DataWeave-Py

A DataWeave data transformation runtime with a Rust-native engine package and a Python bridge, providing powerful data transformation capabilities without requiring the JVM.

Install from PyPI:

uv add dataweave-py

or

pip install dataweave-py

Install the terminal command with uv:

uv tool install dataweave-py
dw run '%dw 2.0\noutput application/json\n---\n{message: upper(payload.name)}' --payload '{"name":"dw"}'

DataWeave Playground

For the best DataWeave Playground a without payload size limits DataWeave Playground, visit: https://dataweavelang.org

Optional extras:

# pandas helpers (DataFrame/Series input normalization)
pip install "dataweave-py[pandas]"

# pydantic helpers
pip install "dataweave-py[pydantic]"

# everything
pip install "dataweave-py[full]"

Overview

DataWeave-Py (dwpy) is a Python-facing interpreter for the DataWeave language, originally developed by MuleSoft for data transformation in the Mule runtime. The runtime is migrating to a Rust core while preserving the existing Python API, enabling:

• Data transformation: Convert between JSON, XML, CSV and other formats
• Functional programming: Leverage map, filter, reduce, and other functional operators
• Pattern matching: Use powerful match expressions with guards and bindings
• Safe navigation: Handle null values gracefully with null-safe operators
• Rich built-ins: Access 100+ built-in functions for strings, numbers, dates, arrays, and objects

Requirements

• Python 3.10 or higher
• Rust stable toolchain with cargo
• Node.js, npm, wasm-pack, and the wasm32-unknown-unknown target for the WASM backend
• Dependencies managed via uv (recommended) or pip

Rust Engine And Python Bridge

The default runtime path is the Rust engine exposed through the Python package as dwpy._dwpy_rust. The legacy Python interpreter is still available as an explicit fallback backend.

Build and install the Rust-backed Python bridge into the local virtual environment:

uv venv --python 3.12
source .venv/bin/activate
UV_CACHE_DIR=.uv-cache uv run maturin develop --release

Run the Python-packaged CLI from the current checkout:

uv tool install .
dw run '%dw 2.0\noutput application/python\n---\n{message: upper(payload.name)}' --payload '{"name":"dw"}'

Or run it without installing globally:

uvx --from . dw run '%dw 2.0\noutput application/json\n---\npayload' --payload '{"name":"dw"}'

Run the standalone native Rust binary:

cargo run -p dwpy-cli -- run '%dw 2.0\noutput application/json\n---\npayload' --payload '{"name":"dw"}'

Rust embedders can compile once and execute repeatedly. Lazy collection pipelines are enabled by default, and materializing a lazy result is limited to 256 MiB unless the caller supplies a different budget:

use dwpy_core::{compile, ExecutionOptions};
use serde_json::Value;

let script = compile(
    "%dw 2.0\noutput application/json\n---\n\
     1 to 1000000 reduce ((item, accum = 0) -> item + accum)",
)?;
let result = script.execute(Value::Null, None, &ExecutionOptions::default())?;
assert_eq!(result, Value::String("500000500000".to_string()));

// Render directly to any std::io::Write sink. Streaming sink failures are
// non-atomic and may leave a valid output prefix in the writer.
let mut bytes = Vec::new();
script.execute_to_writer(
    Value::Null,
    None,
    &ExecutionOptions::default(),
    &mut bytes,
)?;

The native CLI uses the sink API for streaming JSON, NDJSON, and CSV output. Expressions outside the compiled lazy subset continue through the compatible legacy Rust evaluator.

Homebrew

The Rust-native CLI is distributed through a separate Homebrew tap repository: estebanwasinger/homebrew-tap.

