How can I find polygons that stored in elastic index.
Simple mapping:
PUT /regions
{
"mappings": {
"properties": {
"location": {
"type": "geo_shape"
}
}
}
}
And simple polygon:
/regions/_doc/1
{
"location" : {
"type" : "polygon",
"coordinates" : [
[
[53.847332102970626,27.485155519098047],
[53.84626875748117,27.487134989351038],
[53.8449047241684,27.48501067981124],
[53.84612634308789,27.482945378869765],
[53.847411219859,27.48502677306532],
[53.847332102970626,27.485155519098047]
]
]
}
}
According to documentation I can only search coordinates within polygon only if the polygon is contained in the request Geo-polygon query, but I need to find polygons by coordinates in query. Elasticsearch 7.6 version.
Query:
{
"query": {
"match_all": {}
},
"filter": {
"geo_shape": {
"geometry": {
"shape": {
"coordinates": [
53.846415,
27.485756
],
"type": "point"
},
"relation": "whithin"
}
}
}
}
You were on the right path but your query was heavily malformed. Here's the fix:
{
"query": {
"bool": {
"filter": {
"geo_shape": {
"location": {
"shape": {
"coordinates": [
53.846415,
27.485756
],
"type": "point"
},
"relation": "intersects"
}
}
}
}
}
}
Notice how I used intersects instead of within. The reason is explained in this GIS StackExchange answer.
Related
I used geo_shape for this problem, want to know if there are some other better ways(faster) to solve this problem, ES version is 6.5.
mapping:
{
"index": {
"mappings": {
"merchant": {
"_all": {
"enabled": false
},
"properties": {
"delivery_circle": {
"type": "geo_shape",
"tree": "quadtree",
"precision": "50.0m",
"distance_error_pct": 0.025
}
}
}
}
}
}
document example:
{
"_source": {
"id": 1,
"delivery_circle": {
"coordinates": [ // merchant location, its radius is 4km
123.456,
1.2345
],
"radius": "4000m",
"type": "circle"
}
}
}
{
"_source": {
"id": 2,
"delivery_circle": {
"coordinates": [ // merchant location, its radius is 5km
123.567,
1.3456
],
"radius": "5000m",
"type": "circle"
}
}
}
search query example:
{
"query": {
"bool": {
"filter": [
{
"geo_shape": {
"delivery_circle": {
"relation": "contains",
"shape": {
"coordinates": [ // user location
123,
1
],
"type": "point"
}
}
}
}
]
}
}
}
This is off topic suggestion, but you can consider using geohash for the same. This can reduce your search time complexity.
https://en.wikipedia.org/wiki/Geohash
I am trying to understand how elastic search supports Geo Spatial search internally.
For the basic search, it uses the inverted index; but how does it combine with the additional search criteria like searching for a particular text within a certain radius.
I would like to understand the internals of how the index would be stored and queried to support these queries
Text & geo queries are executed separately of one another. Let's take a concrete example:
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"
]
}
You'd then match a text field and apply a 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"
}
}
}
]
}
}
]
}
}
}
Since the geo_distance query only assigns a boolean value (--> score=1; only checking if the location is within a given radius), you may want to apply a gaussian function_score to boost the locations that are closer to a given origin.
Finally, these scores are overridable by using a _geo_distance sort where you'd order by the proximity (while of course keeping the match query intact):
...
"query: {...},
"sort": [
{
"_geo_distance": {
"location": {
"lat": 40.7388,
"lon": -73.9982
},
"order": "asc"
}
}
]
}
Suppose I have the following mapping:
"mappings": {
"doc": {
"properties": {
"name": {
"type": "text"
},
"location": {
"type": "nested",
"properties": {
"point": {
"type": "geo_shape"
}
}
}
}
}
}
}
There is one document in the index:
POST /example/doc?refresh
{
"name": "Wind & Wetter, Berlin, Germany",
"location": {
"type": "point",
"coordinates": [13.400544, 52.530286]
}
}
How can I make a nested geo-shape query?
