An index for relationships is just like an index for nodes, extended by providing support to constrain a search to relationships with a specific start and/or end nodes These extra methods reside in the RelationshipIndex interface which extends Index<Relationship>.
Example of querying a relationship index:
// find relationships filtering on start node // using exact matches IndexHits<Relationship> reevesAsNeoHits; reevesAsNeoHits = roles.get( "name", "Neo", reeves, null ); Relationship reevesAsNeo = reevesAsNeoHits.iterator().next(); reevesAsNeoHits.close(); // find relationships filtering on end node // using a query IndexHits<Relationship> matrixNeoHits; matrixNeoHits = roles.query( "name", "*eo", null, theMatrix ); Relationship matrixNeo = matrixNeoHits.iterator().next(); matrixNeoHits.close();
And here’s an example for the special case of searching for a specific relationship type:
// find relationships filtering on end node
// using a relationship type.
// this is how to add it to the index:
roles.add( reevesAsNeo, "type", reevesAsNeo.getType().name() );
// Note that to use a compound query, we can't combine committed
// and uncommitted index entries, so we'll commit before querying:
tx.success();
tx.close();
// and now we can search for it:
try ( Transaction tx = graphDb.beginTx() )
{
IndexHits<Relationship> typeHits = roles.query( "type:ACTS_IN AND name:Neo", null, theMatrix );
Relationship typeNeo = typeHits.iterator().next();
typeHits.close();
Such an index can be useful if your domain has nodes with a very large number of relationships between them, since it reduces the search time for a relationship between two nodes. A good example where this approach pays dividends is in time series data, where we have readings represented as a relationship per occurrence.