AnormCypher

This is a Neo4j library purely for REST, using the Jerkson JSON parser and the Dispatch REST client library.

The goals of this library are to provide a great API to use Cypher, and it will be modeled after Anorm from Play, which I found to be pleasant to use with SQL. More info about Anorm can be found here: http://www.playframework.org/documentation/2.0.4/ScalaAnorm

Integration tests currently run against neo4j-community-1.9.M01

SBT Console Demo

Switch to an empty folder and create a build.sbt file with the following:

resolvers += "anormcypher" at "http://repo.anormcypher.org/"

libraryDependencies ++= Seq(
  "org.anormcypher" %% "anormcypher" % "0.2.1"
)

Run sbt console

Assuming you have a local Neo4j Server running on the default port, try (note: this will create nodes on your database):

import org.anormcypher._

// create some test nodes
Cypher("""create (anorm {name:"AnormCypher"}), (test {name:"Test"})""").execute()

// a simple query
val req = Cypher("start n=node(*) return n.name")

// get a stream of results back
val stream = req()

// get the results and put them into a list
stream.map(row => {row[String]("n.name")}).toList

Usage

You'll probably notice that this usage is very close to Play's Anorm. That is the idea!

Configuring a server

The default is localhost, but you can specify a special server when your app is starting via the setServer or setURL options. Authentication and multi-server support will come soon.

import org.anormcypher._

Neo4jREST.setServer("localhost", 7474, "/db/data/")

Executing Cypher Queries

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

First, import org.anormcypher._, and then simply use the Cypher object to create queries.

import org.anormcypher._ 

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 the number of (Nodes, Relationships, Properties) updated. Note: NOT SUPPORTED YET.

val result: (Int, Int, Int) = Cypher("START n=node(0) DELETE n").executeUpdate()

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

// create some sample data
val result = Cypher("""
  create (germany {name:"Germany", population:81726000, type:"Country", code:"DEU"}),
         (france {name:"France", population:65436552, type:"Country", code:"FRA", indepYear:1790}),
         (monaco {name:"Monaco", population:32000, type:"Country", code:"MCO"});
  """).execute()
// result: Boolean = true
 
val cypherQuery = Cypher(
  """
    start n=node(*) 
    match n-->m
    where n.code = 'FRA';
    return n,m;
  """
)

If your Cypher query needs dynamic parameters, you can declare placeholders like {name} in the query string, and later assign a value to them with on:

Cypher(
  """
    start n=node(*) 
    where n.type! = "Country"
      and n.code! = {countryCode}
    return n.name
  """
).on("countryCode" -> "FRA")

Retrieving data using the Stream API

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

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

// Create Cypher query
val allCountries = Cypher("start n=node(*) where n.type! = 'Country' return n.code as code, n.name as name")
 
// Transform the resulting Stream[CypherRow] to a List[(String,String)]
val countries = allCountries.apply().map(row => 
  row[String]("code") -> row[String]("name")
).toList

In the following example we will count the number of Country entries in the database, so the result set will be a single row with a single column:

// First retrieve the first row
val firstRow = Cypher("start n=node(*) where n.type! = 'Country' return count(n) as c").apply().head
 
// Next get the content of the 'c' column as Long
val countryCount = firstRow[Long]("c")
// countryCount: Long = 3

Using Pattern Matching

You can also use Pattern Matching to match and extract the CypherRow 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

// NOTE: case CypherRow syntax is NOT YET SUPPORTED
val countries = Cypher("start n=node(*) where n.type! = 'Country' return n.name as name, n.population as pop")().collect {
  case CypherRow("France", _) => France()
  case CypherRow(name:String, pop:Int) if(pop > 1000000) => BigCountry(name)
  case CypherRow(name:String, _) => SmallCountry(name)      
}
// countries: scala.collection.immutable.Stream[Product with Serializable] = Stream(BigCountry(Germany), ?)

val countryList = countries.toList
// countryList: List[Product with Serializable] = List(BigCountry(Germany), France(), SmallCountry(Monaco))

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.

Special data types

Nodes

Nodes can be extracted as so:

// NOTE: case CypherRow syntax is NOT YET SUPPORTED
Cypher("start n=node(*) where n.type! = 'Country' return n.name as name, n")().map {
  case CypherRow(name: String, n: org.anormcypher.NeoNode) => name -> n
}

Here we specifically chose to use map, as we want an exception if the row isn’t in the format we expect.

A Node is just a simple Scala case class, not quite as type-safe as configuring your own:

case class NeoNode(id:Int, props:Map[String,Any])

Relationships

Relationships can be extracted as so:

// NOTE: case CypherRow syntax is NOT YET SUPPORTED
Cypher("start n=node(*) match n-[r]-m where has(n.name) return n.name as name, r;")().map {
  case CypherRow(name: String, r: org.anormcypher.NeoRelationship) => name -> r
}

Here we specifically chose to use map, as we want an exception if the row isn’t in the format we expect.

