Fetch

A library for Simple & Efficient data access in Scala and Scala.js

Documentation

Installation

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

For Scala 2.11.x:

"com.fortysevendeg" %% "fetch" % "0.3.0"

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

"com.fortysevendeg" %%% "fetch" % "0.3.0"

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 fetchOne and fetchMany methods that define how to fetch such a piece of data.

Data Sources take two type parameters:

Identity is a type that has enough information to fetch the data. For a users data source, this would be a user's unique ID.
Result is the type of data we want to fetch. For a users data source, this would the User type.

import cats.data.NonEmptyList

trait DataSource[Identity, Result]{
  def fetchOne(id: Identity): Query[Option[Result]]
  def fetchMany(ids: NonEmptyList[Identity]): Query[Map[Identity, Result]]
}

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.data.NonEmptyList
import cats.instances.list._
import fetch._

implicit object ToStringSource extends DataSource[Int, String]{
  override def fetchOne(id: Int): Query[Option[String]] = {
    Query.sync({
      println(s"[${Thread.currentThread.getId}] One ToString $id")
      Option(id.toString)
    })
  }
  override def fetchMany(ids: NonEmptyList[Int]): Query[Map[Int, String]] = {
    Query.sync({
      println(s"[${Thread.currentThread.getId}] Many ToString $ids")
      ids.toList.map(i => (i, i.toString)).toMap
    })
  }
}

def fetchString(n: Int): Fetch[String] = Fetch(n) // or, more explicitly: Fetch(n)(ToStringSource)

Creating and running a fetch

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

import fetch.syntax._

val fetchOne: Fetch[String] = fetchString(1)

We'll run our fetches to the ambien Id monad in our examples. Note that in real-life scenarios you'll want to run a fetch to a concurrency monad such as Future or Task, synchronous execution of a fetch is only supported in Scala and not Scala.js and is meant for experimentation purposes.

import cats.Id
import fetch.unsafe.implicits._
import fetch.syntax._

Let's run it and wait for the fetch to complete:

fetchOne.runA[Id]
// [62] One ToString 1
// res3: cats.Id[String] = 1

Batching

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.

import cats.syntax.cartesian._

val fetchThree: Fetch[(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.

fetchThree.runA[Id]
// [62] Many ToString NonEmptyList(1, 2, 3)
// res5: cats.Id[(String, String, String)] = (1,2,3)

Parallelism

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.

This time, instead of creating the results with Query#sync we are going to do it with Query#async for emulating an asynchronous data source.

implicit object LengthSource extends DataSource[String, Int]{
  override def fetchOne(id: String): Query[Option[Int]] = {
    Query.async((ok, fail) => {
      println(s"[${Thread.currentThread.getId}] One Length $id")
      ok(Option(id.size))
    })
  }
  override def fetchMany(ids: NonEmptyList[String]): Query[Map[String, Int]] = {
    Query.async((ok, fail) => {
      println(s"[${Thread.currentThread.getId}] Many Length $ids")
      ok(ids.toList.map(i => (i, i.size)).toMap)
    })
  }
}

def fetchLength(s: String): Fetch[Int] = Fetch(s)

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

val fetchMulti: Fetch[(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.

fetchMulti.runA[Id]
// [62] One ToString 1
// [63] One Length one
// res7: cats.Id[(String, Int)] = (1,3)

Caching

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.

val fetchTwice: Fetch[(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.

fetchTwice.runA[Id]
// [62] One ToString 1
// res8: cats.Id[(String, String)] = (1,1)

lambdista/fetch