Combining Search Across Multiple Geolocations in Multiple Indices - elasticsearch

Question

I'm afraid I don't know the terminology to succinctly describe what I'm trying to do, but I will explain what I'm currently doing and what I'd like to do. I'm trying to converge two search queries into a single query, taking geo point data from one index to use a search parameter for searching in a second index/doctype.
My current ES set up:
Indices and DocTypes:
|- 1) locations
|---- 1a) UK_postcode
|- 2) accounts
|---- 2a) client
Each of the Doctypes has a field names 'location' which is mapped to a GeoPoint type.
My Current Process:
1) Users search for clients based on keywords and distance from location (a UK postcode).
2) System takes the postcode and searches for the matching results to get the geo_point latitude and longitude data from the locations.UK_postcode.
3) System uses the provided keywords and latitude and longitude to search on the accounts.client index/doctype.
4) System returns nice looking results to the user, based on ES search results.
My Question:
Can steps 2 and 3 be rolled into a single search query? If yes how do I do this? I want to pass a postcode to the search query and for ES to find the geo_point data for fulfilling the requirements of a geo distance query on the client doctype.

Answer 1

Using pre-indexed shapes, you can definitely eliminate step 2. Note that this solution only works with pre-defined distances.
The main idea would be:
to store in your locations index a geo_shape of type circle for each postcode and each pre-defined distances.
to store in your accounts index a geo_shape of type Point for your client location
create a geo_shape query of type circle which would leverage the pre-indexed postcode shapes.
So as a quick example, you'd have this:
A. Create the postcode locations index:
PUT /locations
{
"mappings": {
"UK_postcode": {
"properties": {
"location": { "type" : "geo_shape" }
}
}
}
}
B. Create client locations index
PUT /accounts
{
"mappings": {
"client": {
"properties": {
"name": { "type": "string" }
"location": { "type" : "geo_shape" }
}
}
}
}
C. Create sample postcode circle of 1, 2, 3 mile radius for "M32 0JG"
PUT /locations/UK_postcode/M320JG-1
{
"location": {
"type" : "circle",
"coordinates" : [-2.30283674284007, 53.4556572899372],
"radius": "1mi"
}
}
PUT /locations/UK_postcode/M320JG-2
{
"location": {
"type" : "circle",
"coordinates" : [-2.30283674284007, 53.4556572899372],
"radius": "2mi"
}
}
# ... repeat until radius = 10
D. Create sample client very close to "M32 0JG"
PUT /accounts/client/1234
{
"name": "Big Corp"
"location": {
"type" : "point",
"coordinates" : [-2.30293674284007, 53.4557572899372]
}
}
E. Query all clients whose name matches "big" and who are in a 2-mile radius of the postcode "M32 0JG"
POST /accounts/client/_search
{
"bool": {
"must": [
{
"match": {
"name": "big" <--- free text name match
}
}
],
"filter": {
"geo_shape": {
"location": {
"indexed_shape": {
"id": "M320JG-2", <--- located within two miles of M32 0JG
"type": "UK_postcode",
"index": "locations",
"path": "location"
}
}
}
}
}
}