Packages

•  package root

  Definition Classes

  root

•  package com

  Definition Classes

  root

•  package spotify

  Definition Classes

  com

•  package scio

  Definition Classes

  spotify

•  package extra

  Definition Classes

  scio

•  package annoy

  Main package for Annoy side input APIs.

  Main package for Annoy side input APIs. Import all.

  import com.spotify.scio.extra.annoy._

  Two metrics are available, Angular and Euclidean.

  To save an SCollection[(Int, Array[Float])] to an Annoy file:

  val s = sc.parallelize(Seq( 1-> Array(1.2f, 3.4f), 2 -> Array(2.2f, 1.2f)))

  Save to a temporary location:

  val s1 = s.asAnnoy(Angular, 40, 10)

  Save to a specific location:

  val s1 = s.asAnnoy(Angular, 40, 10, "gs://<bucket>/<path>")

  SCollection[AnnoyUri] can be converted into a side input:

  val s = sc.parallelize(Seq( 1-> Array(1.2f, 3.4f), 2 -> Array(2.2f, 1.2f)))
  val side = s.asAnnoySideInput(metric, dimension, numTrees)

  There's syntactic sugar for saving an SCollection and converting it to a side input:

  val s = sc
    .parallelize(Seq( 1-> Array(1.2f, 3.4f), 2 -> Array(2.2f, 1.2f)))
    .asAnnoySideInput(metric, dimension, numTrees)

  An existing Annoy file can be converted to a side input directly:

  sc.annoySideInput(metric, dimension, numTrees, "gs://<bucket>/<path>")

  AnnoyReader provides nearest neighbor lookups by vector as well as item lookups:

  val data = (0 until 1000).map(x => (x, Array.fill(40)(r.nextFloat())))
  val main = sc.parallelize(data)
  val side = main.asAnnoySideInput(metric, dimension, numTrees)
  
  main.keys.withSideInput(side)
    .map { (i, s) =>
      val annoyReader = s(side)
  
      // get vector by item id, allocating a new Array[Float] each time
      val v1 = annoyReader.getItemVector(i)
  
      // get vector by item id, copy vector into pre-allocated Array[Float]
      val v2 = Array.fill(dim)(-1.0f)
      annoyReader.getItemVector(i, v2)
  
      // get 10 nearest neighbors by vector
      val results = annoyReader.getNearest(v2, 10)
    }

  Definition Classes

  extra

•  package bigquery

  Definition Classes

  extra

•  package csv

  Main package for CSV type-safe APIs.

  Main package for CSV type-safe APIs. Import all.

  import com.spotify.scio.extra.csv._

  Definition Classes

  extra

•  package hll

  Definition Classes

  extra

•  package json

  Main package for JSON APIs.

  Main package for JSON APIs. Import all.

  This package uses Circe for JSON handling under the hood.

  import com.spotify.scio.extra.json._
  
  // define a type-safe JSON schema
  case class Record(i: Int, d: Double, s: String)
  
  // read JSON as case classes
  sc.jsonFile[Record]("input.json")
  
  // write case classes as JSON
  sc.parallelize((1 to 10).map(x => Record(x, x.toDouble, x.toString))
    .saveAsJsonFile("output")

  Definition Classes

  extra

• DecodeError
• JsonIO
• JsonSCollection
• JsonScioContext
•  package rollup

  Definition Classes

  extra

•  package sorter

  Definition Classes

  extra

•  package sparkey

  Main package for Sparkey side input APIs.

  Main package for Sparkey side input APIs. Import all.

  import com.spotify.scio.extra.sparkey._

  To save an SCollection[(String, String)] to a Sparkey fileset:

  val s = sc.parallelize(Seq("a" -> "one", "b" -> "two"))
  s.saveAsSparkey("gs://<bucket>/<path>/<sparkey-prefix>")
  
  // with multiple shards, sharded by MurmurHash3 of the key
  s.saveAsSparkey("gs://<bucket>/<path>/<sparkey-dir>", numShards=2)

  A previously-saved sparkey can be loaded as a side input:

  sc.sparkeySideInput("gs://<bucket>/<path>/<sparkey-prefix>")

  A sharded collection of Sparkey files can also be used as a side input by specifying a glob path:

  sc.sparkeySideInput("gs://<bucket>/<path>/<sparkey-dir>/part-*")

  When the sparkey is needed only temporarily, the save step can be elided:

  val side: SideInput[SparkeyReader] = sc
    .parallelize(Seq("a" -> "one", "b" -> "two"))
    .asSparkeySideInput

  SparkeyReader can be used like a lookup table in a side input operation:

  val main: SCollection[String] = sc.parallelize(Seq("a", "b", "c"))
  val side: SideInput[SparkeyReader] = sc
    .parallelize(Seq("a" -> "one", "b" -> "two"))
    .asSparkeySideInput
  
  main.withSideInputs(side)
    .map { (x, s) =>
      s(side).getOrElse(x, "unknown")
    }

  A SparkeyMap can store any types of keys and values, but can only be used as a SideInput:

  val main: SCollection[String] = sc.parallelize(Seq("a", "b", "c"))
  val side: SideInput[SparkeyMap[String, Int]] = sc
    .parallelize(Seq("a" -> 1, "b" -> 2, "c" -> 3))
    .asLargeMapSideInput()
  
  val objects: SCollection[MyObject] = main
    .withSideInputs(side)
    .map { (x, s) => s(side).get(x) }
    .toSCollection

  To read a static Sparkey collection and use it as a typed SideInput, use TypedSparkeyReader. TypedSparkeyReader can also accept a Caffeine cache to reduce IO and deserialization load:

  val main: SCollection[String] = sc.parallelize(Seq("a", "b", "c"))
  val cache: Cache[String, MyObject] = ...
  val side: SideInput[TypedSparkeyReader[MyObject]] = sc
    .typedSparkeySideInput("gs://<bucket>/<path>/<sparkey-prefix>", MyObject.decode, cache)
  
  val objects: SCollection[MyObject] = main
    .withSideInputs(side)
    .map { (x, s) => s(side).get(x) }
    .toSCollection

  Definition Classes

  extra

•  package voyager

  Main package for Voyager side input APIs.

  Main package for Voyager side input APIs.

  Definition Classes

  extra

com.spotify.scio.extra

json