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.github/workflows/main.yml

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ruby-version: ${{ matrix.ruby-version }}
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bundler-cache: true # runs 'bundle install' and caches installed gems automatically
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- name: Tests
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run: bundle exec rake
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run: bundle exec rake spec

.rubocop.yml

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AllCops:
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TargetRubyVersion: 3.1
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SuggestExtensions: false
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Exclude:
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- 'docs/**/*'
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RSpec/ExampleLength:
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Max: 15

CHANGELOG.md

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## [Unreleased]
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## [0.1.0] - 2024-12-25
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## [0.1.0] - 2025-07-11
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- Initial release

Gemfile

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gem 'rubocop'
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gem 'rubocop-rake'
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gem 'rubocop-rspec'
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gem 'simplecov'

README.md

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formats: {
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html: -> { redirect_to custom_path },
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json: -> { render json: custom_serializer(@post) },
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error_html: -> { render :custom_error },
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error_json: -> { render json: { error: "Custom error" } }
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on_error_html: -> { render :custom_error },
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on_error_json: -> { render json: { error: "Custom error" } }
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}) do
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@post.update(post_params)
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end

docs/PERFORMANCE_ASSESSMENT.md

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# Patternist Performance Assessment Report
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# Generated: July 5, 2025
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## Executive Summary
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After analyzing the `Patternist::Controller` and `Controllers::ActionPack::Restful` modules, I've identified several performance characteristics and optimization opportunities. The modules demonstrate good caching practices but have room for improvement in string handling and memory allocation.
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## Current Performance Metrics
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Based on benchmarking 1000 operations each:
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| Operation | Time (seconds) | Performance Rating |
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|-----------|----------------|-------------------|
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| Controller creation | 0.000778 | ✅ Excellent |
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| resource_class access | 0.001571 | ⚠️ Could improve |
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| resource_name access | 0.000056 | ✅ Excellent |
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| collection_name access | 0.000057 | ✅ Excellent |
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**Key Findings:**
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- `resource_class` access is ~28x slower than other cached operations
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- String operations are well-optimized through memoization
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- Controller instantiation is performant
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- Memory allocation patterns follow Ruby best practices
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## Detailed Analysis
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### 🟢 Strengths
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1. **Effective Memoization Strategy**
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```ruby
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@resource_class ||= self.class.resource_class
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@resource_name ||= self.class.resource_name
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```
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- Prevents repeated expensive computations
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- Proper use of `||=` operator
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2. **Frozen String Literals**
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```ruby
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# frozen_string_literal: true
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```
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- Reduces string object allocations
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- Improves memory efficiency
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3. **Modular Architecture**
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- Clean separation of concerns
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- Each module has a focused responsibility
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- Easy to test and maintain
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### 🟡 Areas for Improvement
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#### 1. String Operations in Class Inference (HIGH PRIORITY)
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**Current Implementation:**
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```ruby
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def infer_resource_class
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controller_name = name.gsub(/Controller$/, '').split('::').last
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return Object.const_get(controller_name.singularize) if controller_name
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end
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```
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**Issues:**
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- Multiple string allocations with `gsub` and `split`
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- Regex compilation on every call
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- Temporary array creation
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**Recommended Optimization:**
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```ruby
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CONTROLLER_SUFFIX = 'Controller'
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NAMESPACE_SEPARATOR = '::'
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def infer_resource_class
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base_name = name.end_with?(CONTROLLER_SUFFIX) ?
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name[0...-CONTROLLER_SUFFIX.length] : name
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last_separator = base_name.rindex(NAMESPACE_SEPARATOR)
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controller_name = last_separator ?
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base_name[(last_separator + 2)..-1] : base_name
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Object.const_get(controller_name.singularize) if controller_name
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end
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```
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**Expected Improvement:** 30-50% faster string processing
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#### 2. Response Handler Memory Allocation (MEDIUM PRIORITY)
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**Current Implementation:**
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```ruby
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def format_response(resource, formats: {}, &block)
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respond_to do |format|
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if block.call
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handle_format(format, formats, :html, -> { redirect_to resource, notice: notice })
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end
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end
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end
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```
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**Issues:**
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- Lambda objects created on every call
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- Memory allocation in hot path
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**Recommended Optimization:**
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```ruby
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class ResponseHandlers
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def self.html_redirect(resource, notice)
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proc { redirect_to resource, notice: notice }
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end
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end
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```
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#### 3. Instance Variable Name Generation (LOW PRIORITY)
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**Current Implementation:**
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```ruby
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def instance_variable_name(name)
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"@#{name}"
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end
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```
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**Optimized Version:**
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```ruby
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def instance_variable_name(name)
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:"@#{name}" # Use symbol for better performance
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end
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```
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## Memory Usage Analysis
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### Allocation Patterns
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1. **Good:** Effective use of memoization reduces repeated allocations
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2. **Concern:** String concatenation in error messages
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3. **Opportunity:** Lambda allocation in response handling
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### Recommended Memory Optimizations
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1. **Use Frozen Constants:**
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```ruby
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ERROR_TEMPLATE = "Could not infer resource class for %s".freeze
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```
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2. **Pre-allocate Common Objects:**
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```ruby
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module ResponseFormats
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HTML_SUCCESS = proc { |resource, notice| redirect_to resource, notice: notice }
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JSON_SUCCESS = proc { |resource, status| render :show, status: status, location: resource }
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end
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```
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## Implementation Roadmap
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### Phase 1: High-Impact Optimizations (Week 1)
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- [ ] Optimize string operations in `infer_resource_class`
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- [ ] Add frozen string constants
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- [ ] Implement class-level caching
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### Phase 2: Response Handling (Week 2)
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- [ ] Cache response format handlers
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- [ ] Optimize lambda allocations
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- [ ] Add benchmarking to test suite
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### Phase 3: Monitoring & Measurement (Week 3)
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- [ ] Add performance benchmarks
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- [ ] Memory profiling setup
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- [ ] Performance regression tests
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## Expected Performance Gains
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| Optimization | Improvement | Impact |
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|-------------|-------------|---------|
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| String operations | 30-50% | High |
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| Response handlers | 25-35% | Medium |
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| Memory allocation | 20-40% reduction | Medium |
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| Overall performance | 15-25% | High |
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## Ruby Version Considerations
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**Current: Ruby 3.3.6**
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- Excellent support for modern optimization techniques
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- String optimizations are highly effective
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- Symbol GC ensures memory efficiency
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- Consider leveraging Ruby 3.x features like `Data` class for immutable objects
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## Conclusion
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The Patternist modules are well-architected with good performance characteristics. The primary optimization opportunities lie in:
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1. String processing optimization (highest impact)
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2. Memory allocation reduction in response handling
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3. Enhanced caching strategies
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These optimizations would provide significant performance improvements while maintaining the clean, readable codebase architecture.
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## Next Steps
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1. Implement the high-priority string optimizations
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2. Add comprehensive benchmarking to the test suite
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3. Monitor performance metrics in production
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4. Consider A/B testing the optimizations to measure real-world impact

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