Object and nested types in Elasticsearch - Deep Dive

Overview - Object and nested types

What is it?

In Elasticsearch, object and nested types let you store complex data structures inside a document. An object type holds JSON objects as a single unit, while a nested type stores arrays of objects separately to keep their relationships intact. This helps Elasticsearch understand and search data that has multiple layers or lists of items. These types make it easier to work with real-world data like addresses, orders, or comments inside one document.

Why it matters

Without object and nested types, Elasticsearch would treat all data as flat fields, losing the connection between related pieces inside arrays. This would cause wrong search results, like mixing up details from different items in a list. Using these types preserves the structure and meaning of your data, so searches return accurate and relevant results. This is crucial for applications like e-commerce, social media, or any system with complex data.

Where it fits

Before learning object and nested types, you should understand basic Elasticsearch documents and fields. After this, you can explore advanced querying techniques like nested queries and aggregations. Later, you might learn about performance tuning and mapping strategies for large datasets with complex structures.

Mental Model

Core Idea

Object and nested types organize complex, multi-level data inside Elasticsearch documents to keep related information connected and searchable.

Think of it like...

Imagine a filing cabinet where each folder (document) can hold simple papers (fields) or smaller folders inside it (objects). Nested folders keep their papers grouped separately, so you don’t mix up notes from different topics.

Document
├── Simple fields (title, date)
├── Object type (address)
│   ├── street
│   ├── city
│   └── zip
└── Nested type (comments)
    ├── [0]
    │   ├── user
    │   └── message
    └── [1]
        ├── user
        └── message

Build-Up - 7 Steps

1

Foundation

Basic document structure in Elasticsearch

Concept: Elasticsearch stores data as JSON documents with fields and values.

A document is like a record with keys (field names) and values. For example, a blog post document might have a title, author, and content fields. Each field holds simple data like text or numbers.

Result

You can store and retrieve simple data easily using Elasticsearch.

Understanding that documents are JSON objects is the base for working with more complex data types.

2

Foundation

What is an object type in Elasticsearch?

Concept: Object type lets you store a JSON object inside a document field as a single unit.

Instead of flat fields, you can group related fields inside an object. For example, an address field can hold street, city, and zip as one object. Elasticsearch treats this as one field with subfields.

Result

You can organize related data together, making your documents clearer and easier to manage.

Knowing object types helps you model real-world entities inside documents naturally.

3

Intermediate

Limitations of object type with arrays

🤔Before reading on: do you think object type keeps each item in an array separate or mixes them together? Commit to your answer.

Concept: Object type does not keep array items separate, which can cause data mixing in queries.

If you store an array of objects using object type, Elasticsearch flattens the data. This means fields from different array items can mix during searches, causing wrong matches.

Result

Searches on arrays of objects may return incorrect results because relationships between fields are lost.

Understanding this limitation is key to choosing the right type for arrays of objects.

4

Intermediate

Nested type solves array relationship issues

🤔Before reading on: do you think nested type stores array items together or separately? Commit to your answer.

Concept: Nested type stores each object in an array as a separate hidden document to keep relationships intact.

With nested type, Elasticsearch indexes each array item separately but links them to the parent document. This preserves the connection between fields inside each item during searches.

Result

Queries on nested fields return accurate matches without mixing data from different array items.

Knowing how nested type works prevents common search errors with arrays of objects.

5

Intermediate

How to query nested fields correctly

Concept: Nested queries let you search inside nested objects while keeping their structure.

You use a special nested query to search inside nested fields. This query targets the nested path and matches conditions inside each nested object separately.

Result

You get precise search results that respect the grouping of fields inside nested arrays.

Mastering nested queries is essential for working with nested types effectively.

6

Advanced

Performance considerations with nested types

🤔Before reading on: do you think nested types are faster or slower than object types? Commit to your answer.

Concept: Nested types add overhead because each nested object is indexed separately.

While nested types improve accuracy, they require more storage and slower queries compared to object types. You should balance accuracy needs with performance.

Result

Choosing nested types impacts your cluster’s speed and resource use.

Knowing the tradeoff helps you design efficient Elasticsearch mappings.

7

Expert

Internal indexing of nested documents

🤔Before reading on: do you think nested objects are stored as part of the main document or separately? Commit to your answer.

Concept: Nested objects are indexed as hidden separate documents linked to the parent document internally.

Elasticsearch creates internal hidden documents for each nested object. These are linked by a special join mechanism, allowing queries to match nested objects independently but return the parent document.

Result

This design enables precise nested queries but adds complexity to indexing and searching.

Understanding this internal mechanism explains why nested queries behave differently and why they have performance costs.

