Nested and Object Field Types
Learn how Elasticsearch handles JSON objects and arrays, why the default object type flattens data, and how the nested type preserves relationships inside arrays of objects.
Nested and Object Field Types is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Storing Structured Data
Real-world documents often contain nested structures: a blog post with comments, a product with variants, or an order with line items. Elasticsearch must decide how to index these JSON objects so they stay searchable.
This lesson covers the two main approaches: the default object type and the specialized nested type.
The Default object Type
By default, any JSON object inside a document is mapped as the object type. Elasticsearch flattens the inner fields into dotted paths.
A field author.name simply becomes a normal Lucene field. This is efficient and works perfectly for single objects.
PUT my_index/_doc/1
{
"author": { "first": "Jane", "last": "Doe" }
}How Flattening Looks
Internally the object above is stored as two flat fields: author.first = Jane and author.last = Doe. The hierarchy is only conceptual; Lucene sees independent fields.
This is fine until you have an array of objects.
The Flattening Problem
Consider an array of users. After flattening, Elasticsearch loses the link between which first name belongs to which last name.
The arrays become user.first = [Alice, John] and user.last = [White, Smith] separately.
PUT my_index/_doc/2
{
"user": [
{ "first": "Alice", "last": "White" },
{ "first": "John", "last": "Smith" }
]
}Why It Matters
A query for first = Alice AND last = Smith would incorrectly match the document above, because the cross-object relationship is gone. The values are pooled together.
The nested type solves this.
Declaring a Nested Field
Set the field type to nested in the mapping. Each object in the array is then indexed as a hidden, separate Lucene document, preserving its internal field relationships.
PUT my_index
{
"mappings": {
"properties": {
"user": { "type": "nested" }
}
}
}Querying Nested Fields
You must use a nested query and specify the path. Conditions inside are evaluated against a single sub-document, so cross-object false matches disappear.
GET my_index/_search
{
"query": {
"nested": {
"path": "user",
"query": {
"bool": { "must": [
{ "match": { "user.first": "Alice" }},
{ "match": { "user.last": "Smith" }}
]}
}
}
}
}Inner Hits
Add inner_hits to a nested query to return which specific sub-document(s) matched, not just the parent document. This is essential for highlighting the relevant array element.
"nested": {
"path": "user",
"inner_hits": {},
"query": { "match": { "user.first": "Alice" } }
}Costs of Nested
Nested fields are powerful but have trade-offs:
- Each array element is a separate Lucene doc, increasing index size.
- Updating one element re-indexes the whole parent document.
- Deeply nested or large arrays can hurt performance.
Use the index.mapping.nested_objects.limit setting to cap counts.
Nested vs join
For tightly coupled data that updates together, nested is ideal. For independently updated, high-cardinality relationships, consider the join (parent-child) field type instead, which decouples updates at a higher query cost.
When to Choose Which
Use object when arrays do not require cross-field correlation. Use nested when you must match multiple fields within the same array element. Defaulting to nested for everything wastes resources.
Quick Check
Test your understanding of nested mappings.
Recap
You learned how Elasticsearch indexes JSON objects:
- The default
objecttype flattens fields and pools array values. - The
nestedtype indexes each array element separately to preserve relationships. - Query nested fields with the
nestedquery plus apath, and useinner_hitsto find matching elements. - Nested types cost more storage and require full re-indexing on element updates.
Frequently asked questions
Is the “Nested and Object Field Types” lesson free?
Yes — the full text of “Nested and Object Field Types” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.
What will I learn in “Nested and Object Field Types”?
Learn how Elasticsearch handles JSON objects and arrays, why the default object type flattens data, and how the nested type preserves relationships inside arrays of objects. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Elasticsearch & Full Text Search Systems?
No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Nested and Object Field Types” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?
Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Customizing Field Mappings
- Dynamic vs. Explicit Mappings
- Index Templates and Aliases
- Nested and Object Field Types