Elasticsearch(GEO)空间检索查询python版本
1、Elasticsearch
ES的强大就不用多说了，当你安装上插件，搭建好集群，你就拥有了一个搜索系统。
当然，ES的集群优化和查询优化就是另外一个议题了。这里mark一个最近使用的es空间检索的功能。
2、ES GEO空间检索
空间检索顾名思义提供了通过空间距离和位置关系进行检索的能力。有很多空间索引算法和类库可供选择。
ES内置了这种索引方式。下面详细介绍。
step1：创建索引
代码语言：javascript复制def create_index():
mapping = {
"mappings": {
"poi": {
"_routing": {
"required": "true",
"path": "city_id"
},
"properties": {
"id": {
"type": "integer"
},
"geofence_type": {
"type": "integer"
},
"city_id": {
"type": "integer"
},
"city_name": {
"type": "string",
"index": "not_analyzed"
},
"activity_id": {
"type": "integer"
},
"post_date": {
"type": "date"
},
"rank": {
"type": "float"
},
# 不管是point还是任意shape, 都用geo_shape,通过type来设置
# type在数据里
"location_point": {
"type": "geo_shape"
},
"location_shape": {
"type": "geo_shape"
},
# 在计算点间距离的时候, 需要geo_point类型变量
"point": {
"type": "geo_point"
}
}
}
}
}
# 创建索引的时候可以不 mapping
es.create_index(index='mapapp', body=mapping)
# set_mapping = es_dsl.set_mapping('mapapp', 'poi', body=mapping)这里我们创建了一个名叫mapapp的索引，映射的设置如mapping所示。
2、批量插入数据bulk
代码语言：javascript复制def bulk():
# actions 是一个可迭代对象就行, 不一定是list
workbooks = xlrd.open_workbook('./geo_data.xlsx')
table = workbooks.sheets()[1]
colname = list()
actions = list()
for i in range(table.nrows):
if i == 0:
colname = table.row_values(i)
continue
geo_shape_point = json.loads(table.row_values(i)[7])
geo_shape_shape = json.loads(table.row_values(i)[8])
geo_point = json.loads(table.row_values(i)[9])
raw_data = table.row_values(i)[:7]
raw_data.extend([geo_shape_point, geo_shape_shape, geo_point])
source = dict(zip(colname, raw_data))
geo = GEODocument(**source)
action = {
"_index": "mapapp",
"_type": "poi",
"_id": table.row_values(i)[0],
"_routing": geo.city_id,
#"_source": source,
"_source": geo.to_json(),
}
actions.append(action)
es.bulk(index='mapapp', actions=actions, es=es_handler, max=25)刷入测试数据，geo_data数据形如：
代码语言：javascript复制id geofence_type city_id city_name activity_id post_date rank location_point location_shape point
1 1 1 北京 100301 2016/10/20 100.30 {"type":"point","coordinates":[55.75,37.616667]} {"type":"polygon","coordinates":[[[22,22],[4.87463,52.37254],[4.87875,52.36369],[22,22]]]} {"lat":55.75,"lon":37.616667}
2 1 1 北京 100302 2016/10/21 12.00 {"type":"point","coordinates":[55.75,37.616668]} {"type":"polygon","coordinates":[[[0,0],[4.87463,52.37254],[4.87875,52.36369],[0,0]]]} {"lat":48.8567,"lon":2.3508}
3 1 1 北京 100303 2016/10/22 3432.23 {"type":"point","coordinates":[55.75,37.616669]} {"type":"polygon","coordinates":[[[4.8833,52.38617],[4.87463,52.37254],[4.87875,52.36369],[4.8833,52.38617]]]} {"lat":32.75,"lon":37.616668}
4 1 1 北京 100304 2016/10/23 246.80 {"type":"point","coordinates":[52.4796, 2.3508]} {"type":"polygon","coordinates":[[[4.8833,52.38617],[4.87463,52.37254],[4.87875,52.36369],[4.8833,52.38617]]]} {"lat":11.56,"lon":37.616669}3、GEO查询：两点间距离
