Elasticsearch’s flexibility extends beyond simple flat documents — it supports advanced data structures like nested, object, and geo fields that empower developers to model complex, real-world data effectively. Understanding how to use these field types properly is key to unlocking powerful search capabilities while maintaining high performance and scalability.

This post targets intermediate to advanced Elasticsearch users who want to deepen their mastery of these specialized data types and design efficient, production-ready indexes.

Understanding Object and Nested Fields

Object Fields
  • Represent JSON objects within a document as a flat structure with dot notation (e.g., user.name).
  • By default, Elasticsearch treats nested JSON objects as a single flattened document internally, which is efficient for indexing and querying.
  • Suitable for simple, one-to-one relationships where array elements do not need isolated querying.
Nested Fields
  • Handle arrays of objects where each element is indexed as an independent hidden document linked to the parent.
  • Enables accurate queries on individual objects inside arrays without false positives caused by flattened indexing.
  • Essential for complex one-to-many relationships, e.g., products with multiple reviews or orders with multiple line items.
  • Comes with a performance cost due to increased document count and more complex query execution.
When to Use Object vs Nested
  • Use object fields for simple hierarchical data where array element independence is not required.
  • Choose nested fields when precise querying inside arrays of objects is critical.
Types of Geo Fields
  • geo_point: Stores latitude and longitude for point locations, supporting distance queries, bounding box, and geo-distance filters.
  • geo_shape: Supports complex geometries such as polygons, multipolygons, lines, and enables spatial queries like intersects and contains.
Use Cases
  • Geo_point for applications like store locators, geofencing, or proximity search.
  • Geo_shape for mapping, route planning, and spatial analytics requiring area and shape relationships.

Modeling Best Practices for Advanced Data Structures

Optimize Nested Field Usage
  • Limit nested objects to essential fields and keep arrays reasonably sized.
  • Use nested queries and filters to target specific nested documents, avoiding costly full nested document scans.
  • Consider denormalization or parent-child relationships if nested query performance degrades.
Design Object Fields Thoughtfully
  • Explicitly define mappings to avoid dynamic mapping pitfalls.
  • Avoid overly deep object nesting that can complicate query logic and increase index size.
Efficient Geo Data Modeling
  • Use geo_point when you only need simple radius or bounding box searches.
  • Choose geo_shape for complex spatial queries but balance with performance trade-offs.
  • Preprocess geo data to reduce precision or complexity where possible.

Querying Advanced Data Types

  • Use the nested query for precise matching within nested objects, combining with inner_hits to retrieve matching nested documents.
  • Leverage geo_distance, geo_bounding_box, and geo_shape queries to exploit Elasticsearch’s spatial capabilities fully.
  • Combine nested and geo queries for geospatial data inside nested structures for advanced use cases.

Performance Considerations

  • Nested fields increase document count internally; monitor shard size and query latency closely.
  • Geo_shape indexing is more resource-intensive than geo_point; optimize shapes and simplify geometries.
  • Use filters over queries where possible to benefit from caching.
  • Monitor and tune based on actual query patterns and cluster metrics.

Conclusion

Mastering Elasticsearch’s nested, object, and geo fields unlocks the power to model and search rich, complex datasets with accuracy and efficiency. These advanced data structures are essential for building sophisticated applications such as e-commerce catalogs, geospatial services, and analytics platforms. By understanding their differences, use cases, and performance trade-offs, you can design indexes that balance functionality with scalable search performance.