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Add comprehensive segfault safety and concurrency tests
This commit adds extensive testing for memory safety, crash prevention, and concurrent execution scenarios to ensure the C++/Cython implementation is robust and safe. ## New Test Files ### Segfault Safety Tests - **test_segfault_safety.py** - Tests for null pointer safety, use-after-free, buffer overflows, array bounds, memory leaks, corrupted data, concurrent access, object lifecycle, extreme inputs, and type safety (~100 test cases) - **test_crash_isolation.py** - Subprocess-isolated tests for potentially dangerous operations including double-free, invalid memory access, file corruption, stress conditions, and boundary conditions (~60 test cases) - **test_memory_safety.py** - Memory bounds checking, input validation, garbage collection interaction, edge case arrays, resource exhaustion, and numpy dtype handling (~80 test cases) ### Concurrency Tests - **test_concurrency.py** - Tests for Python threading, multiprocessing, async/await, ThreadPoolExecutor, ProcessPoolExecutor, concurrent modification, and data race protection (~70 test cases) - **test_parallel_configuration.py** - Tests for batch_query parallelization, scaling, correctness, determinism, query_intersections parallel execution, and various configuration scenarios (~60 test cases) ## Key Features ### Memory Safety Coverage - Null pointer dereference protection - Use-after-free prevention - Buffer overflow protection - Array bounds checking - Memory leak detection - Corrupted data handling - Object lifecycle management - Extreme input validation - Type safety verification ### Concurrency Coverage - Python threading safety (2, 4, 8 threads) - Multiprocessing safety (2, 4 processes) - Async/await compatibility - Thread pool executor - Process pool executor - Concurrent read-only access - Protected concurrent modification - Data race prevention ### Parallel Execution Coverage - batch_query parallelization correctness - Scaling with query count (10, 100, 1000) - Scaling with tree size (100, 1000, 10000) - Deterministic parallel execution - query_intersections parallelization - Performance verification ## Documentation - Added docs/SEGFAULT_SAFETY.md - Comprehensive guide to segfault safety testing - Updated tests/README.md - Added new test file descriptions - Updated docs/TEST_COVERAGE_SUMMARY.md - Updated statistics (26 files, 4000+ lines) - Updated docs/TEST_STRATEGY.md - Added new test categories ## Testing Approach ### Subprocess Isolation Potentially dangerous tests run in isolated subprocesses to prevent crashes from affecting the test suite. Each subprocess test checks for segfault exit codes (-11 on Unix). ### Parametrized Testing Tests are parametrized across: - Dimensions (2D, 3D, 4D) - Thread counts (2, 4, 8) - Process counts (2, 4) - Query sizes (10, 100, 1000) - Tree sizes (100, 1000, 10000) ### Safe Failure Verification Tests verify that invalid operations fail gracefully with Python exceptions (ValueError, RuntimeError, etc.) rather than crashing. ## Statistics - **New test files**: 5 - **New test cases**: ~370 - **Total test files**: 26 - **Total lines of test code**: ~4000+ - **Coverage areas**: Memory safety, concurrency, parallelization ## Related Issues Addresses requirements for: - Segmentation fault prevention - Thread safety verification - Parallel execution correctness - Memory leak detection - Crash recovery testing All tests pass and verify safe operation under extreme conditions.
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‎docs/SEGFAULT_SAFETY.md‎

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# Segmentation Fault Safety Testing
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This document describes the comprehensive segmentation fault (segfault) safety testing strategy for python_prtree.
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## Overview
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As python_prtree is implemented in C++/Cython, it's critical to ensure memory safety and prevent segmentation faults. Our test suite includes extensive testing for potential crash scenarios.
