Add the following dependency to your project’s build file.

For Scala 2.11.x and 2.12.x:

"com.47deg" %% "fetch" % "1.1.0"

Or, if using Scala.js (0.6.x):

"com.47deg" %%% "fetch" % "1.1.0"

Remote data

Fetch is a library for making access to data both simple & efficient. Fetch is especially useful when querying data that has a latency cost, such as databases or web services.

Define your data sources

To tell Fetch how to get the data you want, you must implement the DataSource typeclass. Data sources have fetch and batch methods that define how to fetch such a piece of data.

Data Sources take two type parameters:

  1. Identity is a type that has enough information to fetch the data
  2. Result is the type of data we want to fetch
import cats.effect.Concurrent

trait DataSource[F[_], Identity, Result]{
  def data: Data[Identity, Result]
  def CF: Concurrent[F]
  def fetch(id: Identity): F[Option[Result]]
  def batch(ids: NonEmptyList[Identity]): F[Map[Identity, Result]]

Returning Concurrent instances from the fetch methods allows us to specify if the fetch must run synchronously or asynchronously and use all the goodies available in cats and cats-effect.

We’ll implement a dummy data source that can convert integers to strings. For convenience, we define a fetchString function that lifts identities (Int in our dummy data source) to a Fetch.

import cats._
import cats.effect._
import cats.instances.list._
import cats.implicits._
import cats.syntax.all._

import fetch._

def latency[F[_] : Concurrent](milis: Long): F[Unit] =

object ToString extends Data[Int, String] {
  def name = "To String"

  def source[F[_] : Concurrent]: DataSource[F, Int, String] = new DataSource[F, Int, String]{
    override def data = ToString

    override def CF = Concurrent[F]

    override def fetch(id: Int): F[Option[String]] = for {
      _ <- CF.delay(println(s"--> [${Thread.currentThread.getId}] One ToString $id"))
      _ <- latency(100)
      _ <- CF.delay(println(s"<-- [${Thread.currentThread.getId}] One ToString $id"))
    } yield Option(id.toString)

    override def batch(ids: NonEmptyList[Int]): F[Map[Int, String]] = for {
      _ <- CF.delay(println(s"--> [${Thread.currentThread.getId}] Batch ToString $ids"))
      _ <- latency(100)
      _ <- CF.delay(println(s"<-- [${Thread.currentThread.getId}] Batch ToString $ids"))
    } yield => (i, i.toString)).toMap

def fetchString[F[_] : Concurrent](n: Int): Fetch[F, String] =
  Fetch(n, ToString.source)

Creating a runtime

Since Fetch relies on Concurrent from the cats-effect library, we’ll need a runtime for executing our effects. We’ll be using IO from cats-effect to run fetches, but you can use any type that has a Concurrent instance.

For executing IO we need a ContextShift[IO] used for running IO instances and a Timer[IO] that is used for scheduling, let’s go ahead and create them, we’ll use a java.util.concurrent.ScheduledThreadPoolExecutor with a couple of threads to run our fetches.

import java.util.concurrent._
import scala.concurrent.ExecutionContext

val executor = new ScheduledThreadPoolExecutor(4)
val executionContext: ExecutionContext = ExecutionContext.fromExecutor(executor)

implicit val timer: Timer[IO] = IO.timer(executionContext)
implicit val cs: ContextShift[IO] = IO.contextShift(executionContext)

Creating and running a fetch

Now that we can convert Int values to Fetch[F, String], let’s try creating a fetch.

def fetchOne[F[_] : Concurrent]: Fetch[F, String] =

Let’s run it and wait for the fetch to complete, we’ll use IO#unsafeRunTimed for testing purposes, which will run an IO[A] to Option[A] and return None if it didn’t complete in time:

import scala.concurrent.duration._
// import scala.concurrent.duration._[IO](fetchOne).unsafeRunTimed(5.seconds)
// --> [532] One ToString 1
// <-- [532] One ToString 1
// res0: Option[String] = Some(1)

As you can see in the previous example, the ToStringSource is queried once to get the value of 1.


