Avro4s is a schema/class generation and serializing/deserializing library for Avro written in Scala. The objective is to allow seamless use with Scala without the need to to write boilerplate conversions yourself, and without the runtime overhead of reflection. Hence, this is a macro based library and generates code for use with Avro at compile time.
The features of the library are:
- Schema generation from classes at compile time
- Class generation from schemas at build time
- Boilerplate free serialization of classes to Avro
- Boilerplate free deserialization of Avro to classes
- 1.5.0 - Upgraded to Avro 1.8.1. Deprecated factory methods in io classes in favour of more explicit naming. Added caching for the macro generation (thanks @jtvoorde).
- 1.4.3 - Added support for json serialization
- 1.4.1 - Added support for parameter defaults
- 1.4.0 - Added better support for enums. Added support for UUIDs. Rewrote the macro backend. Added better binary support.
- 1.3.3 - Added missing support for deserializing byte arrays
- 1.3.0 - Added support for Scala 2.12. Removed 2.10 cross build. Fixed issues with private vals. Added binary (no schema) output stream. Exposed RecordFormat[T] typeclass to enable easy conversion of T to/from an Avro Record.
- 1.2.0 - Added support for properties, doc fields, and aliases. These are set via annotations.
- 1.1.0 - Added JSON document to Avro schema converter
- 1.0.0 - Migrated all macros to use Shapeless. Fixed some trickier nested case class issues. Simplified API. Added support for java enums.
- 0.94.0 - Added support for writing/reading Either and Option in serializer/deserializer. Fixed bug with array serialization.
- 0.93.0 - Added support for either and options in schema generator. Added support for aliases via scala annotation.
- 0.92.0 - Added support for unions (and unions of nulls to Options) and enums to class generator.
Avro4s allows us to generate schemas directly from classes in a totally straightforward way. Let's define some classes.
case class Ingredient(name: String, sugar: Double, fat: Double)
case class Pizza(name: String, ingredients: Seq[Ingredient], vegetarian: Boolean, vegan: Boolean, calories: Int)Next is to invoke the apply method of AvroSchema passing in the top level type. This will return an org.apache.avro.Schema instance, from which you can output, write to a file etc.
import com.sksamuel.avro4s.AvroSchema
val schema = AvroSchema[Pizza]Which will output the following schema:
{
"type" : "record",
"name" : "Pizza",
"namespace" : "com.sksamuel.avro4s.json",
"fields" : [ {
"name" : "name",
"type" : "string"
}, {
"name" : "ingredients",
"type" : {
"type" : "array",
"items" : {
"type" : "record",
"name" : "Ingredient",
"fields" : [ {
"name" : "name",
"type" : "string"
}, {
"name" : "sugar",
"type" : "double"
}, {
"name" : "fat",
"type" : "double"
} ]
}
}
}, {
"name" : "vegetarian",
"type" : "boolean"
}, {
"name" : "vegan",
"type" : "boolean"
}, {
"name" : "calories",
"type" : "int"
} ]
}You can see that the schema generator handles nested case classes, sequences, primitives, etc. For a full list of supported object types, see the table later.
Avro4s allows us to easily serialize case classes using an instance of AvroOutputStream which we write to, and close, just like you would any regular output stream. An AvroOutputStream can be created from a File, Path, or by wrapping another OutputStream. When we create one, we specify the type of objects that we will be serializing. Eg, to serialize instances of our Pizza class:
import java.io.File
import com.sksamuel.avro4s.AvroOutputStream
val pepperoni = Pizza("pepperoni", Seq(Ingredient("pepperoni", 12, 4.4), Ingredient("onions", 1, 0.4)), false, false, 98)
val hawaiian = Pizza("hawaiian", Seq(Ingredient("ham", 1.5, 5.6), Ingredient("pineapple", 5.2, 0.2)), false, false, 91)
val os = AvroOutputStream.data[Pizza](new File("pizzas.avro"))
os.write(Seq(pepperoni, hawaiian))
os.flush()
os.close()With avro4s we can easily deserialize a file back into Scala case classes. Given the pizzas.avro file we generated in the previous section on serialization, we will read this back in using the AvroInputStream class. We first create an instance of the input stream specifying the types we will read back, and the file. Then we call iterator which will return a lazy iterator (reads on demand) of the data in the file. In this example, we'll load all data at once from the iterator via toSet.
import com.sksamuel.avro4s.AvroInputStream
val is = AvroInputStream.data[Pizza](new File("pizzas.avro"))
val pizzas = is.iterator.toSet
is.close()
println(pizzas.mkString("\n"))Will print out:
Pizza(pepperoni,List(Ingredient(pepperoni,12.2,4.4), Ingredient(onions,1.2,0.4)),false,false,500) Pizza(hawaiian,List(Ingredient(ham,1.5,5.6), Ingredient(pineapple,5.2,0.2)),false,false,500)You can serialize to Binary using the AvroBinaryOutputStream which you can create directly or by using AvroOutputStream.binary.
The difference with binary serialization is that the schema is not included in the output.
