Two surfaces wired up so the user can "get a vibe of the market": **Per-listing** — each PropertyCard now shows a small pill next to the price when the listing's total_price moved >=1% over a 14-day lookback (e.g. "↓ £200 (-4%) in 14d"). Drops render green, rises render red. Computed from `price_history_json` by the daily aggregator and denormalised onto the listing row so the streaming endpoint just passes it through. **Macro** — new always-visible inline strip above the chip strip showing today's median total price, median £/m², and listing count for the current filter's bedroom band, each with a 30-day % delta: "Rent · 1-2 bed · 30d: Median £2,500 ↓ -4% · £/m² £50 ↓ -2% · Listings 4,200 ↑ +5%". Both data sources are populated daily at 04:00 UTC by a new Celery beat task that fires 1h after the 03:00 RENT scrape and feeds two sinks: a per-listing update pass and an upsert to a new `dailylistingaggregate` table keyed on (snapshot_date, listing_type, min_bedrooms, max_bedrooms). ## Backend - `models/listing.py`: Listing parent gains `price_14d_ago` + `price_ change_pct_14d` nullable floats (inherited by RentListing/BuyListing). New `DailyListingAggregate` table model with unique constraint on (date, type, min_bed, max_bed). - Alembic `a8b9c0d1e2f3`: adds the two columns to both listing tables and creates the aggregate table + date index. - `services/market_aggregator.py` (new): `compute_trend_for_listing`, `update_per_listing_trend` (batched, idempotent), `_stats` (median + mean filtered to positive finite values), `compute_aggregate_ snapshot` (dialect-aware MySQL / SQLite upsert), `fetch_trend_ series` (range query for the API). - `tasks/market_tasks.py` (new): `compute_daily_market_aggregates_task` Celery task wrapping both stages. - `tasks/listing_tasks.py:setup_periodic_tasks`: registers the daily task at 04:00 UTC alongside the existing scrape schedules. - `celery_app.py`: includes the new tasks module. - `api/app.py`: new `GET /api/market_trend?listing_type=&min_bedrooms=& max_bedrooms=&days=` endpoint returning the daily series. - `ui_exporter.py`: GeoJSON feature properties now carry `price_14d_ago` and `price_change_pct_14d` so the frontend can render the badge without an extra round-trip. ## Frontend - `types/index.ts`: new `MarketTrendPoint`; `PropertyProperties` gains the two optional trend fields. - `components/PropertyCard.tsx`: derived `trendBadge` (>=1% threshold, null-safe) rendered as a small pill on both card variants. - `hooks/useMarketTrend.ts` (new): fetches the trend series, derives current-vs-oldest deltas per metric (% change rounded to 1dp). - `components/MarketTrendStrip.tsx` (new): compact inline strip with three metric cells. Hidden when the aggregator hasn't produced any rows yet (graceful start during the first week post-launch). - `App.tsx`: renders the strip above the chip strip whenever the active queryParameters are known. ## Tests - pytest: 10 new (trend math edge cases including null history, malformed JSON, only-recent entries, drops, rises, zero current price; _stats empty / nonpositive filtering; upsert idempotency on an in-memory SQLite seed). 34 decision + aggregator tests pass. - vitest: 8 new (useMarketTrend fetch URL, two-point delta, single-point null delta, empty series; PropertyCard trend badge arrow direction + sign for drops/rises, noise threshold, null guard). 229 tests pass total, tsc clean. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import sys
|
|
import time
|
|
from celery import Celery
|
|
from celery.signals import worker_ready, task_prerun, task_postrun
|
|
from dotenv import load_dotenv
|
|
import os
|
|
|
|
from logging_config import configure_logging
|
|
|
|
load_dotenv()
|
|
|
|
configure_logging(os.getenv("SERVICE_NAME", "celery-worker"))
|
|
|
|
app = Celery(
|
|
"celery_app",
|
|
broker=os.getenv("CELERY_BROKER_URL", "redis://localhost:6379/0"),
|
|
backend=os.getenv("CELERY_RESULT_BACKEND", "redis://localhost:6379/1"),
|
|
include=["tasks.listing_tasks", "tasks.poi_tasks", "tasks.market_tasks"],
|
|
)
|
|
|
|
# Keep broker / result-backend connections alive when sitting behind an
|
|
# HAProxy / load balancer that idles TCP connections (the in-cluster Redis
|
|
# HAProxy reaps idle conns after 30s). Without these options the worker
|
|
# logs a "Connection closed by server" every ~30s and progress publishes
|
|
# silently drop on the closed socket.
|
|
app.conf.update(
|
|
task_serializer="json",
|
|
result_serializer="json",
|
|
accept_content=["json"],
|
|
timezone="UTC",
|
|
enable_utc=True,
|
|
broker_transport_options={
|
|
"socket_keepalive": True,
|
|
"health_check_interval": 25,
|
|
},
|
|
result_backend_transport_options={
|
|
"socket_keepalive": True,
|
|
"health_check_interval": 25,
|
|
},
|
|
broker_heartbeat=10,
|
|
)
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Celery metrics via prometheus_client
|
|
# ---------------------------------------------------------------------------
|
|
CELERY_METRICS_PORT = int(os.getenv("CELERY_METRICS_PORT", "9090"))
|
|
|
|
# Track task start times for duration measurement
|
|
_task_start_times: dict[str, float] = {}
|
|
|
|
# Initialise OTel metrics at module level so prefork children inherit the
|
|
# MeterProvider and PrometheusMetricReader. The prometheus_client collectors
|
|
# are registered in the default registry before fork, so child-process
|
|
# recordings are visible to the HTTP server started in the main process.
|
|
from api.metrics import init_metrics as _init_metrics # noqa: E402
|
|
_init_metrics(os.getenv("SERVICE_NAME", "celery-worker"))
|
|
|
|
|
|
@worker_ready.connect
|
|
def _start_metrics_server(**kwargs: object) -> None:
|
|
"""Start a lightweight Prometheus HTTP server in the main worker process."""
|
|
from prometheus_client import start_http_server
|
|
start_http_server(CELERY_METRICS_PORT)
|
|
|
|
|
|
@task_prerun.connect
|
|
def _on_task_prerun(task_id: str, task: object, **kwargs: object) -> None:
|
|
import api.metrics as m
|
|
task_name = getattr(task, "name", "unknown")
|
|
m.celery_tasks_active.add(1, {"task_name": task_name})
|
|
_task_start_times[task_id] = time.monotonic()
|
|
|
|
|
|
@task_postrun.connect
|
|
def _on_task_postrun(
|
|
task_id: str, task: object, state: str | None = None, **kwargs: object
|
|
) -> None:
|
|
import api.metrics as m
|
|
task_name = getattr(task, "name", "unknown")
|
|
status = state or "UNKNOWN"
|
|
|
|
m.celery_tasks_active.add(-1, {"task_name": task_name})
|
|
m.celery_tasks_total.add(1, {"task_name": task_name, "status": status})
|
|
|
|
start = _task_start_times.pop(task_id, None)
|
|
if start is not None:
|
|
m.celery_task_duration_seconds.record(
|
|
time.monotonic() - start, {"task_name": task_name}
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
try:
|
|
with app.connection() as conn:
|
|
conn.ensure_connection(max_retries=0)
|
|
print("Broker connection OK")
|
|
sys.exit(0)
|
|
except Exception as e:
|
|
print(f"Broker connection failed: {e}")
|
|
sys.exit(1)
|