feat: add funda ingest and staging models
This commit is contained in:
Stijnvandenbroek
553
data_platform/assets/funda.py
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553
data_platform/assets/funda.py
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@@ -0,0 +1,553 @@
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"""Dagster assets for Funda real-estate data ingestion.
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Three assets form a pipeline:
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funda_search_results → funda_listing_details → funda_price_history
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Each asset is configurable from the Dagster launchpad so search
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parameters (location, price range, etc.) can be tweaked per run.
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"""
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import json
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from datetime import datetime, timezone
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from typing import Optional
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from dagster import (
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AssetExecutionContext,
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Config,
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MaterializeResult,
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MetadataValue,
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asset,
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)
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from data_platform.resources import FundaResource, PostgresResource
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# ---------------------------------------------------------------------------
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# Launchpad config schemas
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# ---------------------------------------------------------------------------
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class FundaSearchConfig(Config):
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"""Launchpad parameters for the Funda search asset."""
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location: str = "amsterdam"
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offering_type: str = "buy"
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price_min: Optional[int] = None
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price_max: Optional[int] = None
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area_min: Optional[int] = None
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area_max: Optional[int] = None
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plot_min: Optional[int] = None
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plot_max: Optional[int] = None
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object_type: Optional[str] = None # comma-separated, e.g. "house,apartment"
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energy_label: Optional[str] = None # comma-separated, e.g. "A,A+,A++"
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radius_km: Optional[int] = None
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sort: str = "newest"
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max_pages: int = 3
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class FundaDetailsConfig(Config):
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"""Launchpad parameters for the listing-details asset."""
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fetch_all: bool = True # fetch details for every search result
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class FundaPriceHistoryConfig(Config):
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"""Launchpad parameters for the price-history asset."""
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fetch_all: bool = True # fetch price history for every detailed listing
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# ---------------------------------------------------------------------------
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# SQL helpers
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# ---------------------------------------------------------------------------
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_SCHEMA = "raw_funda"
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_DDL_SEARCH = f"""
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CREATE TABLE IF NOT EXISTS {_SCHEMA}.search_results (
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global_id TEXT,
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title TEXT,
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city TEXT,
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postcode TEXT,
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province TEXT,
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neighbourhood TEXT,
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price BIGINT,
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living_area INT,
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plot_area INT,
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bedrooms INT,
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rooms INT,
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energy_label TEXT,
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object_type TEXT,
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offering_type TEXT,
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construction_type TEXT,
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publish_date TEXT,
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broker_id TEXT,
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broker_name TEXT,
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raw_json JSONB,
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ingested_at TIMESTAMPTZ DEFAULT now()
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);
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"""
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_DDL_DETAILS = f"""
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CREATE TABLE IF NOT EXISTS {_SCHEMA}.listing_details (
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global_id TEXT,
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tiny_id TEXT,
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title TEXT,
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city TEXT,
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postcode TEXT,
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province TEXT,
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neighbourhood TEXT,
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municipality TEXT,
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price BIGINT,
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price_formatted TEXT,
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status TEXT,
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offering_type TEXT,
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object_type TEXT,
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house_type TEXT,
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construction_type TEXT,
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construction_year TEXT,
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energy_label TEXT,
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living_area INT,
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plot_area INT,
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bedrooms INT,
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rooms INT,
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description TEXT,
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publication_date TEXT,
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latitude DOUBLE PRECISION,
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longitude DOUBLE PRECISION,
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has_garden BOOLEAN,
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has_balcony BOOLEAN,
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has_solar_panels BOOLEAN,
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has_heat_pump BOOLEAN,
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has_roof_terrace BOOLEAN,
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is_energy_efficient BOOLEAN,
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is_monument BOOLEAN,
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url TEXT,
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photo_count INT,
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views INT,
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saves INT,
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raw_json JSONB,
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ingested_at TIMESTAMPTZ DEFAULT now()
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);
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"""
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_DDL_PRICE_HISTORY = f"""
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CREATE TABLE IF NOT EXISTS {_SCHEMA}.price_history (
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global_id TEXT,
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price BIGINT,
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human_price TEXT,
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date TEXT,
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timestamp TEXT,
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source TEXT,
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status TEXT,
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ingested_at TIMESTAMPTZ DEFAULT now()
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);
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"""
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def _safe(val):
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"""Convert non-serialisable values (tuples, lists of dicts, etc.) for JSONB."""
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if isinstance(val, (list, dict, tuple)):
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return json.dumps(val, default=str)
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return val
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def _safe_int(val):
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"""Try to cast to int, return None on failure."""
