A Scala API for Datomic

Getting Started

Here is a very simple sample to get started with Datomisca.


Github project

You can find this sample in Datomic Github Samples Getting-Started

#1 Add resolvers to SBT

You can add that in your build.sbt or Build.scala depending on your choice.

resolvers += Resolver.bintrayRepo("dwhjames", "maven")
// to get Datomic free (for pro, you must put in your own repo or local)
resolvers += "clojars" at "https://clojars.org/repo"

#2 Add dependencies

The latest release is 0.7-RC1

libraryDependencies ++= Seq(
  "com.github.dwhjames" %% "datomisca" % "0.7-RC1",
  "com.datomic" % "datomic-free" % "0.9.4724"

#3 Add imports

The following imports should be sufficient to get you started.

import scala.concurrent.ExecutionContext.Implicits.global

import datomisca._

#4 Create a connection

To use Datomisca, you need an implicit connection to Datomic in your scope.

// Datomic URI definition
val uri = "datomic:mem://datomisca-getting-started"

// Datomic Connection as an implicit in scope
implicit val conn = Datomic.connect(uri)

Datomic’s public API is threadsafe, and there is no need to pool the Datomic connection. Datomic will return the same instance of Connection for a given URI, no matter how many times you ask. And Datomic will cache that single instance even if you don’t. (Stuart Halloway 2013-06-25)

#5 Create a database

We start from scratch so let’s first create a DB.


This method returns a boolean. If true, then a fresh database was created, or else a database already existed for the given URI.

#6 Create a schema

Datomisca allows to define your Schema in a programmatic way.

Here you create your:

Attributes and enumerated entites are gathered in a schema representing your entity.

Let’s create four attributes to represent a Person:

object PersonSchema {
  // Namespaces definition to be reused in Schema
  object ns {
    val person = new Namespace("person") {
      val hobby = Namespace("person.hobby")

  // Attributes
  val name = Attribute(
    ns.person / "name",
    Cardinality.one).withDoc("Person's name")
  val home = Attribute(
    ns.person / "home",
    Cardinality.one).withDoc("Person's hometown")
  val birth = Attribute(
    ns.person / "birth",
    Cardinality.one).withDoc("Person's birth date")
  val hobbies = Attribute(
    ns.person / "hobbies",
    Cardinality.many).withDoc("Person's hobbies")

  // hobby enumerated values
  val movies  = AddIdent(ns.person.hobby / "movies")
  val music   = AddIdent(ns.person.hobby / "music")
  val reading = AddIdent(ns.person.hobby / "reading")
  val sports  = AddIdent(ns.person.hobby / "sports")
  val travel  = AddIdent(ns.person.hobby / "travel")

  // Schema
  val txData: Seq[TxData] = Seq(
    name, home, birth, characters, // attributes
    movies, music, reading, sports, travel // ident entities


#7 Transact your schema

Now we have a schema, let’s transact it into our database. This is our first operation using the transactor and as you may know, Datomisca manages transactor’s communication in an asynchronous and non-blocking way based on Scala 2.10 Execution Context.

To ask the transaction to perform some operations, we use the following method:

Datomic.transact(txData: TraversableOnce[TxData])(implicit conn: Connection, ec: ExecutionContext): Future[TxReport]

As you can see, it accepts a collection of transaction data and returns a Future[TxReport].

If you are unfamilar with Scala Future, then consult this overview.

So let’s transact our schema into Datomic:

Datomic.transact(PersonSchema.txData) flatMap { tx =>
  // do something

We use flatMap because we expect to perform other asynchronous operations upon the completion of the transaction.

#8 Define your first entity

The following code will construct the transaction data for a person called John, whose hometown is Brooklyn, was born on Jan, 1 1980, and likes travelling and watching movies.

// John temporary ID
val johnId = DId(Partition.USER)
// John person entity
val john: TxData = (
      += (PersonSchema.name  -> "John")
      += (PersonSchema.home  -> "Brooklyn, NY")
      += (PersonSchema.birth -> new java.util.Date(80, 0, 1))
      ++= (PersonSchema.hobbies -> Set(PersonSchema.movies, PersonSchema.travel))
  ) withId johnId

The transaction data john is equivalent to the following Clojure map.

