This is a community project - PRs will be accepted and releases published by the maintainer
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 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
- Boilerplate free serialization of Scala types into Avro types
- Boilerplate free deserialization of Avro types to Scala types
The master branch contains version 5.0.x which is designed for Scala 3. PRs are welcome. This version may have minor breaking changes compared to previous major release in order to support the new features of Scala 3.
The previous major version is 4.0.x located at branch release/4.0.x and is released for Scala 2.12 and Scala 2.13.
This version is in support mode only. Bug reports are welcome and bug fixes will be released. No new features will be
added.
Please raise PRs using branch names scala2/* and scala3/* depending on which version of Scala your work is
targeting.
Unlike Json, Avro is a schema based format. You'll find yourself wanting to generate schemas frequently, and writing
these by hand or through the Java based SchemaBuilder classes can be tedious for complex domain models. Avro4s allows
us to generate schemas directly from case classes at compile time via macros. This gives you both the convenience of
generated code, without the annoyance of having to run a code generation step, as well as avoiding the peformance
penalty of runtime reflection based code.
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)To generate an Avro Schema, we need to use the AvroSchema object passing in the target type as a type parameter.
This will return an org.apache.avro.Schema instance.
import com.sksamuel.avro4s.AvroSchema
val schema = AvroSchema[Pizza]Where the generated schema is as follows:
{
"type":"record",
"name":"Pizza",
"namespace":"com.sksamuel",
"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.
Avro schemas for complex types (RECORDS) contain a name and a namespace. By default, these are the name of the class
and the enclosing package name, but it is possible to customize these using the annotations AvroName and AvroNamespace.
For example, the following class:
package com.sksamuel
case class Foo(a: String)Would normally have a schema like this:
{
"type":"record",
"name":"Foo",
"namespace":"com.sksamuel",
"fields":[
{
"name":"a",
"type":"string"
}
]
}However we can override the name and/or the namespace like this:
package com.sksamuel
@AvroName("Wibble")
@AvroNamespace("com.other")
case class Foo(a: String)And then the generated schema looks like this:
{
"type":"record",
"name":"Wibble",
"namespace":"com.other",
"fields":[
{
"name":"a",
"type":"string"
}
]
}Note: It is possible, but not necessary, to use both AvroName and AvroNamespace. You can just use either of them if you wish.
The AvroName annotation can also be used to override field names. This is useful when the record instances you are generating or reading need to have field names different from the scala case classes. For example if you are reading data generated by another system, or another language.
Given the following class.
package com.sksamuel
case class Foo(a: String, @AvroName("z") b : String)Then the generated schema would look like this:
{
"type":"record",
"name":"Foo",
"namespace":"com.sksamuel",
"fields":[
{
"name":"a",
"type":"string"
},
{
"name":"z",
"type":"string"
}
]
}Notice that the second field is z and not b.
Note: @AvroName does not add an alternative name for the field, but an override. If you wish to have alternatives then you want to use @AvroAlias.
Avro allows a doc field, and arbitrary key/values to be added to generated schemas. Avro4s supports this through the use of AvroDoc and AvroProp annotations.
These properties works on either complex or simple types - in other words, on both fields and classes. For example:
package com.sksamuel
@AvroDoc("hello, is it me you're looking for?")
case class Foo(@AvroDoc("I am a string") str: String, @AvroDoc("I am a long") long: Long, int: Int)Would result in the following schema:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"doc":"hello, is it me you're looking for?",
"fields": [
{
"name": "str",
"type": "string",
"doc" : "I am a string"
},
{
"name": "long",
"type": "long",
"doc" : "I am a long"
},
{
"name": "int",
"type": "int"
}
]
}An example of properties:
package com.sksamuel
@AvroProp("jack", "bruce")
case class Annotated(@AvroProp("richard", "ashcroft") str: String, @AvroProp("kate", "bush") long: Long, int: Int)Would generate this schema:
{
"type": "record",
"name": "Annotated",
"namespace": "com.sksamuel",
"fields": [
{
"name": "str",
"type": "string",
"richard": "ashcroft"
},
{
"name": "long",
"type": "long",
"kate": "bush"
},
{
"name": "int",
"type": "int"
}
],
"jack": "bruce"
}Behind the scenes, AvroSchema uses an implicit SchemaFor. This is the core typeclass which generates an Avro schema for a given Java or Scala type. There are SchemaFor instances for all the common JDK and SDK types, as well as macros that generate instances for case classes.
In order to override how a schema is generated for a particular type you need to bring into scope an implicit SchemaFor for the type you want to override. As an example, lets say you wanted all integers to be encoded as Schema.Type.STRING rather than the standard Schema.Type.INT.
