711 lines
30 KiB
Python
711 lines
30 KiB
Python
from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any, Iterable
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from uuid import UUID
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from geoalchemy2.functions import ST_Intersects, ST_MakeEnvelope
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from geoalchemy2.shape import from_shape
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from geoalchemy2.shape import to_shape
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from shapely.geometry import mapping
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from shapely.geometry import shape
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from shapely.ops import transform as transform_geometry
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from shapely.validation import make_valid
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from sqlalchemy import Float, cast, func
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from app.core.errors import AppError
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from app.models import Dataset, VectorFeature
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FULL_AREA_CLIPPED_OPERATOR_TOOLS = {
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"provision_mol_population_history.py",
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"provision_official_landuse_timeseries.py",
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"provision_regional_grb_buildings.py",
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"provision_regional_grb_context.py",
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"provision_regional_historical_landuse.py",
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"provision_waterinfo_station_history.py",
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"provision_mol_bwk_natura2000.py",
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"provision_regional_bwk_natura2000.py",
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"provision_agricultural_parcel_history.py",
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"provision_buildings_addresses_register.py",
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}
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SEMANTIC_METRICS_DISABLED_OPERATOR_TOOLS = {
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# Historical land-use themes are polygon map classes. Generic live-theme
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# line metrics (road/watercourse length) would therefore be meaningless.
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"provision_regional_historical_landuse.py",
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}
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PRECLIPPED_MUNICIPALITY_PARTITION_OPERATOR_TOOLS = {
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"provision_regional_bwk_natura2000.py",
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}
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SEMANTIC_SELECTION_METRICS: dict[str, tuple[dict[str, Any], ...]] = {
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"buildings": (
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{
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"metric_key": "footprint_area",
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"method": "intersection_area",
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"label": "Bebouwde grondoppervlakte",
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"unit": "ha",
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"geometry_dimension": 2,
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"warning": "Dit is de grondoppervlakte van gebouwcontouren, niet de totale vloeroppervlakte of het gebouwvolume.",
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},
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),
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"forest": (
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{
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"metric_key": "forest_area",
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"method": "intersection_area",
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"label": "Bosoppervlakte",
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"unit": "ha",
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"geometry_dimension": 2,
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},
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),
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"water": (
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{
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"metric_key": "water_area",
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"method": "intersection_area",
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"label": "Wateroppervlakte",
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"unit": "ha",
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"geometry_dimension": 2,
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"warning": "Watervolume is niet berekenbaar zonder betrouwbare diepte- of bathymetrische gegevens. De kaartbron levert alleen oppervlakte- en lijngeometrie.",
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},
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{
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"metric_key": "watercourse_length",
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"method": "intersection_length",
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"label": "Lengte waterlopen",
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"unit": "km",
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"geometry_dimension": 1,
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},
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),
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"roads": (
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{
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"metric_key": "road_length",
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"method": "intersection_length",
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"label": "Totale weglengte",
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"unit": "km",
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"geometry_dimension": 1,
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"warning": "De lengte volgt de GRB-wegsegmenten en zegt niets over rijstroken, verkeersvolume of verhardingsoppervlakte.",
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},
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),
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"parcels": (
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{
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"metric_key": "parcel_area",
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"method": "intersection_area",
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"label": "Perceeloppervlakte",
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"unit": "ha",
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"geometry_dimension": 2,
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"warning": "GRB-percelen zijn een grafische referentie en vormen geen juridische grensopmeting.",
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},
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),
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"nature_value": (),
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"agriculture": (),
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}
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SEMANTIC_COUNT_LABELS = {
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"buildings": "Gebouwen",
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"population": "Statistische sectoren",
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"forest": "Bosvlakken",
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"water": "Waterobjecten",
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"roads": "Wegsegmenten",
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"parcels": "Percelen",
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"nature_value": "BWK-kaartvlakken",
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"agriculture": "Landbouwgebruikspercelen",
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}
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# Sprint 205 initially normalized two official comma-separated ALZ group labels
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# mechanically. Keep those persisted values queryable while new artifacts use
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# the explicit controlled keys.