Example of usual geo-shape query from the documentation (the "bool" block can be skipped):
{
"query":{
"bool": {
"must": {
"match_all": {}
},
"filter": {
"geo_shape": {
"location": {
"shape": {
"type": "envelope",
"coordinates" : [[13.0, 53.0], [14.0, 52.0]]
},
"relation": "within"
}
}
}
}
}
}
Example of a nested query is:
{
"query": {
"nested" : {
"path" : "obj1",
"score_mode" : "avg",
"query" : {
"bool" : {
"must" : [
{ "match" : {"obj1.name" : "blue"} },
{ "range" : {"obj1.count" : {"gt" : 5}} }
]
}
}
}
}
}
Now how to combine them? In the documentation it is mentioned that nested filter has been replaced by nested query. And that it behaves as a query in “query context” and as a filter in “filter context”.
If I try query for intersect with the point:
{
"query": {
"nested": {
"path": "location",
"query": {
"geo_shape": {
"location.point": {
"shape": {
"type": "point",
"coordinates": [
13.400544,
52.530286
]
},
"relation": "disjoint"
}
}
}
}
}
}
I still get back the document even if relation is "disjoint", so it's not correct. I tried different combinations, with "bool" and "filter", etc. but query is ignored, returning the whole index. Maybe it's impossible with this type of mapping?
Clearly I am missing something here. Can somebody help me out with that, please? Any help is greatly appreciated.
I have multiple polygons in the index and I'm trying to return all the polygons in a given point.
But my query returns all the docs in the index even if it doesn't falls in polygon 1.
It gives me union instead of intersect.
Query is given below
Map Ref. http://geojson.io/#map=7/-31.775/144.382
PUT /example3
{
"mappings": {
"_doc": {
"properties": {
"features": {
"properties": {
"geometry": {
"type": "geo_shape"
}
}
}
}
}
}
}
Sample data:
{
"type": "Feature",
"geometry": {
"name": "Poly - 04",
"type": "Polygon",
"coordinates": [
[
[
144.91670608520508,
-37.82524314302977
],
[
144.96846199035645,
-37.82524314302977
],
[
144.96846199035645,
-37.78787789236923
],
[
144.91670608520508,
-37.78787789236923
],
[
144.91670608520508,
-37.82524314302977
]
]
]
}
}
Query:
GET /example3/_search
{
"query":{
"bool": {
"must": {
"match_all": {}
},
"filter": {
"geo_shape": {
"features.geometry": {
"relation": "intersects",
"shape": {
"type": "point",
"coordinates" :
[
146.0138,
-37.1734
]
}
}
}
}
}
}
}
Sorry for the noob question... I have Restaurant objects in Elasticsearch 2.3, each has a GeoPoint and a home delivery distance preference. In pseudocode
restaurant: {
location: (x, y)
deliveryPreference: 10km
}
and a user:
user {
location: (a,b)
}
How would I issue a search for a user looking for all restaurants that can deliver in his area?
The solution involves using geo_shapes. You need to model the restaurant documents as follows:
PUT restaurants
{
"mappings": {
"restaurant": {
"properties": {
"name": {
"type": "text"
},
"location": {
"type": "geo_point"
},
"delivery_area": {
"type": "geo_shape",
"tree": "quadtree",
"precision": "1m"
}
}
}
}
}
You can then index your restaurants as follows:
POST /restaurants/restaurant/1
{
"name": "My Food place",
"location": [-45.0, 45.0], <-- lon, lat !!!
"delivery_area": {
"type": "circle",
"coordinates" : [-45.0, 45.0], <-- lon, lat !!!
"radius" : "10km"
}
}
Each restaurant will thus be associated with a circle shape centered on its location and with a proper radius.
Finally, when a user wants to know which restaurant can deliver at the location she is currently at, you can issue the following geo_shape query:
POST /restaurants/_search
{
"query":{
"bool": {
"filter": {
"geo_shape": {
"delivery_area": {
"shape": {
"type": "point",
"coordinates" : [<user_lon>, <user_lat>]
},
"relation": "contains"
}
}
}
}
}
}
In this query, we are retrieving restaurants whose delivery_area shape contains the point the user is currently located at.
Index the restraunts documents with following mappings
{
"mappings": {
"type_name": {
"properties": {
"name": {
"type": "text"
},
"location": {
"type": "text"
},
"location_geo": {
"type": "geo_point"
}
}
}
}
}
Use geo_filter query
{
"query": {
"bool": {
"filter": {
"geo_distance": {
"distance": "10km",
"location_geo": {
"lat": 40,
"lon": -70
}
}
}
}
}
}