To do this, we just introduce a new instance of SchemaFor and put it in scope when we generate the schema.
implicit val intOverride = SchemaFor[Int](SchemaBuilder.builder.stringType)
case class Foo(a: Int)
val schema = AvroSchema[Foo]Note: If you create an override like this, be aware that schemas in Avro are mutable, so don't share the values that the typeclasses return.
Avro4s does not support the @transient anotation to mark a field as ignored, but instead supports its own @AvroTransient annotation to do the same job. Any field marked with this will be excluded from the generated schema.
package com.sksamuel
case class Foo(a: String, @AvroTransient b: String)Would result in the following schema:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"fields": [
{
"name": "a",
"type": "string"
}
]
}If you are dealing with Avro data generated in other languages then it's quite likely the field names will reflect the style of that language. For example, Java may prefer camelCaseFieldNames but other languages may use snake_case_field_names or PascalStyleFieldNames. By default the name of the field in the case class is what will be used, and you've seen earlier that you can override a specific field with @AvroName, but doing this for every single field would be insane.
So, avro4s provides a FieldMapper for this. You simply bring into scope an instance of FieldMapper that will convert the scala field names into a target type field names.
For example, lets take a scala case and generate a schema using snake case.
package com.sksamuel
case class Foo(userName: String, emailAddress: String)
implicit val snake: FieldMapper = SnakeCase
val schema = AvroSchema[Foo]Would generate the following schema:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"fields": [
{
"name": "user_name",
"type": "string"
},
{
"name": "email_address",
"type": "string"
}
]
}You can also define your own field mapper:
package com.sksamuel
case class Foo(userName: String, emailAddress: String)
implicit val short: FieldMapper = {
case "userName" => "user"
case "emailAddress" => "email"
}
val schema = AvroSchema[Foo]Would generate the following schema:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"fields": [
{
"name": "user",
"type": "string"
},
{
"name": "email",
"type": "string"
}
]
}Avro4s will take into account default values on fields. For example, the following class case class Wibble(s: String = "foo") would be serialized as:
{
"type": "record",
"name": "Wibble",
"namespace": "com.sksamuel.avro4s.schema",
"fields": [
{
"name": "s",
"type": "string",
"default" : "foo"
}
]
}However if you wish the scala default to be ignored, then you can annotate the field with @AvroNoDefault. So this class case class Wibble(@AvroNoDefault s: String = "foo") would be serialized as:
{
"type": "record",
"name": "Wibble",
"namespace": "com.sksamuel.avro4s.schema",
"fields": [
{
"name": "s",
"type": "string"
}
]
}Avro4s maps scala enums, java enums, and scala sealed traits to the AVRO enum type.
For example, the following scala enum:
object Colours extends Enumeration {
val Red, Amber, Green = Value
}when referenced in a case class:
case class Car(colour: Colours.Value)results in the following AVRO schema (e.g. using val schema = AvroSchema[Car]):
{
"type" : "record",
"name" : "Car",
"fields" : [ {
"name" : "colour",
"type" : {
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ]
}
} ]
}Avro4s will also convert a Java enum such as:
public enum Wine {
Malbec, Shiraz, CabSav, Merlot
}into an AVRO enum type:
{
"type": "enum",
"name": "Wine",
"symbols": [ "Malbec", "Shiraz", "CabSav", "Merlot" ]
}And likewise, avro4s will convert a sealed trait such as:
sealed trait Animal
@AvroSortPriority(0) case object Cat extends Animal
@AvroSortPriority(-1) case object Dog extends Animalinto the following AVRO enum schema:
{
"type" : "enum",
"name" : "Animal",
"symbols" : [ "Cat", "Dog" ]
}With @AvroSortPriority attribute, elements are sorted in descending order, by the priority specified
(the element with the highest priority will be put as first).
According to Avro specification, when an element is not found the first compatible element defined in the union is used. For this reason order of the elements should not be changed when compatibility is important. Add new elements at the end.