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SELECTION_FILTER_VALUE_ALIASES: dict[tuple[str, str], tuple[str, ...]] = {
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("main_crop_group_key", "grains_seeds_legumes"): ("granen,_zaden_en_peulvruchten",),
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("main_crop_group_key", "horticulture"): ("groenten,_kruiden_en_sierplanten",),
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}
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class VectorFeatureService:
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@staticmethod
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def _expanded_selection_filter_values(filter_property: str, filter_values: list[Any]) -> list[str]:
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expanded: list[str] = []
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for value in filter_values:
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normalized = str(value)
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expanded.append(normalized)
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expanded.extend(SELECTION_FILTER_VALUE_ALIASES.get((filter_property, normalized), ()))
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return list(dict.fromkeys(expanded))
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@staticmethod
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def _dataset_theme(dataset: Dataset) -> str | None:
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source_metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
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candidates = (
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source_metadata.get("theme"),
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dataset.reference_layer_name,
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source_metadata.get("layer_type"),
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)
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aliases = {
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"building": "buildings",
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"bebouwing": "buildings",
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"population": "population",
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"forest": "forest",
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"forestry": "forest",
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"waterways": "water",
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"road": "roads",
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"parcel": "parcels",
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"nature": "nature_value",
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"biodiversity": "nature_value",
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"bwk": "nature_value",
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"natura2000": "nature_value",
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"agricultural": "agriculture",
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"landbouw": "agriculture",
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"landbouwgebruik": "agriculture",
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"building_registry": "buildings",
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}
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for candidate in candidates:
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if not isinstance(candidate, str) or not candidate.strip():
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continue
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normalized = candidate.strip().lower()
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if normalized.startswith("regional_"):
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normalized = normalized.removeprefix("regional_")
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normalized = aliases.get(normalized, normalized)
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if normalized in {*SEMANTIC_SELECTION_METRICS, "population"}:
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return normalized
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return None
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@staticmethod
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def supports_selection_summary(dataset: Dataset) -> bool:
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source_metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
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return isinstance(source_metadata.get("selection_aggregation"), dict) or VectorFeatureService._dataset_theme(dataset) is not None
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@staticmethod
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def can_use_full_area_fast_path(dataset: Dataset, selection_area_id: UUID | None) -> bool:
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if selection_area_id is None or dataset.area_id != selection_area_id:
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return False
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source_metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
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if source_metadata.get("geometry_clipped_to_area") is True:
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return True
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provenance = dataset.provenance_metadata if isinstance(dataset.provenance_metadata, dict) else {}
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return provenance.get("operator_tool") in FULL_AREA_CLIPPED_OPERATOR_TOOLS
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@staticmethod
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def preclipped_partition_filter(dataset: Dataset, selection_area_name: str | None) -> tuple[str, str] | None:
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provenance = dataset.provenance_metadata if isinstance(dataset.provenance_metadata, dict) else {}
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if provenance.get("operator_tool") not in PRECLIPPED_MUNICIPALITY_PARTITION_OPERATOR_TOOLS:
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return None
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source_metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
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if (
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source_metadata.get("partitioned_source_audit") is not True
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or source_metadata.get("geometry_clipped_to_area") is not True
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):
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return None
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normalized_name = str(selection_area_name or "").strip()
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prefix = "Gemeente "
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suffixes = (" - officiële grens", " - officiele grens")
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if not normalized_name.startswith(prefix):
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return None
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municipality = normalized_name[len(prefix):]
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for suffix in suffixes:
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if municipality.endswith(suffix):
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municipality = municipality[: -len(suffix)]
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break
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municipality = municipality.strip()
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return ("municipality", municipality) if municipality else None
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@staticmethod
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def _feature_row(dataset_id: UUID, feature: dict[str, Any], index: int, feature_class: str | None) -> VectorFeature | None:
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geometry_payload = feature.get("geometry")
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if geometry_payload is None:
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return None
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try:
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geometry = shape(geometry_payload)
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except Exception as exc:
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raise AppError(code="INVALID_GEOJSON", message=f"Invalid feature geometry at index {index}", status_code=400) from exc
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if geometry.is_empty:
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return None
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if not geometry.is_valid:
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geometry = make_valid(geometry)
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if geometry.is_empty or not geometry.is_valid:
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raise AppError(code="INVALID_GEOMETRY", message=f"Invalid feature geometry at index {index}", status_code=400)