The intended user flow is:

brew tap estebanwasinger/homebrew-tap
brew install dw-cli
dw run "1 to 1"

This repository includes:

• scripts/build_dw_cli_source_archive.sh to build the deterministic source archive used by the formula
• .github/workflows/release-dw-cli-source.yml to upload that archive to a GitHub release

The release split is intentional:

• dataweave-py releases provide the source archive consumed by the formula
• homebrew-tap releases provide the bottled Homebrew binaries

Run the Rust backend from Python:

from dwpy import DataWeaveRuntime

runtime = DataWeaveRuntime(backend="rust")
result = runtime.execute(
    "%dw 2.0\noutput application/json\n---\n{message: upper(payload.message)}",
    {"message": "hello from rust"},
)
print(result)

Backend selection:

• DataWeaveRuntime() or backend="auto" uses the Rust bridge first and falls back to the legacy Python backend only for explicitly unsupported migration gaps.
• DataWeaveRuntime(backend="rust") runs strict Rust mode and fails instead of falling back.
• DataWeaveRuntime(backend="python") uses the legacy Python interpreter.
• DataWeaveRuntime(backend="wasm") runs the Rust evaluator compiled to WASM through the Node.js bridge.
• DWPY_BACKEND=rust forces strict Rust mode for process-wide test runs.

Build a distributable wheel with the Rust extension:

UV_CACHE_DIR=.uv-cache uv run maturin build --release

Run the Rust workspace tests:

cargo test --workspace

Run the Python suite against the Rust backend:

DWPY_BACKEND=rust UV_CACHE_DIR=.uv-cache uv run --extra dev pytest

Run the default Python package path, which exercises the Python bridge:

UV_CACHE_DIR=.uv-cache uv run --extra dev pytest

Run the Python suite in all three runtime modes—legacy Python, native Rust, and Rust compiled to WASM:

bash scripts/test_backends.sh

The script rebuilds the WASM package before its test pass and preserves the individual pytest results. It accepts normal pytest arguments for focused runs, for example:

bash scripts/test_backends.sh tests/test_runtime_basic.py -k dynamic

Quick Start

Basic Usage

from dwpy import DataWeaveRuntime

# Create a runtime instance
runtime = DataWeaveRuntime()

# Define a DataWeave script
script = """%dw 2.0
output application/json
---
{
  message: "Hello, " ++ upper(payload.name),
  timestamp: now()
}
"""

# Execute with a payload
payload = {"name": "world"}
result = runtime.execute(script, payload)

print(result)
# Output: {'message': 'Hello, WORLD', 'timestamp': '2025-11-03T...Z'}

Data Transformation Example

from dwpy import DataWeaveRuntime

runtime = DataWeaveRuntime()

# Transform and enrich order data
script = """%dw 2.0
output application/json
---
{
  orderId: payload.id,
  status: upper(payload.status default "pending"),
  total: payload.items reduce ((item, acc = 0) -> 
    acc + (item.price * (item.quantity default 1))
  ),
  itemCount: sizeOf(payload.items)
}
"""

payload = {
    "id": "ORD-123",
    "status": "confirmed",
    "items": [
        {"price": 29.99, "quantity": 2},
        {"price": 15.50, "quantity": 1}
    ]
}

result = runtime.execute(script, payload)
print(result)
# Output: {'orderId': 'ORD-123', 'status': 'CONFIRMED', 'total': 75.48, 'itemCount': 2}

Using Variables

from dwpy import DataWeaveRuntime

runtime = DataWeaveRuntime()

script = """%dw 2.0
output application/json
var requestTime = vars.requestTime default now()
---
{
  user: payload.userId,
  processedAt: requestTime
}
"""

payload = {"userId": "U-456"}
vars = {"requestTime": "2024-05-05T12:00:00Z"}

result = runtime.execute(script, payload, vars=vars)

Pattern Matching

from dwpy import DataWeaveRuntime

runtime = DataWeaveRuntime()

script = """%dw 2.0
output application/json
---
{
  category: payload.price match {
    case var p when p > 100 -> "premium",
    case var p when p > 50 -> "standard",
    else -> "budget"
  }
}
"""

result = runtime.execute(script, {"price": 75})
# Output: {'category': 'standard'}

String Interpolation

from dwpy import DataWeaveRuntime

runtime = DataWeaveRuntime()

