Elasticsearch geo query with aggregation - elasticsearch

Question

I have an elasticsearch index containing user locations.
I need to perform aggregate query with geo bounding box using geohash grid, and for buckets that have documents count less than some value, i need to return all documents.
How can i do this?

Answer 1

Since you have not given any relevant information about the index which you have created and the user locations.
I am considering the below data:
index Def
{
"mappings": {
"properties": {
"location": {
"type": "geo_point"
}
}
}
}
Index Sample Doc
POST _bulk
{"index":{"_id":1}}
{"location":"52.37408,4.912350","name":"The golden dragon"}
{"index":{"_id":2}}
{"location":"52.369219,4.901618","name":"Burger King"}
{"index":{"_id":3}}
{"location":"52.371667,4.914722","name":"Wendys"}
{"index":{"_id":4}}
{"location":"51.222900,4.405200","name":"Taco Bell"}
{"index":{"_id":5}}
{"location":"48.861111,2.336389","name":"McDonalds"}
{"index":{"_id":6}}
{"location":"48.860000,2.327000","name":"KFC"}
According to your question:
When requesting detailed buckets a filter like geo_bounding_box
should be applied to narrow the subject area
To know more about this, you can refer to this official ES doc
Now, in order to filter data based on doc_count with aggregations, we can use bucket_selector pipeline aggregation.
From documentation
Pipeline aggregations work on the outputs produced from other
aggregations rather than from document sets, adding information to the
output tree.
So, the amount of work that need to be done to calculate doc_count will be the same.
Query
{
"aggs": {
"location": {
"filter": {
"geo_bounding_box": {
"location": {
"top_left": {
"lat": 52.5225,
"lon": 4.5552
},
"bottom_right": {
"lat": 52.2291,
"lon": 5.2322
}
}
}
},
"aggs": {
"around_amsterdam": {
"geohash_grid": {
"field": "location",
"precision": 8
},
"aggs": {
"the_filter": {
"bucket_selector": {
"buckets_path": {
"the_doc_count": "_count"
},
"script": "params.the_doc_count < 2"
}
}
}
}
}
}
}
}
Search Result
"hits": {
"total": {
"value": 6,
"relation": "eq"
},
"max_score": 1.0,
"hits": [
{
"_index": "restaurant",
"_type": "_doc",
"_id": "1",
"_score": 1.0,
"_source": {
"location": "52.37408,4.912350",
"name": "The golden dragon"
}
},
{
"_index": "restaurant",
"_type": "_doc",
"_id": "2",
"_score": 1.0,
"_source": {
"location": "52.369219,4.901618",
"name": "Burger King"
}
},
{
"_index": "restaurant",
"_type": "_doc",
"_id": "3",
"_score": 1.0,
"_source": {
"location": "52.371667,4.914722",
"name": "Wendys"
}
},
{
"_index": "restaurant",
"_type": "_doc",
"_id": "4",
"_score": 1.0,
"_source": {
"location": "51.222900,4.405200",
"name": "Taco Bell"
}
},
{
"_index": "restaurant",
"_type": "_doc",
"_id": "5",
"_score": 1.0,
"_source": {
"location": "48.861111,2.336389",
"name": "McDonalds"
}
},
{
"_index": "restaurant",
"_type": "_doc",
"_id": "6",
"_score": 1.0,
"_source": {
"location": "48.860000,2.327000",
"name": "KFC"
}
}
]
},
"aggregations": {
"location": {
"doc_count": 3,
"around_amsterdam": {
"buckets": [
{
"key": "u173zy3j",
"doc_count": 1
},
{
"key": "u173zvfz",
"doc_count": 1
},
{
"key": "u173zt90",
"doc_count": 1
}
]
}
}
}
}
It will filter out all the documents, whose count is less than 2 based on "params.the_doc_count < 2"