Simply
case class Composer(name: String, birthplace: String, compositions: Seq[String])
val ennio = Composer("ennio morricone", "rome", Seq("legend of 1900", "ecstasy of gold"))
val baos = new ByteArrayOutputStream()
val output = AvroOutputStream.binary[Composer](baos)
output.write(ennio)
output.close()
println(baos.toString("UTF-8"))You can deserialize Binary using the AvroBinaryInputStream which you can create directly or by using AvroInputStream.binary
The difference with binary serialization is that the schema is not included in the data.
val json = "{\"name\":\"ennio morricone\",\"birthplace\":\"rome\",\"compositions\":[\"legend of 1900\",\"ecstasy of gold\"]}"
val in = new ByteInputStream(json.getBytes("UTF-8"), json.size)
val input = new AvroInputStream.binary[Composer](in)
val result = input.singleEntity
result shouldBe Success(ennio)You can serialize to JSON using the AvroJsonOutputStream which you can create directly or by using AvroOutputStream.json
Simply
case class Composer(name: String, birthplace: String, compositions: Seq[String])
val ennio = Composer("ennio morricone", "rome", Seq("legend of 1900", "ecstasy of gold"))
val baos = new ByteArrayOutputStream()
val output = AvroOutputStream.json[Composer](baos)
output.write(ennio)
output.close()
println(baos.toString("UTF-8"))You can deserialize JSON using the AvroJsonInputStream which you can create directly or by using AvroInputStream.json
val json = "{\"name\":\"ennio morricone\",\"birthplace\":\"rome\",\"compositions\":[\"legend of 1900\",\"ecstasy of gold\"]}"
val in = new ByteInputStream(json.getBytes("UTF-8"), json.size)
val input = new AvroInputStream.json[Composer](in)
val result = input.singleEntity
result shouldBe Success(ennio)To interface with the Java API it is sometimes desirable to convert between your classes and the Avro GenericRecord type. You can do this easily in avro4s using the RecordFormat typeclass (this is what the input/output streams use behind the scenes). Eg,
To convert from a class into a record:
case class Composer(name: String, birthplace: String, compositions: Seq[String])
val ennio = Composer("ennio morricone", "rome", Seq("legend of 1900", "ecstasy of gold"))
val format = RecordFormat[Composer]
// record is of type GenericRecord
val record = format.to(ennio)And to go from a record back into a type:
// given some record from earlier
val record = ...
val format = RecordFormat[Composer]
// is an instance of Composer
val ennio = format.from(record)| Scala Type | Avro Type |
|---|---|
| Boolean | boolean |
| Array[Byte] | bytes |
| String | string or fixed |
| Int | int |
| Long | long |
| BigDecimal | decimal with scale 2 and precision 8 |
| Double | double |
| Float | float |
| java.util.UUID | string |
| Java Enums | enum |
| scala.Enumeration | enum |
| sealed trait T | enum |
| scala.collection.Array[T] | array |
| scala.collection.List[T] | array |
| scala.collection.Seq[T] | array |
| scala.collection.Iterable[T] | array |
| scala.collection.Set[T] | array |
| scala.collection.Map[String, T] | map |
| scala.collection.Option[T] | union:null,T |
| scala.collection.Either[L, R] | union:L,R |
| T | record |
It is very easy to add custom type mappings. To do this, you need to create instances of ToSchema, ToValue and FromValue typeclasses.
ToSchema is used to generate an Avro schema for a given JVM type. ToValue is used to convert an instance of a JVM type into an instance of the Avro type. And FromValue is used to convert an instance of the Avro type into the JVM type.
For example, to create a mapping for org.joda.time.DateTime that we wish to store as an ISO Date string, then we can do the following:
implicit object DateTimeToSchema extends ToSchema[DateTime] {
override val schema: Schema = Schema.create(Schema.Type.STRING)
}
implicit object DateTimeToValue extends ToValue[DateTime] {
override def apply(value: DateTime): String = ISODateTimeFormat.dateTime().print(value)
}
implicit object DateTimeFromValue extends FromValue[DateTime] {
override def apply(value: Any, field: Field): DateTime = ISODateTimeFormat.dateTime().parseDateTime(value.toString())
}These typeclasses must be implicit and in scope when you invoke AvroSchema or create an AvroInputStream/AvroOutputStream.
compile 'com.sksamuel.avro4s:avro4s-core_2.11:1.4.3'
libraryDependencies += "com.sksamuel.avro4s" %% "avro4s-core" % "1.4.3"
<dependency>
<groupId>com.sksamuel.avro4s</groupId>
<artifactId>avro4s-core_2.11</artifactId>
<version>1.4.3</version>
</dependency>The above is just an example and is not always up to date with the latest version. Check the latest released version on Maven Central
This project is built with SBT. So to build
sbt compile
And to test
sbt test
Contributions to avro4s are always welcome. Good ways to contribute include:
- Raising bugs and feature requests
- Fixing bugs and enhancing the DSL
- Improving the performance of avro4s
- Adding to the documentation
The MIT License (MIT)
Copyright (c) 2015 Stephen Samuel
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