An alternative solution is to use the @AvroUnionPosition attribute passing a number that will be sorted ascending,
from lower to upper:
sealed trait Fruit
@AvroUnionPosition(0)
case object Unknown extends Fruit
@AvroUnionPosition(1)
case class Orange(size: Int) extends Fruit
@AvroUnionPosition(2)
case class Mango(size: Int) extends FruitThis will generate the following AVRO schema:
[
{
"type" : "record",
"name" : "Unknown",
"fields" : [ ]
},
{
"type" : "record",
"name" : "Orange",
"fields" : [ {
"name" : "size",
"type" : "int"
} ]
},
{
"type" : "record",
"name" : "Mango",
"fields" : [ {
"name" : "size",
"type" : "int"
} ]
}
]As with any AVRO field, you can specify an enum field's default value as follows:
case class Car(colour: Colours.Value = Colours.Red)resulting in the following AVRO schema:
{
"type" : "record",
"name" : "Car",
"fields" : [ {
"name" : "colour",
"type" : {
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ]
},
"default": "Red"
} ]
}One benefit of providing a field default is that the writer can later remove the field without
breaking existing readers. In the Car example, if the writer doesn't provide a value for the
colour field, the reader will default the colour to Red.
But what if the writer would like to extend the Colour enumeration to include the colour Orange:
object Colours extends Enumeration {
val Red, Amber, Green, Orange = Value
}resulting in the following AVRO schema?
{
"type" : "record",
"name" : "Car",
"fields" : [ {
"name" : "colour",
"type" : {
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green", "Orange" ]
},
"default": "Red"
} ]
}If a writer creates an Orange Car:
Car(colours = Colours.Orange)readers using the older schema (the one without the new Orange value), will fail with a backwards compatibility error.
I.e. readers using the previous version of the Car schema don't know the colour Orange, and therefore
can't read the new Car record.
To enable writers to extend enums in a backwards-compatible way, AVRO allows you to specify a default enum value as part of the enum type's definition:
{
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ],
"default": "Amber"
}Note that an enum's default isn't the same as an enum field's default as showed below,
where the enum default is Amber and the field's default is Red:
{
"type" : "record",
"name" : "Car",
"fields" : [ {
"name" : "colour",
"type" : {
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ],
"default": "Amber"
},
"default": "Red"
} ]
}Note that the field's default and the enum's default need not be the same value.
The field's default answers the question:
- What value should the reader use if the writer didn't specify the field's value?
In the schema example above, the answer is Red.
The enum's default value answers the question:
- What value should the reader use if the writer specifies an enum value that the reader doesn't recognize?
In the example above, the answer is Amber.
In summary, as long as a writer specified a the default enum value in previous versions of an enum's schema, the writer can add
new enum values without breaking older readers. For example, we can add
the colour Orange to the Colour enum's list of symbol/values without breaking older readers:
{
"type" : "record",
"name" : "Car",
"fields" : [ {
"name" : "colour",
"type" : {
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green", "Orange" ],
"default": "Amber"
},
"default": "Red"
} ]
}Specifically, given Amber as the enum's default, an older AVRO reader that receives an Orange Car will
default the Car's colour to Amber, the enum's default.
The following sections describe how to define enum defaults through avro4s for scala enums, java enums, and sealed traits.
For scala enums such as:
object Colours extends Enumeration {
val Red, Amber, Green = Value
}avro4s gives you two options:
- You can define an implicit
SchemaForusing theScalaEnumSchemaFor[E].apply(default: E)method where the method'sdefaultargument is one of the enum's values or ... - You can use the
@AvroEnumDefaultannotation to declare the default enum value.
For example, to create an implicit SchemaFor for an scala enum with a default enum value,
use the ScalaEnumSchemaFor[E].apply(default: E) method as follows:
implicit val schemaForColours: SchemaFor[Colours.Value] = ScalaEnumSchemaFor[Colours.Value](default = Colours.Amber)resulting in the following AVRO schema:
{
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ],
"default": "Amber"
}Or, to declare the default enum value, you can use the @AvroEnumDefault annotation as follows:
@AvroEnumDefault(Colours.Amber)
object Colours extends Enumeration {
val Red, Amber, Green = Value
}resulting in the same AVRO schema:
{
"type" : "enum",
"name" : "Colours",
"symbols" : [ "Red", "Amber", "Green" ],
"default": "Amber"
}You can also use the following avro4s annotations to change a scala enum's name, namespace, and to add additional properties:
@AvroName@AvroNamespace@AvroProp
For example:
@AvroName("MyColours")
@AvroNamespace("my.namespace")
@AvroEnumDefault(Colours.Green)
@AvroProp("hello", "world")
object Colours extends Enumeration {
val Red, Amber, Green = Value
}resulting in the following AVRO schema:
{
"type" : "enum",
"name" : "MyColours",
"namespace" : "my.namespace",
"symbols" : [ "Red", "Amber", "Green" ],
"default": "Amber",
"hello" : "world"
}Note that if you're using an enum from, for example, a 3rd party library and without access to the source code, you may
not be able to use the @AvroEnumDefault annotation, in which case you'll need to use the
ScalaEnumSchemaFor[E].apply(default: E) method instead.