代码语言：javascript复制# 点与点之间的距离
# 按照距离升序排列,如果size取1个,就是最近的
def sort_by_distance():
body = {
"from": 0,
"size": 1,
"query": {
"bool": {
"must": [{
"term": {
"geofence_type": 1
}
}, {
"term": {
"city_id": 1
}
}]
}
},
"sort": [{
"_geo_distance": {
"point": {
"lat": 8.75,
"lon": 37.616
},
"unit": "km",
"order": "asc"
}
}]
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['hits']['hits']:
print type(i), i4、GEO查询：边界框过滤
tips：大家都知道，ES的过滤是会生成缓存的，所以在优化查询的时候，常常需要将频繁用到的查询提取出来作为过滤呈现，但不幸的是，对于GEO过滤不会生成缓存，所以没有必要考虑，这里为了做出区分，使用post_filter，查询后再过滤，下面的都类似。
代码语言：javascript复制# 边界框过滤:用框去圈选点和形状
# 这里实现了矩形框选中
# post_filter后置filter, 对查询结果再过滤; aggs常用后置filter
def bounding_filter():
body = {
"from": 0,
"size": 1,
"query": {
"bool": {
"must": [{
"term": {
"geofence_type": 1
}
}, {
"term": {
"city_id": 1
}
}]
}
},
"post_filter": {
"geo_shape": {
"location_point": {
"shape": {
"type": "envelope",
"coordinates": [[52.4796, 2.3508], [48.8567, -1.903]]
},
"relation": "within"
}
}
}
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['hits']['hits']:
print type(i), i5、GEO查询：圆形圈选
代码语言：javascript复制# 边界框过滤: 圆形圈选
# post_filter后置filter, 对查询结果再过滤; aggs常用后置filter
def circle_filter():
body = {
"from": 0,
"size": 1,
"query": {
"bool": {
"must": [{
"term": {
"geofence_type": 1
}
}, {
"term": {
"city_id": 1
}
}]
}
},
"post_filter": {
"geo_shape": {
"location_point": {
"shape": {
"type": "circle",
"radius": "10000km",
"coordinates": [22, 45]
},
"relation": "within"
}
}
}
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['hits']['hits']:
print type(i), i6、GEO查询：反选
代码语言：javascript复制# 边界框反选:点落在框中,框被查询出来
# post_filter后置filter, 对查询结果再过滤; aggs常用后置filter
# 包含正则匹配regexp
def intersects():
body = {
"from": 0,
"size": 1,
"query": {
"bool": {
"must": [{
"term": {
"geofence_type": 1
}
}, {
"regexp": {
"city_name": u".*北京.*"
}
}, {
"term": {
"city_id": 1
}
}]
}
},
"post_filter": {
"geo_shape": {
"location_shape": {
"shape": {
"type": "point",
"coordinates": [22,22]
},
"relation": "intersects"
}
}
}
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['hits']['hits']:
print type(i), i7、最后粘两个空间聚合的例子，作为参考
代码语言：javascript复制# 空间聚合
# 按照与中心点距离聚合
def aggs_geo_distance():
body = {
"aggs": {
"aggs_geopoint": {
"geo_distance": {
"field": "point",
"origin": {
"lat": 51.5072222,
"lon": -0.1275
},
"unit": "km",
"ranges": [
{
"to": 1000
},
{
"from": 1000,
"to": 3000
},
{
"from": 3000
}
]
}
}
}
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['aggregations']['aggs_geopoint']['buckets']:
print type(i), i
# 空间聚合
# geo_hash算法, 网格聚合grid
# 两次聚合
def aggs_geohash_grid():
body = {
"aggs": {
"new_york": {
"geohash_grid": {
"field": "point",
"precision": 5
}
},
"map_zoom": {
"geo_bounds": {
"field": "point"
}
}
}
}
for i in es.search(index='mapapp', doc_type='poi', body=body)['aggregations']['new_york']['buckets']:
print type(i), i
