Generating fake data for the JVM (Java, Kotlin, Groovy) has never been easier!
See the code
This library is a modern fork of java-faker with up to date libraries and several newly added Fake Generators.
Datafaker 2.x has Java 17 as the minimum requirement.
If Java 17 is not an option for you, you can choose to use Datafaker 1.x. Datafaker 1.x is built on Java 8, but this version is no longer maintained. We recommend all users to upgrade to Datafaker 2.x.
This library generates fake data, similar to other fake data generators, such as:
It's useful when you're developing a new project and need some pretty data for showcase.
In the pom.xml, add the following fragment to the dependencies section:
<dependency>
<groupId>net.datafaker</groupId>
<artifactId>datafaker</artifactId>
<version>2.7.0</version>
</dependency>
For Gradle users, add the following to your build.gradle file.
dependencies {
implementation 'net.datafaker:datafaker:2.7.0'
}
You can also use the snapshot version (3.0.0-SNAPSHOT), which automatically gets published
after every push to the main branch of this repository. Binary repository URL for snapshots download is
https://central.sonatype.com/repository/maven-snapshots/.
In your Java code:
Faker faker = new Faker();
String name = faker.name().fullName(); // Miss Samanta Schmidt
String firstName = faker.name().firstName(); // Emory
String lastName = faker.name().lastName(); // Barton
String streetAddress = faker.address().streetAddress(); // 60018 Sawayn Brooks Suite 449
Or in your Kotlin code:
val faker = Faker()
val name = faker.name().fullName() // Miss Samanta Schmidt
val firstName = faker.name().firstName() // Emory
val lastName = faker.name().lastName() // Barton
val streetAddress = faker.address().streetAddress() // 60018 Sawayn Brooks Suite 449
JShell
# from project root folder
jshell --class-path $(ls -d target/*.jar | tr '\n' ':')
or JShell w/JBang
# can be run anywhere, no project root/download needed.
jbang -i net.datafaker:datafaker:2.7.0
| Welcome to JShell -- Version 17.0.4
| For an introduction type: /help intro
jshell> import net.datafaker.Faker;
jshell> var faker = new Faker();
faker ==> net.datafaker.Faker@c4437c4
jshell> faker.address().city();
$3 ==> "Brittneymouth"
jshell> faker.name().fullName();
$5 ==> "Vernie Schmidt"
Faker faker = new Faker();
faker.expression("#{letterify 'test????test'}"); // testqwastest
faker.expression("#{numerify '#test#'}"); // 3test5
faker.expression("#{templatify 'test','t','q','@'}"); // @esq
faker.expression("#{examplify 'test'}"); // ghjk
faker.expression("#{regexify '[a-z]{4,10}'}"); // wbevoa
faker.expression("#{options.option '23','2','5','$','%','*'}"); // *
faker.expression("#{date.birthday 'yy DDD hh:mm:ss'}"); // 61 327 08:11:45
faker.expression("#{csv '1','name_column','#{Name.first_name}','last_name_column','#{Name.last_name}'}");
// "name_column","last_name_column"
// "Sabrina","Kihn"
faker.expression("#{json 'person','#{json ''first_name'',''#{Name.first_name}'',''last_name'',''#{Name.last_name}''}','address','#{json ''country'',''#{Address.country}'',''city'',''#{Address.city}''}'}");
// {"person": {"first_name": "Barbie", "last_name": "Durgan"}, "address": {"country": "Albania", "city": "East Catarinahaven"}}
also more examples at https://www.datafaker.net/documentation/expressions/
Faker faker = new Faker();
List<String> names = faker.collection(
() -> faker.name().firstName(),
() -> faker.name().lastName())
.len(3, 5)
.generate();
System.out.println(names);
// [Skiles, O'Connell, Lorenzo, West]
more examples about that at https://www.datafaker.net/documentation/sequences/
Faker faker = new Faker();
// generate an infinite stream
Stream<String> names = faker.stream(
() -> faker.name().firstName(),
() -> faker.name().lastName())
.generate();
There are 2 ways of data generation in specific formats
For both cases we need a Schema which could describe fields and a way of data generation.
