Design and Manage Elasticsearch Databases with DbSchema

Build a clearer workflow for Elasticsearch: reverse engineer existing schemas into interactive ER diagrams, model changes visually, and generate reviewed SQL scripts before deployment.

DbSchema is built for visual modeling, schema documentation, and deployment. Keep an offline model in Git, collaborate across teams, and publish documentation that developers, analysts, and stakeholders can navigate in minutes.

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Elasticsearch stores documents in indices, and each index has a mapping that defines field names, data types, analyzer configurations, and nested object structures. While Elasticsearch is primarily a search engine, its SQL interface — available from the Basic license tier upward — allows standard SQL queries against index data. DbSchema treats each Elasticsearch index as a table, visualizes field mappings as a schema diagram, and provides SQL and data exploration tools for Elasticsearch environments.

Exploring Elasticsearch Index Mappings and Field Types

Elasticsearch field types — keyword, text, nested, geo_point, dense_vector, and others — determine how documents are indexed and queried. DbSchema reads the mapping from the Elasticsearch REST API and displays each index's field structure, including nested object hierarchies, giving engineers a clear picture of the data model without reading raw mapping JSON.

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Browsing Elasticsearch Document Data

The DbSchema data explorer fetches documents from Elasticsearch indices and displays them in a tabular format. You can apply field filters to narrow the result set, paginate through large indices, and inspect individual field values — all without writing DSL queries or using the Kibana Discover interface.

Schema Documentation for Elasticsearch Indices

DbSchema generates HTML documentation from the Elasticsearch mapping, capturing index names, field types, analyzer assignments, and nested structures as human-readable reference material. For teams managing dozens of indices across environments, this documentation provides an accessible overview of the full data model without requiring Elasticsearch API knowledge.

Connecting DbSchema to Elasticsearch

DbSchema connects to Elasticsearch using the Elasticsearch JDBC driver, available from Elastic's download page. The default HTTP port is 9200. The JDBC URL format is jdbc:es://http://host:9200. The Elasticsearch SQL feature must be enabled — it is included in the Basic license tier and above with no additional configuration required. For Elasticsearch Service on Elastic Cloud, use the HTTPS cluster endpoint URL and supply your API key as the password in the connection dialog. Self-signed certificates require adding the Elasticsearch CA certificate to the Java truststore before connecting.

Why Teams Use DbSchema with Elasticsearch

• Mapping visualization — view nested field hierarchies and analyzer assignments as a schema diagram instead of raw JSON.
• SQL query access — run Elasticsearch SQL queries from the built-in editor without learning the Query DSL.
• Document browsing — explore index contents in a tabular data explorer with column filtering and pagination.
• Index documentation — generate HTML reference documentation covering all index mappings for team knowledge sharing.
• Elastic Cloud support — connect to cloud-hosted Elasticsearch clusters using API key authentication over HTTPS.