Elasticsearch Geo Bounds Aggregation - Syntax, Example, and Tips

The Geo Bounds Aggregation is a metric aggregation in Elasticsearch that computes the bounding box containing all geo_point and geo_shape values for a field. It's useful for determining the geographical extent of a set of locations.

Syntax

{
  "aggs": {
    "geo_bounds_name": {
      "geo_bounds": {
        "field": "location"
      }
    }
  }
}

For more details, refer to the official Elasticsearch documentation.

Example Usage

GET /my-index/_search
{
  "size": 0,
  "aggs": {
    "viewport": {
      "geo_bounds": {
        "field": "location",
        "wrap_longitude": true
      }
    }
  }
}

This query will return the bounding box for all locations in the "location" field.

Common Issues

1. Ensure that the field specified is of type geo_point or geo_shape.
2. Be aware that the wrap_longitude parameter (default: false) affects how the bounding box is calculated when it crosses the antimeridian.

Best Practices

1. Use wrap_longitude: true when dealing with global data to handle cases where the bounding box crosses the antimeridian.
2. Combine with other geo aggregations for more complex geospatial analysis.
3. Consider using geo_bounds aggregation as a preliminary step before applying more specific geo filters or aggregations.

Frequently Asked Questions

Q: What's the difference between geo_bounds and geo_bounding_box?
A: Geo_bounds is an aggregation that calculates the bounding box containing all geo points in a result set, while geo_bounding_box is a query used to filter documents based on a predefined bounding box.

Q: Can geo_bounds aggregation work with geo_shape fields?
A: Yes, geo_bounds aggregation can work with both geo_point and geo_shape field types.

Q: How does the wrap_longitude parameter affect the results?
A: When wrap_longitude is set to true, the bounding box can "wrap" around the globe, which is useful for datasets that cross the antimeridian (180th meridian).

Q: Is there a performance impact when using geo_bounds on large datasets?
A: Geo_bounds aggregation is generally efficient, but like all aggregations, it can impact performance on very large datasets. Consider using sampling or filtering if performance becomes an issue.

Q: Can I use geo_bounds aggregation in a nested aggregation?
A: Yes, you can use geo_bounds as part of a nested aggregation to compute bounding boxes for subsets of your data.