Nested queries for nested objects in Elasticsearch - Deep Dive

Overview - Nested queries for nested objects

What is it?

Nested queries in Elasticsearch allow you to search within nested objects inside a document. Nested objects are like small groups of related data stored inside a bigger document. These queries help find documents where specific conditions match inside these nested groups. This is important because nested objects are stored differently than normal fields.

Why it matters

Without nested queries, searching inside nested objects would give wrong or mixed results because Elasticsearch treats nested objects as separate hidden documents. This would make it hard to find exactly what you want, like matching a person's name with their specific phone number in a list. Nested queries solve this by keeping nested data together and searching it correctly. This makes your search results accurate and meaningful.

Where it fits

Before learning nested queries, you should understand basic Elasticsearch queries and how documents and fields work. After mastering nested queries, you can explore advanced query types like nested aggregations and parent-child relationships to handle complex data structures.

Mental Model

Core Idea

Nested queries let you search inside groups of related data stored as separate mini-documents within a bigger document, keeping their connections intact.

Think of it like...

Imagine a filing cabinet where each folder contains several sheets of paper. Each sheet is related to the folder but has its own details. Nested queries are like looking inside each folder and checking the sheets one by one to find exactly the right sheet that matches your search.

Document
├─ Field A
├─ Field B
└─ Nested Objects (array)
   ├─ Nested Object 1
   │  ├─ Nested Field 1
   │  └─ Nested Field 2
   ├─ Nested Object 2
   │  ├─ Nested Field 1
   │  └─ Nested Field 2
   └─ Nested Object 3
      ├─ Nested Field 1
      └─ Nested Field 2

Nested Query searches inside each Nested Object separately.

Build-Up - 7 Steps

1

Foundation

Understanding nested objects in Elasticsearch

Concept: Nested objects are special fields that hold arrays of objects, stored separately to keep their internal structure.

In Elasticsearch, a document can have fields that are arrays of objects. These are called nested objects. Unlike normal arrays, nested objects keep each object as a separate hidden document linked to the main one. This prevents mixing data from different objects when searching.

Result

You can store complex data like a list of addresses or phone numbers inside one document without losing the connection between fields inside each object.

Understanding that nested objects are stored separately helps explain why normal queries don't work well on them.

2

Foundation

Basic Elasticsearch query structure

Concept: Elasticsearch queries use JSON to describe what to search for in documents.

A simple query looks like this: { "query": { "match": { "field": "value" } } } This searches documents where 'field' contains 'value'.

Result

Elasticsearch returns documents matching the condition.

Knowing the basic query format is essential before adding nested queries.

3

Intermediate

Why normal queries fail on nested objects

🤔Before reading on: do you think a normal match query on a nested field will correctly match related nested data? Commit to yes or no.

Concept: Normal queries treat nested objects as flattened fields, mixing data from different nested objects.

If you have a nested array of objects with fields 'name' and 'phone', a normal query searching for name='John' and phone='123' might match if 'John' is in one nested object and '123' in another, which is wrong.

Result

Search results may include documents that don't have the exact pair of values in the same nested object.

Knowing this limitation shows why nested queries are necessary for accurate results.

4

Intermediate

Using nested query syntax

🤔Before reading on: do you think nested queries require specifying the path to nested objects? Commit to yes or no.

Concept: Nested queries specify the path to nested objects and the query inside them to search correctly.

A nested query looks like this: { "query": { "nested": { "path": "nested_field", "query": { "bool": { "must": [ {"match": {"nested_field.name": "John"}}, {"match": {"nested_field.phone": "123"}} ] } } } } } This searches nested objects where both conditions match inside the same nested object.

Result

Only documents with nested objects matching both conditions together are returned.

Understanding the path and inner query is key to using nested queries effectively.

5

Intermediate

Combining nested queries with bool queries

🤔Before reading on: can nested queries be combined with other queries using bool? Commit to yes or no.

Concept: Nested queries can be combined with other queries using bool to build complex search logic.

You can combine nested queries with other queries like this: { "query": { "bool": { "must": [ {"match": {"status": "active"}}, { "nested": { "path": "nested_field", "query": {"match": {"nested_field.name": "John"}} } } ] } } } This finds documents with status 'active' and nested objects with name 'John'.

Result

Search results match multiple conditions across nested and normal fields.

Knowing how to combine nested queries expands your ability to filter complex data.

6

Advanced

Nested query scoring and inner hits

🤔Before reading on: do you think nested queries can return which nested objects matched? Commit to yes or no.

Concept: Nested queries can return details about which nested objects matched using inner_hits, and scoring is calculated per nested object.

Adding inner_hits to a nested query: { "query": { "nested": { "path": "nested_field", "query": {"match": {"nested_field.name": "John"}}, "inner_hits": {} } } } This returns matching nested objects inside each document in the results.

Result

Search results include nested objects that matched, helping understand why a document matched.

Understanding inner_hits helps debug and explain search results involving nested data.

7

Expert

Performance considerations and mapping design

🤔Before reading on: do you think nested fields always perform better than flattened arrays? Commit to yes or no.

Concept: Nested fields improve query accuracy but add overhead; mapping design affects performance and storage.

Nested objects are stored as hidden documents, which means more storage and slower indexing. Overusing nested fields can slow searches. Sometimes flattening data or using parent-child relationships is better. Choosing nested mapping depends on query needs and data structure.

Result

Well-designed nested mappings balance accuracy and performance for your use case.