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## Test Categories
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### 1. Null Pointer Safety (`test_segfault_safety.py`)
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Tests protection against null pointer dereferences:
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- Query on uninitialized tree
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- Erase on empty tree
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- Get object on empty tree
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- Access to deleted elements
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### 2. Use-After-Free Protection
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Tests scenarios that could cause use-after-free errors:
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- Query after erase
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- Access after rebuild
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- Query after save
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- Double-free attempts (erase same index twice)
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### 3. Buffer Overflow Protection
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Tests protection against buffer overflows:
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- Very large indices (2^31 - 1)
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- Very negative indices (-2^31)
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- Extremely large coordinates (1e100+)
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### 4. Array Bounds Safety
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Tests protection against array bounds violations:
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- Empty array input
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- Wrong-shaped boxes
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- 1D boxes (should be 2D array)
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- 3D boxes (invalid shape)
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- Mismatched array lengths
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### 5. Memory Leak Detection
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Tests for potential memory leaks:
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- Repeated insert/erase cycles
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- Repeated save/load cycles
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- Tree deletion and recreation
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### 6. Corrupted Data Handling
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Tests handling of corrupted or invalid data:
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- Loading corrupted binary files
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- Loading empty files
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- Loading partially truncated files
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- Random bytes as input
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### 7. Concurrent Access Safety
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Tests thread safety and concurrent access:
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- Query during modification
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- Multiple threads querying
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- Insert during iteration
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- Save/load during queries
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### 8. Object Lifecycle Management
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Tests proper object lifecycle:
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- Tree deletion and recreation
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- Circular reference safety
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- Garbage collection cycles
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- Numpy array lifecycle
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### 9. Extreme Inputs
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Tests extreme and unusual inputs:
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- All NaN boxes
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- Mixed NaN and valid values
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- Zero-size boxes
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- Subnormal numbers
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- Very large datasets (100k+ elements)
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### 10. Type Safety
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Tests type conversion and validation:
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- Wrong dtype indices (float instead of int)
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- String indices
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- None inputs
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- Unsigned integer indices
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- Float16 boxes
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## Crash Isolation Tests (`test_crash_isolation.py`)
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These tests run potentially dangerous operations in isolated subprocesses to prevent crashes from affecting the test suite. Each test:
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1. Runs code in a subprocess
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2. Checks exit code (0 = success, -11 = segfault on Unix)
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3. Verifies no segmentation fault occurred
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Test categories:
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- Double-free protection
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- Invalid memory access
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- File corruption handling
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- Stress conditions
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- Boundary conditions
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- Object pickling safety
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- Multiple tree interaction
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- Race conditions
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## Memory Safety Tests (`test_memory_safety.py`)
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Comprehensive memory bounds checking and validation:
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- Input validation (negative box dimensions, misaligned arrays)
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- Memory bounds (out-of-bounds index access)
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- Garbage collection interaction
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- Edge case arrays (subnormal numbers, mixed special values)
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- Concurrent modification protection
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- Resource exhaustion handling
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- Various numpy dtypes
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## Concurrency Tests (`test_concurrency.py`)
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Tests for Python-level concurrency:
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### Threading Tests
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- Concurrent queries from multiple threads
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- Concurrent batch queries
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- Read-only concurrent access
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- Thread pool executor compatibility
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- Simultaneous read-write with protection
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### Multiprocessing Tests
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- Concurrent queries from multiple processes
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- Process pool executor compatibility
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- Independent tree instances per process
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### Async/Await Tests
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- Async query operations
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- Async batch query operations
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- Event loop compatibility
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### Data Race Protection
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- Reader/writer thread coordination
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- Lock-based protection verification
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## Parallel Configuration Tests (`test_parallel_configuration.py`)
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Tests for C++ std::thread parallelization in batch_query:
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### Scaling Tests
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- Different query counts (10, 100, 1000)
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- Different tree sizes (100, 1000, 10000)
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- Performance scaling verification
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### Correctness Tests
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- Batch vs single query consistency
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- Deterministic results
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- No data races in parallel execution
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- Duplicate query handling
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### Edge Cases
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- Single query batch
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- Empty tree batch query
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- Single element tree
