Table of Contents
By default, JanusGraph will encode property keys to generate a unique field name for the property key in the mixed index. If one wants to query the mixed index directly in the external index backend can be difficult to deal with and are illegible. For this use case, the field name can be explicitly specified through a parameter.
mgmt = graph.openManagement() name = mgmt.makePropertyKey('bookname').dataType(String.class).make() mgmt.buildIndex('booksBySummary', Vertex.class).addKey(name, Parameter.of('mapped-name', 'bookname')).buildMixedIndex("search") mgmt.commit()
With this field mapping defined as a parameter, JanusGraph will use the same name for the field in the booksBySummary index created in the external index system as for the property key. Note, that it must be ensured that the given field name is unique in the index.
Instead of individually adjusting the field mapping for every key added to a mixed index, one can instruct JanusGraph to always set the field name in the external index to be identical to the property key name. This is accomplished by enabling the configuration option map-name which is configured per indexing backend. If this option is enabled for a particular indexing backend, then all mixed indexes defined against said backend will use field names identical to the property key names.
However, this approach has two limitations: 1) The user has to ensure that the property key names are valid field names for the indexing backend and 2) renaming the property key will NOT rename the field name in the index which can lead to naming collisions that the user has to be aware of and avoid.
Note, that individual field mappings as described above can be used to overwrite the default name for a particular key.
By default, JanusGraph will use the default analyzer from the indexing backend for properties with Mapping.TEXT, and no analyzer for properties with Mapping.STRING. If one wants to use another analyzer, it can be explicitly specified through a parameter : ParameterType.TEXT_ANALYZER for Mapping.TEXT and ParameterType.STRING_ANALYZER for Mapping.STRING.
The name of the analyzer must be set as parameter value.
mgmt = graph.openManagement() string = mgmt.makePropertyKey('string').dataType(String.class).make() text = mgmt.makePropertyKey('text').dataType(String.class).make() textString = mgmt.makePropertyKey('textString').dataType(String.class).make() mgmt.buildIndex('string', Vertex.class).addKey(string, Mapping.STRING.asParameter(), Parameter.of(ParameterType.STRING_ANALYZER.getName(), 'standard')).buildMixedIndex("search") mgmt.buildIndex('text', Vertex.class).addKey(text, Mapping.TEXT.asParameter(), Parameter.of(ParameterType.TEXT_ANALYZER.getName(), 'english')).buildMixedIndex("search") mgmt.buildIndex('textString', Vertex.class).addKey(text, Mapping.TEXTSTRING.asParameter(), Parameter.of(ParameterType.STRING_ANALYZER.getName(), 'standard'), Parameter.of(ParameterType.TEXT_ANALYZER.getName(), 'english')).buildMixedIndex("search") mgmt.commit()
With these settings, JanusGraph will use the standard analyzer for property key string and the english analyzer for property key text.
The class of the tokenizer must be set as parameter value.
mgmt = graph.openManagement() string = mgmt.makePropertyKey('string').dataType(String.class).make() text = mgmt.makePropertyKey('text').dataType(String.class).make() mgmt.buildIndex('string', Vertex.class).addKey(string, Mapping.STRING.asParameter(), Parameter.of(ParameterType.STRING_ANALYZER.getName(), 'org.apache.lucene.analysis.standard.StandardTokenizer')).buildMixedIndex("search") mgmt.buildIndex('text', Vertex.class).addKey(text, Mapping.TEXT.asParameter(), Parameter.of(ParameterType.TEXT_ANALYZER.getName(), 'org.apache.lucene.analysis.core.WhitespaceTokenizer')).buildMixedIndex("search") mgmt.commit()
With these settings, JanusGraph will use the standard tokenizer for property key string and the whitespace tokenizer for property key text.
The name of the analyzer must be set as parameter value or it defaults to KeywordAnalyzer for Mapping.STRING and to StandardAnalyzer for Mapping.TEXT.
mgmt = graph.openManagement() string = mgmt.makePropertyKey('string').dataType(String.class).make() text = mgmt.makePropertyKey('text').dataType(String.class).make() name = mgmt.makePropertyKey('name').dataType(String.class).make() document = mgmt.makePropertyKey('document').dataType(String.class).make() mgmt.buildIndex('string', Vertex.class).addKey(string, Mapping.STRING.asParameter(), Parameter.of(ParameterType.STRING_ANALYZER.getName(), org.apache.lucene.analysis.core.SimpleAnalyzer.class.getName())).buildMixedIndex("search") mgmt.buildIndex('text', Vertex.class).addKey(text, Mapping.TEXT.asParameter(), Parameter.of(ParameterType.TEXT_ANALYZER.getName(), org.apache.lucene.analysis.en.EnglishAnalyzer.class.getName())).buildMixedIndex("search") mgmt.buildIndex('name', Vertex.class).addKey(string, Mapping.STRING.asParameter()).buildMixedIndex("search") mgmt.buildIndex('document', Vertex.class).addKey(text, Mapping.TEXT.asParameter()).buildMixedIndex("search") mgmt.commit()
With these settings, JanusGraph will use a SimpleAnalyzer analyzer for property key string, an EnglishAnalyzer analyzer for property key text, a KeywordAnalyzer analyzer for property name and a StandardAnalyzer analyzer for property document.
Sometimes it is required to set additional parameters on mappings (other than mapping type, mapping name and analyzer). For example, when we would like to use a different similarity algorithm (to modify the scoring algorithm of full text search) or if we want to use a custom boosting on some fields in Elasticsearch we can set custom parameters (right now only Elasticsearch supports custom parameters).
The name of the custom parameter must be set through ParameterType.customParameterName("yourProperty").
mgmt = graph.openManagement() myProperty = mgmt.makePropertyKey('my_property').dataType(String.class).make() mgmt.buildIndex('custom_property_test', Vertex.class).addKey(myProperty, Mapping.TEXT.asParameter(), Parameter.of(ParameterType.customParameterName("boost"), 5), Parameter.of(ParameterType.customParameterName("similarity"), "boolean")).buildMixedIndex("search") mgmt.commit()
With these settings, JanusGraph will use the boost 5 and boolean similarity algorithm for property key my_property. Possible mapping parameters depend on Elasticsearch version. See mapping parameters for current Elasticsearch version.