The shape field data type in Elasticsearch is used for indexing and querying arbitrary two-dimensional geometries. It supports points, lines, circles, polygons, multi-polygons, and other complex shapes. This data type is particularly useful for geospatial applications that require more advanced spatial operations than simple point-based queries.
While the geo_point data type is suitable for simple latitude and longitude coordinates, the shape data type is preferred when dealing with complex geometries or when you need to perform advanced spatial queries like intersections, containment, or disjoint relationships.
Example
PUT my_index
{
"mappings": {
"properties": {
"location": {
"type": "shape"
}
}
}
}
PUT my_index/_doc/1
{
"location": {
"type": "polygon",
"coordinates": [
[ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ]
]
}
}
Common Issues and Misuses
Performance impact: Shape fields can be resource-intensive, especially for complex geometries or large datasets. Be mindful of the performance implications when using shape fields extensively.
Precision issues: The shape field uses a quad-tree-based indexing strategy, which may lead to some precision loss for very small or very large geometries.
Confusion with geo_shape: The shape field is sometimes confused with the geo_shape field. While similar, geo_shape is specifically optimized for geographic coordinates and supports additional geographic-specific operations.
Incorrect GeoJSON formatting: Ensure that the GeoJSON data is correctly formatted to avoid indexing errors.
Frequently Asked Questions
Q: What's the difference between shape and geo_shape field types?
A: While both can handle complex geometries, geo_shape is optimized for geographic coordinates and supports additional geographic-specific operations. The shape field is more general-purpose and can be used for non-geographic spatial data as well.
Q: Can I use shape fields for 3D geometries?
A: No, the shape field type in Elasticsearch only supports 2D geometries. For 3D data, you might need to consider alternative solutions or split the data into multiple 2D representations.
Q: How does indexing shape fields affect performance?
A: Shape fields can be more resource-intensive than simpler data types, especially for complex geometries. They may increase index size and slow down indexing and query performance. It's important to benchmark and optimize your usage based on your specific use case.
Q: What spatial relationships can I query with shape fields?
A: Shape fields support various spatial queries including INTERSECTS, DISJOINT, WITHIN, and CONTAINS. These allow you to find shapes that intersect, are completely within, or contain other shapes.
Q: Can I convert between geo_point and shape field types?
A: While you can't directly convert between field types, you can represent a geo_point as a shape by using a point geometry. However, keep in mind that shape fields are more complex and may not be as efficient for simple point-based queries as geo_point fields.
