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Discover how to effectively send queries from Elasticsearch to Neo4j using Devtools, paving the way for powerful data visualizations.
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How to Successfully Connect Elasticsearch with Neo4j: A Step-by-Step Guide

If you're trying to utilize Neo4j's graph data within Elasticsearch for advanced data visualizations, you're likely faced with the challenge of establishing a connection between the two systems. You might have a setup running on Docker, and also be using the APOC plugin — but finding the right syntax to send queries can be tricky. In this post, we’ll walk you through the connection process and what to expect when working with these powerful tools.

The Challenge: Connecting Elasticsearch to Neo4j

You've set up a Neo4j instance and an Elasticsearch instance using Docker Compose, and you want to harness the insights from your Neo4j graph database by integrating it with Elasticsearch. You may have already tried to send queries directly to Neo4j from Elasticsearch's Devtools, only to find that those attempts didn’t yield the expected results.

Here's a snippet of what works on Neo4j:

[[See Video to Reveal this Text or Code Snippet]]

And here's the snippet that doesn’t work in Elasticsearch:

[[See Video to Reveal this Text or Code Snippet]]

Understanding the Limitation

First and foremost, it's essential to clarify that Kibana, the visualization tool for Elasticsearch, can only communicate with Elasticsearch instances it is configured to interact with. This means that you cannot send direct queries from Kibana (or the Elasticsearch Devtools) to Neo4j. The integration isn't straightforward, and you'll need to explore alternative methods to bridge the gap between these systems.

Solution: Alternative Approaches for Integration

While the query you attempted won’t work directly due to communication restrictions, there are still effective strategies you can implement to leverage Neo4j data in Elasticsearch. Below, we outline some methods you can consider:

1. Use an ETL Pipeline

An Extract, Transform, Load (ETL) pipeline can efficiently transfer data from your Neo4j database to Elasticsearch. Tools such as Apache NiFi, Logstash, or custom scripts can help you achieve this integration.

Steps to Implement an ETL Pipeline:

Extract data from Neo4j using Neo4j's query language (Cypher).

Transform the data into the desired format for Elasticsearch.

Load the transformed data into Elasticsearch.

2. Utilize the APOC Plugin

Since you have the APOC plugin installed, you can leverage it to perform data exports. Although you can’t run APOC calls directly from Elasticsearch, you can use APOC functions within Neo4j itself to prepare data for use in Elasticsearch.

Example Steps:

Use APOC to query your Neo4j database and then export the results into a CSV file or directly to a JSON format.

Ingest the CSV/JSON file into Elasticsearch.

3. Integrate Using Custom Applications

If your data visualization needs are more complex, consider building a custom application that directly fetches data from Neo4j and sends it to Elasticsearch using their respective APIs:

Fetch Neo4j data using Cypher queries via Neo4j's REST API.

Push the data to Elasticsearch using its REST API.

Conclusion

Although directly sending a query from Elasticsearch Devtools to a Neo4j server isn’t possible, by employing ETL pipelines, utilizing the APOC plugin for data export, or building a custom integration, you can effectively leverage Neo4j data within Elasticsearch for powerful visualizations. Remember, the key lies in understanding the communication paths and leveraging the strengths of each platform to achieve your data goals.

Feel free to revisit this guide whenever you need to tackle the integration challenge between these two robust database systems!