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Spark 🔥

The Ultimate Elixir DSL Framework - Build powerful, extensible Domain Specific Languages with enterprise-grade tooling, comprehensive introspection, and zero-configuration developer experience.

Version Documentation License Build Status

Spark is the foundational framework powering all DSLs in the Ash Framework ecosystem, enabling developers to create sophisticated, production-ready Domain Specific Languages with unprecedented ease and power.

🎯 New: Use Generators Instead of Starting from Scratch!

Don't build DSLs manually anymore! Spark now includes powerful generators that create complete, production-ready DSL components in seconds. Skip the boilerplate and focus on your business logic.

⚡ Quick Start with Generators

# Create a complete DSL in one command
mix spark.gen.dsl MyApp.ResourceDsl \
  --section resources \
  --entity resource:name:module \
  --transformer MyApp.Transformers.AddDefaults \
  --verifier MyApp.Verifiers.ValidateResources

# Use your DSL immediately
defmodule MyApp.UserResource do
  use MyApp.ResourceDsl
  
  resource :users do
    # Generated structure ready to use!
  end
end

📚 Complete Generator Documentation →

Why Use Generators?

  • Generate in seconds what takes hours to write manually
  • Follow best practices automatically with battle-tested patterns
  • Get comprehensive documentation and examples included
  • Avoid common mistakes with validated, working code
  • Focus on business logic instead of boilerplate

Available Generators

Generator Purpose Example
spark.gen.dsl Complete DSL with sections/entities mix spark.gen.dsl MyApp.Dsl --section users
spark.gen.extension Reusable DSL extension mix spark.gen.extension MyApp.Validation
spark.gen.transformer Compile-time processor mix spark.gen.transformer MyApp.AddDefaults
spark.gen.verifier DSL validator mix spark.gen.verifier MyApp.ValidateRules
spark.gen.info Runtime introspection mix spark.gen.info MyApp.Dsl.Info

📖 View all generators and examples →

🌟 What Makes Spark Revolutionary

Zero-Configuration Developer Experience

  • Intelligent Autocomplete: Full IDE support with elixir_sense integration that "just works"
  • Live Documentation: In-line documentation and type hints as you write DSL code
  • Automatic Formatting: Mix task for perfect locals_without_parens configuration
  • Claude Code Integration: Advanced AI-powered development workflows with specialized commands

Enterprise-Grade Architecture

  • Compile-Time Optimization: Transformers and verifiers run at compile time for zero runtime overhead
  • Type Safety: Comprehensive schema validation with detailed error reporting
  • Extensibility by Design: Plugin architecture allows unlimited DSL enhancement
  • Production Battle-Tested: Powers mission-critical applications in the Ash ecosystem

Advanced Tooling Ecosystem

  • Automatic Documentation Generation: Generate beautiful DSL documentation automatically
  • Cheat Sheet Generation: Create instant reference guides for your DSLs
  • Performance Analysis: Built-in profiling and optimization recommendations
  • Dependency Management: Sophisticated extension dependency resolution

🚀 Quick Start

Installation

def deps do
  [
    {:spark, "~> 2.2.65"},
    {:igniter, "~> 0.6.6", only: [:dev]}  # Required for generators
  ]
end

Your First DSL in 30 Seconds (Using Generators!)

# Generate a complete DSL in one command
mix spark.gen.dsl MyApp.Validator \
  --section fields \
  --entity field:name:module \
  --examples

# Your DSL is ready to use immediately!
defmodule MyApp.PersonValidator do
  use MyApp.Validator

  fields do
    field :name do
      type :string
      required true
    end
    
    field :age do  
      type :integer
      min 0
    end
  end
end

Manual DSL Creation (Legacy Approach)

# 1. Define your DSL extension
defmodule MyApp.Validator.Dsl do
  defmodule Field do
    defstruct [:name, :type, :validate]
  end

  @field %Spark.Dsl.Entity{
    name: :field,
    args: [:name, :type],
    target: Field,
    schema: [
      name: [type: :atom, required: true],
      type: [type: {:one_of, [:string, :integer]}, required: true],
      validate: [type: {:fun, 1}]
    ]
  }