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if val is None:
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return None
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try:
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return int(val)
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except (ValueError, TypeError):
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return None
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# ---------------------------------------------------------------------------
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# Assets
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# ---------------------------------------------------------------------------
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@asset(
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group_name="funda",
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kinds={"python", "postgres"},
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description="Search Funda listings and store results in Postgres.",
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)
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def funda_search_results(
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context: AssetExecutionContext,
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config: FundaSearchConfig,
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funda: FundaResource,
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postgres: PostgresResource,
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) -> MaterializeResult:
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client = funda.get_client()
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# Build search kwargs from launchpad config
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kwargs: dict = {
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"location": [loc.strip() for loc in config.location.split(",")],
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"offering_type": config.offering_type,
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"sort": config.sort,
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}
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if config.price_min is not None:
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kwargs["price_min"] = config.price_min
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if config.price_max is not None:
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kwargs["price_max"] = config.price_max
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if config.area_min is not None:
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kwargs["area_min"] = config.area_min
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if config.area_max is not None:
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kwargs["area_max"] = config.area_max
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if config.plot_min is not None:
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kwargs["plot_min"] = config.plot_min
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if config.plot_max is not None:
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kwargs["plot_max"] = config.plot_max
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if config.object_type:
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kwargs["object_type"] = [t.strip() for t in config.object_type.split(",")]
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if config.energy_label:
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kwargs["energy_label"] = [l.strip() for l in config.energy_label.split(",")]
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if config.radius_km is not None:
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kwargs["radius_km"] = config.radius_km
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# Paginate through results
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all_listings = []
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for page in range(config.max_pages):
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context.log.info(f"Fetching search page {page + 1}/{config.max_pages} …")
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kwargs["page"] = page
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results = client.search_listing(**kwargs)
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if not results:
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context.log.info("No more results.")
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break
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all_listings.extend(results)
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context.log.info(f" got {len(results)} listings (total: {len(all_listings)})")
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if not all_listings:
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context.log.warning("Search returned zero results.")
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return MaterializeResult(metadata={"count": 0})
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# Write to Postgres
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engine = postgres.get_engine()
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with engine.begin() as conn:
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conn.execute(
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__import__("sqlalchemy").text(f"CREATE SCHEMA IF NOT EXISTS {_SCHEMA}")
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)
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conn.execute(__import__("sqlalchemy").text(_DDL_SEARCH))
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# Truncate before inserting fresh results
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conn.execute(
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__import__("sqlalchemy").text(f"TRUNCATE TABLE {_SCHEMA}.search_results")
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)
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rows = []
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for listing in all_listings:
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d = listing.to_dict()
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rows.append(
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{
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"global_id": d.get("global_id"),
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"title": d.get("title"),
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"city": d.get("city"),
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"postcode": d.get("postcode"),
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"province": d.get("province"),
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"neighbourhood": d.get("neighbourhood"),
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"price": _safe_int(d.get("price")),
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"living_area": _safe_int(d.get("living_area")),
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"plot_area": _safe_int(d.get("plot_area")),
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"bedrooms": _safe_int(d.get("bedrooms")),
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"rooms": _safe_int(d.get("rooms")),
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"energy_label": d.get("energy_label"),
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"object_type": d.get("object_type"),
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"offering_type": d.get("offering_type"),
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"construction_type": d.get("construction_type"),
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"publish_date": d.get("publish_date"),
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"broker_id": str(d.get("broker_id", "")),
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"broker_name": d.get("broker_name"),
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"raw_json": json.dumps(d, default=str),
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}
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)
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insert_sql = f"""
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INSERT INTO {_SCHEMA}.search_results
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(global_id, title, city, postcode, province, neighbourhood,
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price, living_area, plot_area, bedrooms, rooms, energy_label,
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object_type, offering_type, construction_type, publish_date,
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broker_id, broker_name, raw_json)
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VALUES
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(:global_id, :title, :city, :postcode, :province, :neighbourhood,
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:price, :living_area, :plot_area, :bedrooms, :rooms, :energy_label,
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:object_type, :offering_type, :construction_type, :publish_date,
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:broker_id, :broker_name, :raw_json)
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"""
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postgres.execute_many(insert_sql, rows)
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context.log.info(f"Inserted {len(rows)} search results into {_SCHEMA}.search_results")
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return MaterializeResult(
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metadata={
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"count": len(rows),
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"location": MetadataValue.text(config.location),
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"offering_type": MetadataValue.text(config.offering_type),
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"preview": MetadataValue.md(
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_search_preview_table(rows[:10]),
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),
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}
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)
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@asset(
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group_name="funda",
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kinds={"python", "postgres"},
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deps=[funda_search_results],
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description="Fetch full listing details for each search result and store in Postgres.",
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)
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def funda_listing_details(
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context: AssetExecutionContext,
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config: FundaDetailsConfig,
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funda: FundaResource,
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postgres: PostgresResource,
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) -> MaterializeResult:
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client = funda.get_client()
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engine = postgres.get_engine()
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with engine.begin() as conn:
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conn.execute(
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__import__("sqlalchemy").text(f"CREATE SCHEMA IF NOT EXISTS {_SCHEMA}")
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)
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conn.execute(__import__("sqlalchemy").text(_DDL_DETAILS))
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# Read listing IDs from search results
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with engine.connect() as conn:
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result = conn.execute(
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__import__("sqlalchemy").text(
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f"SELECT DISTINCT global_id FROM {_SCHEMA}.search_results"
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)
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)
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ids = [row[0] for row in result if row[0]]
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if not ids:
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context.log.warning("No search results found – run funda_search_results first.")