For java enums such as:
public enum Wine {
Malbec,
Shiraz,
CabSav,
Merlot
}avro4s gives you two options to define an enum's default value:
- You can define an implicit
SchemaForusing theJavaEnumSchemaFor[E].apply(default: E)method where the method'sdefaultargument is one of the enum's values or ... - You can use the
@AvroJavaEnumDefaultannotation to declare the default enum value.
For example, to create an implicit SchemaFor for an enum with a default enum value,
use the JavaEnumSchemaFor[E].apply(default: E) method as follows:
implicit val schemaForWine: SchemaFor[Wine] = JavaEnumSchemaFor[Wine](default = Wine.Merlot)Or, to declare the default enum value, use the @AvroJavaEnumDefault annotation as follows:
public enum Wine {
Malbec,
Shiraz,
@AvroJavaEnumDefault CabSav,
Merlot
}Avro4s also supports the following java annotations for java enums:
@AvroJavaName@AvroJavaNamespace@AvroJavaProp
Putting it all together, you can define a java enum with using avro4s's annotations as follows:
@AvroJavaName("MyWine")
@AvroJavaNamespace("my.namespace")
@AvroJavaProp(key = "hello", value = "world")
public enum Wine {
Malbec,
Shiraz,
@AvroJavaEnumDefault CabSav,
Merlot
}resulting in the following AVRO schema:
{
"type": "enum",
"name": "MyWine",
"namespace": "my.namespace",
"symbols": [
"Malbec",
"Shiraz",
"CabSav",
"Merlot"
],
"default": "CabSav",
"hello": "world"
}For sealed traits, you can define the trait's default enum using the @AvroEnumDefault annotation as follows:
@AvroEnumDefault(Dog)
sealed trait Animal
@AvroSortPriority(0) case object Cat extends Animal
@AvroSortPriority(-1) case object Dog extends Animalresulting in the following AVRO schema:
{
"type" : "enum",
"name" : "Animal",
"symbols" : [ "Cat", "Dog" ],
"default" : "Dog"
}Avro supports the idea of fixed length byte arrays. To use these we can either override the schema generated for a type to return Schema.Type.Fixed. This will work for types like String or UUID. You can also annotate a field with @AvroFixed(size).
For example:
package com.sksamuel
case class Foo(@AvroFixed(7) mystring: String)
val schema = AvroSchema[Foo]Will generate the following schema:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"fields": [
{
"name": "mystring",
"type": {
"type": "fixed",
"name": "mystring",
"size": 7
}
}
]
}If you have a value type that you always want to be represented as fixed, then rather than annotate every single location it is used, you can annotate the value type itself.
package com.sksamuel
@AvroFixed(4)
case class FixedA(bytes: Array[Byte]) extends AnyVal
case class Foo(a: FixedA)
val schema = AvroSchema[Foo]And this would generate:
{
"type": "record",
"name": "Foo",
"namespace": "com.sksamuel",
"fields": [
{
"name": "a",
"type": {
"type": "fixed",
"name": "FixedA",
"size": 4
}
}
]
}Finally, these annotated value types can be used as top level schemas too:
package com.sksamuel
@AvroFixed(6)
case class FixedA(bytes: Array[Byte]) extends AnyVal
val schema = AvroSchema[FixedA]{
"type": "fixed",
"name": "FixedA",
"namespace": "com.sksamuel",
"size": 6
}The order of types in a union is significant in Avro, e.g the schemas type: ["int", "float"] and type: ["float", "int"] are different. This can cause problems when generating schemas for sealed trait hierarchies. Ideally we would generate schemas using the source code declaration order of the types. So for example:
sealed trait Animal
case class Dog(howFriendly: Float) extends Animal
case class Fish(remembersYou: Boolean) extends AnimalShould generate a schema where the order of types in the unions is Dog, Fish. Unfortunately, the SchemaFor macro can sometimes lose track of what the declaration order is - especially with larger hierarchies. In any situation where this is happening you can use the @AvroSortPriority annotation to explicitly control what order the types appear in. @AvroSortPriority takes a single float argument, which is the priority this field should be treated with, higher priority means closer to the beginning of the union. For example:
sealed trait Animal
@AvroSortPriority(1)
case class Dog(howFriendly: Float) extends Animal
@AvroSortPriority(2)
case class Fish(remembersYou: Boolean) extends AnimalWould output the types in the union as Fish,Dog.
Avro4s supports recursive schemas. Customizing them requires some thought, so if you can stick with the out-of-the-box provided schemas and customization via annotations.
