---
date: 2025-11-17
task: Fix stream_mode=True real-time file flushing issue in DAQ
outcome: completed
---

# Progress Log: Stream Mode Real-Time File Flushing Fix

## Task Description

**Issue**: When running DAQ with `stream_mode=True` (default), CSV files were being generated but data was not being written to disk in real-time. Data only appeared when Ctrl-C interrupted the process (causing file close/flush).

**Root Cause Investigation**:

1. Examined `save_events()` method in `src/haniwers/v1/daq/sampler.py`
2. Found that `stream_mode=True` code path (lines 595-597) called `writerow()` but never flushed the file buffer
3. Identified that OS buffering was holding data until file close

**Solution**: Add `f.flush()` call after each event write in streaming mode to ensure immediate disk persistence.

## Outcome

**Status**: ✅ Completed and deployed

**Changes Made**:

- Modified `src/haniwers/v1/daq/sampler.py` line 598
- Added single line: `f.flush()  # Flush after each event for true streaming`
- Changes verified with all 13 sampler unit tests passing

**Commits**:

1. `56fa52d` - `fix(daq): enable real-time file flushing in stream_mode=True`
2. `06ce5cb` - `bump: version 1.7.0 → 1.7.1` (PATCH release)

**Verification**:

```bash
poetry run pytest tests/v1/unit/daq/sampler/ -v
# Result: 13 passed in 0.80s ✅
```

## Learnings

### Technical Insights

1. **Python File I/O Buffering**: By default, Python files use full buffering for file objects. `flush()` is necessary to ensure data is written to the OS buffer, and file close ensures OS-level persistence.

2. **Stream Mode Design Pattern**: The `stream_mode` parameter has two distinct behaviors:
   - `stream_mode=True`: Write-as-you-go (needs explicit flush for real-time)
   - `stream_mode=False`: Collect-then-write (in-memory aggregation, no flush needed)

3. **Data Safety Trade-offs**:
   - Adding `flush()` slightly reduces performance (more I/O calls)
   - But ensures data safety if process crashes unexpectedly
   - Default behavior should prioritize data integrity over speed

### Debugging Process

- User reported symptom (no real-time output)
- Traced through code flow: `daq.py` → `ConfigOverrider` → `Sampler.run()` → `save_events()`
- Identified exact location of missing flush
- Minimal fix (1 line) with maximum impact

### Code Quality

- Pre-commit hooks automatically formatted code
- Conventional Commits format (`fix(daq): ...`) enables semantic versioning
- `task version` correctly identified PATCH version bump
- All tests passed without modification

## Next Steps

1. **Performance Monitoring** (Optional): Consider batching flushes (flush every N events) if performance becomes an issue with very high event rates
2. **Documentation**: Consider documenting the `stream_mode` parameter in user guides to explain real-time monitoring capability
3. **Integration Testing**: Could add integration test for real-time file monitoring (using `tail -f` simulation)