ContextLab / ContextLab/clustrix

Error Handling & Edge Cases

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task
Dominant language
Python
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10
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6h 27m
Merged PRs (30d)
9

Description

name: "Error Handling & Edge Cases"
status: "open"
created: "2025-09-04T00:46:14Z"
updated: "2025-09-04T00:46:14Z"
github: "[Will be updated when synced to GitHub]"
depends_on: ["001", "002", "003", "004", "005"]
parallel: false
conflicts_with: []
---

## Description

Implement comprehensive testing for failure scenarios, error handling, and edge cases across all Clustrix modules. This task focuses on ensuring robust error handling and graceful degradation when external services fail, networks are unreliable, or inputs are invalid.

## Acceptance Criteria

- [ ] Test network failure scenarios (connection timeouts, SSH failures)
- [ ] Test API error conditions (authentication failures, rate limits)
- [ ] Test invalid input validation across all public APIs
- [ ] Test resource exhaustion scenarios (disk space, memory)
- [ ] Test concurrent access and race conditions
- [ ] Test malformed configuration files and missing dependencies
- [ ] Verify graceful degradation when optional services unavailable
- [ ] Test cleanup procedures after failures
- [ ] Validate error messages are user-friendly and actionable

## Technical Details

### Key Error Scenarios to Test

**Network & SSH Failures**:
- Connection timeouts during job submission
- SSH authentication failures
- Network interruptions during file transfer
- SFTP upload/download failures
- Cluster unavailability

**API & Service Errors**:
- Invalid cluster configurations
- Scheduler API failures (SLURM, PBS, SGE)
- Kubernetes API errors
- File system permission errors
- Job submission rejections

**Input Validation**:
- Invalid function signatures for @cluster decorator
- Malformed configuration files
- Missing required cluster parameters
- Invalid resource specifications (cores, memory, time)

**Resource & State Issues**:
- Disk space exhaustion on remote systems
- Memory limitations during serialization
- Concurrent job conflicts
- Stale job state recovery

### Testing Approach
```python
# Network failure simulation
@pytest.fixture
def mock_network_failure():
with patch('paramiko.SSHClient.connect', side_effect=socket.timeout):
yield

def test_ssh_connection_failure(mock_network_failure):
# Test graceful handling of SSH failures
pass

def test_invalid_cluster_config():
# Test validation of cluster configurations
pass

def test_resource_exhaustion():
# Test handling of resource limitations
pass
```

### Coverage Focus Areas
- Exception handling paths in all modules
- Validation logic in config.py and decorator.py
- Recovery mechanisms in executor.py
- Error reporting in utils.py and filesystem.py

## Dependencies

- **Depends On**: Tasks 001-005 (needs core modules tested first to build upon)
- **Technical**: pytest-mock, network simulation tools
- **Logical**: Requires understanding of normal operation paths before testing failures

## Effort Estimate

**Size**: M (3-4 days)

**Breakdown**:
- Day 1: Analyze existing error handling patterns, setup test infrastructure
- Day 2: Test network and SSH failure scenarios
- Day 3: Test API errors and input validation
- Day 4: Test resource exhaustion and cleanup procedures

**Complexity**: Medium-High - requires understanding of failure modes across distributed systems

## Definition of Done

- [ ] All identified error scenarios have test coverage
- [ ] Network failure simulation works reliably in tests
- [ ] Input validation is comprehensively tested
- [ ] Error messages are validated for clarity and actionability
- [ ] Cleanup procedures are tested after various failure types
- [ ] Coverage reports show significant improvement in error handling paths
- [ ] No unhandled exceptions in failure scenarios
- [ ] Documentation updated with error handling best practices

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