The simplest way to customize schemas for recursive types is to provide custom SchemaFor instances for all types that
form the recursion. Given for example the following recursive Tree type,
sealed trait Tree[+T]
case class Branch[+T](left: Tree[T], right: Tree[T]) extends Tree[T]
case class Leaf[+T](value: T) extends Tree[T]it is easy to customize recursive schemas by providing a SchemaFor for both Tree and Branch:
import scala.collection.JavaConverters._
val leafSchema = AvroSchema[Leaf[Int]]
val branchSchema = Schema.createRecord("CustomBranch", "custom schema", "custom", false)
val treeSchema = Schema.createUnion(leafSchema, branchSchema)
branchSchema.setFields(Seq(new Schema.Field("left", treeSchema), new Schema.Field("right", treeSchema)).asJava)
val treeSchemaFor: SchemaFor[Tree[Int]] = SchemaFor(treeSchema)
val branchSchemaFor: SchemaFor[Branch[Int]] = SchemaFor(branchSchema)If you want to customize the schema for one type that is part of a type recursion (e.g., Branch[Int]) while using
generated schemas, this can be done as follows (sticking with the above example):
// 1. Use implicit def here so that this SchemaFor gets summoned for Branch[Int] in steps 6. and 10. below
// 2. Implement a ResolvableSchemaFor instead of SchemaFor directly so that SchemaFor creation can be deferred
implicit def branchSchemaFor: SchemaFor[Branch[Int]] = new ResolvableSchemaFor[Branch[Int]] {
def schemaFor(env: DefinitionEnvironment[SchemaFor], update: SchemaUpdate): SchemaFor[Branch[Int]] =
// 3. first, check whether SchemaFor[Branch[Int]] is already defined and return that if it is
env.get[Branch[Int]].getOrElse {
// 4. otherwise, create an incomplete SchemaFor (it initially lacks fields)
val record: SchemaFor[Branch[Int]] = SchemaFor(Schema.createRecord("CustomBranch", "custom schema", "custom", false))
// 5. extend the definition environment with the created SchemaFor[Branch[Int]]
val nextEnv = env.updated(record)
// 6. summon a schema for Tree[Int] (using the Branch[Int] from step 1. through implicits)
// 7. resolve the schema to get a finalized schema for Tree[Int]
val treeSchema = SchemaFor[Tree[Int]].resolveSchemaFor(nextEnv, NoUpdate).schema
// 8. close the reference cycle between Branch[Int] and Tree[Int]
val fields = Seq(new Schema.Field("left", treeSchema), new Schema.Field("right", treeSchema))
record.schema.setFields(fields.asJava)
// 9. return the final SchemaFor[Branch[Int]]
record
}
}
// 10. summon Schema for tree and kick off encoder resolution.
val treeSchema = AvroSchema[Tree[Int]]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 and provide a writer schema.
For example, 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, 598)
val hawaiian = Pizza("hawaiian", Seq(Ingredient("ham", 1.5, 5.6), Ingredient("pineapple", 5.2, 0.2)), false, false, 391)
val schema = AvroSchema[Pizza]
val os = AvroOutputStream.data[Pizza].to(new File("pizzas.avro")).build()
os.write(Seq(pepperoni, hawaiian))
os.flush()
os.close()We can easily deserialize a file back into 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, the source file, and then build it using a reader schema.
Once the input stream is created, we can invoke iterator which will return a lazy iterator that reads on demand 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 schema = AvroSchema[Pizza]
val is = AvroInputStream.data[Pizza].from(new File("pizzas.avro")).build(schema)
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 as binary or json
by specifying the format when creating the input or output stream. In the earlier example we use data which is considered the "default" for Avro.
To use json or binary, you can do the following:
AvroOutputStream.binary.to(...).build(...)
AvroOutputStream.json.to(...).build(...)
AvroInputStream.binary.from(...).build(...)
AvroInputStream.json.from(...).build(...)Note: Binary serialization does not include the schema in the output.
In Avro there are two container interfaces designed for complex types - GenericRecord, which is the most commonly used, along with the lesser used SpecificRecord.
These record types are used with a schema of type Schema.Type.RECORD.
To interface with the Avro Java API or with third party frameworks like Kafka it is sometimes desirable to convert between your case classes and these records, rather than using the input/output streams that avro4s provides.
To perform conversions, use the RecordFormat typeclass which converts to/from case classes and Avro records.
Note: In Avro, GenericRecord and SpecificRecord don't have a common Record interface (just a Container interface which simply provides for a schema without any methods for accessing values), so
avro4s has defined a Record trait, which is the union of the GenericRecord and SpecificRecord interfaces. This allows avro4s to generate records which implement both interfaces at the same time.