Knowing tradeoffs prevents performance problems and guides better data modeling.

Under the Hood

Elasticsearch stores nested objects as separate hidden documents linked to the main document by an internal ID. When a nested query runs, it searches these hidden documents and matches only those nested objects that satisfy the query. Then it returns the main document if any nested object matches. This prevents mixing fields from different nested objects during search.

Why designed this way?

This design was chosen to keep nested objects isolated for accurate matching. Alternatives like flattening nested data caused incorrect matches because fields from different nested objects mixed. Storing nested objects as separate documents preserves their structure but adds complexity and storage cost.

Main Document
┌─────────────────────────────┐
│ Document ID: 1              │
│ Fields:                    │
│  - title: "Example"        │
│  - nested_objects:          │
│     ┌───────────────┐       │
│     │ Nested Doc 1  │◄──────┤
│     │ Fields:       │       │
│     │  name: John   │       │
│     │  phone: 123   │       │
│     └───────────────┘       │
│     ┌───────────────┐       │
│     │ Nested Doc 2  │◄──────┤
│     │ Fields:       │       │
│     │  name: Jane   │       │
│     │  phone: 456   │       │
│     └───────────────┘       │
└─────────────────────────────┘

Nested Query searches Nested Docs separately, then returns Main Document if any match.

Myth Busters - 4 Common Misconceptions

Quick: Does a normal match query on nested fields always return correct results? Commit to yes or no.

Common Belief: Normal queries on nested fields work just like on regular fields and return accurate matches.

Reality: Normal queries flatten nested objects, mixing fields from different nested objects and causing incorrect matches.

Why it matters: This leads to wrong search results, confusing users and making data unreliable.

Quick: Do nested queries always slow down Elasticsearch significantly? Commit to yes or no.

Common Belief: Nested queries always cause big performance problems and should be avoided.

Reality: Nested queries add overhead but are efficient when used properly; poor mapping or overuse causes slowdowns.

Why it matters: Avoiding nested queries blindly can force bad data models and inaccurate searches.

Quick: Can nested queries return which nested objects matched inside a document? Commit to yes or no.

Common Belief: Nested queries only return the whole document, not details about matching nested objects.

Reality: Nested queries can return matching nested objects using inner_hits, helping explain results.

Why it matters: Without inner_hits, debugging complex queries is harder and less transparent.

Quick: Are nested objects the only way to model arrays of objects in Elasticsearch? Commit to yes or no.

Common Belief: Nested objects are always the best way to store arrays of objects.

Reality: Sometimes flattening or parent-child relationships are better depending on query needs and performance.

Why it matters: Choosing nested objects blindly can cause unnecessary complexity or slow queries.

Expert Zone

1

Nested queries score each nested object separately, affecting overall document score in subtle ways.

2

Inner_hits can be customized to control how much nested data is returned, balancing detail and performance.

3

Mapping nested fields requires careful planning to avoid excessive storage and indexing overhead.

When NOT to use

Avoid nested queries when your data does not require strict matching inside nested objects or when performance is critical. Alternatives include flattening data into simple arrays or using parent-child relationships for very large datasets.

Production Patterns

In production, nested queries are often combined with filters and aggregations to analyze nested data. Inner_hits are used for debugging and UI display. Mapping is optimized by limiting nested fields to only those needed for precise queries.

Connections

Relational database foreign keys

Nested objects in Elasticsearch are similar to related tables linked by foreign keys in relational databases.

Understanding foreign keys helps grasp why nested objects are stored separately but linked, preserving relationships.

Object-oriented programming encapsulation

Nested objects encapsulate related data inside a parent object, like objects inside classes.

Knowing encapsulation clarifies why nested queries keep nested data together and separate from other data.

Document Object Model (DOM) in web browsers

Nested objects resemble nested elements in the DOM tree, where queries target specific nested nodes.

Understanding DOM traversal helps visualize how nested queries navigate and match nested data.

Common Pitfalls

#1 Using a normal match query on nested fields expecting correct matches.

Wrong approach:

{
"query": {
"match": {
"nested_field.name": "John"
}
}
}

Correct approach:

{
"query": {
"nested": {
"path": "nested_field",
"query": {
"match": {
"nested_field.name": "John"
}
}
}
}
}

Root cause:

Misunderstanding that nested fields require special queries to keep their internal structure intact.

#2 Omitting the 'path' parameter in a nested query.

Wrong approach:

{
"query": {
"nested": {
"query": {
"match": {
"nested_field.name": "John"
}
}
}
}
}

Correct approach:

{
"query": {
"nested": {
"path": "nested_field",
"query": {
"match": {
"nested_field.name": "John"
}
}
}
}
}

Root cause:

Not specifying the path causes Elasticsearch to not know which nested objects to search.

#3 Expecting nested queries to perform well without considering mapping design.

Wrong approach:

Mapping many fields as nested without analyzing query needs or data size.

Correct approach:

Carefully map only necessary fields as nested and consider alternatives like flattening or parent-child.

Root cause:

Ignoring performance tradeoffs and data modeling best practices.

Key Takeaways

Nested queries are essential for searching inside arrays of objects stored as nested fields in Elasticsearch.

They keep nested objects isolated to avoid mixing data from different objects, ensuring accurate matches.

Using the 'path' parameter and inner queries correctly is key to effective nested queries.

Nested queries can return matching nested objects with inner_hits, aiding result explanation.

Proper mapping and understanding performance tradeoffs are crucial for using nested queries in production.