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if geometry.has_z:
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geometry = transform_geometry(lambda x, y, z=None: (x, y), geometry)
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properties = feature.get("properties") if isinstance(feature.get("properties"), dict) else {}
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source_feature_id = feature.get("id")
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if source_feature_id is None:
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source_feature_id = properties.get("id") or properties.get("source_feature_id")
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return VectorFeature(
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dataset_id=dataset_id,
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feature_class=feature_class,
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source_feature_id=str(source_feature_id) if source_feature_id is not None else None,
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properties_json=properties,
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geometry=from_shape(geometry, srid=4326),
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)
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@staticmethod
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def _normalize_selection_bbox(bbox: dict[str, Any]) -> dict[str, float | str]:
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try:
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min_x = float(bbox["min_x"])
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min_y = float(bbox["min_y"])
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max_x = float(bbox["max_x"])
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max_y = float(bbox["max_y"])
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except (KeyError, TypeError, ValueError) as exc:
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raise AppError(
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code="INVALID_SELECTION_BBOX",
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message="Selection bbox must include numeric min_x, min_y, max_x and max_y values",
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status_code=400,
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) from exc
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crs = str(bbox.get("crs") or "EPSG:4326").upper()
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if crs != "EPSG:4326":
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raise AppError(
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code="UNSUPPORTED_SELECTION_CRS",
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message="Map selection currently supports EPSG:4326 bbox coordinates only",
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details={"crs": crs},
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status_code=400,
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)
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if min_x >= max_x or min_y >= max_y:
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raise AppError(
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code="INVALID_SELECTION_BBOX",
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message="Selection bbox must have min_x < max_x and min_y < max_y",
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status_code=400,
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)
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if min_x < -180 or max_x > 180 or min_y < -90 or max_y > 90:
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raise AppError(
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code="INVALID_SELECTION_BBOX",
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message="Selection bbox is outside EPSG:4326 longitude/latitude bounds",
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status_code=400,
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)
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return {"min_x": min_x, "min_y": min_y, "max_x": max_x, "max_y": max_y, "crs": "EPSG:4326"}
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@staticmethod
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def _row_to_geojson_feature(row: VectorFeature) -> dict[str, Any]:
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geometry_value = row.geometry
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try:
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geometry = geometry_value if hasattr(geometry_value, "__geo_interface__") else to_shape(geometry_value)
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except Exception as exc:
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raise AppError(
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code="INVALID_VECTOR_FEATURE_GEOMETRY",
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message="Persisted vector feature geometry could not be converted to GeoJSON",
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details={"vector_feature_id": str(row.id)},
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status_code=500,
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) from exc
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properties = dict(row.properties_json or {})
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properties.update(
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{
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"vector_feature_id": str(row.id),
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"dataset_id": str(row.dataset_id),
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"source_feature_id": row.source_feature_id,
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"feature_class": row.feature_class,
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}
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)
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return {
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"type": "Feature",
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"id": str(row.id),
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"geometry": mapping(geometry),
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"properties": properties,
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}
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@staticmethod
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def select_features_by_bbox(
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db,
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dataset_id: UUID,
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bbox: dict[str, Any],
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limit: int = 100,
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dataset: Dataset | None = None,
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selection_geometry: Any | None = None,
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selection_area_id: UUID | None = None,
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full_dataset_area: bool = False,
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preclipped_partition_filter: tuple[str, str] | None = None,
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) -> dict[str, Any]:
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normalized_bbox = VectorFeatureService._normalize_selection_bbox(bbox)
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safe_limit = max(1, min(int(limit), 1000))
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selection_shape = selection_geometry
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if selection_shape is None:
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selection_shape = ST_MakeEnvelope(
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normalized_bbox["min_x"],
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normalized_bbox["min_y"],
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normalized_bbox["max_x"],
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normalized_bbox["max_y"],
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4326,
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)
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query = db.query(VectorFeature).filter(VectorFeature.dataset_id == dataset_id)
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if preclipped_partition_filter is not None:
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partition_property, partition_value = preclipped_partition_filter
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query = query.filter(VectorFeature.properties_json.op("->>")(partition_property) == partition_value)
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elif not full_dataset_area:
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query = query.filter(ST_Intersects(VectorFeature.geometry, selection_shape))
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if hasattr(query, "count"):
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total_feature_count = int(query.count())
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else: # Lightweight unit-test sessions do not always implement Query.count().