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 schema: Schema = AvroSchema[Composer]
implicit val toRecord: ToRecord[Composer] = ToRecord.apply[Composer](schema)
implicit val fromRecord: FromRecord[Composer] = FromRecord.apply[Composer](schema)
val format: RecordFormat[Composer] = RecordFormat.apply[Composer](schema)
// record is a type that implements both GenericRecord and Specific Record
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]
val ennio = format.from(record)The com.sksamuel.avro4s.kafka.GenericSerde class can be used as a Kafka Serdes to serialize/deserialize case classes into Avro records with Avro4s. Note that this class is not integrated with the schema registry.
import java.util.Properties
import org.apache.kafka.clients.CommonClientConfigs
import org.apache.kafka.clients.producer.ProducerConfig
import com.sksamuel.avro4s.BinaryFormat
case class TheKafkaKey(id: String)
case class TheKafkaValue(name: String, location: String)
val producerProps = new Properties();
producerProps.put(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, "...")
producerProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, new GenericSerde[TheKafkaKey](BinaryFormat))
producerProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, new GenericSerde[TheKafkaValue](BinaryFormat))
new ProducerConfig(producerProps)Avro4s defines two typeclasses, Encoder and Decoder which do the work
of mapping between scala values and Avro compatible values. Avro has no understanding of Scala types, or anything outside of it's built in set of supported types, so all values must be converted to something that is compatible with Avro. There are built in encoders and decoders for all the common JDK and Scala SDK types, including macro generated instances for case classes.
For example a java.sql.Timestamp is usually encoded as a Long, and a java.util.UUID is encoded as a String.
Decoders do the same work, but in reverse. They take an Avro value, such as null and return a scala value, such as Option.
Some values can be mapped in multiple ways depending on how the schema was generated. For example a String, which is usually encoded as
org.apache.avro.util.Utf8 could also be encoded as an array of bytes if the generated schema for that field was Schema.Type.BYTES. Therefore some encoders will take into account the schema passed to them when choosing the avro compatible type. In the schemas section you saw how you could influence which schema is generated for types.
import scala.collection.{Array, List, Seq, Iterable, Set, Map, Option, Either}
import shapeless.{:+:, CNil}The following table shows how types used in your code will be mapped / encoded in the generated Avro schemas and files. If a type can be mapped in multiple ways, it is listed more than once.
| Scala Type | Schema Type | Logical Type | Encoded Type |
|---|---|---|---|
| String | STRING | Utf8 | |
| String | FIXED | GenericFixed | |
| String | BYTES | ByteBuffer | |
| Boolean | BOOLEAN | java.lang.Boolean | |
| Long | LONG | java.lang.Long | |
| Int | INT | java.lang.Integer | |
| Short | INT | java.lang.Integer | |
| Byte | INT | java.lang.Integer | |
| Double | DOUBLE | java.lang.Double | |
| Float | FLOAT | java.lang.Float | |
| UUID | STRING | UUID | Utf8 |
| LocalDate | INT | Date | java.lang.Int |
| LocalTime | INT | time-millis | java.lang.Int |
| LocalDateTime | LONG | timestamp-nanos | java.lang.Long |
| java.sql.Date | INT | Date | java.lang.Int |
| Instant | LONG | Timestamp-Millis | java.lang.Long |
| Timestamp | LONG | Timestamp-Millis | java.lang.Long |
| BigDecimal | BYTES | Decimal<8,2> | ByteBuffer |
| BigDecimal | FIXED | Decimal<8,2> | GenericFixed |
| BigDecimal | STRING | Decimal<8,2> | String |
| Option[T] | UNION<null,T> | null, T | |
| Array[Byte] | BYTES | ByteBuffer | |
| Array[Byte] | FIXED | GenericFixed | |
| ByteBuffer | BYTES | ByteBuffer | |
| Seq[Byte] | BYTES | ByteBuffer | |
| List[Byte] | BYTES | ByteBuffer | |
| Vector[Byte] | BYTES | ByteBuffer | |
| Array[T] | ARRAY | Array[T] | |
| Vector[T] | ARRAY | Array[T] | |
| Seq[T] | ARRAY | Array[T] | |
| List[T] | ARRAY | Array[T] | |
| Set[T] | ARRAY | Array[T] | |
| sealed trait of case classes | UNION<A,B,..> | A, B, ... | |
| sealed trait of case objects | ENUM<A,B,..> | GenericEnumSymbol | |
| Map[String, V] | MAP | java.util.Map[String, V] | |
| Either[A,B] | UNION<A,B> | A, B | |
| A :+: B :+: C :+: CNil | UNION<A,B,C> | A, B, ... | |
| case class T | RECORD | GenericRecord with SpecificRecord | |
| Scala enumeration | ENUM | GenericEnumSymbol | |
| Java enumeration | ENUM | GenericEnumSymbol | |
| Scala tuples | RECORD | GenericRecord with SpecificRecord | |
| Option[Either[A,B]] | UNION<null,A,B> | null, A, B | |
| option of sealed trait of case classes | UNION<null,A,B,..> | null, A, B, ... | |
| option of sealed trait of case objects | UNION<null,A,B,..> | null, GenericEnumSymbol |
To select the encoding in case multiple encoded types exist, create a new Encoder with a corresponding SchemaFor
instance to the via withSchema. For example, creating a string encoder that uses target type BYTES works like this:
val stringSchemaFor = SchemaFor[String](Schema.create(Schema.Type.BYTES))
val stringEncoder = Encoder[String].withSchema(stringSchemaFor)It is very easy to add custom type mappings. To do this, we bring into scope a custom implicit of Encoder[T] and/or Decoder[T].