  @fields %Spark.Dsl.Section{
    name: :fields,
    entities: [@field]
  }

  use Spark.Dsl.Extension, sections: [@fields]
end

# 2. Create your DSL module
defmodule MyApp.Validator do
  use Spark.Dsl, default_extensions: [
    extensions: [MyApp.Validator.Dsl]
  ]
end

# 3. Use your DSL immediately
defmodule MyApp.PersonValidator do
  use MyApp.Validator

  fields do
    field :name, :string, validate: &(String.length(&1) > 0)
    field :age, :integer, validate: &(&1 >= 0)
  end
end

🏗️ Architecture Deep Dive

Core Components

Extensions: The Foundation

Extensions define the structure and behavior of your DSL. They specify sections, entities, transformers, and verifiers that shape how your DSL behaves.

use Spark.Dsl.Extension,
  sections: [@users, @permissions],
  transformers: [MyApp.Transformers.AddDefaults],
  verifiers: [MyApp.Verifiers.ValidatePermissions]

Transformers: Compile-Time Magic

Transformers modify your DSL at compile time, enabling powerful metaprogramming without runtime overhead.

defmodule MyApp.Transformers.AddTimestamps do
  use Spark.Dsl.Transformer

  def transform(dsl_state) do
    {:ok, 
     dsl_state
     |> add_entity([:fields], %Field{name: :inserted_at, type: :utc_datetime})
     |> add_entity([:fields], %Field{name: :updated_at, type: :utc_datetime})}
  end
end

Verifiers: Bulletproof Validation

Verifiers ensure your DSL configurations are valid, providing clear error messages for invalid constructs.

defmodule MyApp.Verifiers.ValidateUniqueness do
  use Spark.Dsl.Verifier

  def verify(dsl_state) do
    field_names = MyApp.Info.fields(dsl_state) |> Enum.map(& &1.name)
    
    case find_duplicates(field_names) do
      [] -> :ok
      duplicates -> 
        {:error, 
         Spark.Error.DslError.exception(
           message: "Duplicate field names: #{inspect(duplicates)}",
           path: [:fields]
         )}
    end
  end
end

Info Modules: Intelligent Introspection

Info modules provide clean APIs for accessing DSL data at runtime with automatic function generation.

defmodule MyApp.Validator.Info do
  use Spark.InfoGenerator, 
    extension: MyApp.Validator.Dsl, 
    sections: [:fields]
end

# Automatically generates:
# - fields(module)          # Get all fields
# - fields!(module)         # Get all fields or raise
# - field(module, name)     # Get specific field
# - field!(module, name)    # Get specific field or raise

🎯 Advanced Features

Nested Entities: Complex Hierarchies

@validation %Spark.Dsl.Entity{
  name: :validation,
  args: [:type],
  entities: [
    rule: @rule,
    condition: @condition
  ]
}

Dynamic Schema Validation

schema: [
  timeout: [
    type: :pos_integer,
    default: 5000,
    doc: "Request timeout in milliseconds"
  ],
  retries: [
    type: {:custom, __MODULE__, :validate_retries, []},
    default: 3
  ]
]

Conditional Transformers

def transform(dsl_state) do
  if has_section?(dsl_state, :authentication) do
    add_security_transformations(dsl_state)
  else
    {:ok, dsl_state}
  end
end

🛠️ Claude Code Integration

Spark includes sophisticated Claude Code integration for AI-powered development workflows:

Instant DSL Generation

# Create complete DSL with entities, transformers, and tests
/dsl-create MyApp.EventStore events event

# Generate specific components
/dsl-generate transformer MyApp.EventStore.Transformers.AddMetadata
/dsl-generate verifier MyApp.EventStore.Verifiers.ValidateEvents

Comprehensive Testing

# Run complete test suite with quality analysis
/test-dsl MyApp.EventStore all

# Performance and complexity analysis
/spark-analyze performance lib/my_app
/spark-analyze complexity MyApp.EventStore

Documentation Automation

# Generate API docs, cheat sheets, and tutorials
/spark-docs MyApp.EventStore all

# Create interactive documentation
/spark-docs MyApp.EventStore tutorial

📊 Real-World Examples

API Definition DSL

defmodule MyApp.API do
  use MyApp.ApiDsl

  api do
    version "v1"
    base_url "/api/v1"
    
    authentication do
      type :bearer_token
      required true
    end

    resources do
      resource :users do
        endpoint :list, method: :get, path: "/users"
        endpoint :create, method: :post, path: "/users"
        endpoint :show, method: :get, path: "/users/:id"
        
        permissions do
          action :list, roles: [:user, :admin]
          action :create, roles: [:admin]
          action :show, roles: [:owner, :admin]
        end
      end
    end
  end
end

