AshProfiler

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Performance profiling and optimization toolkit for Ash Framework applications.

Installation

Add ash_profiler to your list of dependencies in mix.exs:

def deps do
  [
    {:ash_profiler, "~> 0.1.0"}
  ]
end

Quick Start

# Basic analysis
AshProfiler.analyze()

# Generate HTML report
AshProfiler.analyze(output: :html, file: "performance_report.html")

# Profile specific domains
AshProfiler.analyze(domains: [MyApp.CoreDomain])

Command Line Usage

# Basic profiling
mix ash_profiler

# Generate detailed report
mix ash_profiler --output html --file report.html

# Container-specific analysis
mix ash_profiler --container-mode --threshold 50

Features

  • DSL Complexity Analysis - Identifies expensive Ash DSL patterns
  • Compilation Profiling - Tracks compilation performance bottlenecks
  • Container Detection - Specialized analysis for containerized environments
  • Optimization Suggestions - Actionable recommendations for improvements
  • Multiple Output Formats - Console, JSON, and HTML reporting

API Reference

AshProfiler.analyze/1

Runs comprehensive performance analysis of Ash resources.

Options:

  • :domains - List of domains to analyze (default: auto-discover)
  • :output - Output format :console, :json, :html (default: :console)
  • :file - Output file path for JSON/HTML reports
  • :threshold - Complexity threshold for warnings (default: 100)
  • :container_mode - Enable container-specific analysis (default: auto-detect)
  • :include_optimizations - Include optimization suggestions (default: true)

Examples:

# Analyze all domains with default settings
AshProfiler.analyze()

# Custom analysis with specific options
AshProfiler.analyze(
  domains: [MyApp.CoreDomain, MyApp.UserDomain],
  output: :html,
  file: "ash_profile.html",
  threshold: 50
)

# JSON output for CI/CD integration
AshProfiler.analyze(
  output: :json,
  file: "performance_metrics.json",
  include_optimizations: false
)

DSL Complexity Scoring

AshProfiler analyzes various aspects of your Ash resources and assigns complexity scores:

Resource Sections

  • Attributes - Base attributes (1 point each), computed attributes (3 points each), constraints (1 point per constraint)
  • Relationships - Base relationships (2 points each), many-to-many (5 bonus points), through relationships (3 bonus points)
  • Policies - Base policies (5 points each), expression complexity varies, bypasses (2 points each)
  • Actions - Base actions (1 point each), plus complexity from changes and validations
  • Changes - 2 points per change
  • Preparations - 2 points per preparation
  • Validations - 1 point per validation

Severity Levels

  • Low (< 50): Well-optimized resource
  • Medium (50-100): Moderate complexity
  • High (100-150): Complex resource, review recommended
  • Critical (> 150): Very complex, optimization needed

Container Environment Analysis

When running in containers (Docker, etc.), AshProfiler provides additional insights:

System Resource Analysis

  • Memory allocation and usage
  • CPU core count and scheduler information
  • Disk space and I/O performance

Performance Characteristics

  • File I/O performance testing
  • Memory pressure detection
  • CPU throttling detection

Container-Specific Recommendations

  • Memory allocation optimization
  • Multi-stage Docker build suggestions
  • Erlang VM tuning for containers
  • Compilation caching strategies

Optimization Recommendations

AshProfiler provides actionable optimization suggestions:

Policy Optimizations

  • Extract complex expressions to computed attributes
  • Simplify authorize_if conditions
  • Use policy composition patterns

Relationship Optimizations

  • Move complex relationships to separate resources
  • Use manual relationships for complex queries
  • Consider data layer optimizations

Domain-Level Recommendations

  • Domain splitting suggestions for large domains
  • Resource organization improvements
  • Compilation performance optimizations

Performance Boost Tips

Based on real-world performance improvements (98.2% speed improvement achieved):

Environment Variables

# Erlang scheduler optimizations
export ELIXIR_ERL_OPTIONS="+sbwt none +sbwtdcpu none +sbwtdio none"
export ERL_FLAGS="+S 4:4 +P 1048576"

