Implicits and types for helping with query parameter specifications, result handling and streaming, and other nice scala wrappers for java-based driver primitives and semantics.
Neo4j is a natural fit for storing knowledge graphs and representing and querying large scale data based upon its internal relationships. Scala is a very nice fit for representing linked information in a scalable and type-safe way. Moving data from Neo4j query results to scala types in a safe and idiomatic way makes the mariage of these two technologies a safer and easier. That is what the neo4s-query-support project does.
Specifically, this package:
- Supports lifting Neo4j query results to Scala case classes.
- Supports memory-efficient streaming of results to scala collections
libraryDependencies += "io.github.neo4s" %% "neo4s-query-support" % "0.1.2"NOTE - neo4s-query-support supports the lifing of results into the Stream class, which is deprecated in scala 2.13. To address
this we've created type alias for the new LazyList class so that, when the target project utilizes scala 2.12, LazyList is a Stream
class, otherwise it is LazyList
The following example makes use of the neo4s cypher dsl available at (TODO).
import aif.dna.neo4j.QueryTools._
import aif.dna.cypher.dsl.syntax._
import java.util.UUID
case class Person(firstName: String, lastName: String, id: UUID)
val anyPerson = any[Person]
val query = cypher.MATCH(anyPerson).toQuery()
// A durable session is not closed when we leave the `withDriver` scope
val resultList = withDriver(_.withDurableSession(AccessMode.READ) { session =>
// Attaches the durabale session to the list
session.run(query.query,query.queryMap).recordList(session)
})
// The durable session is closed when the lazy list is at end. Forcing the lazy list will close it.
resultList.foreach { record =>
println(record.get("person").asMap().asScala)
}For a database that has two Person nodes present the example above yields:
Map(firstName -> Chuck, lastName -> Norris, id -> 614a23a5-00e2-4951-8689-b6fa1ca54f28)
Map(firstName -> Vin, lastName -> Diesel, id -> 4a707a16-aba2-4ab7-aec4-e29b05efcf1d)
import aif.dna.neo4j.QueryTools._
import aif.dna.cypher.dsl.syntax._
import java.util.UUID
case class Person(id: UUID, firstName: String, lastName: String)
// Extraction behavior. Overrides for type conversion that is one post-read and pre-lift to node case classes
implicit val extractionBehavior = ExtractionBehavior(typeMapper = {
case tuple@("id", idString: String) => (tuple._1,UUID.fromString(idString))
})
// Generates a typeclass extractor for the Person case class
implicit val personExtractor = extractorFor[Person]
val query = cyper.MATCH(any[Person]).toQuery()
// The foregoing is all we need to define to get some really nice help from QueryTools...
val people: LazyList[Person] = withDriver(_withDurableSession(AccessMode.READ) { session =>
// The person node will be labelled "person" in the generated query
session.run(query.query,query.queryMap).stream(session,"person")
}
people.foreach(println) // consume the lazy list to close the sessionThe foregoing yields the following output:
Person(Chuck,Norris,a50d1008-889f-40e3-bfda-5719703331c1)
Person(Vin,Diesel,5f1c7234-b81c-4089-b582-dc75d69ab780)
Edges are (node) -[:relation]-> (node) patterns. The principle is exactly the same,
but we need more extractors. There are two patterns that are supported; either including
the relationship for the edge or just the nodes. The following example
provides for the latter.
case class Person(firstName: String, lastName: String, id: UUID)
case class Article(name: String, descriptor: String)
case class Wears(consistency: String)
// Extraction behavior. Overrides for type conversion that is one post-read and pre-lift to node case classes
implicit val extractionBehavior = ExtractionBehavior(typeMapper = {
case tuple@("id", idString: String) => (tuple._1,UUID.fromString(idString))
})
implicit val PersonExtractor = extractorFor[Person]
implicit val DepartmentExtractor = extractorFor[Department]
implicit val WorksForExtractor = extractorFor[WorksFor]
val anyPerson = any[Person]
val anyWears = any[Wears]
val anyArticle = any[Article]
val queryAllPeople = cypher
.MATCH(anyPerson -| anyWears |-> anyArticle)
.RETURN(anyPerson,anyWears,anyArticle)
val query = queryAllPeople.toQuery()
// Query returns a list of edges in the records. For instance a person who reports to two departments would be
// LazyList((person: Person) -[wears:WEARS]-> (article: Article), (person: Person) -[wears:WEARS]-> (article: Article))
val people = withDriver(_.withDurableSession { session =>
// Edges extracts the record items labelled "person", "works_for", and "department", which are leftnode, relationship, rightnode, respectively
// The type for the edge with these extractors is `(Person,Option[Wears,Article]))
session.run(query.query,query.queryMap).edges[Department,Wears,Article](session,"person","wears","article")
})
// Remember to exhaust the lazy list to close the session.. .
people.foreach(println)This example produces:
(Person(Chuck,Norris,2f374677-4dae-47d9-9e50-2e69c75abe3c),Some((Wears(sometimes),Article(hat,black))))
(Person(Chuck,Norris,2f374677-4dae-47d9-9e50-2e69c75abe3c),Some((Wears(usually),Article(boots,dusty))))
(Person(Vin,Diesel,e8f5ba47-93d9-45a5-9597-3b848293defa),Some((Wears(always),Article(shirt,tight))))
