dagex 2026.14

A pure Rust DAG executor supporting implicit node connections, branching, and config sweeps

A pure Rust DAG executor with Python bindings

dagex

A pure Rust DAG (Directed Acyclic Graph) executor with Python bindings for building and executing computational pipelines.

Features

• Implicit Node Connections: Nodes automatically connect based on execution order
• Parallel Branching: Create fan-out execution paths with .branch()
• Configuration Variants: Use .variant() to create parameter sweeps
• Mermaid Visualization: Generate diagrams with .to_mermaid()
• Parallel Execution: Execute independent branches concurrently
• Native Performance: Rust-powered execution with Python convenience
• Memory Efficient: Zero-copy data sharing between nodes using Arc (18x performance improvement for large datasets)

Installation

pip install dagex

No Rust toolchain required - prebuilt wheels are available for all major platforms.

Quick Start

Basic Pipeline

import dagex

def data_source(inputs, variant_params):
    """Generate initial data"""
    return {"value": 42}

def multiply(inputs, variant_params):
    """Process the data"""
    val = inputs.get("x", 0)
    return {"doubled": val * 2}

# Create graph
graph = dagex.Graph()

# Add nodes
graph.add(
    function=data_source,
    label="DataSource",
    inputs=None,
    outputs=[("value", "data")]
)

graph.add(
    function=multiply,
    label="Multiply",
    inputs=[("data", "x")],
    outputs=[("doubled", "result")]
)

# Build and execute
dag = graph.build()
context = dag.execute()

print(f"Result: {context['result']}")  # Output: Result: 84

Parallel Branching

Create multiple parallel processing paths:

import dagex
import time

def source(inputs, variant_params):
    return {"data": 100}

def branch_a(inputs, variant_params):
    time.sleep(0.1)  # Simulate work
    return {"result_a": inputs["x"] * 2}

def branch_b(inputs, variant_params):
    time.sleep(0.1)  # Simulate work
    return {"result_b": inputs["x"] * 3}

def branch_c(inputs, variant_params):
    time.sleep(0.1)  # Simulate work
    return {"result_c": inputs["x"] + 50}

graph = dagex.Graph()

# Source node
graph.add(source, label="Source", outputs=[("data", "shared_data")])

# Create parallel branches
graph.branch()
graph.add(branch_a, label="BranchA", inputs=[("shared_data", "x")], outputs=[("result_a", "a")])

graph.branch()
graph.add(branch_b, label="BranchB", inputs=[("shared_data", "x")], outputs=[("result_b", "b")])

graph.branch()
graph.add(branch_c, label="BranchC", inputs=[("shared_data", "x")], outputs=[("result_c", "c")])

dag = graph.build()
result = dag.execute(parallel=True)

print(f"Branch A (100*2): {result['a']}")  # 200
print(f"Branch B (100*3): {result['b']}")  # 300
print(f"Branch C (100+50): {result['c']}")  # 150

Visualization

Generate Mermaid diagrams to visualize your pipeline:

print(dag.to_mermaid())

Output:

graph TD
    0["Source"]
    1["BranchA"]
    2["BranchB"]
    3["BranchC"]
    0 -->|shared_data → x| 1
    0 -->|shared_data → x| 2
    0 -->|shared_data → x| 3
    style 1 fill:#e1f5ff
    style 2 fill:#e1f5ff
    style 3 fill:#e1f5ff

Working with NumPy Arrays

dagex seamlessly handles NumPy arrays and complex numbers:

import dagex
import numpy as np

def generate_signal(inputs, variant_params):
    """Generate a complex signal"""
    samples = 256
    t = np.linspace(0, 1, samples)
    signal = np.exp(2j * np.pi * 10 * t)  # 10 Hz complex exponential
    return {
        "signal": signal,  # numpy array passed directly
        "num_samples": samples
    }

def process_signal(inputs, variant_params):
    """Process with FFT"""
    signal = np.array(inputs["signal"], dtype=complex)
    spectrum = np.fft.fft(signal)
    magnitude = np.abs(spectrum)
    peak_freq = np.argmax(magnitude)
    
    return {
        "spectrum": spectrum,
        "peak_frequency": int(peak_freq)
    }

graph = dagex.Graph()

graph.add(
    generate_signal,
    label="GenerateSignal",
    outputs=[("signal", "sig"), ("num_samples", "n")]
)

graph.add(
    process_signal,
    label="ProcessSignal",
    inputs=[("sig", "signal")],
    outputs=[("spectrum", "spec"), ("peak_frequency", "peak")]
)

dag = graph.build()
result = dag.execute()

print(f"Peak frequency bin: {result['peak']}")

Data Types

dagex supports any Python data type:

• Primitives: int, float, str, bool
• Complex numbers: Built-in complex and numpy.complex128
• Collections: list, dict, tuple
• NumPy arrays: All dtypes including complex arrays
• Custom objects: Any Python object can be passed through the graph

The framework doesn't impose type restrictions - your node functions determine what data flows through the pipeline.

API Reference

Graph

• Graph() - Create a new graph builder
• graph.add(function, label=None, inputs=None, outputs=None) - Add a node
  • function: Callable with signature (inputs: dict, variant_params: dict) -> dict
  • label: Optional node name (str)
  • inputs: List of (broadcast_var, node_input_var) tuples
  • outputs: List of (node_output_var, broadcast_var) tuples
• graph.branch(subgraph=None) - Create a parallel branch
• graph.build() - Build the DAG and return a Dag object

Dag

• dag.execute(parallel=False, max_threads=None) - Execute the graph
  • parallel: Enable parallel execution of independent nodes
  • max_threads: Limit concurrent threads (None = unlimited)
  • Returns: dict with all broadcast variables
• dag.to_mermaid() - Generate Mermaid diagram (str)

Examples

The package includes several example scripts:

• python_demo.py - Basic pipeline construction
• python_comprehensive_demo.py - Multiple pipeline patterns
• python_parallel_demo.py - Parallel execution examples
• python_data_types_demo.py - Working with various data types
• python_radar_demo.py - Signal processing pipeline with NumPy

Find them in the examples/py directory on GitHub.

Performance Notes

• The DAG executor is written in Rust for performance
• Python functions are called from Rust with proper GIL handling
• Parallel execution releases the GIL during graph traversal
• For CPU-bound Python code, use libraries like NumPy that release the GIL internally
• Best performance gains with parallel execution of I/O-bound or NumPy-based operations

License

MIT License - see LICENSE for details.

Links

• GitHub: https://github.com/briday1/dagex
• Documentation: https://docs.rs/dagex
• PyPI: https://pypi.org/project/dagex
• Crates.io: https://crates.io/crates/dagex