# Ash compilation optimizations
export ASH_DISABLE_COMPILE_DEPENDENCY_TRACKING=true

Container Optimizations

  • Use multi-stage Docker builds with proper layer caching
  • Increase container memory allocation (minimum 4GB, 8GB recommended)
  • Apply Erlang VM scheduler optimizations for containers
  • Enable Ash-specific compilation performance flags
  • Set appropriate CPU limits and resource reservations
  • Cache compilation artifacts using Docker BuildKit

Quick Docker Setup

Generate optimized Docker configurations instantly:

# Generate complete optimized Docker setup
mix ash_profiler.docker --complete

# Generate just an optimized Dockerfile
mix ash_profiler.docker --dockerfile

# Generate CI/CD workflow with performance monitoring
mix ash_profiler.docker --cicd github

Real-World Use Cases & Optimizations

Case Study 1: E-commerce Platform (98.2% Performance Improvement)

Before AshProfiler:

$ time mix compile
real    2m0.450s  # 120+ seconds compilation

AshProfiler Analysis Identified:

  • Complex policy expressions (complexity score: 180)
  • Nested many-to-many relationships (15+ per resource)
  • Heavy computed attributes in hot paths

Applied Optimizations:

# Before: Complex policy expression
policy action(:read) do
  authorize_if expr(user.role == "admin" or 
    (user.department == resource.department and 
     user.permissions.read_products == true and
     resource.status in ["active", "pending"]))
end

# After: Extracted to computed attribute  
attribute :user_can_read, :boolean, allow_nil?: false do
  calculation UserReadPermission
end

policy action(:read) do
  authorize_if expr(resource.user_can_read == true)
end

Results After Optimization:

$ time mix compile  
real    0m2.100s  # 2.1 seconds! 🚀

Case Study 2: SaaS Multi-tenant App

Challenge: Slow CI/CD builds in containerized environment

AshProfiler Container Analysis:

$ mix ash_profiler --container-mode
=== Container Performance Issues Detected ===
- Memory pressure: 85% usage during compilation
- CPU throttling: detected in 67% of builds
- Inefficient Docker layer caching

Recommendations:
✓ Increase Docker memory from 2GB → 8GB  
✓ Apply Erlang scheduler optimizations
✓ Implement multi-stage builds with dependency caching

Dockerfile Optimization:

# Before: Single stage build
FROM elixir:1.15-alpine
COPY . .
RUN mix deps.get && mix compile

# After: Optimized multi-stage
FROM elixir:1.15-alpine AS deps
ENV ELIXIR_ERL_OPTIONS="+sbwt none +sbwtdcpu none +sbwtdio none"
ENV ERL_FLAGS="+S 4:4 +P 1048576"  
COPY mix.exs mix.lock ./
RUN mix deps.get --only prod && mix deps.compile

FROM deps AS compile  
COPY lib ./lib
RUN mix compile

# Result: Build time reduced from 8min → 45sec

Case Study 3: Legacy Code Refactoring

Scenario: Inherited Ash codebase with performance issues

AshProfiler Report Highlights:

=== Critical Complexity Detected ===
UserDomain.Account: 245 complexity points
├── Relationships: 45 points (18 associations)  
├── Policies: 120 points (complex authorization)
└── Actions: 80 points (12 custom actions)

Optimization Suggestions:
🔴 Split UserDomain.Account into separate resources
🟡 Simplify policy expressions using computed attributes
🟡 Move secondary relationships to dedicated resources

Refactoring Strategy:

# Before: Monolithic Account resource (245 complexity)
defmodule UserDomain.Account do
  # 18 relationships, complex policies, many actions...
end

# After: Split into focused resources (< 50 complexity each)
defmodule UserDomain.Account do        # Core account data
defmodule UserDomain.AccountProfile do # Profile information  
defmodule UserDomain.AccountSettings do # User preferences
defmodule UserDomain.AccountMetrics do  # Analytics data

Measurable Results:

  • Compilation time: 45s → 8s
  • Test suite: 2.3s → 0.7s
  • Memory usage during compilation: -60%

Integration with CI/CD

Use AshProfiler in your continuous integration pipeline:

# Generate JSON report for automated analysis
mix ash_profiler --output json --file metrics.json --threshold 80

# Fail build if complexity exceeds threshold
mix ash_profiler --threshold 100 || exit 1

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/nocsi/ash_profiler.

License

This package is available as open source under the terms of the MIT License.