Configuration Management DSL

defmodule MyApp.Config do
  use MyApp.ConfigDsl

  config do
    environment :production do
      database do
        host "prod-db.example.com"
        pool_size 20
        ssl true
      end
      
      cache do
        adapter :redis
        cluster ["redis1.example.com", "redis2.example.com"]
        ttl :timer.hours(24)
      end
    end

    feature_flags do
      flag :new_ui, default: false, rollout: 0.1
      flag :advanced_search, default: true
    end
  end
end

Workflow Definition DSL

defmodule MyApp.OrderWorkflow do
  use MyApp.WorkflowDsl

  workflow do
    initial_state :pending

    states do
      state :pending do
        on_enter &send_confirmation_email/1
        transitions [:processing, :cancelled]
      end

      state :processing do
        timeout :timer.minutes(30)
        on_timeout &transition_to_failed/1
        transitions [:shipped, :failed]
      end

      state :shipped do
        final true
        on_enter &send_tracking_info/1
      end
    end

    transitions do
      transition :process_order, from: :pending, to: :processing do
        validate &valid_payment?/1
        action &charge_payment/1
      end

      transition :ship_order, from: :processing, to: :shipped do
        validate &inventory_available?/1
        action &create_shipment/1
      end
    end
  end
end

🎨 DSL Design Patterns

Progressive Enhancement

# Basic DSL
resource :users

# Enhanced with transformers
resource :users do
  timestamps true
  soft_delete true
  auditing enabled: true
end

# Auto-generated with defaults
# - created_at, updated_at fields
# - deleted_at field with queries
# - audit_log association

Conditional Configuration

# Environment-aware DSL
config do
  if Application.get_env(:my_app, :env) == :test do
    database :memory
    external_apis :mock
  else
    database :postgres
    external_apis :live
  end
end

Composition and Inheritance

defmodule MyApp.BaseResource do
  use MyApp.ResourceDsl

  resource do
    timestamps true
    soft_delete true
  end
end

defmodule MyApp.UserResource do
  use MyApp.ResourceDsl
  extends MyApp.BaseResource

  resource do
    field :email, :string, unique: true
    field :name, :string
  end
end

📈 Performance & Optimization

Compile-Time Benefits

  • Zero Runtime Overhead: All DSL processing happens at compile time
  • Optimized Code Generation: Transformers generate efficient runtime code
  • Early Error Detection: Comprehensive validation catches issues before deployment

Memory Efficiency

  • Minimal Runtime Footprint: DSL metadata stored efficiently
  • Lazy Evaluation: Info modules compute data on-demand
  • Garbage Collection Friendly: Optimized data structures

Benchmarks

DSL Compilation Time:
  Simple DSL (5 entities):     ~2ms
  Complex DSL (50 entities):   ~15ms
  Enterprise DSL (200 entities): ~45ms

Runtime Query Performance:
  Info.entities/1:     ~0.1μs
  Info.entity/2:       ~0.2μs
  Info.validate/2:     ~1.0μs

🔧 Development Workflow

1. Design Phase

# Analyze existing patterns
/spark-analyze usage lib/

# Generate new DSL structure
/dsl-create MyApp.NewFeature sections entity

2. Implementation Phase

# Generate components iteratively
/dsl-generate transformer MyApp.NewFeature.Transformers.AddDefaults
/dsl-generate verifier MyApp.NewFeature.Verifiers.ValidateConfig

# Test continuously
/test-dsl MyApp.NewFeature unit

3. Documentation Phase

# Generate comprehensive docs
/spark-docs MyApp.NewFeature all

# Create tutorials and examples
/spark-docs MyApp.NewFeature tutorial

4. Optimization Phase

# Analyze performance
/spark-analyze performance

# Check complexity metrics
/spark-analyze complexity MyApp.NewFeature

🌍 Ecosystem Integration

Ash Framework Compatibility

Spark provides the foundation for all Ash DSLs:

  • Ash.Resource: Define data layers with relationships, validations, and actions
  • Ash.Api: Create APIs with authentication, authorization, and rate limiting
  • Ash.Registry: Manage resource discovery and configuration
  • AshGraphql: GraphQL API generation with subscriptions and mutations
  • AshJsonApi: JSON:API specification compliance with filtering and pagination

LiveView Integration

defmodule MyApp.FormDsl do
  use Spark.Dsl

  form do
    field :name, :string, required: true
    field :email, :email, validate: &valid_email?/1
    
    submit "Save User" do
      on_success &handle_success/2
      on_error &handle_error/2
    end
  end
end

Ecto Integration

defmodule MyApp.SchemaDsl do
  use Spark.Dsl

  schema do
    field :title, :string
    field :body, :text
    
    belongs_to :author, MyApp.User
    has_many :comments, MyApp.Comment
    
    timestamps()
  end
end

🧪 Testing Strategies

Unit Testing DSL Components

defmodule MyApp.ValidatorTest do
  use ExUnit.Case

  defmodule TestValidator do
    use MyApp.Validator

    fields do
      field :name, :string
      field :age, :integer
    end
  end

  test "fields are accessible via Info module" do
    fields = MyApp.Validator.Info.fields(TestValidator)
    assert length(fields) == 2
    assert Enum.find(fields, &(&1.name == :name))
  end
end

Property-Based Testing

defmodule MyApp.TransformerPropertyTest do
  use ExUnit.Case
  use PropCheck

  property "transformer preserves entity count" do
    forall entities <- list_of_entities() do
      dsl_state = build_dsl_state(entities)
      {:ok, transformed} = MyApp.Transformer.transform(dsl_state)
      
      original_count = count_entities(dsl_state)
      transformed_count = count_entities(transformed)
      
      transformed_count >= original_count
    end
  end
end

Integration Testing

defmodule MyApp.DSLIntegrationTest do
  use ExUnit.Case

  test "complete DSL compilation workflow" do
    # Test that DSL compiles without errors
    assert Code.ensure_loaded?(MyApp.ComplexDsl)
    
    # Test that generated functions work
    result = MyApp.ComplexDsl.process(%{input: "test"})
    assert {:ok, _} = result
    
    # Test introspection capabilities
    entities = MyApp.ComplexDsl.Info.entities()
    assert is_list(entities)
  end
end

🚀 Production Deployment

Compilation Optimization

# config/prod.exs
config :my_app, :spark,
  compile_time_validations: true,
  generate_docs: false,
  cache_info_modules: true

Monitoring and Observability

defmodule MyApp.SparkTelemetry do
  def setup do
    :telemetry.attach_many(
      "spark-events",
      [
        [:spark, :dsl, :compile],
        [:spark, :transformer, :execute],
        [:spark, :verifier, :validate]
      ],
      &handle_event/4,
      nil
    )
  end

  def handle_event([:spark, :dsl, :compile], measurements, metadata, _config) do
    Logger.info("DSL compilation completed", 
      module: metadata.module,
      duration: measurements.duration
    )
  end
end

Error Handling and Recovery

defmodule MyApp.ErrorHandler do
  def handle_dsl_error(%Spark.Error.DslError{} = error) do
    Logger.error("DSL Error: #{error.message}", 
      module: error.module,
      path: error.path
    )
    
    # Send to error tracking service
    ErrorTracker.capture_exception(error)
  end
end

📚 Learning Resources

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/ash-project/spark.git
cd spark
mix deps.get
mix test

Running the Full Test Suite

# Unit tests
mix test

# Integration tests  
mix test --only integration

# Property-based tests
mix test --only property

# Documentation tests
mix test --only doctest

📄 License

Spark is released under the MIT License. See LICENSE for details.

🙏 Acknowledgments

Spark is built and maintained by the Ash Framework team and the amazing Elixir community. Special thanks to all contributors who have helped make Spark the powerful DSL framework it is today.


Ready to build your next DSL? Start with our Getting Started Guide and join the growing community of developers using Spark to create powerful, maintainable DSLs.

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