ALF vs Broadway: which one should I use for Elixir event processing?

ALF is best for structuring general business logic using FBP principles with visual pipelines, while Broadway is tailored for data ingestion and processing with built-in adapters. Use ALF for application layer design and Broadway for stream processing with external sources.

How to visualize ALF pipelines in production?

ALF generates pipeline diagrams automatically from component definitions, as shown in the README with images like bubble sort. In production, you can use these diagrams for monitoring by integrating with logging or custom tooling based on the DSL structure.

What's the performance impact of ALF's component graphs?

ALF's graph-based processing introduces communication overhead between GenStage processes, which can affect latency. For performance-critical applications, benchmark against simpler functions and consider the sync mode for testing overhead in development.

How to handle errors in ALF components?

Implement error handling within component functions using Elixir's try/rescue or return error tuples. Use `done` components for early exits on errors, and leverage Elixir's supervision trees since components are separate processes.

Can ALF integrate with Phoenix web applications?

Yes, ALF pipelines can be started in Phoenix supervision trees and called from controllers or channels. Use `call/2` for synchronous responses in web requests or `cast/2` for background processing with async event handling.

How to manage state in ALF composers?

Composer components use a 'memo' for stateful logic, initialized via options and updated on each event. Implement a 3-arity function that returns {list(event), new_memo}, as shown in the composer examples for accumulative operations.

alf — Flow-Based Framework for Elixir

What is alf?

ALF is a flow-based application layer framework for Elixir that implements Flow-Based Programming (FBP) to structure business logic. It allows developers to model application logic as graphs of components that process events (information packets), providing high design and runtime observability. Unlike data-processing tools, ALF focuses on organizing general business logic in a clear, maintainable way.

Target Audience

Elixir developers building complex application layers who want to structure business logic using Flow-Based Programming principles for better observability and maintainability.

Value Proposition

Developers choose ALF for its expressive DSL that brings FBP to Elixir, enabling clear visualization of logic flows, parallel processing capabilities, and a focus on application-layer design over raw data processing.

Flow-based Application Layer Framework

Use Cases

Best For

Structuring complex business logic with clear visual pipelines
Building event-driven application layers in Elixir
Implementing parallel processing of multiple event streams
Creating reusable and composable logic components
Debugging and visualizing application flows through diagrams
Transitioning from Flowex with advanced features like branching and cloning

Not Ideal For

Projects focused on data ingestion with pre-built source/sink connectors (e.g., using Broadway for Kafka or SQS)
Applications with simple, linear business logic that doesn't benefit from parallel component graphs
High-performance systems where the overhead of per-component GenStage processes is prohibitive
Teams preferring traditional MVC or context-based patterns over Flow-Based Programming abstractions

Pros & Cons

Pros

Expressive FBP DSL

The component DSL enables defining logic as graphs of stages, switches, and composers, making complex flows manageable and visually representable, as shown in examples like bubble sort and tic-tac-toe.

Parallel Stream Processing

Leverages Elixir's concurrency to handle multiple event streams concurrently, improving throughput for event-driven systems, demonstrated in the parallel stream examples with Task.async.

Debugging and Visualization

Supports synchronous mode for in-process evaluation and generates pipeline diagrams, enhancing observability during development and runtime, as highlighted in the visualization features and examples.

Component Reusability

Macros like `from` and `plug_with` facilitate reusing and adapting components across pipelines, promoting modular and maintainable code, as detailed in the DSL documentation.

Cons

Breaking Interface Changes

Version 0.8 introduced significant interface changes, indicating potential instability and the need for code updates in future releases, as warned in the README's 'Important notice!' section.

Process Overhead

Each component runs in a separate GenStage process, which can add latency and resource consumption, making it less ideal for low-latency or resource-constrained environments.

Niche Learning Curve

Requires understanding of Flow-Based Programming concepts, which might be unfamiliar to developers used to more conventional Elixir patterns, limiting adoption and increasing onboarding time.

What is alf?

Target Audience

Elixir developers building complex application layers who want to structure business logic using Flow-Based Programming principles for better observability and maintainability.

Value Proposition

Use Cases

Best For

Structuring complex business logic with clear visual pipelines
Building event-driven application layers in Elixir
Implementing parallel processing of multiple event streams
Creating reusable and composable logic components
Debugging and visualizing application flows through diagrams
Transitioning from Flowex with advanced features like branching and cloning

Not Ideal For

Projects focused on data ingestion with pre-built source/sink connectors (e.g., using Broadway for Kafka or SQS)
Applications with simple, linear business logic that doesn't benefit from parallel component graphs
High-performance systems where the overhead of per-component GenStage processes is prohibitive
Teams preferring traditional MVC or context-based patterns over Flow-Based Programming abstractions

Pros & Cons

Pros

Expressive FBP DSL

Parallel Stream Processing

Leverages Elixir's concurrency to handle multiple event streams concurrently, improving throughput for event-driven systems, demonstrated in the parallel stream examples with Task.async.

Debugging and Visualization

Supports synchronous mode for in-process evaluation and generates pipeline diagrams, enhancing observability during development and runtime, as highlighted in the visualization features and examples.

Component Reusability

Macros like `from` and `plug_with` facilitate reusing and adapting components across pipelines, promoting modular and maintainable code, as detailed in the DSL documentation.

Cons

Breaking Interface Changes

Version 0.8 introduced significant interface changes, indicating potential instability and the need for code updates in future releases, as warned in the README's 'Important notice!' section.

Process Overhead

Each component runs in a separate GenStage process, which can add latency and resource consumption, making it less ideal for low-latency or resource-constrained environments.

Niche Learning Curve

Requires understanding of Flow-Based Programming concepts, which might be unfamiliar to developers used to more conventional Elixir patterns, limiting adoption and increasing onboarding time.

alf

What is alf?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

alf

What is alf?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?