Elasticsearch: How to Join Data from Two Indexes

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At its core, Elasticsearch is a distributed search and analytics engine designed for horizontal scalability, reliability, and real-time search. It excels in managing and querying large datasets, offering powerful full-text search capabilities alongside various complex queries. However, one common question is how to “join” data across two or more indexes, similar to relational database joins.

Elasticsearch is fundamentally a schema-less and non-relational database, which doesn’t support traditional SQL-style joins.

This design choice is key to its performance and scalability. However, there are strategies to model and query data in ways that mimic the effect of joins, leveraging Elasticsearch’s strengths. This article explores techniques for joining data from two indexes, focusing on application-side joins, nested documents, and parent-child relationships.

Table of Contents

Application-Side Joins

The simplest approach to joining data from two indexes in Elasticsearch is to perform application-side joins. This involves executing separate queries to each index and combining the results in your application logic. While this approach offers flexibility and is straightforward to implement, it requires careful consideration of performance and data consistency, especially when dealing with large datasets or real-time requirements.

Steps for Application-Side Joins:

Query the First Index: Execute a search query against the first index and retrieve the relevant documents.
Extract Join Keys: From the results of the first query, extract the keys or identifiers that will be used to join with the second index.
Query the Second Index: Using the extracted keys from the first step, query the second index to retrieve the related documents.
Combine Results: In your application logic, merge or combine the results from both indexes based on your specific requirements.

Application-Side Join Example with Python

Step 1: Query the `users` Index to Find the User ID

from elasticsearch import Elasticsearch

# Initialize the Elasticsearch client
es = Elasticsearch("http://localhost:9200")

# The username we're searching for
username = "john_doe"

# Search for the user to get the user_id
user_search_body = {
  "query": {
    "match": {
      "username": username
    }
  }
}

user_response = es.search(index="users", body=user_search_body)

# Assuming usernames are unique and we have a hit
user_id = user_response['hits']['hits'][0]['_id']

Step 2: Query the `orders` Index Using the Retrieved `user_id`

# Now, use the user_id to find all orders by this user
order_search_body = {
  "query": {
    "match": {
      "user_id": user_id
    }
  }
}

orders_response = es.search(index="orders", body=order_search_body)

# Print out the orders
for order in orders_response['hits']['hits']:
    print(order['_source'])

Explanation

Initialize Elasticsearch Client: Establish a connection to your Elasticsearch cluster.
Find User ID: Perform a search query against the users index to find the document representing the user of interest and retrieve the user’s ID.
Query Orders: With the user’s ID, execute a second search query against the orders index to find all orders associated with that user ID.
Combine Results: The application manually correlates the data from these two queries to present a combined view, similar to a join operation in a relational database.

Best Practices and Considerations for Application-Side Joins

Performance and Scalability: This approach requires multiple queries and additional application logic, which may not be as efficient as database-level joins, especially for large datasets.
Data Consistency: Ensure data consistency across indexes (e.g., consistent IDs) to prevent mismatches or errors in joining logic.
Use of Elasticsearch Features: While not a direct substitute for joins, features like nested documents and parent-child relationships can sometimes offer more efficient ways to model and query related data within a single index, depending on the use case.

This example demonstrates how to achieve join-like functionality between two Elasticsearch indexes through application-side logic. It’s a practical approach in scenarios where Elasticsearch’s data modeling capabilities (such as nested documents or parent-child relationships) cannot be applied due to data being spread across separate indexes.

Nested Documents

For scenarios where related data can be modeled as nested objects within a single document, Elasticsearch’s support for nested documents offers a way to encapsulate this relationship directly in the index. This approach avoids application-side joins by ensuring all related data is stored together, allowing for efficient querying.

Modeling with Nested Documents:

Define a Nested Mapping: When creating your index, define the nested fields using the nested type in your mappings.
Index Nested Documents: Store related data together in nested JSON structures within a single document.
Querying Nested Fields: Use nested queries to search within nested fields, treating them as separate documents while maintaining the relationship to the parent document.

Parent-Child Relationships

Elasticsearch also supports parent-child relationships, allowing you to define a one-to-many relationship between documents in the same index. This model can be useful for hierarchical data structures, such as categories and products or blogs and comments.

Implementing Parent-Child Relationships:

Define Parent-Child Mapping: Use the join datatype to define the relationship between parent and child documents within your mappings.
Index Parent and Child Documents: When indexing documents, specify the relationship type and the parent’s ID for child documents.
Querying Parent-Child Relationships: Elasticsearch provides specialized queries, such as has_child and has_parent, to retrieve documents based on the defined relationships.

Best Practices and Considerations for Nested Documents & Parent-Child Relationships

Data Modeling: Carefully model your data and relationships based on your query requirements. Depending on your data’s nature and how you intend to query it, you can choose between nested documents and parent-child relationships.
Performance: Consider the performance implications of your chosen strategy. Application-side joins can be flexible but may involve more network overhead and complex application logic. Nested documents and parent-child relationships offer efficient querying but require careful data modeling and can increase index size.
Data Consistency: To avoid data integrity issues, ensure consistency in managing and updating related data, especially when using application-side joins or parent-child relationships.

Conclusion

While Elasticsearch does not support traditional joins like relational databases, its flexible data modeling capabilities offer powerful alternatives to represent and query related data. Whether through application-side joins, nested documents, or parent-child relationships, you can achieve complex data retrieval patterns that cater to a wide range of use cases. Choosing the right approach depends on your specific data relationships, performance requirements, and how you plan to query your data. With careful planning and understanding of Elasticsearch’s features, you can effectively model and query related data across multiple indexes.

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Anastasios Antoniadis

Anastasios Antoniadis is the founder and editor-in-chief of BORDERPOLAR... He is a software engineer, blogger, and avid gamer covering tech, gaming, and coding guides for over 4 years. He is a 2014 graduate of the Department of Informatics and Telecommunications of the University of Athens, an M.Sc. holder in Computer Science, and a Ph.D. student in Program Analysis.