | Smaller ecosystem, mainly focused on building reliable backend services and leveraging Erlang/Elixir libraries. |
| Community and support | Large, mature community with extensive documentation and enterprise support. | Growing but small community, with active development and increasing resources. |
| Learning curve | Moderate for Python users, but distributed computing concepts can be challenging. | Steep for those new to functional programming and static typing, but approachable for those familiar with BEAM languages. |
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