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total_feature_count = len(query.all())
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rows = (
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query.order_by(VectorFeature.created_at.asc())
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.limit(safe_limit + 1)
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.all()
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)
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truncated = total_feature_count > safe_limit
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selected_rows = rows[:safe_limit]
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features = [VectorFeatureService._row_to_geojson_feature(row) for row in selected_rows]
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summary = None
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if dataset and VectorFeatureService.supports_selection_summary(dataset):
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summary = VectorFeatureService.summarize_features_by_bbox(
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db,
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dataset=dataset,
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bbox=normalized_bbox,
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total_feature_count=total_feature_count,
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selection_geometry=selection_geometry,
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full_dataset_area=full_dataset_area,
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preclipped_partition_filter=preclipped_partition_filter,
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)
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result = {
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"selection_bbox": normalized_bbox,
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"feature_count": len(features),
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"total_feature_count": total_feature_count,
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"limit": safe_limit,
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"truncated": truncated,
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"geojson": {
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"type": "FeatureCollection",
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"features": features,
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},
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"summary": summary,
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}
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if selection_area_id is not None:
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result["selection_area_id"] = str(selection_area_id)
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return result
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@staticmethod
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def summarize_features_by_bbox(
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db,
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*,
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dataset: Dataset,
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bbox: dict[str, Any],
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total_feature_count: int | None = None,
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selection_geometry: Any | None = None,
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full_dataset_area: bool = False,
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preclipped_partition_filter: tuple[str, str] | None = None,
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) -> dict[str, Any]:
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normalized_bbox = VectorFeatureService._normalize_selection_bbox(bbox)
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selection_shape = selection_geometry
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if selection_shape is None:
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selection_shape = ST_MakeEnvelope(
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normalized_bbox["min_x"],
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normalized_bbox["min_y"],
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normalized_bbox["max_x"],
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normalized_bbox["max_y"],
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4326,
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)
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selection_filter = (VectorFeature.dataset_id == dataset.id,)
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if preclipped_partition_filter is not None:
