Geo-point and geo-shape types in Elasticsearch - Mini Project: Build & Apply

Geo-point and Geo-shape Types in Elasticsearch

📖 Scenario: You are working on a location-based service that stores places and their areas on a map. You want to use Elasticsearch to store and search these locations efficiently.

🎯 Goal: Build an Elasticsearch index mapping that uses geo_point for storing exact locations and geo_shape for storing areas. Then, add sample documents and query them.

📋 What You'll Learn

Create an index mapping with location as geo_point

Add area field as geo_shape

Index sample documents with both fields

Query documents by location and shape

💡 Why This Matters

🌍 Real World

Location-based apps like delivery services, real estate, or mapping use geo_point and geo_shape to store and search places and areas.

💼 Career

Understanding geo data types in Elasticsearch is important for backend developers and data engineers working on geospatial search and analytics.

1

Create index mapping with geo_point and geo_shape

Create an Elasticsearch index called places with a mapping that has a location field of type geo_point and an area field of type geo_shape.

Elasticsearch

# Create the index mapping with geo_point and geo_shape fields
# Your code here

Hint

Use the PUT method to create the index and define location as geo_point and area as geo_shape in the mappings.

2

Index sample documents with geo_point and geo_shape

Index a document into the places index with location set to latitude 40.7128 and longitude -74.0060, and area set to a polygon shape with coordinates [[[-74.0, 40.7], [-74.0, 40.8], [-73.9, 40.8], [-73.9, 40.7], [-74.0, 40.7]]].

Elasticsearch

PUT /places/_doc/1
{
  "location": {"lat": 0, "lon": 0},
  "area": {
    "type": "polygon",
    "coordinates": []
  }
}
# Replace the values above with the correct latitude, longitude, and polygon coordinates
# Your code here

Hint

Use PUT /places/_doc/1 to add a document. Set location with lat and lon. Define area as a polygon with the given coordinates.

3

Query documents by geo_point location

Write a search query on the places index to find documents where the location is within 5km of latitude 40.7130 and longitude -74.0070 using a geo_distance query.

Elasticsearch

GET /places/_search
{
  "query": {
    "geo_distance": {
      "distance": "5km",
      "location": {
        "lat": 0,
        "lon": 0
      }
    }
  }
}
# Replace lat and lon with 40.7130 and -74.0070
# Your code here

Hint

Use a geo_distance query with distance set to "5km" and location coordinates as given.

4

Query documents by geo_shape area

Write a search query on the places index to find documents where the area intersects with a polygon shape defined by coordinates [[[-74.005, 40.705], [-74.005, 40.715], [-73.995, 40.715], [-73.995, 40.705], [-74.005, 40.705]]].

Elasticsearch

GET /places/_search
{
  "query": {
    "geo_shape": {
      "area": {
        "shape": {
          "type": "polygon",
          "coordinates": []
        },
        "relation": "intersects"
      }
    }
  }
}
# Replace the coordinates array with the polygon coordinates given
# Your code here

Hint

Use a geo_shape query with relation set to "intersects" and the polygon coordinates given.

Practice

1.

What is the main difference between geo_point and geo_shape types in Elasticsearch?

easy

A. geo_point stores a single location; geo_shape stores complex shapes like polygons.

B. geo_point stores polygons; geo_shape stores single points.

C. geo_point is used for text data; geo_shape is used for numbers.

D. geo_point stores multiple points; geo_shape stores only lines.

Solution

1. Step 1: Understand geo_point type

   geo_point stores a single geographic location using latitude and longitude coordinates.

2. Step 2: Understand geo_shape type

   geo_shape stores complex geographic shapes such as polygons, lines, or multi-points.

3. Final Answer:

   geo_point stores a single location; geo_shape stores complex shapes like polygons. -> Option A

4. Quick Check:

   Single point vs complex shape = C [OK]

Hint: Remember: point = one spot, shape = many spots connected [OK]

Common Mistakes:

• Confusing points with polygons
• Thinking geo_point stores multiple points
• Mixing data types with geo types

2.

Which of the following is the correct way to define a geo_point field in an Elasticsearch mapping?

