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 dynamic

    CSV package for dynamic destinations.

    CSV package for dynamic destinations. Import All.

    import com.spotify.scio.extra.csv.dynamic._
  • package syntax
  • CsvIO
  • CsvScioContext
  • ReadableCsvFileSCollection
  • WritableCsvSCollection
  • 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
  • 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

package csv

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

import com.spotify.scio.extra.csv._
Source
package.scala
Linear Supertypes
AllSyntax, ScioContextSyntax, SCollectionSyntax, AnyRef, Any
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Inherited
  1. csv
  2. AllSyntax
  3. ScioContextSyntax
  4. SCollectionSyntax
  5. AnyRef
  6. Any
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Package Members

  1. package dynamic

    CSV package for dynamic destinations.

    CSV package for dynamic destinations. Import All.

    import com.spotify.scio.extra.csv.dynamic._
  2. package syntax

Type Members

  1. implicit final class ReadableCsvFileSCollection extends AnyRef
    Definition Classes
    SCollectionSyntax
  2. implicit final class WritableCsvSCollection[T] extends AnyRef
    Definition Classes
    SCollectionSyntax
  3. implicit final class CsvScioContext extends AnyRef
    Definition Classes
    ScioContextSyntax

Value Members

  1. object CsvIO

    This package uses a CSV mapper called Kantan.

    This package uses a CSV mapper called Kantan.

    import the following:

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

    Reading

    with a header

    Kantan docs

    case class User(name: String, age: Int)
implicit val decoder = HeaderDecoder.decoder("fullName", "userAge")(User.apply _)
val users: SCollection[User] = scioContext.csvFile(path)

    without a header

    Kantan docs

    case class User(name: String, age: Int)
implicit val decoder = RowDecoder.ordered { (name: String, age: Int) => User(name, age) }
val csvConfiguration = CsvIO.ReadParam(csvConfiguration = CsvIO.DefaultCsvConfiguration.withoutHeader)
val users: SCollection[User] = scioContext.csvFile(path, csvConfiguration)

    Writing

    with a header

    Kantan docs

    case class User(name: String, age: Int)
implicit val encoder = HeaderEncoder.caseEncoder("fullName", "age")(User.unapply)

val users: SCollection[User] = ???
users.saveAsCsvFile(path)

    without a header

    Kantan docs

    case class User(name: String, age: Int)
implicit val encoder = RowEncoder.encoder(0, 1)((u: User) => (u.name, u.age))

val users: SCollection[User] = ???
users.saveAsCsvFile(path)

Inherited from AllSyntax

Inherited from ScioContextSyntax

Inherited from SCollectionSyntax

Inherited from AnyRef

Inherited from Any

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