Reactive Scala Driver for MongoDB

Asynchronous & Non-Blocking


Instead of accumulating documents in memory like in the two previous examples, we can process them as a stream.

ReactiveMongo can be used with several streaming frameworks: Play Iteratees, Akka Streams, or with custom processors using foldWhile.

Play Iteratees

The Play Iteratees library can work with document streams as follows.

import scala.concurrent.Future

import play.api.libs.iteratee._
import reactivemongo.bson.BSONDocument
import reactivemongo.api.collections.bson.BSONCollection

def processPerson1(collection: BSONCollection, query: BSONDocument): Future[Unit] = {
  val enumeratorOfPeople: Enumerator[BSONDocument] =

  val processDocuments: Iteratee[BSONDocument, Unit] =
    Iteratee.foreach { person =>
      val lastName = person.getAs[String]("lastName")
      val prettyBson = BSONDocument.pretty(person)
      println(s"found $lastName: $prettyBson")

The method cursor.enumerate() returns an Enumerator[T]. In this case, we get a producer of documents (of type BSONDocument).

Now that we have the producer, we need to define how the documents are processed: that is the Iteratee’s job. Iteratees, as the opposite of Enumerators, are consumers: they are fed in by enumerators and do some computation with the chunks they get.

Here, we build an Iteratee[BSONDocument, Unit] that takes BSONDocument as an input and eventually returns Unit – which is normal because we just print the results without computing any final value. Each time it gets a document, it extracts the lastName and prints it on the console along with the whole document. Note that none of these operations are blocking: when the running thread is not processing the callback of our Iteratee, it can be used to compute other things.

When this snippet is run, we get the following:

found London: {
  _id: BSONObjectID("4f899e7eaf527324ab25c56b"),
  firstName: BSONString(Jack),
  lastName: BSONString(London),
  age: BSONInteger(40)
found Whitman: {
  _id: BSONObjectID("4f899f9baf527324ab25c56c"),
  firstName: BSONString(Walt),
  lastName: BSONString(Whitman),
  age: BSONInteger(72)
found Hemingway: {
  _id: BSONObjectID("4f899f9baf527324ab25c56d"),
  firstName: BSONString(Ernest),
  lastName: BSONString(Hemingway),
  age: BSONInteger(61)

The line returns a Future[Unit]; it will eventually return the final value of the Iteratee, which is Unit in our case.

The run method on Enumerator has an operator alias, |>>>. So we can rewrite the last line like this: enumeratorOfPeople |>>> processDocuments.

Obviously, we may use a pure Iteratee that performs some computation:

import scala.concurrent.Future

import play.api.libs.iteratee._
import reactivemongo.bson.BSONDocument

def processPerson2(enumeratorOfPeople: Enumerator[BSONDocument]) = {
  val cumulateAge: Iteratee[BSONDocument, (Int, Int)] =
    Iteratee.fold(0 -> 0) {
      case ((cumulatedAge, n), doc) =>
        val age = doc.getAs[Int]("age").getOrElse(0)
        (cumulatedAge + age, n + 1)

  val cumulated: Future[(Int, Int)] = enumeratorOfPeople |>>> cumulateAge

  val meanAge: Future[Float] = { case (cumulatedAge, n) =>
      if (n == 0) 0
      else cumulatedAge / n


At each step, this Iteratee will extract the age from the document and add it to the current result; it also counts the number of documents processed. It eventually produces a tuple of two integers; in our case (173, 3). When the cumulated future is completed, we divide the cumulated age by the number of documents to get the mean age.

Custom streaming

ReactiveMongo streaming is based on the function Cursor.foldWhile[A], which also allows you to implement a custom stream processor.

import scala.concurrent.Future

import reactivemongo.api.Cursor

def streaming(c: Cursor[String]): Future[List[String]] =
  c.foldWhile(List.empty[String], 1000/* optional: max doc */)(
    { (ls, str) => // process next String value
      if (str startsWith "#") Cursor.Cont(ls) // Skip: continue unchanged `ls`
      else if (str == "_end") Cursor.Done(ls) // End processing
      else Cursor.Cont(str :: ls) // Continue with updated `ls`
    { (ls, err) => // handle failure
      err match {
        case e: RuntimeException => Cursor.Cont(ls) // Skip error, continue
        case _ => Cursor.Fail(err) // Stop with current failure -> Future.failed

At each streaming step, for each new value or error, you choose how you want to proceed, using the cases Cursor.{ Cont, Done, Fail }.