# Simple interpolation
script = """%dw 2.0
output application/json
---
{
  greeting: "Hello $(payload.name)!",
  total: "Total: $(payload.price * payload.quantity)",
  status: "Order $(payload.orderId) is $(upper(payload.status))"
}
"""

payload = {
    "name": "Alice",
    "price": 10.5,
    "quantity": 3,
    "orderId": "ORD-123",
    "status": "confirmed"
}

result = runtime.execute(script, payload)
# Output: {
#   'greeting': 'Hello Alice!',
#   'total': 'Total: 31.5',
#   'status': 'Order ORD-123 is CONFIRMED'
# }

String interpolation allows you to embed expressions directly within strings using the $(expression) syntax. The expression can be:

• Property access: $(payload.name)
• Nested properties: $(payload.user.email)
• Expressions: $(payload.price * 1.1)
• Function calls: $(upper(payload.status))
• Any valid DataWeave expression

Output Formats

The runtime supports these output directives:

• application/python (native Python objects)
• application/json
• application/x-ndjson (also accepts ndjson and application/x-ldjson)
• application/csv
• application/xml
• text/plain
• text/markdown

Format-specific notes:

• application/x-ndjson maps newline-delimited JSON records to an array. Empty lines are ignored by default; use skipInvalid=true to skip malformed records. Writers emit one compact JSON record per line and support skipNullOn and writeAttributes.
• output text/plain only works when the final script result is a string.
• output text/markdown expects a tabular value (list or dict) and renders a Markdown table.
• output text/markdown header=false is rejected because Markdown table rendering requires headers.
• payload_format="text/markdown" parses Markdown pipe tables into structured rows (Array<Object> by default, or Array<Array<String>> with payload_format_options={"header": False}).

Supported Features

DataWeave-Py currently supports a wide range of DataWeave language features:

Core Language Features

• ✅ Header directives (%dw 2.0, output, var, import)
• ✅ Payload and variable access
• ✅ Object and array literals
• ✅ Field selectors (.field, ?.field, [index])
• ✅ Comments (line // and block /* */)
• ✅ Default values (payload.field default "fallback")
• ✅ String interpolation ("Hello $(payload.name)")

Operators

• ✅ Concatenation (++)
• ✅ Difference (--)
• ✅ Arithmetic (+, -, *, /)
• ✅ Comparison (==, !=, >, <, >=, <=)
• ✅ Logical (and, or, not)
• ✅ Range (to)

Control Flow

• ✅ Conditional expressions (if-else)
• ✅ Pattern matching (match-case)
• ✅ Match guards (case var x when condition)

Collection Operations

• ✅ map - Transform elements
• ✅ filter - Select elements
• ✅ reduce - Aggregate values
• ✅ flatMap - Map and flatten
• ✅ distinctBy - Remove duplicates
• ✅ groupBy - Group by criteria
• ✅ orderBy - Sort elements

Built-in Functions

String Functions

upper, lower, trim, contains, startsWith, endsWith, isBlank, splitBy, joinBy, find, match, matches

Numeric Functions

abs, ceil, floor, round, pow, mod, sum, avg, max, min, random, randomInt, isDecimal, isInteger, isEven, isOdd

Array/Object Functions

sizeOf, isEmpty, flatten, indexOf, lastIndexOf, distinctBy, filterObject, keysOf, valuesOf, entriesOf, pluck, maxBy, minBy

Date Functions

now, isLeapYear, daysBetween

Utility Functions

log, logInfo, logDebug, logWarn, logError

Running Tests

The project includes comprehensive test coverage:

# Run all tests
pytest

# Run specific test file
pytest tests/test_runtime_basic.py

# Run with verbose output
pytest -v

# Run with coverage
pytest --cov=dwpy

Browser WASM (Pyodide)

The project includes a browser-worker runtime for WASM execution with Pyodide and wheel-based loading.

