By default, strings are indexed as text. To make this indexing option explicit, one can define a mapping when indexing a property key as text.

mgmt = graph.openManagement()
summary = mgmt.makePropertyKey('booksummary').dataType(String.class).make()
mgmt.buildIndex('booksBySummary', Vertex.class).addKey(summary, Mapping.TEXT.asParameter()).buildMixedIndex("search")
mgmt.commit()

This is identical to a standard mixed index definition with the only addition of an extra parameter that specifies the mapping in the index - in this case Mapping.TEXT.

When a string property is indexed as text, the string value is tokenized into a bag of tokens. The exact tokenization depends on the indexing backend and its configuration. JanusGraph’s default tokenization splits the string on non-alphanumeric characters and removes any tokens with less than 2 characters. The tokenization used by an indexing backend may differ (e.g. stop words are removed) which can lead to minor differences in how full-text search queries are handled for modifications inside a transaction and committed data in the indexing backend.

When a string property is indexed as text, only full-text search predicates are supported in graph queries by the indexing backend. Full-text search is case-insensitive.

import static org.janusgraph.core.attribute.Text.*
g.V().has('booksummary', textContains('unicorns'))
g.V().has('booksummary', textContainsPrefix('uni'))
g.V().has('booksummary', textContainsRegex('.*corn.*'))
g.V().has('booksummary', textContainsFuzzy('unicorn'))

String search predicates (see below) may be used in queries, but those require filtering in memory which can be very costly.

To index string properties as character sequences without any analysis or tokenization, specify the mapping as Mapping.STRING:

mgmt = graph.openManagement()
name = mgmt.makePropertyKey('bookname').dataType(String.class).make()
mgmt.buildIndex('booksBySummary', Vertex.class).addKey(name, Mapping.STRING.asParameter()).buildMixedIndex("search")
mgmt.commit()

When a string mapping is configured, the string value is indexed and can be queried "as-is" - including stop words and non-letter characters. However, in this case the query must match the entire string value. Hence, the string mapping is useful when indexing short character sequences that are considered to be one token.

When a string property is indexed as string, only the following predicates are supported in graph queries by the indexing backend. String search is case-sensitive.

import static org.apache.tinkerpop.gremlin.process.traversal.P.*
import static org.janusgraph.core.attribute.Text.*
g.V().has('bookname', eq('unicorns'))
g.V().has('bookname', neq('unicorns'))
g.V().has('bookname', textPrefix('uni'))
g.V().has('bookname', textRegex('.*corn.*'))
g.V().has('bookname', textFuzzy('unicorn'))

Full-text search predicates may be used in queries, but those require filtering in memory which can be very costly.

If you are using Elasticsearch it is possible to index properties as both text and string allowing you to use all of the predicates for exact and fuzzy matching.

mgmt = graph.openManagement()
summary = mgmt.makePropertyKey('booksummary').dataType(String.class).make()
mgmt.buildIndex('booksBySummary', Vertex.class).addKey(summary, Mapping.TEXTSTRING.asParameter()).buildMixedIndex("search")
mgmt.commit()

Note that the data will be stored in the index twice, once for exact matching and once for fuzzy matching.

It is also possible to use the TinkerPop text predicates with JanusGraph, but these predicates do not make use of indices which means that they require filtering in memory which can be very costly.

import static org.apache.tinkerpop.gremlin.process.traversal.TextP.*;
g.V().has('bookname', startingWith('uni'))
g.V().has('bookname', endingWith('corn'))
g.V().has('bookname', containing('nico'))