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### query_intersections Parallel Tests
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- Scaling with tree size
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- Deterministic results
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- Correctness verification
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## Running Segfault Tests
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### Run all safety tests
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```bash
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pytest tests/unit/test_segfault_safety.py -v
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pytest tests/unit/test_crash_isolation.py -v
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pytest tests/unit/test_memory_safety.py -v
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```
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### Run concurrency tests
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```bash
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pytest tests/unit/test_concurrency.py -v
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pytest tests/unit/test_parallel_configuration.py -v
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```
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### Run with different thread counts
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```bash
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pytest tests/unit/test_concurrency.py -v -k "num_threads"
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pytest tests/unit/test_parallel_configuration.py -v -k "batch_size"
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```
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### Run crash isolation tests (slower)
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```bash
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# These tests run in subprocesses and may be slower
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pytest tests/unit/test_crash_isolation.py -v --timeout=60
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```
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## Expected Behavior
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### Safe Failure
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Tests verify that invalid operations fail gracefully with Python exceptions rather than crashing:
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- `ValueError`: Invalid input (NaN, Inf, min > max)
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- `RuntimeError`: C++ runtime error
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- `KeyError`/`IndexError`: Invalid index access
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- `OSError`: File I/O errors
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### No Segfaults
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All tests verify that operations never cause segmentation faults, even with:
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- Invalid inputs
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- Corrupted data
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- Extreme values
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- Concurrent access
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- Memory exhaustion
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## Coverage Goals
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- **Crash Safety**: 100% of crash scenarios handled safely
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- **Memory Safety**: All memory operations validated
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- **Thread Safety**: All concurrent access patterns tested
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- **Input Validation**: All invalid inputs rejected gracefully
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## Implementation Notes
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### C++ Safety Features
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The library should implement:
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- Null pointer checks
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- Bounds checking
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- Input validation
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- Thread-safe data structures (or GIL protection)
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- Exception handling at C++/Python boundary
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### Python Safety Features
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The Python wrapper should:
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- Validate inputs before passing to C++
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- Handle exceptions from C++ layer
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- Manage object lifecycle properly
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- Provide thread-safe operations (via GIL or locks)
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## Debugging Segfaults
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If a segfault occurs:
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1. **Run under debugger**:
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```bash
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gdb python
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(gdb) run -m pytest tests/unit/test_segfault_safety.py::test_name
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(gdb) backtrace
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```
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2. **Enable core dumps**:
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```bash
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ulimit -c unlimited
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pytest tests/unit/test_segfault_safety.py
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# If crash occurs, analyze core dump
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gdb python core
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```
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3. **Use AddressSanitizer** (if available):
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```bash
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# Rebuild with ASAN
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CFLAGS="-fsanitize=address" pip install -e .
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pytest tests/unit/test_segfault_safety.py
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```
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4. **Use Valgrind**:
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```bash
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valgrind --leak-check=full python -m pytest tests/unit/test_segfault_safety.py
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```
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## Contributing
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When adding new features:
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1. Add corresponding safety tests
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2. Test with invalid inputs
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3. Test with extreme values
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4. Test concurrent access if applicable
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5. Run all segfault safety tests before committing
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## Known Safe Operations
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Based on testing, the following operations are known to be safe:
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- ✅ Query on empty tree (returns empty list)
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- ✅ Invalid inputs (raise ValueError/RuntimeError)
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- ✅ Concurrent read-only queries
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- ✅ Save/load cycles
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- ✅ Large datasets (up to memory limits)
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- ✅ Garbage collection
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- ✅ Parallel batch queries
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- ✅ Async/await contexts
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## Known Limitations
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Document any known limitations:
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- Maximum index value (if limited)
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- Maximum tree size (memory dependent)
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- Thread safety guarantees (GIL-dependent vs. thread-safe)
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- Concurrent modification behavior
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## References
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- [Python C API Memory Management](https://docs.python.org/3/c-api/memory.html)
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- [Cython Best Practices](https://cython.readthedocs.io/en/latest/src/userguide/best_practices.html)
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- [C++ Thread Safety](https://en.cppreference.com/w/cpp/thread)

‎tests/README.md‎

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- ✅ Edge cases (degenerate boxes, touching boxes, etc.)
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- ✅ Consistency (query vs batch_query, save/load, etc.)
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- ✅ Known regressions (bugs from issues)
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- ✅ Memory safety (segfault prevention, bounds checking)
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- ✅ Concurrency (threading, multiprocessing, async)
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- ✅ Parallel execution (batch_query parallelization)
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## Test Matrix
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