(let [johnId (d/tempid :db.part/user)]
  {:db/id (d/tempid :db.part/user)
   :person/name "John"
   :person/home "Brooklyn, NY"
   :person/birth (java.util.Date 80 0 1)
   :person/hobbies [:person.hobby/movies :person.hobby/travel]})

In Datomisca, the DId type is one of the ways of constructing entity ids, and here we are constructing a temporary entity id in the user partition.

The SchemaEntity builder follows Scala’s Builder for collections. This is an idiom for incrementally building collections. To build up transaction data for a new entity, we use attribute–value pairs, rather than keyword–value pairs. This provides a level of type-safety, as the attribute stores the schema type and the cardinality along with the keyword ident. The value of the pair is statically checked against the attribute’s type and cardinality.

#9 Transact your entity

Transacting regular data and schema data is no different.

// creates an entity
Datomic.transact(john) map { tx =>
  val realJohnId = tx.resolve(johnId)
  // Do something else
val Seq(realId1, realId2, realId3) = tx.resolve(id1, id2, id3)

#10 Write a query

So now that we have an entity in our DB, let’s try to query for it.

In Datomisca, you write your queries in Datalog exactly in the same way as Clojure. Leveraging Scala’s 2.10 macros, Datomisca validates the syntax of your query at compile-time and also deduces the number of input/output parameters (more features are also in the roadmap).

Let’s write a “find person by name” query:

val queryFindByName = Query("""
  [:find ?e ?home
   :in $ ?name
   [?e :person/name ?name]
   [?e :person/home ?home]]

Datomisca’s query macro also supports string interpolation, which means that the query can be written as follows.

val queryFindByName = Query(s"""
  [:find ?e ?home
   :in $$ ?name
   [?e ${PersonSchema.name} ?name]
   [?e ${PersonSchema.home} ?home]]

Remember to watch out for escaping the datasource $ as $$. The toString method is called on the values of expressions that are interpolated. The string representation of attributes is their keyword, which is why we can rewrite the query this way. The query treats expressions of type String specially, by double quoting them, so,

val name = "John"
val queryFindByName = Query(s"""
  [:find ?e ?home
   :in $$
   [?e ${PersonSchema.name} $name]
   [?e ${PersonSchema.home} ?home]]

will result in a query with a clause

[?e :person/name "John"]

#11 Execute a query

Queries are executed using the Datomic.q method, with your query and the appropriate input parameters.

val results = Datomic.q(queryFindByName, conn.database, "John")

#12 Use the query result

The query results are bound to the name results. According to the input query, the compiler has inferred that there should be two output parameters.

Thus, results is a Iterable[(Any, Any)].

results.headOption map {
  case (eid: Long, home: String) =>
    // do something

Note that results is a Iterable[(Any, Any)] and not Iterable[(Long, String)] as you might hope. Why? Because with the info provided in the query, it’s impossible to infer those types directly. In the future, we hope to extend the power of the query macro to provide type-safety for output parameters using schema information. Therefore, for now, you must type match with a case.

#13 Traverse the entity graph

With the previous query, we retrieved eid, which is an entity id, and now we can get the entity from the database and inspect it.

val entity: Entity = conn.database.entity(eid)

As before, conn.database retrieves the currently available value of the database, and the entity method looks up the entity map for a given identifier.

The Entity and RichEntity apis provide various ways of interact with entities. The apply method on the implicit RichEntity allows us to use attributes rather than keywords to retrieve values, in a similar fashion to how we constructed transaction data above.

val johnName: String = entity(PersonSchema.name)
val johnHome: String = entity(PersonSchema.home)
val johnBirth: java.util.Date = entity(PersonSchema.birth)

The attributes possess the type information, so Datomisca computes the correct return type.

Datomisca is able to do this for all primitives, of cardinality one or many, but it can’t do this for reference attributes as Datomic will return values of type Entity in most cases, but Keyword if the referenced entity has an ident attribute, which is the case here:

val johnHobbies = entity.read[Set[Keyword]](PersonSchema.hobbies)

The read method allows us to do a type-safe cast.

And much more…

Read the more detailed guides and the API docs for more details about what was covered here.