For example, to create a custom type mapping for a type Foo which writes out the contents in upper case, but always reads the contents in lower case, we can do the following:
case class Foo(a: String, b: String)
implicit object FooEncoder extends Encoder[Foo] {
override val schemaFor = SchemaFor[Foo]
override def encode(foo: Foo) = {
val record = new GenericData.Record(schema)
record.put("a", foo.a.toUpperCase)
record.put("b", foo.b.toUpperCase)
record
}
}
implicit object FooDecoder extends Decoder[Foo] {
override val schemaFor = SchemaFor[Foo]
override def decode(value: Any) = {
val record = value.asInstanceOf[GenericRecord]
Foo(record.get("a").toString.toLowerCase, record.get("b").toString.toLowerCase)
}
}Another example is changing the way we serialize LocalDateTime to store these dates as ISO strings. In this case, we are
writing out a String rather than the default Long so we must also change the schema type. Therefore, we must add an implicit SchemaFor as well as the encoders
and decoders.
implicit val LocalDateTimeSchemaFor = SchemaFor[LocalDateTime](Schema.create(Schema.Type.STRING))
implicit object DateTimeEncoder extends Encoder[LocalDateTime] {
override val schemaFor = LocalDateTimeSchemaFor
override def encode(value: LocalDateTime) =
ISODateTimeFormat.dateTime().print(value)
}
implicit object DateTimeDecoder extends Decoder[LocalDateTime] {
override val schemaFor = LocalDateTimeSchemaFor
override def decode(value: Any) =
ISODateTimeFormat.dateTime().parseDateTime(value.toString)
}These typeclasses must be implicit and in scope when you use AvroSchema or RecordFormat.
Avro supports generalised unions, eithers of more than two values.
To represent these in scala, we use shapeless.:+:, such that A :+: B :+: C :+: CNil represents cases where a type is A OR B OR C.
See shapeless' documentation on coproducts for more on how to use coproducts.
Scala sealed traits/classes are supported both when it comes to schema generation and conversions to/from GenericRecord.
Generally sealed hierarchies are encoded as unions - in the same way like Coproducts.
Under the hood, shapeless Generic is used to derive Coproduct representation for sealed hierarchy.
When all descendants of sealed trait/class are singleton objects, optimized, enum-based encoding is used instead.
In order to customize the scale and precision used by BigDecimal schema generators, bring an implicit ScalePrecision instance into scope.before using AvroSchema.
import com.sksamuel.avro4s.ScalePrecision
case class MyDecimal(d: BigDecimal)
implicit val sp = ScalePrecision(4, 20)
val schema = AvroSchema[MyDecimal]{
"type":"record",
"name":"MyDecimal",
"namespace":"com.foo",
"fields":[{
"name":"d",
"type":{
"type":"bytes",
"logicalType":"decimal",
"scale":"4",
"precision":"20"
}
}]
}When encoding values, it may be necessary to round values if they need to be converted to the scale used by the schema. By default this is RoundingMode.UNNECESSARY which will throw an exception if rounding is required.
In order to change this, add an implicit RoundingMode value before the Encoder is generated.
case class MyDecimal(d: BigDecimal)
implicit val sp = ScalePrecision(4, 20)
val schema = AvroSchema[MyDecimal]
implicit val roundingMode = RoundingMode.HALF_UP
val encoder = Encoder[MyDecimal]When serializing a class with one or more type parameters, the avro name used in a schema is the name of the raw type, plus the actual type parameters. In other words, it would be of the form rawtype__typeparam1_typeparam2_..._typeparamN. So for example, the schema for a type Event[Foo] would have the avro name event__foo.
