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Performance Test Functions

Clean view of test functions only | Generated from 4 test files

This page shows only the test functions without class setup/teardown methods.

Benchmarks

  • Single Call Performance Local File - Benchmark single get_path call for local file - Target: <0.1 seconds
  • Single Call Performance Nested File - Benchmark single get_path call for nested file - Target: <0.1 seconds
  • Multiple Calls Performance - Benchmark multiple sequential get_path calls - Target: <1.0 seconds for 100 calls
  • Missing File Resolution Performance - Benchmark get_path performance for missing files - Target: <0.2 seconds
  • Setup - Set up test fixtures
  • Array Operations Benchmark - Simple array operations benchmark
  • File Io Benchmark - Simple file I/O operations benchmark
  • Project Flow Creation Benchmark - Benchmark ProjectFlow creation performance
  • Hazelbean Temp Benchmark - Benchmark hazelbean temp file operations
  • Numpy Save Load Benchmark - Benchmark numpy array save/load operations with hazelbean
  • Data Processing Workflow Benchmark - Benchmark complete data processing workflow
  • Multi File Processing Benchmark - Benchmark processing multiple files
  • Path Resolution Stress Test - Stress test path resolution performance with many files

Source: test_benchmarks.py

Functions

  • Get Path Function Overhead - Benchmark just the get_path function call overhead
  • Get Path Cache Performance - Benchmark get_path caching efficiency
  • Get Path Different Patterns - Benchmark get_path with different file name patterns
  • Absolute Path Resolution - Benchmark absolute path resolution performance
  • Relative Path Resolution - Benchmark relative path resolution performance
  • Nonexistent Path Resolution - Benchmark performance when resolving non-existent paths
  • Path Normalization Performance - Benchmark path normalization and cleanup performance
  • Array Tiling Performance - Benchmark array tiling operations
  • Small Array Tiling Performance - Benchmark tiling performance for small arrays
  • Tile Reassembly Performance - Benchmark tile reassembly performance
  • Memory Efficient Tiling - Benchmark memory-efficient tiling operations

Source: test_functions.py

Project Flow Scalability

  • Add Task Single Performance Baseline - Establish baseline performance for single add_task() call
  • Add Task Moderate Load 100 Tasks - Test add_task() performance with 100 tasks
  • Add Task High Load 500 Tasks - Test add_task() performance with 500 tasks - may expose scalability limits
  • Add Task Extreme Load 1000 Tasks - Test add_task() performance with 1000 tasks - extreme load may expose significant issues
  • Add Iterator Single Performance Baseline - Establish baseline performance for single add_iterator() call
  • Add Iterator Moderate Load 50 Iterators - Test add_iterator() performance with 50 iterators
  • Add Iterator High Load 200 Iterators - Test add_iterator() performance with 200 iterators - may expose scalability limits
  • Iterator Parallel Flag Performance Comparison - Compare performance characteristics of parallel vs serial iterator creation
  • Task Tree Memory Growth Pattern - Analyze memory growth pattern during large task tree creation
  • Task Tree Cleanup Memory Recovery - Test memory recovery after task tree cleanup
  • Mixed Task Iterator Memory Pattern - Test memory usage with mixed task and iterator creation
  • Anytree Hierarchy Navigation Performance - Test performance of anytree hierarchy navigation operations
  • Anytree Deep Hierarchy Performance - Test anytree performance with deep nested hierarchies
  • Anytree Wide Hierarchy Performance - Test anytree performance with wide hierarchies (many children)
  • Performance Baseline Establishment - Establish performance baselines for regression detection
  • Regression Detection Simulation - Simulate regression detection by comparing current performance to mock baseline
  • Complex Mixed Hierarchy Stress - Stress test with complex mixed task and iterator hierarchies
  • Edge Case Massive Flat Hierarchy Stress - Stress test edge case: massive flat hierarchy (many siblings)
  • Edge Case Rapid Creation Destruction Stress - Stress test edge case: rapid creation and destruction cycles

Source: test_project_flow_scalability.py

Workflows

  • Json Artifact Storage Performance - Test JSON artifact storage and version control integration performance
  • Performance Baseline Validation Workflow - Test performance baseline establishment and validation workflow
  • Ci Cd Performance Integration - Test integration with CI/CD pipeline performance validation
  • Performance Metrics Aggregation - Test aggregation of performance metrics from multiple sources
  • Performance Trend Analysis - Test performance trend analysis workflow
  • Performance Report Generation - Test performance report generation workflow
  • Cross Platform Performance Consistency - Test performance consistency across different environments

Source: test_workflows.py

Running Performance Tests

# Activate environment
conda activate hazelbean_env

# Run all performance tests
pytest hazelbean_tests/performance/ -v

# Run specific test file  
pytest hazelbean_tests/performance/test_example.py -v

Complete Documentation

For full test context including class structure and setup methods, see the complete performance test documentation.

Generated automatically from 4 test files (50 test functions)