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partition_property, partition_value = preclipped_partition_filter
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selection_filter += (
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VectorFeature.properties_json.op("->>")(partition_property) == partition_value,
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)
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elif not full_dataset_area:
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selection_filter += (ST_Intersects(VectorFeature.geometry, selection_shape),)
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selection_is_preclipped = full_dataset_area or preclipped_partition_filter is not None
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feature_count = total_feature_count
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if feature_count is None:
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feature_count = int(db.query(func.count(VectorFeature.id)).filter(*selection_filter).scalar() or 0)
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source_metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
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config = source_metadata.get("selection_aggregation")
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if not isinstance(config, dict):
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config = {}
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theme = VectorFeatureService._dataset_theme(dataset)
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configured_metric = {
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"metric_key": str(config.get("metric_key") or config.get("method") or "feature_count"),
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"method": str(config.get("method") or "feature_count"),
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"label": str(config.get("label") or SEMANTIC_COUNT_LABELS.get(theme or "", "Objecten")),
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"unit": str(config.get("unit") or "objecten"),
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"warning": str(config["warning"]) if config.get("warning") else None,
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"is_estimate": bool(config.get("is_estimate", False)),
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**({"property": config.get("property")} if config.get("property") else {}),
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}
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provenance = dataset.provenance_metadata if isinstance(dataset.provenance_metadata, dict) else {}
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semantic_metrics_disabled = (
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source_metadata.get("semantic_metrics") is False
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or provenance.get("operator_tool") in SEMANTIC_METRICS_DISABLED_OPERATOR_TOOLS
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)
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semantic_metrics = (
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[]
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if semantic_metrics_disabled
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else [dict(metric) for metric in SEMANTIC_SELECTION_METRICS.get(theme or "", ())]
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)
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primary_config = configured_metric
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if configured_metric["method"] == "feature_count" and semantic_metrics:
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primary_config = semantic_metrics[0]
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metric_configs = [primary_config]
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configured_metrics = source_metadata.get("selection_metrics")
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if isinstance(configured_metrics, list):
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existing_metric_keys = {str(primary_config.get("metric_key") or "")}
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for configured_item in configured_metrics:
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if not isinstance(configured_item, dict):
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continue
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metric_key = str(configured_item.get("metric_key") or "").strip()
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if not metric_key or metric_key in existing_metric_keys:
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continue
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metric_configs.append(dict(configured_item))
