lane2lane 1.5.7

A Python library for creating flexible, chainable, and prioritized processing pipelines.

Lane2Lane

Lane2Lane is a Python library for creating flexible, chainable, and prioritized processing pipelines. It allows you to define sequential processing steps (lanes) that can be executed in a specific order with dependency relationships.

For detailed documentation, check out our Wiki.

Installation

pip install git+https://github.com/Talisik/lane2lane.git

Requirements

• Python 3.8+
• fun-things
• simple-chalk

Quick Start

from l2l import Lane, PrimaryLane

# Define a simple processing lane
class ProcessingLane(Lane):
    def process(self, value):
        processed_value = f"{value} - processed"
        yield processed_value

# Define a primary lane that uses the processing lane
class Main(PrimaryLane):
    lanes = {
        -10: ProcessingLane,  # Run ProcessingLane before this lane
    }

    def process(self, value):
        result = f"{value} - main"
        yield result

# Run the pipeline
results = Lane.start("MAIN")

# Process the results
for result in results:
    print(result)

Concepts

Lanes

A Lane is a processing unit that can transform or act on data. Lanes can be:

• Primary Lanes: Entry points that can be directly executed
• Regular Lanes: Processing stages that run as part of a lane chain

Lane Ordering

Lanes are executed in a specific order defined by:

• Priority: Integer values that determine execution order
• Before/After Relationships: Negative priorities run before, positive priorities run after

Basic Usage

Creating a Lane

from l2l import Lane

class MyLane(Lane):
    # Process data and optionally yield results
    def process(self, value):
        processed_value = do_something(value)
        yield processed_value

Creating a Primary Lane

Primary lanes are entry points for execution:

from l2l import PrimaryLane

class MyPrimaryLane(PrimaryLane):
    def process(self, value):
        # Process the input value
        result = transform_data(value)
        yield result

You can also override the 'primary' class method in a Lane class:

from l2l import Lane

class MyPrimaryLane(Lane):
    @classmethod
    def primary(cls) -> bool:
        return True  # This makes it a primary lane

    def process(self, value):
        # Process the input value
        result = transform_data(value)
        yield result

Defining Lane Order

Lanes can specify other lanes to run before and after them:

class MainLane(PrimaryLane):
    # Define lanes to run before and after this lane
    lanes = {
        -10: "PreprocessLane",   # Run PreprocessLane before this lane (higher negative priority runs first)
        -5: ValidationLane,      # Run ValidationLane after PreprocessLane but before this lane
        0: PostProcessLane,      # Run PostProcessLane after this lane
        10: CleanupLane,         # Run CleanupLane after PostProcessLane
        20: None,                # Use None to remove a lane at this priority
    }

    def process(self, value):
        # Process after PreprocessLane and ValidationLane
        # but before PostProcessLane and CleanupLane
        return transform_data(value)

Running Lanes

# Start a specific primary lane
result = Lane.start("MAIN_LANE")

# Start all primary lanes that match a name
results = Lane.start_all("MAIN")

Subscriber Example

Subscriber is a pre-defined lane class that provides a standard way to generate data. Rather than processing input from previous lanes, Subscriber lanes generate their own payloads:

from l2l import Subscriber

class DataSourceLane(Subscriber):
    def get_payloads(self, value):
        # Fetch data from some source
        data = fetch_data_from_source()
        for item in data:
            yield item

Instead of implementing process(), you only need to implement get_payloads() to define where your data comes from. The Subscriber class handles the rest automatically.

Advanced Features

Conditional Execution

Lanes can have conditions for execution:

class ConditionalLane(Lane):
    @classmethod
    def condition(cls, name: str):
        # Only run this lane if the name contains "SPECIAL"
        return "SPECIAL" in name

Custom Naming

Provide custom names or aliases for lanes:

class CustomNamedLane(Lane):
    @classmethod
    def name(cls) -> Iterable[str]:
        yield "CUSTOM_PROCESS"
        yield "PROCESSOR"  # An alias

Maximum Run Count

Limit how many times a lane can run:

class OneTimeLane(Lane):
    @classmethod
    def max_run_count(cls) -> int:
        return 1  # Run this lane only once

Multiprocessing Support

Lane2Lane supports multiprocessing for parallel data processing:

class ParallelProcessingLane(Lane):
    multiprocessing = True  # Enable multiprocessing for this lane

    def process(self, value):
        # Process data in parallel
        # Each yielded item will be processed by subsequent lanes
        yield processed_item

Error Handling

Lanes provide built-in error handling capabilities:

class ErrorHandlingLane(Lane):
    @classmethod
    def terminate_on_error(cls):
        return True  # Stop processing on error (default behavior)

    def process(self, value):
        try:
            # Process data
            yield processed_data
        except Exception as e:
            # Access errors with self.errors
            # Global errors available via Lane.global_errors()
            pass

Complete Example

Here's a complete example showing a data processing pipeline:

from l2l import Lane, PrimaryLane, Subscriber

# Data source that fetches records
class DataSourceLane(Subscriber):
    def get_payloads(self, value):
        data = [
            {"id": 1, "name": "Alice", "score": 85},
            {"id": 2, "name": "Bob", "score": 92},
            {"id": 3, "name": "Charlie", "score": 78},
        ]
        for item in data:
            yield item

# Validation lane
class ValidationLane(Lane):
    def process(self, value):
        if "id" not in value or "name" not in value:
            raise ValueError(f"Invalid data format: {value}")
        yield value

# Processing lane
class ScoreProcessingLane(Lane):
    def process(self, value):
        # Add grade based on score
        if "score" in value:
            if value["score"] >= 90:
                value["grade"] = "A"
            elif value["score"] >= 80:
                value["grade"] = "B"
            elif value["score"] >= 70:
                value["grade"] = "C"
            else:
                value["grade"] = "D"
        yield value

# Output formatting lane
class FormattingLane(Lane):
    def process(self, value):
        yield f"Student {value['name']} (ID: {value['id']}) - Score: {value['score']}, Grade: {value.get('grade', 'N/A')}"

# Main primary lane that orchestrates the pipeline
class StudentProcessingLane(PrimaryLane):
    lanes = {
        -30: DataSourceLane,       # First fetch the data
        -20: ValidationLane,       # Then validate it
        -10: ScoreProcessingLane,  # Then process scores
        0: FormattingLane,         # Finally format for output
    }

    # Note: No need to implement process() if you're just passing values through
    # The Lane class already handles this behavior by default

# Run the pipeline
results = Lane.start("STUDENT_PROCESSING")
for result in results:
    print(result)

Output:

Student Alice (ID: 1) - Score: 85, Grade: B
Student Bob (ID: 2) - Score: 92, Grade: A
Student Charlie (ID: 3) - Score: 78, Grade: C

License

MIT License