JarCastingshapeless-datatype
Shapeless utilities for common data types. Also see Magnolify for a simpler and faster alternative based on Magnolia.
Modules
This library includes the following modules.
shapeless-datatype-coreshapeless-datatype-avroshapeless-datatype-bigqueryshapeless-datatype-datastoreshapeless-datatype-tensorflow
Core
Core includes the following components.
- A
MappableTypefor generic conversion between case class and other data types, used by BigQuery and Datastore modules. - A
RecordMapperfor generic conversion between case class types. - A
RecordMatcherfor generic type-based equality check bewteen case classes. - A
LensMatcherfor generic lens-based equality check between case classes.
RecordMapper
RecordMapper[A, B] maps instances of case class A and B with different field types.
import shapeless._
import shapeless.datatype.record._
import scala.language.implicitConversions
// records with same field names but different types
case class Point1(x: Double, y: Double, label: String)
case class Point2(x: Float, y: Float, label: String)
// implicit conversion bewteen fields of different types
implicit def f2d(x: Float) = x.toDouble
implicit def d2f(x: Double) = x.toFloat
val m = RecordMapper[Point1, Point2]
m.to(Point1(0.5, -0.5, "a")) // Point2(0.5,-0.5,a)
m.from(Point2(0.5, -0.5, "a")) // Point1(0.5,-0.5,a)
RecordMatcher
RecordMatcher[T] performs equality check of instances of case class T with custom logic based on field types.
import shapeless.datatype.record._
case class Record(id: String, name: String, value: Int)
// custom comparator for String type
implicit def compareStrings(x: String, y: String) = x.toLowerCase == y.toLowerCase
val m = RecordMatcher[Record]
Record("a", "RecordA", 10) == Record("A", "RECORDA", 10) // false
// compareStrings is applied to all String fields
m(Record("a", "RecordA", 10), Record("A", "RECORDA", 10)) // true
LensMatcher
LensMatcher[T] performs equality check of instances of case class T with custom logic based on Lenses.
import shapeless.datatype.record._
case class Record(id: String, name: String, value: Int)
// compare String fields id and name with different logic
val m = LensMatcher[Record]
.on(_ >> 'id)(_.toLowerCase == _.toLowerCase)
.on(_ >> 'name)(_.length == _.length)
Record("a", "foo", 10) == Record("A", "bar", 10) // false
m(Record("a", "foo", 10), Record("A", "bar", 10)) // true
AvroType
AvroType[T] maps bewteen case class T and Avro GenericRecord. AvroSchema[T] generates schema for case class T.
import shapeless.datatype.avro._
case class City(name: String, code: String, lat: Double, long: Double)
val t = AvroType[City]
val r = t.toGenericRecord(City("New York", "NYC", 40.730610, -73.935242))
val c = t.fromGenericRecord(r)
AvroSchema[City]
Custom types are also supported.
import shapeless.datatype.avro._
import java.net.URI
import org.apache.avro.Schema
implicit val uriAvroType = AvroType.at[URI](Schema.Type.STRING)(v => URI.create(v.toString), _.toString)
case class Page(uri: URI, rank: Int)
val t = AvroType[Page]
val r = t.toGenericRecord(Page(URI.create("www.google.com"), 42))
val c = t.fromGenericRecord(r)
AvroSchema[Page]
BigQueryType
BigQueryType[T] maps bewteen case class T and BigQuery TableRow. BigQuerySchema[T] generates schema for case class T.
import shapeless.datatype.bigquery._
case class City(name: String, code: String, lat: Double, long: Double)
val t = BigQueryType[City]
val r = t.toTableRow(City("New York", "NYC", 40.730610, -73.935242))
val c = t.fromTableRow(r)
BigQuerySchema[City]
Custom types are also supported.
import shapeless.datatype.bigquery._
import java.net.URI
implicit val uriBigQueryType = BigQueryType.at[URI]("STRING")(v => URI.create(v.toString), _.toString)
case class Page(uri: URI, rank: Int)
val t = BigQueryType[Page]
val r = t.toTableRow(Page(URI.create("www.google.com"), 42))
val c = t.fromTableRow(r)
BigQuerySchema[Page]
DatastoreType
DatastoreType[T] maps between case class T and Cloud Datastore Entity or Entity.Builder Protobuf types.
import shapeless.datatype.datastore._
case class City(name: String, code: String, lat: Double, long: Double)
val t = DatastoreType[City]
val r = t.toEntity(City("New York", "NYC", 40.730610, -73.935242))
val c = t.fromEntity(r)
val b = t.toEntityBuilder(City("New York", "NYC", 40.730610, -73.935242))
val d = t.fromEntityBuilder(b)
Custom types are also supported.
import shapeless.datatype.datastore._
import com.google.datastore.v1.client.DatastoreHelper._
import java.net.URI
implicit val uriDatastoreType = DatastoreType.at[URI](
v => URI.create(v.getStringValue),
u => makeValue(u.toString).build())
case class Page(uri: URI, rank: Int)
val t = DatastoreType[Page]
val r = t.toEntity(Page(URI.create("www.google.com"), 42))
val c = t.fromEntity(r)
val b = t.toEntityBuilder(Page(URI.create("www.google.com"), 42))
val d = t.fromEntityBuilder(b)
TensorFlowType
TensorFlowType[T] maps between case class T and TensorFlow Example or Example.Builder Protobuf types.
import shapeless.datatype.tensorflow._
case class Data(floats: Array[Float], longs: Array[Long], strings: List[String], label: String)
val t = TensorFlowType[Data]
val r = t.toExample(Data(Array(1.5f, 2.5f), Array(1L, 2L), List("a", "b"), "x"))
val c = t.fromExample(r)
val b = t.toExampleBuilder(Data(Array(1.5f, 2.5f), Array(1L, 2L), List("a", "b"), "x"))
val d = t.fromExampleBuilder(b)
Custom types are also supported.
import shapeless.datatype.tensorflow._
import java.net.URI
implicit val uriTensorFlowType = TensorFlowType.at[URI](
TensorFlowType.toStrings(_).map(URI.create),
xs => TensorFlowType.fromStrings(xs.map(_.toString)))
case class Page(uri: URI, rank: Int)
val t = TensorFlowType[Page]
val r = t.toExample(Page(URI.create("www.google.com"), 42))
val c = t.fromExample(r)
val b = t.toExampleBuilder(Page(URI.create("www.google.com"), 42))
val d = t.fromExampleBuilder(b)
License
Copyright 2016 Neville Li.
Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0