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existing_metric_keys.add(metric_key)
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for semantic_metric in semantic_metrics:
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signature = (semantic_metric["method"], semantic_metric["unit"])
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existing = {
|
|
(item["method"], item["unit"])
|
|
for item in metric_configs
|
|
}
|
|
if signature not in existing:
|
|
metric_configs.append(semantic_metric)
|
|
if not any(item["method"] == "feature_count" for item in metric_configs):
|
|
metric_configs.append(
|
|
{
|
|
"metric_key": "feature_count",
|
|
"method": "feature_count",
|
|
"label": SEMANTIC_COUNT_LABELS.get(theme or "", "Objecten"),
|
|
"unit": "objecten",
|
|
}
|
|
)
|
|
|
|
metrics = [
|
|
VectorFeatureService._calculate_selection_metric(
|
|
db,
|
|
dataset=dataset,
|
|
config=metric_config,
|
|
selection_filter=selection_filter,
|
|
selection_shape=selection_shape,
|
|
feature_count=feature_count,
|
|
full_dataset_area=selection_is_preclipped,
|
|
)
|
|
for metric_config in metric_configs
|
|
]
|
|
primary_metric = metrics[0]
|
|
return {
|
|
"metric_label": primary_metric["metric_label"],
|
|
"metric_value": primary_metric["metric_value"],
|
|
"metric_unit": primary_metric["metric_unit"],
|
|
"aggregation_method": primary_metric["aggregation_method"],
|
|
"primary_metric_key": primary_metric["metric_key"],
|
|
"feature_count": feature_count,
|
|
"is_estimate": primary_metric["is_estimate"],
|
|
"warning": primary_metric.get("warning"),
|
|
"metrics": metrics,
|
|
}
|
|
|
|
@staticmethod
|
|
def _calculate_selection_metric(
|
|
db,
|
|
*,
|
|
dataset: Dataset,
|
|
config: dict[str, Any],
|
|
selection_filter: tuple[Any, ...],
|
|
selection_shape: Any,
|
|
feature_count: int,
|
|
full_dataset_area: bool,
|
|
) -> dict[str, Any]:
|
|
method = str(config.get("method") or "feature_count")
|
|
unit = str(config.get("unit") or "objecten")
|
|
warning = str(config["warning"]) if config.get("warning") else None
|
|
is_estimate = bool(config.get("is_estimate", False))
|
|
metric_value = float(feature_count)
|
|
dimension = config.get("geometry_dimension")
|
|
metric_filter = selection_filter
|
|
if dimension in {1, 2}:
|
|
metric_filter += (func.ST_Dimension(VectorFeature.geometry) == int(dimension),)
|
|
filter_property = str(config.get("filter_property") or "").strip()
|
|
filter_values = config.get("filter_values")
|
|
if filter_property:
|
|
if not isinstance(filter_values, list) or not filter_values:
|
|
raise AppError(
|
|
code="INVALID_SELECTION_AGGREGATION",
|
|
message="Dataset selection metric filter requires one or more values",
|
|
details={"dataset_id": str(dataset.id), "filter_property": filter_property},
|
|
status_code=500,
|
|
)
|
|
normalized_filter_values = VectorFeatureService._expanded_selection_filter_values(
|
|
filter_property,
|
|
filter_values,
|
|
)
|
|
metric_filter += (
|
|
VectorFeature.properties_json.op("->>")(filter_property).in_(normalized_filter_values),
|
|
)
|
|
|
|
if method == "intersection_area":
|
|
measured_geometry = (
|
|
VectorFeature.geometry
|
|
if full_dataset_area
|
|
else func.ST_Intersection(VectorFeature.geometry, selection_shape)
|
|
)
|
|
area_expression = func.ST_Area(func.ST_Transform(measured_geometry, 31370))
|
|
area_m2 = db.query(func.coalesce(func.sum(area_expression), 0.0)).filter(*metric_filter).scalar()
|
|
divisor = 10_000.0 if unit == "ha" else 1.0
|
|
metric_value = float(area_m2 or 0.0) / divisor
|
|
elif method == "intersection_length":
|
|
measured_geometry = (
|
|
VectorFeature.geometry
|
|
if full_dataset_area
|
|
else func.ST_Intersection(VectorFeature.geometry, selection_shape)
|
|
)
|
|
length_expression = func.ST_Length(func.ST_Transform(measured_geometry, 31370))
|
|
length_m = db.query(func.coalesce(func.sum(length_expression), 0.0)).filter(*metric_filter).scalar()
|
|
divisor = 1_000.0 if unit == "km" else 1.0
|
|
metric_value = float(length_m or 0.0) / divisor
|
|
elif method in {"sum", "mean", "area_weighted_sum"}:
|
|
property_name = str(config.get("property") or "").strip()
|
|
if not property_name:
|
|
raise AppError(
|
|
code="INVALID_SELECTION_AGGREGATION",
|
|
message="Dataset selection aggregation requires a numeric property",
|
|
details={"dataset_id": str(dataset.id), "method": method},
|
|
status_code=500,
|
|
)
|
|
numeric_value = cast(VectorFeature.properties_json.op("->>")(property_name), Float)
|
|
value_expression = numeric_value
|
|
if method == "area_weighted_sum" and not full_dataset_area:
|
|
source_area = func.ST_Area(func.ST_Transform(VectorFeature.geometry, 31370))
|
|
intersection_area = func.ST_Area(
|
|
