A data access layer for embedded Neo4j graph databases using the Cypher query language.
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Anorm for Cypher Embedded!

The play! framework includes a simple data access layer called Anorm. It uses plain SQL to interact with the database and provides an API to parse and transform the resulting datasets. Some libraries exist to transfer this concept to the world of Neo4j. The Neo4j Scala wrapper has introduced basic Cypher query support and AnormCypher provides a Anorm-like API purely for REST.

However, if you want to use Neo4j embedded in your applications you will be in need of another library. Ace (i.e. Anorm for Cypher Embedded) closes this gap and provides a data access layer that uses plain Cypher to interact with the database. Ace (much like AnormCypher) is modeled after Anorm for play! and it's API will look extremly similar. If you have worked with Anorm before, you will find Ace very easy to understand and work with.

Current tests run with Scala 2.10.1 and Neo4j 1.9.RC-2.

Executing Cypher queries

To start you need to learn how to execute simple Cypher queries.

First, import org.neo4j.ace._, and then simply use the Cypher object to create queries. You need to provide an implicit GraphDatabaseService to run a query:

import org.neo4j.ace._

Neo4j.withTx { implicit service =>
  val result: Boolean = Cypher("START n=node(0) RETURN n").execute()    

The execute() method returns a boolean value indicating whether the execution was successful.

To execute an update, you can use executeUpdate(), which returns a 5-tuple containing:

  1. The number of nodes created.
  2. The number of relationships created.
  3. The number of properties set.
  4. The number of deleted nodes.
  5. The number of deleted relationships.
val (nodesCreated, relsCreated, propsSet, nodesDeleted, relsDeleted) =
  Cypher("START n=node(0) MATCH n-[r]-() DELETE n, r").executeUpdate()

Since Scala supports multi-line strings, feel free to use them for complex Cypher statements:

val cypherQuery = Cypher(
    START n=node(0)
    MATCH (n)--(x)
    RETURN x

Cypher supports querying with parameters. If your Cypher query needs dynamic parameters, you can assign them as follows:

    START n=node(0,1,2)
    RETURN n
    SKIP {s}
    LIMIT {l}
).on("s" -> 1, "l" -> 1)

Retrieving data using the Stream API

The first way to access the results of a select query is to use the Stream API.

When you call apply() on any Cypher statement, you will receive a Stream of Row instances, where each row can be seen as a dictionary:

// Create a Cypher query
val selectQuery = Cypher("START n=node(0) RETURN n.name, n.value")
// Transform the resulting Stream[Row] to a List[(String,String)]
val selection = selectQuery().map(row => 
  row[String]("n.name") -> row[String]("n.value")

In the following example we will count the number of connected nodes from the start node, so the result set will be a single row with a single column:

// First retrieve the first row
val firstRow = Cypher("START n=node(2) MATCH (n)-->(x) RETURN count(x) AS c").apply().head
// Next get the content of the 'c' column as Long
val nodeCount = firstRow[Long]("c")

Using Pattern Matching

You can also use Pattern Matching to match and extract the Row content. In this case the column name doesn’t matter. Only the order and the type of the parameters is used to match.

The following example transforms each row to the correct Scala type:

case class SmallCountry(name:String) 
case class BigCountry(name:String) 
case class France
val countries = Cypher("START n=node(*) WHERE n.type! = 'Country' RETURN n.name as name, n.population as pop")().collect {
  case Row("France", _) => France()
  case Row(name:String, pop:Int) if(pop > 1000000) => BigCountry(name)
  case Row(name:String, _) => SmallCountry(name)      

Note that since collect(…) ignores the cases where the partial function isn’t defined, it allows your code to safely ignore rows that you don’t expect.

Dealing with Nullable columns

If a column can contain Null values in the database schema, you need to manipulate it as an Option type.

For example, the indepYear of the Country table is nullable, so you need to match it as Option[Int]:

Cypher("START n=node(*) WHERE n.type! = 'Country' RETURN n.name AS name, n.indepYear? AS year")().collect {
  case Row(name:String, Some(year:Int)) => name -> year

If you try to match this column as Int it won’t be able to parse Null values. Suppose you try to retrieve the column content as Int directly from the dictionary:

Cypher("START n=node(*) WHERE n.type! = 'Country' RETURN n.name AS name, n.indepYear? AS year")().map { row =>
  row[String]("name") -> row[Int]("year")

This will produce an UnexpectedNullableFound(year) exception if it encounters a null value, so you need to map it properly to an Option[Int], as:

Cypher("START n=node(*) WHERE n.type! = 'Country' RETURN n.name AS name, n.indepYear? AS year")().map { row =>
  row[String]("name") -> row[Option[Int]]("year")

This is also true for the parser API, as we will see next.

Using the Parser API

You can use the parser API to create generic and reusable parsers that can parse the result of any select query.

Note: This is really useful, since most queries in a web application will return similar data sets. For example, if you have defined a parser able to parse a Country from a result set, and another Language parser, you can then easily compose them to parse both Country and Language from a join query.

First you need to import org.neo4j.ace.CypherParser._

Getting a single result

First you need a RowParser, i.e. a parser able to parse one row to a Scala value. For example we can define a parser to transform a single column result set row, to a Scala Long:

val rowParser = scalar[Long]

Then we have to transform it into a ResultSetParser. Here we will create a parser that parse a single row:

val rsParser = scalar[Long].single

So this parser will parse a result set to return a Long. It is useful to parse to result produced by a simple Cypher COUNT query:

val count: Long = Cypher("START n=node(*) return COUNT(n)").as(scalar[Long].single)

Getting a single optional result

Let's say you want to retrieve a value from a query that might return null. We'll use the singleOpt parser:

val countryId: Option[Long] = Cypher("START n=node(*) WHERE n.type! = 'Country' AND n.country = 'France' RETURN ID(n)").as(scalar[Long].singleOpt)

Handling Neo4j types

The following parsers will retrieve Neo4j nodes and relationships from a query:

val node: org.neo4j.graphdb.Node = Cypher("START n=node(0) RETURN n").as(node("n").single)
val rels: org.neo4j.graphdb.Relationship = Cypher("START n=node(0) MATCH n-[r]->() RETURN r").as(relationship("r") *)

If you need to retrieve Cypher paths, you can use the path parser. It will result in a Seq[org.neo4j.graphdb.PropertyContainer] containing all nodes and relationships along the given path. These will usually emerge in alternate order.

val path: Seq[org.neo4j.graphdb.PropertyContainer] = Cypher("START n=node(0) MATCH p=n-[r]-m RETURN p").as(path("p") *)


  • Anorm - simple SQL data access
  • AnormCypher - a Neo4j library purely for REST
  • Play! - a framework for web applications with Java & Scala
  • Neo4j - an open-source, high-performance, enterprise-grade NOSQL graph database