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Show sort options Show sort options

Show text analysis JSON Hide text analysis JSON

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No differences with {{diffSource}} found.

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Found differences with {{diffSource}}:

Updated Fields

Name Old New
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New Fields

Name Type Indexed Stored MultiValued DocValues Tokenized
{{f.name}} {{f.type}}

Removed Fields

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Updated Field Types

Name Old New
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New Field Types

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Removed Field Types

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Updated Dynamic Fields

Name Old New
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New Dynamic Fields

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Removed Dynamic Fields

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Copy Fields

Source Destination

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Publish schema and associated configs to Zookeeper as a Solr ConfigSet.

Affected collections:

  • {{coll}}
No existing collections

Add new collection with published config?

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Warning: You've chosen to load an existing schema that is already being used by active collections. Making changes to the '{{confirmSchema}}' schema will impact existing documents in these collections. Please proceed with caution.

Collections using the {{confirmSchema}} schema:

  • {{coll}}

Get started building your search app by defining a new schema. A schema defines all the fields in your search index. Solr comes with a _default schema that provides a good starting point to customize based on your specific data.

Copy an existing Configset (schema, solrconfig.xml, and supporting files) as the starting point for your new schema. The _default Configset includes a schema with field guessing enabled, dynamic fields for common field types, and field types for analyzing text data for a number of common languages supported by Lucene. For more information about Configsets, see:

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Sample Documents

Upload a JSON, CSS, or XML file containing sample documents or simply paste some sample documents into the text area below; the Schema Designer supports a maximum of 5MB and 1,000 documents.

Click on the Analyze Documents button to have Solr determine the schema by looking at the sample values for each field. Sample documents are stored on the server so you can make changes to the schema and Schema Designer will automatically re-index the sample documents to apply the changes.

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Schema Editor

Filter fields by: 

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By default, schemas copied from the _default schema include field types (and associated files) for all built-in languages. Select the languages needed for your application and the designer will remove all the unnecessary field types for languages you don't need. For more information about text analysis and languages, see:

Dynamic fields allow Solr to index fields that you did not explicitly define in your schema. Dynamic fields can make your application less brittle by providing some flexibility in the documents you can add to Solr. It is recommended to keep the default set of dynamic fields enabled for your schema. Unchecking this option removes all dynamic fields from your schema. For more information about dynamic fields, see:

Field guessing (aka "schemaless mode") allows Solr to detect the "best" field type for unknown fields encountered during indexing. Field guessing also performs some field transformations, such as removing spaces from field names. If you use the schema designer to create your schema based on sample documents, you may not need to enable this feature. However, with this feature disabled, you need to make sure the incoming data matches the schema exactly or indexing errors may occur. For more information about schemaless mode, see:

Enabling this feature adds the _root_ and _nest_path_ fields to your schema.

For more information about indexing nested child documents, see:

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{{selectedType}}: {{selectedNode.name}}

The field type defines how Solr should interpret data in a field and how the field can be queried. The properties on each field are inherited from the field type if not explicitly overridden in the field definition. In many cases, it's preferable to choose a different field type instead of overriding the field properties. For example, there's typically a multi-valued version of each type, such as strings is the multi-valued version of the string type. For more information about field types, see:

Copy fields allow you to apply several distinct field types to a single piece of incoming information during indexing. For instance, a common approach is to copy important string fields into the catch-all '_text_' field to enable full-text matches on terms contained in string fields. Provide a comma-delimited list of fields to copy into during indexing. For more information about copy fields, see:

Name

This table provides a list of {{containerNodeLabel}} defined for your schema. Click on the name in the first column to edit properties. Click on {{containerNodeLabel}} in the Schema Editor tree on the left to return to this table view.

For more information about field properties, see:

Type Indexed Stored MultiValued DocValues Tokenized
{{f.name}} {{f.type}}
Type

This table provides a list of field types defined for your schema. Click on the type name in the first column to edit properties for that type. Changing properties for a type impacts all fields linked to the type. Click on Field Types in the Schema Editor tree on the left to return to this table view.

For more information about field types, see:

Class Indexed Stored MultiValued DocValues Tokenized
{{f.name}} {{f.class}}

Text Analysis

This Functionality requires the /analysis/field Handler to be registered and active!
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Query Tester

Main query parameter q specifies the ranked matching criteria for documents. If you don't specify a field name in the query, the default text field, typically _text_, is used. If the query results do not contain expected documents, check how the default text field is populated, typically using copy field directives in the schema.

For more information about query parameters, see:

asc desc

Sort fields must be single-valued and indexed (or have docValues enabled). If an expected field is not present in this list, check the active properties in the schema.

For more information about query parameters, see:

Fields must be indexed or have docValues enabled to compute facets.

For more information about faceting, see:

Query Results

Copy the documents from the query results to the Sample Documents text area as JSON. This allows you to refine your schema by changing the sample data sourced from the query results.
{{row.name}}{{row.name}} {{row.value}}