func.ST_Transform(func.ST_Intersection(VectorFeature.geometry, selection_shape), 31370)
|
|
)
|
|
coverage_ratio = intersection_area / func.nullif(source_area, 0.0)
|
|
value_expression = numeric_value * coverage_ratio
|
|
aggregate_function = func.avg if method == "mean" else func.sum
|
|
aggregate_value = (
|
|
db.query(func.coalesce(aggregate_function(value_expression), 0.0))
|
|
.filter(*metric_filter)
|
|
.filter(VectorFeature.properties_json.op("->>")(property_name).isnot(None))
|
|
.scalar()
|
|
)
|
|
metric_value = float(aggregate_value or 0.0)
|
|
if method == "area_weighted_sum" and not full_dataset_area:
|
|
partial_feature_count = (
|
|
db.query(func.count(VectorFeature.id))
|
|
.filter(*metric_filter)
|
|
.filter(coverage_ratio < 0.999999)
|
|
.scalar()
|
|
)
|
|
is_estimate = bool(config.get("is_estimate", False)) or bool(partial_feature_count)
|
|
if not is_estimate and config.get("warning_only_when_estimate", True):
|
|
warning = None
|
|
elif method == "area_weighted_sum":
|
|
is_estimate = bool(config.get("is_estimate", False))
|
|
if not is_estimate and config.get("warning_only_when_estimate", True):
|
|
warning = None
|
|
elif method == "feature_count" and (filter_property or dimension in {1, 2}):
|
|
metric_value = float(
|
|
db.query(func.count(VectorFeature.id)).filter(*metric_filter).scalar() or 0
|
|
)
|
|
elif method != "feature_count":
|
|
raise AppError(
|
|
code="INVALID_SELECTION_AGGREGATION",
|
|
message="Unsupported dataset selection aggregation",
|
|
details={"dataset_id": str(dataset.id), "method": method},
|
|
status_code=500,
|
|
)
|
|
|
|
return {
|
|
"metric_key": str(config.get("metric_key") or method),
|
|
"metric_label": str(config.get("label") or "Objecten"),
|
|
"metric_value": metric_value,
|
|
"metric_unit": unit,
|
|
"aggregation_method": method,
|
|
"is_estimate": is_estimate,
|
|
"warning": warning,
|
|
}
|
|
|
|
@staticmethod
|
|
def persist_geojson_features(
|
|
db,
|
|
dataset_id: UUID,
|
|
payload: dict[str, Any],
|
|
feature_class: str | None = None,
|
|
*,
|
|
commit: bool = True,
|
|
) -> list[VectorFeature]:
|
|
features = payload.get("features")
|
|
if payload.get("type") != "FeatureCollection" or not isinstance(features, list):
|
|
raise AppError(code="INVALID_GEOJSON", message="GeoJSON payload must be a FeatureCollection", status_code=400)
|
|
|
|
persisted: list[VectorFeature] = []
|
|
for index, feature in enumerate(features):
|
|
if not isinstance(feature, dict):
|
|
raise AppError(code="INVALID_GEOJSON", message=f"Feature {index} must be an object", status_code=400)
|
|
row = VectorFeatureService._feature_row(dataset_id, feature, index, feature_class)
|
|
if row is None:
|
|
continue
|
|
db.add(row)
|
|
persisted.append(row)
|
|
|
|
if commit:
|
|
db.flush()
|
|
db.commit()
|
|
return persisted
|
|
|
|
@staticmethod
|
|
def persist_geojson_partitions(
|
|
db,
|
|
dataset_id: UUID,
|
|
partition_paths: Iterable[str | Path],
|
|
feature_class: str | None = None,
|
|
*,
|
|
batch_size: int = 1000,
|
|
) -> int:
|
|
if batch_size <= 0:
|
|
raise ValueError("batch_size must be positive")
|
|
|
|
persisted_count = 0
|
|
source_feature_ids: set[str] = set()
|
|
for partition_path in partition_paths:
|
|
path = Path(partition_path)
|
|
try:
|
|
payload = json.loads(path.read_text(encoding="utf-8"))
|
|
except (OSError, json.JSONDecodeError) as exc:
|
|
raise AppError(
|
|
code="INVALID_GEOJSON_PARTITION",
|
|
message=f"Could not read GeoJSON partition {path.name}",
|
|
status_code=400,
|
|
) from exc
|
|
features = payload.get("features")
|
|
if payload.get("type") != "FeatureCollection" or not isinstance(features, list):
|
|
raise AppError(
|
|
code="INVALID_GEOJSON_PARTITION",
|
|
message=f"GeoJSON partition {path.name} must be a FeatureCollection",
|
|
status_code=400,
|
|
)
|
|
|
|
batch: list[VectorFeature] = []
|
|
for index, feature in enumerate(features):
|
|
if not isinstance(feature, dict):
|
|
raise AppError(
|
|
code="INVALID_GEOJSON_PARTITION",
|
|
message=f"Feature {index} in {path.name} must be an object",
|
|
status_code=400,
|
|
)
|
|
row = VectorFeatureService._feature_row(dataset_id, feature, index, feature_class)
|
|
if row is None:
|
|
continue
|
|
if row.source_feature_id:
|
|
if row.source_feature_id in source_feature_ids:
|
|
raise AppError(
|
|
code="DUPLICATE_SOURCE_FEATURE",
|
|
message=f"Duplicate source feature {row.source_feature_id} across regional partitions",
|
|
status_code=400,
|
|
)
|
|
source_feature_ids.add(row.source_feature_id)
|
|
db.add(row)
|
|
batch.append(row)
|
|
persisted_count += 1
|
|
if len(batch) >= batch_size:
|
|
db.flush()
|
|
for persisted in batch:
|
|
db.expunge(persisted)
|
|
batch.clear()
|
|
if batch:
|
|
db.flush()
|
|
for persisted in batch:
|
|
db.expunge(persisted)
|
|
|
|
return persisted_count
|