Add server-side clustering with pysupercluster
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175
geo_app/cluster_index.py
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175
geo_app/cluster_index.py
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"""
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Cached SuperCluster index for server-side map clustering.
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Uses pysupercluster for fast geospatial point clustering.
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Index is lazily initialized on first request and cached in memory.
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"""
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import logging
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import threading
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import numpy as np
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logger = logging.getLogger(__name__)
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# Global cache for cluster indices
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_cluster_cache = {}
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_cache_lock = threading.Lock()
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def _build_index(nodes, transport_type=None):
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"""
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Build SuperCluster index from node list.
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Args:
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nodes: List of node dicts with latitude, longitude, _key, name
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transport_type: Optional filter for transport type
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Returns:
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Tuple of (SuperCluster index, node_data dict keyed by index)
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"""
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try:
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import pysupercluster
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except ImportError:
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logger.error("pysupercluster not installed")
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return None, {}
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# Filter nodes with valid coordinates
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valid_nodes = []
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for node in nodes:
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lat = node.get('latitude')
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lon = node.get('longitude')
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if lat is not None and lon is not None:
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# Filter by transport type if specified
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if transport_type:
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types = node.get('transport_types') or []
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if transport_type not in types:
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continue
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valid_nodes.append(node)
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if not valid_nodes:
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logger.warning("No valid nodes for clustering")
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return None, {}
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# Build numpy array of coordinates (lon, lat)
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coords = np.array([
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(node['longitude'], node['latitude'])
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for node in valid_nodes
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])
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# Build node data lookup by index
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node_data = {
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i: {
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'uuid': node.get('_key'),
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'name': node.get('name'),
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'latitude': node.get('latitude'),
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'longitude': node.get('longitude'),
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}
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for i, node in enumerate(valid_nodes)
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}
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# Create SuperCluster index
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# min_zoom=0, max_zoom=16 covers typical map zoom range
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# radius=60 pixels for clustering
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index = pysupercluster.SuperCluster(
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coords,
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min_zoom=0,
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max_zoom=16,
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radius=60,
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extent=512,
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)
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logger.info("Built cluster index with %d points", len(valid_nodes))
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return index, node_data
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def get_clustered_nodes(db, west, south, east, north, zoom, transport_type=None):
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"""
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Get clustered nodes for given bounding box and zoom level.
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Args:
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db: ArangoDB connection
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west, south, east, north: Bounding box coordinates
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zoom: Map zoom level (integer)
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transport_type: Optional filter
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Returns:
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List of cluster/point dicts with id, latitude, longitude, count, expansion_zoom, name
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"""
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cache_key = f"nodes:{transport_type or 'all'}"
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with _cache_lock:
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if cache_key not in _cluster_cache:
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# Load all nodes from database
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aql = """
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FOR node IN nodes
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FILTER node.node_type == 'logistics' OR node.node_type == null
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FILTER node.latitude != null AND node.longitude != null
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RETURN node
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"""
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cursor = db.aql.execute(aql)
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all_nodes = list(cursor)
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# Build index
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index, node_data = _build_index(all_nodes, transport_type)
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_cluster_cache[cache_key] = (index, node_data, all_nodes)
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index, node_data, all_nodes = _cluster_cache[cache_key]
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if index is None:
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return []
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# Get clusters for bounding box
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# pysupercluster uses top_left (lon, lat) and bottom_right (lon, lat)
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try:
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clusters = index.getClusters(
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top_left=(west, north),
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bottom_right=(east, south),
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zoom=int(zoom),
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)
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except Exception as e:
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logger.error("getClusters failed: %s", e)
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return []
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results = []
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for cluster in clusters:
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cluster_id = cluster.get('id')
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count = cluster.get('count', 1)
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lat = cluster.get('latitude')
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lon = cluster.get('longitude')
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expansion_zoom = cluster.get('expansion_zoom')
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# For single points (count=1), get the actual node data
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name = None
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uuid = None
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if count == 1 and cluster_id is not None and cluster_id in node_data:
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node_info = node_data[cluster_id]
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name = node_info.get('name')
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uuid = node_info.get('uuid')
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results.append({
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'id': uuid or f"cluster-{cluster_id}",
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'latitude': lat,
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'longitude': lon,
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'count': count,
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'expansion_zoom': expansion_zoom,
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'name': name,
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})
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logger.info("Returning %d clusters/points for zoom=%d", len(results), zoom)
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return results
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def invalidate_cache(transport_type=None):
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"""
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Invalidate cluster cache.
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Call this after nodes are updated in the database.
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"""
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with _cache_lock:
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if transport_type:
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cache_key = f"nodes:{transport_type}"
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if cache_key in _cluster_cache:
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del _cluster_cache[cache_key]
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else:
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_cluster_cache.clear()
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logger.info("Cluster cache invalidated")
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@@ -6,6 +6,7 @@ import requests
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import graphene
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from django.conf import settings
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from .arango_client import get_db, ensure_graph
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from .cluster_index import get_clustered_nodes, invalidate_cache
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logger = logging.getLogger(__name__)
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@@ -80,6 +81,16 @@ class ProductRouteOptionType(graphene.ObjectType):
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routes = graphene.List(RoutePathType)
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class ClusterPointType(graphene.ObjectType):
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"""Cluster or individual point for map display."""
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id = graphene.String(description="UUID for points, 'cluster-N' for clusters")
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latitude = graphene.Float()
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longitude = graphene.Float()
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count = graphene.Int(description="1 for single point, >1 for cluster")
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expansion_zoom = graphene.Int(description="Zoom level to expand cluster")
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name = graphene.String(description="Node name (only for single points)")
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class Query(graphene.ObjectType):
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"""Root query."""
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MAX_EXPANSIONS = 20000
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@@ -161,6 +172,17 @@ class Query(graphene.ObjectType):
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description="Find routes from product offer nodes to destination",
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)
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clustered_nodes = graphene.List(
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ClusterPointType,
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west=graphene.Float(required=True, description="Bounding box west longitude"),
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south=graphene.Float(required=True, description="Bounding box south latitude"),
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east=graphene.Float(required=True, description="Bounding box east longitude"),
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north=graphene.Float(required=True, description="Bounding box north latitude"),
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zoom=graphene.Int(required=True, description="Map zoom level (0-16)"),
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transport_type=graphene.String(description="Filter by transport type"),
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description="Get clustered nodes for map display (server-side clustering)",
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)
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@staticmethod
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def _build_routes(db, from_uuid, to_uuid, limit):
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"""Shared helper to compute K shortest routes between two nodes."""
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@@ -740,6 +762,12 @@ class Query(graphene.ObjectType):
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return found_routes
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def resolve_clustered_nodes(self, info, west, south, east, north, zoom, transport_type=None):
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"""Get clustered nodes for map display using server-side SuperCluster."""
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db = get_db()
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clusters = get_clustered_nodes(db, west, south, east, north, zoom, transport_type)
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return [ClusterPointType(**c) for c in clusters]
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schema = graphene.Schema(query=Query)
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