In case of generation from scratch Suppliers are enough, in case of transformation Functions are required
// transformer could be the same for both
CsvTransformer<Name> transformer =
CsvTransformer.<Name>builder().header(true).separator(",").build();
// Schema for from scratch
Schema<Name, String> fromScratch =
Schema.of(field("firstName", () -> faker.name().firstName()),
field("lastname", () -> faker.name().lastName()));
System.out.println(transformer.generate(fromScratch, 2));
// POSSIBLE OUTPUT
// "firstName","lastname"
// "Kimberely","Considine"
// "Mariela","Krajcik"
// ----------------------
// Schema for transformations
Schema<Name, String> schemaForTransformations =
Schema.of(field("firstName", Name::firstName),
field("lastname", Name::lastName));
// Here we pass a collection of Name objects and extract first and last names from each element
System.out.println(
transformer.generate(
faker.collection(faker::name).maxLen(2).generate(), schemaForTransformations));
// POSSIBLE OUTPUT
// "firstName","lastname"
// "Kimberely","Considine"
// "Mariela","Krajcik"
# from project root folder
jshell --class-path $(ls -d target/*.jar | tr '\n' ':')
| Welcome to JShell -- Version 17.0.4
| For an introduction type: /help intro
jshell> import net.datafaker.Faker;
jshell> import net.datafaker.providers.base.Name;
jshell> import net.datafaker.transformations.Schema;
jshell> import net.datafaker.transformations.CsvTransformer;
jshell> import static net.datafaker.transformations.Field.field;
jshell> var faker = new Faker();
faker ==> net.datafaker.Faker@c4437c4
jshell> Schema fromScratch =
...> Schema.of(field("firstName", () -> faker.name().firstName()),
...> field("lastname", () -> faker.name().lastName()));
fromScratch ==> net.datafaker.transformations.Schema@306a30c7
jshell> CsvTransformer<Name> transformer =
...> CsvTransformer.<Name>builder().header(true).separator(",").build();
transformer ==> net.datafaker.transformations.CsvTransformer@506c589e
jshell> System.out.println(transformer.generate(fromScratch, 2));
"firstName","lastname"
"Darcel","Schuppe"
"Noelle","Smitham"
Schema<Object, ?> schema = Schema.of(
field("firstName", () -> faker.name().firstName()),
field("lastName", () -> faker.name().lastName())
);
JsonTransformer<Object> transformer = JsonTransformer.builder().build();
String json = transformer.generate(schema, 2);
// [{"firstName": "Oleta", "lastName": "Toy"},
// {"firstName": "Gerard", "lastName": "Windler"}]
More complex examples and other formats like YAML, XML could be found at https://www.datafaker.net/documentation/formats/
Faker faker = new Faker();
// The values returned in the following lines will never be the same.
String firstUniqueInstrument = faker.unique().fetchFromYaml("music.instruments"); // "Flute"
String secondUniqueInstrument = faker.unique().fetchFromYaml("music.instruments"); // "Clarinet"
More examples can be found in https://www.datafaker.net/documentation/unique-values
Add your own custom provider in your app following steps from https://www.datafaker.net/documentation/custom-providers/
See CONTRIBUTING.md
If this is your first time contributing then you may find it helpful to read FIRST_TIME_CONTRIBUTOR.md
The list below is not complete and shows only a part of available providers. To view the full list of providers, please follow the link: Full list of providers.
Faker faker = new Faker(Locale.of("lang", "COUNTRY"));
For example:
String californiaZipCode = new Faker(Locale.of("en", "US")).address().zipCodeByState("CA");
String albanianIdNumber = new Faker(Locale.of("sq", "AL")).idNumber().valid();
String moldovanPhone = new Faker(Locale.of("ru", "MD")).phoneNumber().cellPhone();
Note that most of the data depends on language, but some data depends purely on country (personal ID and phone numbers). In the example above,
Address provider with
address-formatter-java
to turn generated components into locale-aware postal addresses.
See Related projects for examples.Since version 2.4.1, Datafaker provides experimental native-image support.
This is done by providing a reachability-metadata.json file in the META-INF directory.
The metadata is generated by running the test suite with the GraalVM tracing agent. Set JAVA_HOME to a GraalVM
installation and run:
./scripts/update-native-image-metadata.sh
The generator requires jq. It filters test runners and test dependencies while tracing, includes resources shipped
by Datafaker and its runtime dependencies, writes to target first, and only replaces the published metadata after
NativeImageMetadataTest validates it. If you encounter unexpected native image behaviour, feel free to report an
issue.
An example usage of this can be found here: https://github.com/datafaker-net/datafaker-native-demo
Copyright (c) 2026 Datafaker.net See the LICENSE file for license rights and limitations.
152 followers · starred May 2024
807 followers · starred Dec 2023
218 followers · starred Oct 2025
162 followers · starred Mar 2023
Generating fake data for the JVM (Java, Kotlin, Groovy) has never been easier!
