Benchmark Report - InjectQ Documentation
Author: InjectQ Contributors
Description: Modern Python dependency injection library

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# InjectQ Performance Benchmark Report

Generated: October 28, 2024
Python Version: 3.13.5
Platform: macOS (Darwin)
Benchmark Tool: pytest-benchmark 5.1.0

## Executive Summary

This report presents comprehensive performance benchmarks for the InjectQ dependency injection library, covering basic operations, dependency resolution, factory functions, scoping mechanisms, load testing, thread safety, and real-world scenarios.

### Key Findings

✅ Ultra-Fast Core Operations: Basic container operations (bind, get, has) execute in ~270-780 nanoseconds
✅ Efficient Dependency Resolution: Simple to complex dependency graphs resolve in 1-25 microseconds
✅ Excellent Scalability: Handles 1,000+ service instances efficiently
✅ Thread-Safe: Concurrent operations maintain performance with minimal overhead
✅ Production-Ready: Real-world scenarios (web requests, API stacks) perform exceptionally well

## Performance Categories

### 1. Ultra-Fast Operations (< 1 microsecond)

These operations execute in nanoseconds, making them virtually zero-overhead:

Operation | Mean Time | Operations/Second | Description
--- | --- | --- | ---
`has_service` | 272.2 ns | 3.67 million | Check if service is registered
`bind_factory` | 271.4 ns | 3.68 million | Register a factory function
`bind_instance` | 784.2 ns | 1.28 million | Register a singleton instance

Analysis: Container lookups and basic registrations are nearly instant, with negligible overhead. Perfect for high-frequency operations.

### 2. Fast Operations (1-10 microseconds)

Core container operations that execute in microseconds:

Operation | Mean Time | Operations/Second | Description
--- | --- | --- | ---
`bind_simple_class` | 924.1 ns | 1.08 million | Register a class binding
`get_simple_service` | 1.02 μs | 977k | Retrieve a simple service
`get_singleton` | 1.02 μs | 976k | Retrieve cached singleton
`resolve_simple_dependency` | 1.05 μs | 955k | Resolve single dependency
`singleton_scope_cached` | 1.02 μs | 976k | Get cached singleton scope
`api_service_stack` | 1.06 μs | 944k | Resolve API service stack
`resolve_deep_dependency_tree` | 1.01 μs | 990k | Resolve deep (5-level) tree
`resolve_multiple_dependencies` | 1.00 μs | 1.00 million | Resolve service with 3 deps
`resolve_nested_dependencies` | 1.01 μs | 990k | Resolve nested (3-level) deps
`container_getitem` | 1.29 μs | 773k | Access via `container[Type]`

Analysis: Dependency resolution is extremely efficient. Even deep dependency trees resolve in ~1 microsecond, demonstrating excellent optimization.

### 3. Moderate Operations (10-100 microseconds)

Operations with slightly more complexity:

Operation | Mean Time | Operations/Second | Description
--- | --- | --- | ---
`container_clear` | 2.60 μs | 385k | Clear container state
`request_scope` | 2.85 μs | 351k | Request scope resolution
`factory_simple` | 3.71 μs | 270k | Simple factory invocation
`override_context` | 4.16 μs | 240k | Override context resolution
`transient_scope_creation` | 4.71 μs | 212k | Create transient instance
`factory_with_dependency` | 6.22 μs | 161k | Factory with dependencies
`thread_safe_container` | 24.7 μs | 40.5k | Thread-safe container ops
`container_creation` | 24.3 μs | 41.2k | Create new container
`get_transient` | 27.2 μs | 36.8k | Get transient instance

Analysis: Factory invocations and transient scope creations involve actual object instantiation, explaining the slightly higher times. Still exceptionally fast for production use.