• Worker bootstrap: web/pyodide-worker.mjs
• Python entrypoint: dwpy.wasm_entry.run_dataweave(...)
• Full instructions: docs/WASM_PYODIDE.md

Language Server (LSP)

The project now includes a stdio Language Server for DataWeave:

• Command: dwpy-lsp
• Module: dwpy.lsp.server
• Engine shared with Monaco + WASM completion bridge: dwpy.lsp.engine

Install

Install the LSP extra:

uv pip install "dataweave-py[lsp]"

Sidecar context files

For structure-aware payload/vars completion in .dwl files, place these JSON files next to the script:

• <file>.payload.json
• <file>.vars.json

Example for transform.dwl:

• transform.dwl.payload.json
• transform.dwl.vars.json

If sidecars are missing or invalid, the server falls back to script-only inference.

VS Code client (example)

{
  "languageserver": {
    "dataweave-py": {
      "command": "dwpy-lsp",
      "filetypes": ["dataweave", "dwl"]
    }
  }
}

Neovim client (example)

require("lspconfig").dwpy_lsp.setup({
  cmd = { "dwpy-lsp" },
  filetypes = { "dataweave", "dwl" },
})

Project Structure

dataweave-py/
├── crates/                    # Rust workspace
│   ├── dwpy-core/             # Core Rust value model and engine foundation
│   ├── dwpy-python/           # PyO3 extension exposed as dwpy._dwpy_rust
│   └── dwpy-wasm/             # WASM wrapper foundation
├── dwpy/                      # Main Python package
│   ├── __init__.py           # Package exports
│   ├── parser.py             # DataWeave parser
│   ├── runtime.py            # Runtime backend facade
│   ├── _python_runtime.py    # Legacy Python interpreter backend
│   └── builtins.py           # Built-in functions
├── tests/                     # Test suite
│   ├── test_runtime_basic.py # Core functionality tests
│   ├── test_builtins.py      # Built-in function tests
│   └── fixtures/             # Test data and fixtures
├── runtime-2.11.0-20250825/  # Original JVM runtime reference
├── docs/                      # Documentation
├── pyproject.toml            # Project configuration
└── README.md                 # This file

Development

Setting Up Development Environment

# Create virtual environment
uv venv --python 3.12
source .venv/bin/activate

# Install Python development dependencies
UV_CACHE_DIR=.uv-cache uv sync --extra dev

# Build and install the Rust-backed Python bridge in editable mode
UV_CACHE_DIR=.uv-cache uv run maturin develop --release

Running the Test Suite

# Run all tests
UV_CACHE_DIR=.uv-cache uv run --extra dev pytest

# Force strict Rust backend
DWPY_BACKEND=rust UV_CACHE_DIR=.uv-cache uv run --extra dev pytest

# Run Rust workspace tests
cargo test --workspace

Code Style

The project follows standard Python conventions:

• PEP 8 style guide
• Type hints where appropriate
• Comprehensive docstrings
• Two-space indentation for consistency with Scala codebase

Comparison with JVM Runtime

DataWeave-Py aims to provide feature parity with the official JVM-based DataWeave runtime. Key differences:

Roadmap

Current Status (v0.1.0)

• ✅ Core language parser
• ✅ Expression evaluation
• ✅ 60+ built-in functions
• ✅ Pattern matching
• ✅ Collection operators

Planned Features

• 🔄 Full module system support
• 🔄 Import statements
• 🔄 Custom function definitions
• 🔄 XML/CSV format support
• 🔄 Streaming for large datasets
• 🔄 Type validation
• 🔄 Performance optimizations

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Write tests for your changes
4. Ensure all tests pass (pytest)
5. Commit your changes (git commit -m 'feat: add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

See the original DataWeave runtime license terms. This project is a reference implementation for educational and development purposes.

Resources

• DataWeave Documentation
• DataWeave Tutorial
• DataWeave Playground

Support

For questions, issues, or contributions:

• Open an issue on GitHub
• Check existing documentation in the docs/ directory
• Review test cases in tests/ for usage examples

Note: This is an independent Python implementation and is not officially supported by MuleSoft. For production use cases requiring full DataWeave compatibility, please use the official JVM-based runtime.