See the code
This library is a modern fork of java-faker with up to date libraries and several newly added Fake Generators.
Datafaker 2.x has Java 17 as the minimum requirement.
If Java 17 is not an option for you, you can choose to use Datafaker 1.x. Datafaker 1.x is built on Java 8, but this version is no longer maintained. We recommend all users to upgrade to Datafaker 2.x.
This library generates fake data, similar to other fake data generators, such as:
It's useful when you're developing a new project and need some pretty data for showcase.
In the pom.xml, add the following fragment to the dependencies section:
<dependency>
<groupId>net.datafaker</groupId>
<artifactId>datafaker</artifactId>
<version>2.7.0</version>
</dependency>
For Gradle users, add the following to your build.gradle file.
dependencies {
implementation 'net.datafaker:datafaker:2.7.0'
}
You can also use the snapshot version (3.0.0-SNAPSHOT), which automatically gets published
after every push to the main branch of this repository. Binary repository URL for snapshots download is
https://central.sonatype.com/repository/maven-snapshots/.
In your Java code:
Faker faker = new Faker();
String name = faker.name().fullName(); // Miss Samanta Schmidt
String firstName = faker.name().firstName(); // Emory
String lastName = faker.name().lastName(); // Barton
String streetAddress = faker.address().streetAddress(); // 60018 Sawayn Brooks Suite 449
Or in your Kotlin code:
val faker = Faker()
val name = faker.name().fullName() // Miss Samanta Schmidt
val firstName = faker.name().firstName() // Emory
val lastName = faker.name().lastName() // Barton
val streetAddress = faker.address().streetAddress() // 60018 Sawayn Brooks Suite 449
JShell
# from project root folder
jshell --class-path $(ls -d target/*.jar | tr '\n' ':')
or JShell w/JBang
# can be run anywhere, no project root/download needed.
jbang -i net.datafaker:datafaker:2.7.0
| Welcome to JShell -- Version 17.0.4
| For an introduction type: /help intro
jshell> import net.datafaker.Faker;
jshell> var faker = new Faker();
faker ==> net.datafaker.Faker@c4437c4
jshell> faker.address().city();
$3 ==> "Brittneymouth"
jshell> faker.name().fullName();
$5 ==> "Vernie Schmidt"
Faker faker = new Faker();
faker.expression("#{letterify 'test????test'}"); // testqwastest
faker.expression("#{numerify '#test#'}"); // 3test5
faker.expression("#{templatify 'test','t','q','@'}"); // @esq
faker.expression("#{examplify 'test'}"); // ghjk
faker.expression("#{regexify '[a-z]{4,10}'}"); // wbevoa
faker.expression("#{options.option '23','2','5','$','%','*'}"); // *
faker.expression("#{date.birthday 'yy DDD hh:mm:ss'}"); // 61 327 08:11:45
faker.expression("#{csv '1','name_column','#{Name.first_name}','last_name_column','#{Name.last_name}'}");
// "name_column","last_name_column"
// "Sabrina","Kihn"
faker.expression("#{json 'person','#{json ''first_name'',''#{Name.first_name}'',''last_name'',''#{Name.last_name}''}','address','#{json ''country'',''#{Address.country}'',''city'',''#{Address.city}''}'}");
// {"person": {"first_name": "Barbie", "last_name": "Durgan"}, "address": {"country": "Albania", "city": "East Catarinahaven"}}
also more examples at https://www.datafaker.net/documentation/expressions/
Faker faker = new Faker();
List<String> names = faker.collection(
() -> faker.name().firstName(),
() -> faker.name().lastName())
.len(3, 5)
.generate();
System.out.println(names);
// [Skiles, O'Connell, Lorenzo, West]
more examples about that at https://www.datafaker.net/documentation/sequences/
Faker faker = new Faker();
// generate an infinite stream
Stream<String> names = faker.stream(
() -> faker.name().firstName(),
() -> faker.name().lastName())
.generate();
There are 2 ways of data generation in specific formats
For both cases we need a Schema which could describe fields and a way of data generation.
