文本查询和地理查询是分开执行的。让我们举一个具体的例子:
PUT restaurants
{
"mappings": {
"properties": {
"location": {
"type": "geo_point"
},
"menu": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
}
}
}
}
POST restaurants/_doc
{
"name": "rest1",
"location": {
"lat": 40.739812,
"lon": -74.006201
},
"menu": [
"european",
"french",
"pizza"
]
}
POST restaurants/_doc
{
"name": "rest2",
"location": {
"lat": 40.7403963,
"lon": -73.9950026
},
"menu": [
"pizza",
"kebab"
]
}
那么你就会match
文本字段并应用geo_distance
filter:
GET restaurants/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"menu": "pizza"
}
},
{
"geo_distance": {
"distance": "0.5mi",
"location": {
"lat": 40.7388,
"lon": -73.9982
}
}
},
{
"function_score": {
"query": {
"match_all": {}
},
"boost_mode": "avg",
"functions": [
{
"gauss": {
"location": {
"origin": {
"lat": 40.7388,
"lon": -73.9982
},
"scale": "0.5mi"
}
}
}
]
}
}
]
}
}
}
自从geo_distance
查询仅分配一个布尔值(-->score=1;仅检查位置是否在给定半径内),您可能需要应用高斯function_score
提升更接近给定原点的位置。
最后,这些分数可以通过使用_geo_distance
按邻近程度对您要订购的位置进行排序(当然,同时保持match
查询完好):
...
"query: {...},
"sort": [
{
"_geo_distance": {
"location": {
"lat": 40.7388,
"lon": -73.9982
},
"order": "asc"
}
}
]
}