Replace the sequential fetch-all-then-process pipeline with a streaming
architecture where listing processing starts as soon as IDs become
available from each subquery. A producer task fetches pages and enqueues
new IDs (filtered inline against DB), while 20 consumer workers process
listings concurrently from the queue.
- Add ListingRepository.get_listing_ids() for fast ID-only projection
- Refactor listing_tasks.py: remove get_ids_to_process/dump_listings_and_monitor,
replace with unified producer/worker/monitor pipeline
- Apply same pattern to CLI path in listing_fetcher.py
- Remove 'filtering' phase from frontend, show combined fetch+process metrics
- Add fetching_done flag to TaskResult for phase transition tracking
- Add phase-aware progress reporting across all crawl phases (splitting,
fetching, filtering, processing) with per-step counters
- Add TaskProgressDrawer component with phase timeline stepper, detail
counters, progress bar with ETA, and live worker log viewer
- Add on_step_complete callback to ListingProcessor for granular tracking
of details/images/OCR steps
- Extend QuerySplitter on_progress callback with structured counter data
- Capture celery worker logs via ring buffer handler and inject into task
state updates for frontend display
- Guard taskResult updates with phase presence check to prevent drawer
from blanking during state transitions
