Elasticsearch Geohash Grid Aggregation - Syntax, Example, and Tips

The Geohash Grid Aggregation in Elasticsearch is a multi-bucket aggregation that groups geo_point and geo_shape values into buckets representing cells in a geohash grid. It provides a way to group and analyze geospatial data efficiently.

Syntax

{
  "aggs": {
    "grid": {
      "geohash_grid": {
        "field": "location",
        "precision": 5
      }
    }
  }
}

For more details, refer to the official Elasticsearch documentation.

Example Usage

GET /my-index/_search
{
  "size": 0,
  "aggs": {
    "locations": {
      "geohash_grid": {
        "field": "location",
        "precision": 5
      }
    }
  }
}

This example groups geo_point data in the "location" field into geohash cells with a precision of 5.

Common Issues

1. Incorrect field type: Ensure the field used is of type geo_point or geo_shape.
2. Precision too high: High precision values can lead to excessive memory usage and slow performance.
3. Missing data: Geohash grid aggregation ignores documents without geo data in the specified field.

Best Practices

1. Choose an appropriate precision level based on your use case and data distribution.
2. Use with other aggregations like avg or sum for more complex geospatial analysis.
3. Consider using geotile_grid aggregation for map visualizations that use Web Mercator projection.

Frequently Asked Questions

Q: What is the difference between geohash_grid and geo_distance aggregations?
A: Geohash_grid groups points into grid cells, while geo_distance groups points based on their distance from a central point.

Q: How does precision affect the geohash_grid aggregation?
A: Higher precision values create smaller, more numerous grid cells, providing finer granularity but potentially impacting performance.

Q: Can geohash_grid aggregation be used with geo_shape fields?
A: Yes, geohash_grid aggregation supports both geo_point and geo_shape field types.

Q: What's the maximum precision allowed in geohash_grid aggregation?
A: The maximum precision is 12, but it's rarely practical to use such high precision due to performance considerations.

Q: How can I visualize geohash_grid aggregation results?
A: Results can be visualized using mapping tools that support geohash, or by converting geohash cells to bounding boxes for rendering.