{"id":814,"date":"2020-04-18T05:44:18","date_gmt":"2020-04-18T05:44:18","guid":{"rendered":"https:\/\/www.aeologic.com\/blog\/?p=814"},"modified":"2026-05-18T13:12:31","modified_gmt":"2026-05-18T07:42:31","slug":"indexing-and-searching-nested-documents-in-solr-ultimate-solr-guide","status":"publish","type":"post","link":"https:\/\/www.aeologic.com\/blog\/indexing-and-searching-nested-documents-in-solr-ultimate-solr-guide\/","title":{"rendered":"Indexing and Searching Nested Documents in Solr – Ultimate Solr Guide"},"content":{"rendered":"

Hi All, today I will be writing about another important aspect of Solr that addresses the needs of many business applications \u2014 Solr Nested Documents<\/strong><\/a>. Suppose we need to index data for an e-commerce company that hosts a variety of products on its website along with associated SKUs. This data must be indexed, updated, and searched efficiently in a way that is meaningful for both end users and merchandisers. A natural question arises: how should this data be represented, and what approach should be followed to manage it effectively while maintaining scalability and search performance?<\/p>\n

Introduction to Nested Documents in Search Engines<\/h2>\n

Modern search applications rarely deal with simple, flat datasets. Industries such as e-commerce, healthcare, logistics, travel, and digital marketplaces often manage information that exists in hierarchical relationships. A product may have multiple variants, an order may contain several items, or a customer profile may include numerous transactions. Representing such interconnected data efficiently becomes a challenge when using traditional document indexing approaches.<\/p>\n

Search engines like Solr and Lucene are optimized for storing and retrieving documents quickly. However, treating every related entity as an independent document can create issues during querying, filtering, faceting, and relevance ranking. For example, an e-commerce product with multiple SKUs stored as separate records may produce duplicate results or inaccurate facets when users search by attributes such as color, size, or material.<\/p>\n

Nested documents solve this problem by allowing multiple child documents to be associated with a parent document within a single indexed block. Rather than indexing each SKU independently, Solr groups all related child entities beneath a parent entity, preserving relationships during indexing and querying.<\/p>\n

To address this problem, Solr provides us with a mechanism to associate multiple docs with each other and put them in a relationship. Also, it provides both indexing and searching mechanisms to get data in and out. What we are referring to here is called a “Nested document structure”, or more technically, a BLOCK in Solr. Put simply, a block is a set of parent-child relation between multiple documents.<\/p>\n

Let’s understand this in two parts.<\/p>\n

Indexing Nested Documents<\/h2>\n

Let’s assume a nested structure as below:<\/p>\n

{
\n“id”:”100″,
\n“scope_ss”:”product”,
\n“category_ss”:”OutdoorSofa”,
\n“productType_ss”:”sofa”,
\n“_childDocuments_<\/strong>“: [
\n{
\n“id”:”101″,
\n“scope_ss”:”sku”,
\n“Fabric_ss”:”Polyester”,
\n“Depth_ss”:”Classic”,
\n“Finish_ss” : “Fine”,
\n“Color_ss”:”Grey”},
\n{
\n“id”:”102″,
\n“scope_ss”:”sku”,
\n“Fabric_ss”:”nylon”,
\n“Depth_ss”:”Petite”,
\n“Finish_ss” : “Grey”,
\n“Color_ss”:”Green”},
\n{
\n“id”:”103″,
\n“scope_ss”:”sku”,
\n“Fabric_ss”:”acrylic”,
\n“Depth_ss”:”Petite”,
\n“Finish_ss” : “Espresso”,
\n“Color_ss”:”Green”},
\n{
\n“id”:”104″,
\n“scope_ss”:”sku”,
\n“Fabric_ss”:”Polyester”,
\n“Depth_ss”: “Classic”,
\n“Finish_ss” : “Grey”,
\n“Color_ss”:”Dove”},<\/p>\n

{“id”:”105″,
\n“scope_ss”:”sku”,
\n“Fabric_ss”:”nylon”,
\n“Depth_ss”: “Classic”,
\n“Finish_ss” : “Grey”,
\n“Color_ss”:”Blue”}]<\/p>\n

}<\/p>\n

_childDocuments_\u00a0<\/strong>tells solr that each object defined within the scope is a child to the doc it belongs to. In essence, we have just created a block of information that has properties for SKU’s which imparts some meaning to the application scenarios. Now let’s see how we can index the doc. Utilizing Solr’s dynamic schema mapping capabilities, we can be certain that all the fields in the document will get indexed and get reflected (Notice _ss <\/strong>in fields, they are stored as String array). Now, one may ask upon careful observation that why the data type used is array when the field is storing String type information. Answer to this question lies in more subtle understanding of inner working of Solr.<\/p>\n

When Solr indexes a nested document, it stores all field values for a field in a set. This effectively means each field has multiple values and hence the type, array of String. If one tries to index data without using _ss or just _s (String type in Solr), solr engine throws an error. Now, assuming we have settled with basics, let’s continue to index the data.<\/p>\n

Fire up UI and index data by navigating to necessary modalities as below:<\/p>\n

\"\"<\/p>\n

Paste the JSON doc in “Documents(s)” section and index the document. If all configuration is proper, then, the index upon querying will appear as below:<\/p>\n

\"\"<\/p>\n

Note:<\/strong> Notice how the parent doc (the product information) is referenced at bottom and child docs are piled upon it. This is not by accident but by design.<\/p>\n

Solr 9.x nested document updates<\/h2>\n

## Nested Documents in Solr 9.x: What Changed?<\/p>\n

Solr 9.x introduced improvements in nested document handling, memory efficiency, and query execution for block joins. Compared to older Solr versions, nested indexing now performs better with large SKU catalogs and deep parent-child hierarchies.<\/p>\n

Key enhancements include:<\/p>\n

\u2022 Improved block join query execution
\n\u2022 Better heap utilization during indexing
\n\u2022 Faster JSON Facet API performance
\n\u2022 Optimized update handling for nested documents
\n\u2022 Reduced latency for large product catalogs<\/p>\n

Businesses handling millions of SKUs can observe noticeable improvements in search throughput and indexing speed after migrating to Solr 9.x.<\/p>\n

Querying Nested Documents<\/h2>\n

Let’s dive into basics of querying nested docs in Solr.<\/p>\n

One of the most needed use-case in context of e-commerce data-set is Faceting. Faceting for nested docs by design is different than traditional docs in Solr.<\/p>\n