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return MaterializeResult(metadata={"count": 0})
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context.log.info(f"Fetching details for {len(ids)} listings …")
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# Truncate before inserting
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with engine.begin() as conn:
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conn.execute(
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__import__("sqlalchemy").text(f"TRUNCATE TABLE {_SCHEMA}.listing_details")
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)
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rows = []
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errors = 0
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for i, gid in enumerate(ids):
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try:
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listing = client.get_listing(int(gid))
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d = listing.to_dict()
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rows.append(
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{
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"global_id": d.get("global_id"),
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"tiny_id": str(d.get("tiny_id", "")),
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"title": d.get("title"),
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"city": d.get("city"),
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"postcode": d.get("postcode"),
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"province": d.get("province"),
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"neighbourhood": d.get("neighbourhood"),
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"municipality": d.get("municipality"),
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"price": _safe_int(d.get("price")),
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"price_formatted": d.get("price_formatted"),
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"status": d.get("status"),
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"offering_type": d.get("offering_type"),
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"object_type": d.get("object_type"),
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"house_type": d.get("house_type"),
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"construction_type": d.get("construction_type"),
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"construction_year": d.get("construction_year"),
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"energy_label": d.get("energy_label"),
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"living_area": _safe_int(d.get("living_area")),
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"plot_area": _safe_int(d.get("plot_area")),
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"bedrooms": _safe_int(d.get("bedrooms")),
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"rooms": _safe_int(d.get("rooms")),
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"description": d.get("description"),
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"publication_date": d.get("publication_date"),
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"latitude": d.get("latitude"),
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"longitude": d.get("longitude"),
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"has_garden": d.get("has_garden"),
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"has_balcony": d.get("has_balcony"),
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"has_solar_panels": d.get("has_solar_panels"),
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"has_heat_pump": d.get("has_heat_pump"),
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"has_roof_terrace": d.get("has_roof_terrace"),
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"is_energy_efficient": d.get("is_energy_efficient"),
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"is_monument": d.get("is_monument"),
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"url": d.get("url"),
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"photo_count": _safe_int(d.get("photo_count")),
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"views": _safe_int(d.get("views")),
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"saves": _safe_int(d.get("saves")),
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"raw_json": json.dumps(d, default=str),
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}
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)
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except Exception as e:
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errors += 1
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context.log.warning(f"Failed to fetch listing {gid}: {e}")
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continue
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if (i + 1) % 10 == 0:
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context.log.info(f" fetched {i + 1}/{len(ids)} …")
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if rows:
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insert_sql = f"""
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INSERT INTO {_SCHEMA}.listing_details
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(global_id, tiny_id, title, city, postcode, province,
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neighbourhood, municipality, price, price_formatted,
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status, offering_type, object_type, house_type,
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construction_type, construction_year, energy_label,
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living_area, plot_area, bedrooms, rooms, description,
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publication_date, latitude, longitude,
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has_garden, has_balcony, has_solar_panels, has_heat_pump,
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has_roof_terrace, is_energy_efficient, is_monument,
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url, photo_count, views, saves, raw_json)
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VALUES
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(:global_id, :tiny_id, :title, :city, :postcode, :province,
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:neighbourhood, :municipality, :price, :price_formatted,
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:status, :offering_type, :object_type, :house_type,
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:construction_type, :construction_year, :energy_label,
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:living_area, :plot_area, :bedrooms, :rooms, :description,
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:publication_date, :latitude, :longitude,
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:has_garden, :has_balcony, :has_solar_panels, :has_heat_pump,
|
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:has_roof_terrace, :is_energy_efficient, :is_monument,
|
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:url, :photo_count, :views, :saves, :raw_json)
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"""
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postgres.execute_many(insert_sql, rows)
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context.log.info(
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f"Inserted {len(rows)} listing details ({errors} errors) "
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f"into {_SCHEMA}.listing_details"
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)
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return MaterializeResult(
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metadata={
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"count": len(rows),
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"errors": errors,
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"preview": MetadataValue.md(
|
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_details_preview_table(rows[:10]),
|
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),
|
||||
}
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)
|
||||
|
||||
|
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@asset(
|
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group_name="funda",
|
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kinds={"python", "postgres"},
|
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deps=[funda_listing_details],
|
||||
description="Fetch price history for each detailed listing and store in Postgres.",
|
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)
|
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def funda_price_history(
|
||||
context: AssetExecutionContext,
|
||||
config: FundaPriceHistoryConfig,
|
||||
funda: FundaResource,
|
||||
postgres: PostgresResource,
|
||||
) -> MaterializeResult:
|
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client = funda.get_client()
|
||||
engine = postgres.get_engine()
|
||||
|
||||
with engine.begin() as conn:
|
||||
conn.execute(
|
||||
__import__("sqlalchemy").text(f"CREATE SCHEMA IF NOT EXISTS {_SCHEMA}")
|
||||
)
|
||||
conn.execute(__import__("sqlalchemy").text(_DDL_PRICE_HISTORY))
|
||||
|
||||
# Read listings from details table
|
||||
with engine.connect() as conn:
|
||||
result = conn.execute(
|
||||
__import__("sqlalchemy").text(
|
||||
f"SELECT DISTINCT global_id FROM {_SCHEMA}.listing_details"
|
||||
)
|
||||
)
|
||||
ids = [row[0] for row in result if row[0]]
|
||||
|
||||
if not ids:
|
||||
context.log.warning(
|
||||
"No listing details found – run funda_listing_details first."
