In this blog, I am going to cover how you can measure the distance using Elasticsearch. Geo distance measurement is a very important aspect of many important use cases. Now let us take a very simple example where when we move to any new location Google start showing us the nearest attractions or any cab aggregating service uses your current location to book the nearest cab for you. Geo distance based search is also a very important use case where we try to find the nearest restaurant, ATM, hospital, or practically anything.  

So till now we have discussed different use cases where geo distance based search is very important and for some businesses it is critical but the question arises: How to do the geo search? So let us find out how we can perform a geo search in Elasticsearch.

Elasticsearch also supports geo data types and these can be of two types that are geo-point and geo-shape. In this blog, we will focus on geo-point which is a point representation and a point is nothing but a geocoordinate having latitude and longitude. Although Elasticsearch accepts data without explicit mapping creation for geodata we need to explicitly create the mapping as it is the first step for executing geo queries.

But before creating the mapping let us understand what we are going to achieve in this blog. So the idea is to create an index in Elasticsearch along with a field of a geo-point type that will hold the latitude and longitude. Then we will add some geo data representing a fake location like some parking locations. After adding those parking locations we will pass an arbitrary user current location and will try to find the nearest parking location.

So let us start with an index creation along with the location field mapping of geo-point type. We need to execute the following command to create an index along with location mapping:

PUT parking_locations
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_point"
      }
    }
  }
}

After executing the above command we can get the following response on success:

{
  "acknowledged" : true,
  "shards_acknowledged" : true,
  "index" : "parking_locations"
}

Our parking location index is created along with the mapping of the location field. Now let us add some arbitrary locations so that we can apply geo search later. We will be adding the following data:

Parking 1              28.594817, 77.046608
Parking 2              28.593168, 77.041244
Parking 3              28.601345, 77.026352
Parking 4              28.590606, 77.062959

To add these data we can execute the following query for each document:

POST parking_locations/_doc
{
  "name": "Parking 1",
  "location": "28.594817, 77.046608",
  "address": "street 1, sec abc"
}

After executing the above command we can get the following response:

{
  "_index" : "parking_locations",
  "_type" : "_doc",
  "_id" : "uDz1HnIBtlnNM0yOpqq6",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 1,
    "failed" : 0
  },
  "_seq_no" : 0,
  "_primary_term" : 1
}

We can follow the same for the other three documents we can also use bulk indexing to add all of these documents in a single query. Now we have an index with four different parking locations. To verify if all documents are added we can run the following command:

GET parking_locations/_search 

The above command will list all documents of the parking_locations index and we can verify if all four documents are created in the index. So now our index is all set for a geo query. Now assume that I am a new user in the city with a current location as 28.580116, 77.051673. I am searching for a parking location to park my car so how will I get the nearest parking address?

Let us take an example where I want to search all parking located within a range of 2 KM from my current location. So to get the parking location within 2 KM range we need to execute the following query:

GET parking_locations/_search
{
    "query": {
        "bool" : {
            "must" : {
                "match_all" : {}
            },
            "filter" : {
                "geo_distance" : {
                    "distance" : "2km",
                    "location" : "28.580116, 77.051673"
                }
            }
        }
    }
}

After executing the following command we can see that there are three parking locations within 2 KM from my location. Please refer to the following response which we can receive after executing the above query:

{
  "took" : 8,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "parking_locations",
        "_type" : "_doc",
        "_id" : "uDz1HnIBtlnNM0yOpqq6",
        "_score" : 1.0,
        "_source" : {
          "name" : "Parking 1",
          "location" : "28.594817, 77.046608",
          "address" : "street 1, sec abc"
        }
      },
      {
        "_index" : "parking_locations",
        "_type" : "_doc",
        "_id" : "uTz3HnIBtlnNM0yOj6qg",
        "_score" : 1.0,
        "_source" : {
          "name" : "Parking 2",
          "location" : "28.593168, 77.041244",
          "address" : "street 3, sec wer"
        }
      },
      {
        "_index" : "parking_locations",
        "_type" : "_doc",
        "_id" : "uzz3HnIBtlnNM0yO_aou",
        "_score" : 1.0,
        "_source" : {
          "name" : "Parking 4",
          "location" : "28.590606, 77.062959",
          "address" : "street 4, sec qer"
        }
      }
    ]
  }
}

In this way, we can fetch the documents having geolocation from our current location by providing different distance range. We can change this 2 KM range to any other range and can get a different set of documents.

Other Blogs on Elastic Stack:

Configure Logstash to push MongoDB data into Elasticsearch
Load CSV Data into Elasticsearch
Introduction to Elasticsearch
Elasticsearch Installation and Configuration on Ubuntu 14.04
Log analysis with Elastic stack 
Elasticsearch Rest API
Basics of Data Search in Elasticsearch
Elasticsearch Rest API
Wildcard and Boolean Search in Elasticsearch
Metrics Aggregation in Elasticsearch
Bucket Aggregation in Elasticsearch
How to create Elasticsearch Cluster