fix: v3.1.1 - Test suite compatibility and cache improvements

[TexasCoding]

Summary

This release fixes all test suite compatibility issues with the v3.1.0 performance optimizations and improves cache serialization for datetime handling.

Changes

🐛 Bug Fixes

• Test Suite Compatibility: Fixed all failing tests for optimized cache implementation
  • Updated test references from old cache variables (_instrument_cache) to new optimized ones (_opt_instrument_cache)
  • Fixed datetime serialization/deserialization in cached DataFrames to properly preserve timezone information
  • Resolved BatchedWebSocketHandler flush mechanism with event-based signaling for immediate message processing
  • Fixed race condition in BatchedWebSocketHandler task creation
  • Corrected SignalR mock methods in connection management tests (changed from AsyncMock to MagicMock for synchronous methods)

✨ Improvements

• Cache Serialization: Enhanced datetime handling in msgpack cache
  • Proper timezone preservation for datetime columns in Polars DataFrames
  • More robust deserialization with fallback handling
  • Better datetime string format compatibility

Test Results

• ✅ All 438 tests now passing
• ✅ Full test suite compatibility with optimized cache implementation
• ✅ Comprehensive test coverage for new cache features

Files Modified

• src/project_x_py/client/cache.py - Enhanced datetime serialization
• src/project_x_py/realtime/batched_handler.py - Fixed flush mechanism and race conditions
• tests/client/test_client_integration.py - Updated cache variable names
• tests/client/test_market_data.py - Updated cache variable names
• tests/realtime/test_batched_handler.py - Fixed flush test timing
• tests/realtime/test_connection_management.py - Fixed mock methods
• Documentation and version updates

Type of Change

• Bug fix (non-breaking change which fixes an issue)
• Performance improvement
• New feature
• Breaking change

Checklist

• All tests pass
• Documentation updated
• CHANGELOG.md updated
• Version bumped to 3.1.1

🤖 Generated with Claude Code

Co-Authored-By: Claude [email protected]

[claude]

Claude finished @TexasCoding's task —— View job

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Code Review Complete ✅

I've thoroughly reviewed this PR focusing on code quality, potential issues, performance, security, and test coverage. Here's my comprehensive analysis:

Review Checklist:

• Analyze cache serialization improvements in cache.py
• Review WebSocket handler fixes in batched_handler.py
• Check test file updates and compatibility fixes
• Evaluate overall code quality and best practices
• Assess security and performance implications
• Provide constructive feedback and recommendations

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✅ Positive Aspects

Excellent Cache Serialization Improvements (cache.py)

• Robust datetime handling: The regex-based timezone extraction (re.search(r"time_zone='([^']+)'", dtype_str)) properly preserves timezone info during msgpack serialization
• Smart fallback strategy: Lines 132-134 and 138-139 provide graceful degradation if decompression fails
• Efficient compression: LZ4 compression with configurable thresholds (1KB default) balances performance and memory usage
• Type safety: Proper type hints and None handling throughout

Well-Architected WebSocket Handler (batched_handler.py)

• Race condition fix: Event-based flush mechanism (_flush_event) eliminates timing issues
• Proper task management: Lines 82-85 correctly handle task lifecycle and prevent duplicate processing
• Memory safety: Circuit breaker pattern (lines 148-153) prevents runaway failures
• Performance optimizations: Deduplication for quotes/depth updates (lines 315-321, 340-344) reduces redundant processing

Comprehensive Test Updates

• Consistent naming: All test files properly updated from _instrument_cache to _opt_instrument_cache
• Improved mock accuracy: Fixed SignalR mocks from AsyncMock to MagicMock for synchronous methods
• Better timing: Test flush mechanism uses realistic timeouts (0.1s vs 0.05s)

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🔍 Areas for Improvement

Minor Issues

1. Cache Serialization Robustness (cache.py:163-172)

# Current: Could fail on malformed datetime strings
df = df.with_columns(
    pl.col(col_name)
    .str.strptime(pl.Datetime("us", time_zone), "%Y-%m-%d %H:%M:%S%.f", strict=False)
)

Suggestion: Add explicit exception handling around strptime parsing for better error recovery.

2. WebSocket Handler Resource Management (batched_handler.py:171-174)

# Fire and forget task creation without proper cleanup tracking
task = asyncio.create_task(self._process_batch())
task.add_done_callback(lambda t: None)

Suggestion: Consider tracking these background tasks for proper cleanup during shutdown.

3. Test Timing Dependencies (test_batched_handler.py:116-124)
The flush test relies on precise timing which could be flaky in CI environments. Consider using more deterministic synchronization.

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🚀 Performance Considerations

Strengths

• Memory efficiency: LZ4 compression reduces cache memory by ~70% according to docstrings
• Serialization performance: msgpack provides 2-5x faster serialization vs JSON
• Batch deduplication: Smart handling of redundant quote/depth updates reduces processing overhead
• O(1) queue operations: Uses deque for efficient message queuing

Optimization Opportunities

• Cache key normalization: symbol.upper() is called repeatedly - consider caching normalized keys
• Regex compilation: The timezone regex in _deserialize_dataframe should be compiled once and reused

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🔒 Security Assessment

Good Practices

• ✅ No hardcoded secrets or credentials
• ✅ Proper input validation and type checking
• ✅ Exception handling prevents information leakage
• ✅ No SQL injection or XSS vulnerabilities

Recommendations

• Consider adding rate limiting to batch processing to prevent resource exhaustion
• The circuit breaker (lines 148-153) is good but could benefit from exponential backoff

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📊 Test Coverage Analysis

Strengths

• Comprehensive scenarios: Tests cover batch size triggers, timeouts, errors, and edge cases
• Async safety: Proper use of pytest.mark.asyncio and timing considerations
• Mock accuracy: Corrected mock types improve test reliability
• Performance testing: Statistics validation ensures metrics work correctly

Suggestions

• Add tests for datetime serialization edge cases (malformed timezones, DST transitions)
• Consider property-based testing for cache serialization with various DataFrame schemas

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🎯 Overall Assessment

This is a high-quality PR that addresses critical compatibility issues while improving system robustness. The changes demonstrate:

• Deep understanding of async programming patterns
• Attention to performance with smart optimizations
• Proper error handling and edge case consideration
• Good testing practices with realistic scenarios

Recommendation: ✅ APPROVE

The improvements outweigh the minor issues identified. The datetime serialization and WebSocket handler fixes are particularly well-implemented. The test updates ensure long-term maintainability of the optimized cache system.

Risk Level: Low - Changes are backward compatible and well-tested
Performance Impact: Positive - Improved caching and reduced WebSocket processing overhead

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