{
  "properties": {
    "location": {
      "type": "?"
    }
  }
}

easy

A. "geo_shape"

B. "geo_point"

C. "point_geo"

D. "location_point"

Solution

1. Step 1: Recall Elasticsearch geo types

   Elasticsearch uses "geo_point" as the type name for single geographic points.

2. Step 2: Match the correct type in mapping

   The mapping must specify "type": "geo_point" to store latitude and longitude.

3. Final Answer:

   "geo_point" -> Option B

4. Quick Check:

   Mapping type for point = "geo_point" [OK]

Hint: Use exact type names: geo_point for points [OK]

Common Mistakes:

• Using incorrect type names like geo_shape for points
• Typos in type string
• Confusing field name with type

3.

Given this Elasticsearch document indexed with a geo_point field location:

{ "location": { "lat": 40.7128, "lon": -74.0060 } }

Which query will correctly find documents within 10km of this point?

medium

A. { "range": { "location": { "gte": "10km" } } }

B. { "geo_shape": { "location": { "shape": { "type": "point", "coordinates": [40.7128, -74.0060] }, "relation": "within" } } }

C. { "term": { "location": "40.7128,-74.0060" } }

D. { "geo_distance": { "distance": "10km", "location": { "lat": 40.7128, "lon": -74.0060 } } }

Solution

1. Step 1: Identify correct query type for geo_point

   For geo_point, geo_distance query finds documents within a distance from a point.

2. Step 2: Check query syntax and parameters

   { "geo_distance": { "distance": "10km", "location": { "lat": 40.7128, "lon": -74.0060 } } } uses geo_distance with correct distance and point coordinates.

3. Final Answer:

   { "geo_distance": { "distance": "10km", "location": { "lat": 40.7128, "lon": -74.0060 } } } -> Option D

4. Quick Check:

   geo_distance query = { "geo_distance": { "distance": "10km", "location": { "lat": 40.7128, "lon": -74.0060 } } } [OK]

Hint: Use geo_distance for points, geo_shape for polygons [OK]

Common Mistakes:

• Using geo_shape query on geo_point field
• Wrong coordinate order in geo_shape
• Using term or range queries for geo data

4.

What is wrong with this Elasticsearch mapping for a geo_shape field named area?

{
  "properties": {
    "area": {
      "type": "geo_point"
    }
  }
}

The data to be stored is a polygon shape.

medium

A. The type should be geo_shape to store polygons.

B. The field name area is invalid for geo data.

C. The mapping must include coordinates property.

D. The type geo_point supports polygons by default.

Solution

1. Step 1: Check field type for polygon data

   Polygons require geo_shape type, not geo_point.

2. Step 2: Identify mapping error

   Mapping uses geo_point which only supports single points, so it cannot store polygons.

3. Final Answer:

   The type should be geo_shape to store polygons. -> Option A

4. Quick Check:

   Polygon needs geo_shape type = D [OK]

Hint: Polygons need geo_shape, not geo_point [OK]

Common Mistakes:

• Using geo_point for polygon shapes
• Assuming field name affects type
• Adding coordinates in mapping instead of data

5.

You want to store a city boundary as a polygon and find all points inside it. Which Elasticsearch setup is correct?

{
  "mappings": {
    "properties": {
      "city_boundary": { "type": "?" },
      "user_location": { "type": "?" }
    }
  }
}

Choose the correct types for city_boundary and user_location.

hard

A. "geo_shape" for both city_boundary and user_location

B. "geo_point" for city_boundary and "geo_shape" for user_location

C. "geo_shape" for city_boundary and "geo_point" for user_location

D. "geo_point" for both city_boundary and user_location

Solution

1. Step 1: Determine type for city boundary polygon

   City boundary is a polygon, so it requires geo_shape type.

2. Step 2: Determine type for user location point

   User location is a single point, so it requires geo_point type.

3. Step 3: Match types to fields

   Assign geo_shape to city_boundary and geo_point to user_location.

4. Final Answer:

   "geo_shape" for city_boundary and "geo_point" for user_location -> Option C

5. Quick Check:

   Polygon = geo_shape, Point = geo_point [OK]

Hint: Polygon needs geo_shape; point needs geo_point [OK]

Common Mistakes:

• Swapping types between polygon and point
• Using geo_point for polygons
• Using geo_shape for single points unnecessarily