# Vert.x Json Schema

Vert.x Json Schema provides an extendable and asynchronous implementation for Json Schema specification. You can use Json Schemas to validate every json structure. This module provides:

- Implementation of draft 2020-12

- Implementation of draft 2019-09

- Implementation of draft 7

- Implementation of draft 4

- Dereferencing of `$ref` resolution and caching

- DSL to build schemas programmatically

## Using Vert.x Json Schema

To use Vert.x Json Schema, add the following dependency to the dependencies section of your build descriptor:

- Maven (in your `pom.xml`):

    <dependency>
     <groupId>io.vertx</groupId>
     <artifactId>vertx-json-schema</artifactId>
     <version>4.3.0</version>
    </dependency>

- Gradle (in your `build.gradle` file):

    dependencies {
     compile 'io.vertx:vertx-json-schema:4.3.0'
    }

## Concepts

### JsonSchema

Schemas can exist in 2 flavours:

- JSON as in JSON natation

- Boolean as in `true/false`

The `JsonSchema` interface allows both types to be handled without the constant check of the underlying type.

### SchemaRepository

The `SchemaRepository` holds `JsonSchema` instances. It performs dereferencing of schemas to speed up validation. The repository is a simple key store, this means that it does not allow duplicate ids.

The repository can then create `Validator` instances aware of all sub schemas in the repository.

### Validator

As the name implies the `Validator` validates an object using a start schema. The output format is dependent of the configuration.

## Parse a schema

When working with multiple schemas or sub-schemas, it is recommended to use a `Repository`.

To parse a schema you first need a `JsonSchema` and some initial configuration. Since schemas can contain references it is required for the validator and repository to be aware of your application `baseUri`. This allows you to reference your own schemas in other sub-schemas. For the purpose of dereferencing, you don’t need to configure a draft.

    SchemaRepository repository =
      SchemaRepository.create(new JsonSchemaOptions().setBaseUri("https://vertx.io"));

You can use `JsonSchema` instances for different `Validator` and you can parse different `JsonSchema` with `JsonParser` directly.

Now you can parse the schema:

    JsonSchema schema = JsonSchema.of(object);

    // Or

    repository.dereference(JsonSchema.of(object));

Important | Remember that for security reasons, this module will not attempt to download any referenced sub-schema. All required sub-schemas should be provided to a repository object.

## Validate

Given the dynamic nature of json-schema and the conditional `if-then-else` it is not possible to validate in a streaming scenario. Validation is for this reason a blocking operation. If you are aware that validation will be a very expensive process, then it is advisable to run the validation on a dedicated thread pool or using `executeBlocking`. A schema could have two states:

To validate a schema:

    OutputUnit result = Validator.create(
        schema,
        new JsonSchemaOptions().setDraft(Draft.DRAFT7))
      .validate(json);

    if (result.getValid()) {
      // Successful validation
    }

## Custom formats

Adding custom formats is not allowed. This may sound like a limitation but it arises from the validation spec. Validators are expected to assume unknown formats as valid input, delegating to the application business code the role of validator for custom values.

## Building your schemas from code

If you want to build schemas from code, you can use the included DSL. Only Draft-7 is supported for this feature.

To start, add static imports for `Schemas` and `Keywords`

### Creating the schema

Inside `Schemas` there are static methods to create the schema:

    SchemaBuilder intSchemaBuilder = intSchema();
    SchemaBuilder objectSchemaBuilder = objectSchema();

### Using the keywords

For every schema you can add keywords built with `Keywords` methods, depending on the type of the schema:

    stringSchema()
      .with(format(StringFormat.DATETIME));
    arraySchema()
      .with(maxItems(10));
    schema() // Generic schema that accepts both arrays and integers
      .with(type(SchemaType.ARRAY, SchemaType.INT));

### Defining the schema structure

Depending on the schema you create, you can define a structure.

To create an object schema with some properties schemas and additional properties schema:

    objectSchema()
      .requiredProperty("name", stringSchema())
      .requiredProperty("age", intSchema())
      .additionalProperties(stringSchema());

To create an array schema:

    arraySchema()
      .items(stringSchema());

To create a tuple schema:

    tupleSchema()
      .item(stringSchema()) // First item
      .item(intSchema()) // Second item
      .item(booleanSchema()); // Third item

### `$ref` and aliases

To add a `$ref` schema you can use the `Schemas.ref` method. To assign an `$id` keyword to a schema, use `id`

You can also refer to schemas defined with this dsl using aliases. You can use `alias` to assign an alias to a schema. Then you can refer to a schema with an alias using `Schemas.refToAlias`:

    intSchema()
      .alias("myInt");

    objectSchema()
      .requiredProperty("anInteger", refToAlias("myInt"));

### Using the schema

After you defined the schema, you can call `toJson` to return the JSON notation of the schema:

    JsonObject schema = objectSchema()
      .requiredProperty("name", stringSchema())
      .requiredProperty("age", intSchema())
      .additionalProperties(stringSchema())
      .toJson();