Similarly, a Relationship is just a simple Scala case class, not quite as type-safe as configuring your own:

case class NeoRelationship(id:Int, props:Map[String,Any])

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]:

// NOTE: case CypherRow syntax is NOT YET SUPPORTED
Cypher("start n=node(*) where n.type! = 'Country' return n.name as name, n.indepYear? as year;")().collect {
  case CypherRow(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 indepYear;")().map { row =>
  row[String]("name") -> row[Int]("indepYear")
}

This will produce an UnexpectedNullableFound(COUNTRY.INDEPYEAR) 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 indepYear;")().map { row =>
  row[String]("name") -> row[Option[Int]]("indepYear")
}

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

Using the Parser API (the Parser API is a work in progress)

You can use the parser API to create generic and reusable parsers that can parse the result of any Cypher 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 single return.

First you need to import org.anormcypher.CypherParser._

First you need a CypherRowParser, 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 CypherResultSetParser. 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 results produced by a simple Cypher count query:

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

Let’s write a more complicated parser:

str("name") ~ int("population"), will create a CypherRowParser able to parse a row containing a String name column and an Integer population column. Then we can create a ResultSetParser that will parse as many rows of this kind as it can, using *:

val populations:List[String~Int] = {
  Cypher("start n=node(*) where n.type! = 'Country' return n.*").as( str("n.name") ~ int("n.population") * ) 
}

As you see, this query’s result type is List[String~Int] - a list of country name and population items.

You can also rewrite the same code as:

val result:List[String~Int] = {
  Cypher("start n=node(*) where n.type! = 'Country' return n.*").as(get[String]("n.name")~get[Int]("n.population")*) 
}

Now what about the String~Int type? This is an AnormCypher type that is not really convenient to use outside of your database access code. You would rather have a simple tuple (String, Int) instead. You can use the map function on a CypherRowParser to transform its result to a more convenient type:

str("n.name") ~ int("n.population") map { case n~p => (n,p) }

Note: We created a tuple (String,Int) here, but there is nothing stopping you from transforming the CypherRowParser result to any other type, such as a custom case class.

Now, because transforming A~B~C types to (A,B,C) is a common task, we provide a flatten function that does exactly that. So you finally write:

val result:List[(String,Int)] = {
  Cypher("start n=node(*) where n.type! = 'Country' return n.*").as(
    str("n.name") ~ int("n.population") map(flatten) *
  ) 
}

Now let’s try with a more complicated example. How to parse the result of the following query to retrieve the country name and all spoken languages for a country code?

start country=node_auto_index(code="FRA")
match country-[:speaks]->language
return country.name, language.name;

Let's start by parsing all rows as a List[(String,String)] (a list of name,language tuples):

var p: CypherResultSetParser[List[(String,String)] = {
  str("country.name") ~ str("language.name") map(flatten) *
}

Now we get this kind of result:

List(
  ("France", "Arabic"), 
  ("France", "French"), 
  ("France", "Italian"), 
  ("France", "Portuguese"), 
  ("France", "Spanish"), 
  ("France", "Turkish")
)

We can then use the Scala collection API, to transform it to the expected result:

case class SpokenLanguages(country:String, languages:Seq[String])

languages.headOption.map { f =>
  SpokenLanguages(f._1, languages.map(_._2))
}

Finally, we get this convenient function:

case class SpokenLanguages(country:String, languages:Seq[String])

def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
  val languages: List[(String, String)] = Cypher(
    """
      start country=node_auto_index(code="{code}")
      match country-[:speaks]->language
      return country.name, language.name;
    """
  )
  .on("code" -> countryCode)
  .as(str("country.name") ~ str("language.name") map(flatten) *)

  languages.headOption.map { f =>
    SpokenLanguages(f._1, languages.map(_._2))
  }
}

To continue, let’s complicate our example to separate the official language from the others:

case class SpokenLanguages(
  country:String, 
  officialLanguage: Option[String], 
  otherLanguages:Seq[String]
)

def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
  val languages: List[(String, String, Boolean)] = Cypher(
    """
      start country=node_auto_index(code="{code}")
      match country-[:speaks]->language
      return country.name, language.name, language.isOfficial;
    """
  )
  .on("code" -> countryCode)
  .as {
    str("country.name") ~ str("language.name") ~ str("language.isOfficial") map {
      case n~l~"T" => (n,l,true)
      case n~l~"F" => (n,l,false)
    } *
  }

  languages.headOption.map { f =>
    SpokenLanguages(
      f._1, 
      languages.find(_._3).map(_._2),
      languages.filterNot(_._3).map(_._2)
    )
  }
}

If you try this on the world sample database, you will get:

$ spokenLanguages("FRA")
> Some(
    SpokenLanguages(France,Some(French),List(
      Arabic, Italian, Portuguese, Spanish, Turkish
    ))
)

Contributors

• Wes Freeman: @wfreeman on github
• Jason Jackson: @jasonjackson on github

Thanks

• The Play Framework team for providing the Anorm library, the basis for this library.
• Coda Hale, for the Jerkson library
• Databinder.net, for the Dispatch library
• Neo Technologies for Neo4j!

License LGPL

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this program. If not, see http://www.gnu.org/licenses/