You can disable this by annotating the class with @AvroErasedName which uses the JVM erased name - in other words, it drops type parameter information. So the aforementioned Event[Foo] would be simply event.
You can selectively customise the way Avro4s generates certain parts of your hierarchy, thanks to implicit precedence. Suppose you have the following classes:
case class Product(name: String, price: Price, litres: BigDecimal)
case class Price(currency: String, amount: BigDecimal)And you want to selectively use different scale/precision for the price and litres quantities. You can do this by forcing the implicits in the corresponding companion objects.
object Price {
implicit val sp = ScalePrecision(10, 2)
implicit val schema = SchemaFor[Price]
}
object Product {
implicit val sp = ScalePrecision(8, 4)
implicit val schema = SchemaFor[Product]
}This will result in a schema where both BigDecimal quantities have their own separate scale and precision.
If you use cats in your domain objects, then Avro4s provides a cats module with schemas, encoders and decoders for some cats types.
Just import import com.sksamuel.avro4s.cats._ before calling into the macros.
case class Foo(list: NonEmptyList[String], vector: NonEmptyVector[Boolean])
val schema = AvroSchema[Foo]If you use refined in your domain objects, then Avro4s provides a refined module with schemas, encoders and decoders for refined types.
Just import import com.sksamuel.avro4s.refined._ before calling into the macros.
case class Foo(nonEmptyStr: String Refined NonEmpty)
val schema = AvroSchema[Foo]Avro4s supports encoders and decoders for recursive types. Customizing them is possible, but involved. As with customizing SchemaFor instances for recursive types, the simplest way to customize encoders and decoders is to provide a custom encoder and decoder for all types that form the recursion.
If that isn't possible, you can customize encoders / decoders for one single type and participate in creating a cyclic graph of encoders / decoders. To give an example, consider the following recursive type for trees.
sealed trait Tree[+T]
case class Branch[+T](left: Tree[T], right: Tree[T]) extends Tree[T]
case class Leaf[+T](value: T) extends Tree[T]For this, a custom Branch[Int] encoder can be defined as follows.
// 1. use implicit def so that Encoder.apply[Tree[Int]] in step 7. and 10. below picks this resolvable encoder for branches.
// 2. implement a ResolvableEncoder instead of Encoder directly so that encoder creation can be deferred
implicit def branchEncoder: Encoder[Branch[Int]] = new ResolvableEncoder[Branch[Int]] {
def encoder(env: DefinitionEnvironment[Encoder], update: SchemaUpdate): Encoder[Branch[Int]] =
// 3. lookup in the definition environment whether we already have created an encoder for branch.
env.get[Branch[Int]].getOrElse {
// 4. use var here to first create an acyclic graph and close it later.
var treeEncoder: Encoder[Tree[Int]] = null
// 5. create a partially initialized encoder for branches (it lacks a value for treeEncoder on creation).
val encoder = new Encoder[Branch[Int]] {
val schemaFor: SchemaFor[Branch[Int]] = SchemaFor[Branch[Int]]
def encode(value: Branch[Int]): AnyRef =
ImmutableRecord(schema, Seq(treeEncoder.encode(value.left), treeEncoder.encode(value.right)))
}
// 6. extend the definition environment with the newly created encoder so that subsequent lookups (step 3.) can return it
val nextEnv = env.updated(encoder)
// 7. resolve the tree encoder with the extended environment; the extended env will be passed back to the lookup
// performed in step 3. above.
// 9. complete the initialization by closing the reference cycle: the branch encoder and tree encoder now
// reference each other.
treeEncoder = Encoder.apply[Tree[Int]].resolveEncoder(nextEnv, NoUpdate)
encoder
}
}
// 10. summon encoder for tree and kick off encoder resolution.
val toRecord = ToRecord[Tree[Int]]Why is this so complicated? Glad you asked! Turns out that caring for performance, providing customization via annotations, and using Magnolia for automatic typeclass derivation (which is great in itself) are three constraints that aren't easy to combine. This design is the best we came up with; if you have a better design for this, please contribute it!
compile 'com.sksamuel.avro4s:avro4s-core_2.12:xxx'
libraryDependencies += "com.sksamuel.avro4s" %% "avro4s-core" % "xxx"
<dependency>
<groupId>com.sksamuel.avro4s</groupId>
<artifactId>avro4s-core_2.12</artifactId>
<version>xxx</version>
</dependency>Check the latest released version on Maven Central
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
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