### 4. Load Testing (100+ operations)

Performance under load conditions:

Test | Mean Time | Operations/Second | Load Size
--- | --- | --- | ---
`web_request_simulation` | 142.1 μs | 7,037 | 10 services per request
`load_complex_graph` | 178.6 μs | 5,598 | Complex dependency graph
`load_many_services` | 779.3 μs | 1,283 | 100 services
`load_repeated_gets` | 739.5 μs | 1,352 | 1,000 get operations
`concurrent_gets` | 884.4 μs | 1,131 | 10 threads, 100 ops each
`stress_resolution_mix` | 849.0 μs | 1,178 | 100 mixed operations
`stress_sequential_binds` | 1.96 ms | 509 | 500 sequential binds
`load_transient_creation` | 24.8 ms | 40 | 1,000 transient instances

Analysis:

- ✅ Can handle 1,000 get operations in under 1 millisecond
- ✅ Web request simulation (realistic scenario) completes in 142 microseconds
- ✅ Creating 100 services takes only 779 microseconds
- ⚠️ Creating 1,000 transient instances takes 24.8ms (expected, each requires object instantiation)

## Detailed Performance Analysis

### Container Lifecycle

    Container Creation:    24.3 μs  (41,192 ops/sec)
    Container Clear:        2.6 μs  (384,784 ops/sec)
    Container GetItem:      1.3 μs  (772,729 ops/sec)

Insight: Container creation is very fast. Clearing is even faster, making it ideal for test isolation.

### Binding Performance

    Bind Instance:     784 ns  (1.28M ops/sec)
    Bind Class:        924 ns  (1.08M ops/sec)
    Bind Factory:      271 ns  (3.68M ops/sec)

Insight: Factory bindings are fastest (just storing function references), while instance bindings are still sub-microsecond.

### Retrieval Performance

    Has Service:       272 ns  (3.67M ops/sec)
    Get Singleton:    1.02 μs  (976K ops/sec)
    Get Transient:    27.2 μs  (36.8K ops/sec)
    Get Simple:       1.02 μs  (977K ops/sec)

Insight: Singleton retrieval benefits from caching. Transient creation is slower due to instantiation overhead.

### Dependency Resolution

    Simple (1 dep):        1.05 μs  (955K ops/sec)
    Nested (3 levels):     1.01 μs  (990K ops/sec)
    Multiple (3 deps):     1.00 μs  (1.00M ops/sec)
    Deep Tree (5 levels):  1.01 μs  (990K ops/sec)

Insight: Resolution time is remarkably consistent regardless of dependency complexity. Excellent graph traversal optimization.

### Scope Performance Comparison

Scope Type | Mean Time | Operations/Second | Notes
--- | --- | --- | ---
Singleton (cached) | 1.02 μs | 976K | Fastest - retrieves from cache
Transient | 4.71 μs | 212K | Creates new instance each time
Request | 2.85 μs | 351K | Scoped to request context

Insight: Singleton scope offers best performance for shared services. Transient scope is still very fast for most use cases.

### Thread Safety

    Single-threaded:       1.02 μs  (976K ops/sec)
    Thread-safe:          24.7 μs  (40.5K ops/sec)
    Concurrent (10x100): 884.4 μs  (1,131 ops/sec)

Insight: Thread-safe container adds ~24x overhead for locking, but still delivers 40,500+ operations/second. Concurrent access from 10 threads maintains excellent throughput.

### Real-World Scenarios

#### Web Request Simulation (10 services)

    Mean:     142.1 μs
    Min:      93.5 μs
    Max:      8.5 ms
    Ops/sec:  7,037

Analysis: Typical web request resolving 10 services completes in 142 microseconds. Even worst case (8.5ms) is negligible.

#### API Service Stack (4 layers)

    Mean:     1.06 μs
    Ops/sec:  944,257

Analysis: Resolving a 4-layer API stack (controller → service → repository → cache) takes just 1 microsecond.

## Load Testing Results

### Scenario 1: High-Volume Service Registration (100 services)

- Time: 779 microseconds
- Throughput: 1,283 operations/second
- Result: ✅ Excellent performance for dynamic service registration

### Scenario 2: Repeated Access Pattern (1,000 gets)

- Time: 739 microseconds
- Per-operation: 739 nanoseconds
- Result: ✅ Sub-microsecond average per operation under heavy load

### Scenario 3: Concurrent Access (10 threads × 100 operations)

- Time: 884 microseconds
- Result: ✅ Handles 1,000 concurrent operations in under 1 millisecond

### Scenario 4: Stress Test - Mixed Operations (100 operations)

- Time: 849 microseconds
- Mix: Bind + resolve + clear
- Result: ✅ Consistent performance under mixed workload

### Scenario 5: Extreme Load - Sequential Binds (500 binds)

- Time: 1.96 milliseconds
- Per-bind: 3.92 microseconds
- Result: ✅ Linear scaling maintained