In case of generation from scratch Suppliers are enough, in case of transformation Functions are required
// transformer could be the same for both
CsvTransformer<Name> transformer =
CsvTransformer.<Name>builder().header(true).separator(",").build();
// Schema for from scratch
Schema<Name, String> fromScratch =
Schema.of(field("firstName", () -> faker.name().firstName()),
field("lastname", () -> faker.name().lastName()));
System.out.println(transformer.generate(fromScratch, 2));
// POSSIBLE OUTPUT
// "firstName","lastname"
// "Kimberely","Considine"
// "Mariela","Krajcik"
// ----------------------
// Schema for transformations
Schema<Name, String> schemaForTransformations =
Schema.of(field("firstName", Name::firstName),
field("lastname", Name::lastName));
// Here we pass a collection of Name objects and extract first and last names from each element
System.out.println(
transformer.generate(
faker.collection(faker::name).maxLen(2).generate(), schemaForTransformations));
// POSSIBLE OUTPUT
// "firstName","lastname"
// "Kimberely","Considine"
// "Mariela","Krajcik"
# from project root folder
jshell --class-path $(ls -d target/*.jar | tr '\n' ':')
| Welcome to JShell -- Version 17.0.4
| For an introduction type: /help intro
jshell> import net.datafaker.Faker;
jshell> import net.datafaker.providers.base.Name;
jshell> import net.datafaker.transformations.Schema;
jshell> import net.datafaker.transformations.CsvTransformer;
jshell> import static net.datafaker.transformations.Field.field;
jshell> var faker = new Faker();
faker ==> net.datafaker.Faker@c4437c4
jshell> Schema fromScratch =
...> Schema.of(field("firstName", () -> faker.name().firstName()),
...> field("lastname", () -> faker.name().lastName()));
fromScratch ==> net.datafaker.transformations.Schema@306a30c7
jshell> CsvTransformer<Name> transformer =
...> CsvTransformer.<Name>builder().header(true).separator(",").build();
transformer ==> net.datafaker.transformations.CsvTransformer@506c589e
jshell> System.out.println(transformer.generate(fromScratch, 2));
"firstName","lastname"
"Darcel","Schuppe"
"Noelle","Smitham"
Schema<Object, ?> schema = Schema.of(
field("firstName", () -> faker.name().firstName()),
field("lastName", () -> faker.name().lastName())
);
JsonTransformer<Object> transformer = JsonTransformer.builder().build();
String json = transformer.generate(schema, 2);
// [{"firstName": "Oleta", "lastName": "Toy"},
// {"firstName": "Gerard", "lastName": "Windler"}]
More complex examples and other formats like YAML, XML could be found at https://www.datafaker.net/documentation/formats/
Faker faker = new Faker();
// The values returned in the following lines will never be the same.
String firstUniqueInstrument = faker.unique().fetchFromYaml("music.instruments"); // "Flute"
String secondUniqueInstrument = faker.unique().fetchFromYaml("music.instruments"); // "Clarinet"
More examples can be found in https://www.datafaker.net/documentation/unique-values
Add your own custom provider in your app following steps from https://www.datafaker.net/documentation/custom-providers/
See CONTRIBUTING.md
If this is your first time contributing then you may find it helpful to read FIRST_TIME_CONTRIBUTOR.md
The list below is not complete and shows only a part of available providers. To view the full list of providers, please follow the link: Full list of providers.
Faker faker = new Faker(Locale.of("lang", "COUNTRY"));
For example:
String californiaZipCode = new Faker(Locale.of("en", "US")).address().zipCodeByState("CA");
String albanianIdNumber = new Faker(Locale.of("sq", "AL")).idNumber().valid();
String moldovanPhone = new Faker(Locale.of("ru", "MD")).phoneNumber().cellPhone();
Note that most of the data depends on language, but some data depends purely on country (personal ID and phone numbers). In the example above,
Address provider with
address-formatter-java
to turn generated components into locale-aware postal addresses.
See Related projects for examples.Since version 2.4.1, Datafaker provides experimental native-image support.
This is done by providing a reachability-metadata.json file in the META-INF directory.
The metadata is generated by running the test suite with the GraalVM tracing agent. Set JAVA_HOME to a GraalVM
installation and run:
./scripts/update-native-image-metadata.sh
The generator requires jq. It filters test runners and test dependencies while tracing, includes resources shipped
by Datafaker and its runtime dependencies, writes to target first, and only replaces the published metadata after
NativeImageMetadataTest validates it. If you encounter unexpected native image behaviour, feel free to report an
issue.
An example usage of this can be found here: https://github.com/datafaker-net/datafaker-native-demo
Copyright (c) 2026 Datafaker.net See the LICENSE file for license rights and limitations.
152 followers · starred May 2024
807 followers · starred Dec 2023
218 followers · starred Oct 2025
162 followers · starred Mar 2023