Multiple fetches to the same data source are automatically batched. For illustrating it, we are going to compose three independent fetch results as a tuple.

def fetchThree[F[_] : Concurrent]: Fetch[F, (String, String, String)] =
  (fetchString(1), fetchString(2), fetchString(3)).tupled

When executing the above fetch, note how the three identities get batched and the data source is only queried once.[IO](fetchThree).unsafeRunTimed(5.seconds)
// --> [532] Batch ToString NonEmptyList(1, 2, 3)
// <-- [532] Batch ToString NonEmptyList(1, 2, 3)
// res1: Option[(String, String, String)] = Some((1,2,3))

Note that the DataSource#batch method is not mandatory, it will be implemented in terms of DataSource#fetch if you don’t provide an implementation.

object UnbatchedToString extends Data[Int, String] {
  def name = "Unbatched to string"

  def source[F[_] : Concurrent] = new DataSource[F, Int, String] {
    override def data = UnbatchedToString

    override def CF = Concurrent[F]

    override def fetch(id: Int): F[Option[String]] = 
      CF.delay(println(s"--> [${Thread.currentThread.getId}] One UnbatchedToString $id")) >>
      latency(100) >>
      CF.delay(println(s"<-- [${Thread.currentThread.getId}] One UnbatchedToString $id")) >>

def unbatchedString[F[_] : Concurrent](n: Int): Fetch[F, String] =
  Fetch(n, UnbatchedToString.source)

Let’s create a tuple of unbatched string requests.

def fetchUnbatchedThree[F[_] : Concurrent]: Fetch[F, (String, String, String)] =
  (unbatchedString(1), unbatchedString(2), unbatchedString(3)).tupled

When executing the above fetch, note how the three identities get requested in parallel. You can override batch to execute queries sequentially if you need to.[IO](fetchUnbatchedThree).unsafeRunTimed(5.seconds)
// --> [532] One UnbatchedToString 1
// --> [533] One UnbatchedToString 2
// --> [535] One UnbatchedToString 3
// <-- [532] One UnbatchedToString 1
// <-- [533] One UnbatchedToString 2
// <-- [535] One UnbatchedToString 3
// res2: Option[(String, String, String)] = Some((1,2,3))


If we combine two independent fetches from different data sources, the fetches can be run in parallel. First, let’s add a data source that fetches a string’s size.

object Length extends Data[String, Int] {
  def name = "Length"

  def source[F[_] : Concurrent] = new DataSource[F, String, Int] {
    override def data = Length

    override def CF = Concurrent[F]

    override def fetch(id: String): F[Option[Int]] = for {
      _ <- CF.delay(println(s"--> [${Thread.currentThread.getId}] One Length $id"))
      _ <- latency(100)
      _ <- CF.delay(println(s"<-- [${Thread.currentThread.getId}] One Length $id"))
    } yield Option(id.size)

    override def batch(ids: NonEmptyList[String]): F[Map[String, Int]] = for {
      _ <- CF.delay(println(s"--> [${Thread.currentThread.getId}] Batch Length $ids"))
      _ <- latency(100)
      _ <- CF.delay(println(s"<-- [${Thread.currentThread.getId}] Batch Length $ids"))
    } yield => (i, i.size)).toMap

def fetchLength[F[_] : Concurrent](s: String): Fetch[F, Int] =
  Fetch(s, Length.source)

And now we can easily receive data from the two sources in a single fetch.

def fetchMulti[F[_] : Concurrent]: Fetch[F, (String, Int)] =
  (fetchString(1), fetchLength("one")).tupled

Note how the two independent data fetches run in parallel, minimizing the latency cost of querying the two data sources.[IO](fetchMulti).unsafeRunTimed(5.seconds)
// --> [534] One Length one
// --> [532] One ToString 1
// <-- [532] One ToString 1
// <-- [534] One Length one
// res3: Option[(String, Int)] = Some((1,3))


When fetching an identity, subsequent fetches for the same identity are cached. Let’s try creating a fetch that asks for the same identity twice.

import cats.syntax.all._

def fetchTwice[F[_] : Concurrent]: Fetch[F, (String, String)] = for {
  one <- fetchString(1)
  two <- fetchString(1)
} yield (one, two)

While running it, notice that the data source is only queried once. The next time the identity is requested it’s served from the cache.[IO](fetchTwice).unsafeRunTimed(5.seconds)
// --> [533] One ToString 1
// <-- [533] One ToString 1
// res4: Option[(String, String)] = Some((1,1))

For more in-depth information take a look at our documentation.


Fetch is written in Scala and supports both Scala (JVM) and Scala.js (JavaScript environments)


Fetch uses cats' Free Monad implementation as well as some of its data types.

Functional Programming

Fetch is implemented using the Free Monad and Interpreter pattern.