Under the Hood

Elasticsearch stores nested objects as separate hidden documents internally linked to the parent document. This allows queries to match nested objects individually without mixing fields from different objects. Object types are stored as flattened JSON fields, which can cause field mixing in arrays. Nested documents use a special internal join to maintain relationships during search and aggregation.

Why designed this way?

The nested type was created to solve the problem of inaccurate search results caused by flattening arrays of objects. Before nested types, Elasticsearch could not distinguish between fields from different array items, leading to false matches. The design trades extra storage and query complexity for accurate, reliable search results on complex data.

Parent Document
┌─────────────────────────────┐
│ id: 1                      │
│ title: "Order 123"         │
│ nested comments:            │
│ ┌─────────────┐ ┌─────────┐ │
│ │ Nested Doc1 │ │Nested Doc2│ │
│ │ user: Ann   │ │ user: Bob │ │
│ │ message: Hi │ │ message: Bye│ │
│ └─────────────┘ └─────────┘ │
└─────────────────────────────┘

Nested docs are hidden but linked to parent for queries.

Myth Busters - 4 Common Misconceptions

Quick: Does using object type for arrays keep each array item separate? Commit yes or no.

Common Belief: Object type keeps each item in an array separate and preserves their relationships.

Reality: Object type flattens arrays of objects, mixing fields from different items during searches.

Why it matters: This causes incorrect search results, like matching a user from one comment with a message from another.

Quick: Are nested types always faster than object types? Commit yes or no.

Common Belief: Nested types are faster because they organize data better.

Reality: Nested types are slower and use more storage because they index each nested object separately.

Why it matters: Using nested types unnecessarily can degrade performance and increase resource costs.

Quick: Can you query nested fields with normal match queries? Commit yes or no.

Common Belief: You can use normal queries on nested fields just like any other field.

Reality: Nested fields require special nested queries to maintain object boundaries during search.

Why it matters: Using normal queries on nested fields leads to wrong results or no matches.

Quick: Does nested type store data outside the main document? Commit yes or no.

Common Belief: Nested data is stored inside the main document as a single JSON object.

Reality: Nested data is stored as separate hidden documents internally linked to the main document.

Why it matters: This affects how queries and aggregations work and explains performance characteristics.

Expert Zone

1

Nested documents are internally stored as hidden Lucene documents, which affects how scoring and filtering behave in queries.

2

When updating nested fields, Elasticsearch rewrites the entire parent document, which can impact update performance.

3

Nested aggregations require special handling because they operate on nested documents, not the parent document directly.

When NOT to use

Avoid nested types when your data does not require strict relationship preservation between array items. Use object types or flatten your data if performance is critical and slight inaccuracies are acceptable. Alternatively, use parent-child relationships for very large or deeply nested data.

Production Patterns

In production, nested types are commonly used for user comments, product attributes, or order line items where each item has multiple fields. Developers combine nested queries with filters and aggregations to build precise search and analytics features. Careful mapping design and query optimization are essential to balance accuracy and performance.

Connections

Relational database normalization

Nested types in Elasticsearch are similar to normalized tables with foreign keys in relational databases.

Understanding relational normalization helps grasp why nested documents are stored separately to preserve relationships.

JSON data structures

Object and nested types directly map to JSON objects and arrays, which are common in web APIs and configurations.

Knowing JSON helps you design Elasticsearch mappings that reflect real-world data naturally.

Document Object Model (DOM) in web browsers

Both nested types and DOM represent hierarchical data structures with parent-child relationships.

Understanding DOM trees aids in visualizing how nested documents relate to their parent documents.

Common Pitfalls

#1 Using object type for arrays of objects expecting separate item matching.

Wrong approach:

"comments": { "type": "object" } with array of comment objects

Correct approach:

"comments": { "type": "nested" } to keep each comment separate

Root cause:

Misunderstanding that object type flattens arrays and loses item boundaries.

#2 Querying nested fields with normal match query instead of nested query.

Wrong approach:

{ "match": { "comments.user": "Ann" } }

Correct approach:

{ "nested": { "path": "comments", "query": { "match": { "comments.user": "Ann" } } } }

Root cause:

Not knowing nested queries are required to maintain nested object context.

#3 Expecting nested types to have no performance impact.

Wrong approach:

Using nested types everywhere without considering query speed or storage.

Correct approach:

Use nested types only when necessary and optimize mappings and queries accordingly.

Root cause:

Ignoring the internal complexity and overhead of nested document indexing.

Key Takeaways

Elasticsearch object type stores JSON objects inside documents but flattens arrays, which can mix data from different items.

Nested type stores each object in an array as a separate hidden document to preserve relationships and enable accurate queries.

Nested queries are required to search inside nested fields correctly and avoid false matches.

Nested types improve search accuracy but add storage and query overhead, so use them thoughtfully.

Understanding the internal storage of nested documents explains their behavior and performance characteristics.