Progress Log: Stream Mode Real-Time File Flushing Fix

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:

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)

目次