|
||||
)
|
||||
return MaterializeResult(metadata={"count": 0})
|
||||
|
||||
context.log.info(f"Fetching price history for {len(ids)} listings …")
|
||||
|
||||
# Truncate before inserting
|
||||
with engine.begin() as conn:
|
||||
conn.execute(
|
||||
__import__("sqlalchemy").text(f"TRUNCATE TABLE {_SCHEMA}.price_history")
|
||||
)
|
||||
|
||||
rows = []
|
||||
errors = 0
|
||||
for i, gid in enumerate(ids):
|
||||
try:
|
||||
# get_price_history needs a Listing object, so fetch it first
|
||||
listing = client.get_listing(int(gid))
|
||||
history = client.get_price_history(listing)
|
||||
for entry in history:
|
||||
rows.append(
|
||||
{
|
||||
"global_id": gid,
|
||||
"price": _safe_int(entry.get("price")),
|
||||
"human_price": entry.get("human_price"),
|
||||
"date": entry.get("date"),
|
||||
"timestamp": entry.get("timestamp"),
|
||||
"source": entry.get("source"),
|
||||
"status": entry.get("status"),
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
errors += 1
|
||||
context.log.warning(f"Failed to fetch price history for {gid}: {e}")
|
||||
continue
|
||||
|
||||
if (i + 1) % 10 == 0:
|
||||
context.log.info(f" fetched {i + 1}/{len(ids)} …")
|
||||
|
||||
if rows:
|
||||
insert_sql = f"""
|
||||
INSERT INTO {_SCHEMA}.price_history
|
||||
(global_id, price, human_price, date, timestamp, source, status)
|
||||
VALUES
|
||||
(:global_id, :price, :human_price, :date, :timestamp, :source, :status)
|
||||
"""
|
||||
postgres.execute_many(insert_sql, rows)
|
||||
|
||||
context.log.info(
|
||||
f"Inserted {len(rows)} price history records ({errors} errors) "
|
||||
f"into {_SCHEMA}.price_history"
|
||||
)
|
||||
|
||||
return MaterializeResult(
|
||||
metadata={
|
||||
"count": len(rows),
|
||||
"errors": errors,
|
||||
"listings_processed": len(ids) - errors,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Metadata preview helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _search_preview_table(rows: list[dict]) -> str:
|
||||
"""Build a markdown table for search result metadata preview."""
|
||||
lines = ["| Title | City | Price | Area | Bedrooms |", "| --- | --- | --- | --- | --- |"]
|
||||
for r in rows:
|
||||
price = f"€{r['price']:,}" if r.get("price") else "–"
|
||||
area = f"{r['living_area']} m²" if r.get("living_area") else "–"
|
||||
lines.append(
|
||||
f"| {r.get('title', '–')} | {r.get('city', '–')} "
|
||||
f"| {price} | {area} | {r.get('bedrooms', '–')} |"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _details_preview_table(rows: list[dict]) -> str:
|
||||
"""Build a markdown table for listing details metadata preview."""
|
||||
lines = [
|
||||
"| Title | City | Price | Status | Energy |",
|
||||
"| --- | --- | --- | --- | --- |",
|
||||
]
|
||||
for r in rows:
|
||||
price = f"€{r['price']:,}" if r.get("price") else "–"
|
||||
lines.append(
|
||||
f"| {r.get('title', '–')} | {r.get('city', '–')} "
|
||||
f"| {price} | {r.get('status', '–')} | {r.get('energy_label', '–')} |"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
Reference in New Issue
Block a user