### Scenario 6: Mass Instantiation (1,000 transient objects)

- Time: 24.8 milliseconds
- Per-object: 24.8 microseconds
- Result: ✅ Reasonable for bulk object creation

## Performance Recommendations

### ✅ Best Practices

1. Use Singleton Scope for Shared Services
2. 1.02 μs retrieval time (cached)

3. Ideal for database connections, API clients, configuration

4. Leverage Factory Functions

5. 271 ns binding time

6. Perfect for lazy initialization patterns

7. Batch Service Registration

8. 100 services in 779 μs

9. Register all services at startup for optimal performance

10. Cache Container Lookups

11. `has_service` check is only 272 ns

12. Use early checks to avoid unnecessary resolution

13. Use Request Scope for Web Applications

14. 2.85 μs resolution
15. Balance between singleton and transient

### ⚠️ Performance Considerations

1. Minimize Transient Scope Overuse
2. 4.71 μs per creation (vs 1.02 μs for singleton)

3. Use only when instance isolation is required

4. Thread-Safe Container Overhead

5. 24x slower than single-threaded (still fast at 24.7 μs)

6. Use only when concurrent access is needed

7. Deep Dependency Trees

8. Still fast at 1.01 μs for 5-level trees
9. No significant performance penalty

## Comparison with Manual DI

    # Manual DI
    time_per_instantiation = ~50-100 ns (raw Python)

    # InjectQ DI
    time_per_resolution = ~1,000 ns (1 μs)

    # Overhead = ~10x raw instantiation
    # Trade-off: Automatic dependency management, scoping, lifecycle

Verdict: The 10x overhead is negligible (< 1 microsecond) and provides massive developer productivity gains through automatic dependency injection, scope management, and cleaner code architecture.

## Benchmarking Methodology

### Test Environment

- CPU: Apple Silicon (M-series)
- Python: 3.13.5
- Timer: `time.perf_counter` (41 ns precision)
- Warm-up: Enabled (100,000 iterations)
- Min Rounds: 5
- Max Time: 1 second per benchmark

### Statistical Metrics

- Min/Max: Range of execution times
- Mean: Average execution time
- Median: 50th percentile
- IQR: Interquartile range (25th-75th percentile)
- StdDev: Standard deviation
- Outliers: Values > 1.5 IQR from quartiles

### Benchmark Categories

1. Basic Operations: Container creation, binding, retrieval
2. Dependency Resolution: Simple to complex dependency graphs
3. Factory Functions: Simple and parameterized factories
4. Scope Management: Singleton, transient, request scopes
5. Load Testing: 100-1,000 operation batches
6. Thread Safety: Concurrent access patterns
7. Real-World Scenarios: Web requests, API stacks
8. Stress Testing: Sequential binds, mixed operations

## Conclusion

InjectQ demonstrates exceptional performance across all tested scenarios:

✅ Sub-microsecond operations for core functionality
✅ Linear scaling under load (1,000+ operations)
✅ Thread-safe with acceptable overhead
✅ Production-ready for high-performance applications
✅ Negligible overhead compared to manual dependency injection

The library is suitable for:

- High-traffic web applications (sub-millisecond request handling)
- Microservices (efficient service resolution)
- Real-time systems (predictable sub-microsecond latency)
- CLI applications (instant startup)
- Testing frameworks (fast container creation/cleanup)

## Appendix: Raw Benchmark Data

All benchmark data is saved in: - JSON: `.benchmarks/Darwin-CPython-3.13-64bit/` - Compare: Use `pytest-benchmark compare` for historical tracking

### Running Benchmarks Yourself

    # Run all benchmarks
    pytest tests/test_benchmarks.py --benchmark-only

    # Run with verbose output
    pytest tests/test_benchmarks.py --benchmark-only --benchmark-verbose

    # Save results for comparison
    pytest tests/test_benchmarks.py --benchmark-only --benchmark-autosave

    # Compare with previous runs
    pytest-benchmark compare 0001 0002

### Generating HTML Reports

    # Install optional dependencies
    pip install pytest-benchmark[histogram]

    # Generate histogram
    pytest tests/test_benchmarks.py --benchmark-only --benchmark-histogram

    # Generate comparison charts
    pytest-benchmark compare --histogram

Report Version: 1.0
Benchmark Suite: test_benchmarks.py (30 tests)
Total Runtime: ~56 seconds
Test Status: ✅ All 30 benchmarks passed