Validation In


There are many situations in which incoming or outgoing data should be validated. For example, when creating a record which stores user information, you may want to check that the username field doesn’t contain special characters, or you may want to ensure that all required (non-optional) fields are present before processing the incoming record. When a required field is not present (and doesn’t have a default value either), calling getXXX() will throw a RequiredFieldNotPresentException. So validating the entity beforehand can simplify the error handling code because you wouldn’t need to call hasXXX() for every single field before calling getXXX(). Also, you may want to check that clients are not trying to modify certain fields, like server-generated ids or timestamps. validation provides a mechanism to perform such validation.

To use’s validation feature, you need to specify validation rules and choose how to enable validation.

Specifying Validation Rules validates using three types of rules:

  1. Schema validation rules (checking the type of field values and the presence of required fields)
  2. Custom validation rules (checking whether a number is in some range, matching a string to a regular expression, etc)
  3. validation annotations (@ReadOnly and @CreateOnly)

The first two rules are specified in the data schema (.pdl file), and the third one is specified in the resource implementation. This is because the first two rules only deal with information in the data itself, but the third rule also needs additional context such as the method type. For example, a read only field shouldn’t be included in a create request, but should be included in a get response.

For partial update requests (patches), the goal is to ensure that if the patch is applied to a valid entity, the modified entity will also be valid. For example, a patch that deletes a required field is invalid because the modified entity will be missing that field. If there is a custom validation rule on the username field that it must be at least 3 characters, a patch setting the username to “AA” is invalid.

You can read more about required and optional fields in Data Schemas - PDL Syntax - Record Type.

Custom Validation Rules includes some customizable validators, such as “strlen” and “regex”, that can be added to schemas. Developers can write additional validators for any specific need.

For example, to use “strlen” to validate a string between 1 and 20 chars long, we add it to the “validate” map of the field in the schema, for example:

namespace com.example

record Fortune {
  @validate.strlen = {"min": 1, "max": 20}
  message: string

Validator names are case sensitive and must have a matching validator Java class in the current classpath. finds the validator class by uppercasing the first letter of the validator name and appending the “Validator” suffix. For example, “strlen” maps to StrlenValidator. Developers writing additional validators only need to write a class extending AbstractValidator and include it in the classpath to use it.

Additional details are described in the javadoc for DataSchemaAnnotationValidator. Validation Annotations

Fields in a pegasus data schema can be either required or optional. However, there are certain cases where this distinction is not expressive enough. For example, a client shouldn’t send a server-generated id in a create request (i.e. id is optional), but the server must send the id when responding to a get request (i.e. id is required). To cover cases like this, we introduce two new validation annotations: @ReadOnly and @CreateOnly.

A @ReadOnly field cannot be set or changed by the client, ex. server-generated ID field. A @CreateOnly field can be set the client when creating the entity, but cannot be changed by the client afterwards. This annotation implies that the field is immutable, ex. a purchase price. The client will send the price to the CREATE method while creating a purchase entry.

As a best practice, validation annotation should not conflict with schema validation rules and custom validation rules specified in the schema. For example, @ReadOnly should only be used to enforce that an optional field is not present. It should not be specified for a required field, making missing required field value valid.

The data validator will enforce the following rules for fields in request data based on the annotation:

Note that validation is not turned on by default, and servers have to manually call the validator or use the validation filter.

  Create Partial Update
@ReadOnly Must not be present (See notes below) Must not be present
@CreateOnly N/A Must not be present

Batch create, batch update, and batch partial update are treated the same as create, update, and partial update respectively.

Notes on @ReadOnly Validation Rules for Create Request

If @ReadOnly is specified to a field that is required in schema, the field is treated as optional and the validation rule enforces that the field is not present in the Create request data.

Validation for Update Requests

Update methods can be used in two scenarios:

  1. As a PUT method, that updates the whole entity. The client should’ve fetched the original entity, updated it and then called UPDATE with the full entity. In this case, both @ReadOnly and @CreateOnly marked fields should be present.
  2. As CREATE when UPDATE is used to as UPSERT method. In this scenario, UPDATE is used both to update or create the entity (if it is not already present). When the entity is being created, the @ReadOnly fields may be absent and the @CreateOnly fields may be present. Similarly, when the entity is be updated, both sets of fields will be present (similar to scenario 1).

So to support both these scenarios, the validation for Update requests is relaxed to allow @ReadOnly and @CreateOnly fields to be present and for @ReadOnly fields to be optional (when the field is marked as required in schema).

In the update request, when the @ReadOnly or @CreateOnly fields are present, and if the request is updating an existing entity, they are expected to have the same value as the original entity (if the field was missing from the original entity, it should be missing in the update request too). However, this is not checked by the framework and should be checked manually in the resource implementation.

Specifying Validation Annotations validation annotations are specified on top of the resource. For example,

@RestLiCollection(name = "photos", namespace = "")
@CreateOnly({"/id", "/EXIF"})
public class PhotoResource extends CollectionResourceTemplate<Long, Photo>

Every path should correspond to a field in a record, and not an enum constant, a member of a union, etc. Paths should be specified in the format used by “PathSpec”: Note that the first / character can be either specified or omitted. You can check the correct path for a field by getting its PathSpec and calling toString(). For example, if the ValidationDemo record contains an array field like this:

record ValidationDemo {
  ArrayWithInlineRecord: optional array[record myItem {
    bar1: string,
    bar2: string
  // ...

ValidationDemo.fields().ArrayWithInlineRecord().items().bar1().toString() will return /ArrayWithInlineRecord/*/bar1.

You can also refer to these rules:

  • For a non-nested field, put the field name. e.g. “stringA”
  • For a nested field, put the full path separated by / characters. e.g. “location/latitude”
  • For a field of an array item, specify the array name followed by the wildcard and the field name. e.g. “ArrayWithInlineRecord/*/bar1”
  • Similarly, for a field of a map value, specify the map name followed by the wildcard and the field name.
  • For a field of a record inside a union, specify the union name, followed by the fully qualified record schema name, and then the field name. e.g. “UnionFieldWithInlineRecord/com.linkedin.restli.examples.greetings.api.myRecord/foo2”

Because full paths are listed, different rules can be specified for records that have the same schema. For example, if the schema contains two Photos, you can make the id of photo1 ReadOnly and id of photo2 non-ReadOnly. This is different from the optional/required distinction specified in data schemas, where if the id of photo1 is required, the id of photo2 will also be required.

Using the Data Validator For Servers

The data validator can be called directly or indirectly using the validation filters. When the resource calls it directly, it gets specific error messages about where and why the validation failed, and can decide how to handle the error. When a filter is used instead, the resource does not get to examine why validation failed. Instead, the client gets an error response with a message describing why the validation failed. The validation filters are convenient if you want to simply discard invalid requests or responses. On the other hand, if you need to log the error or fail requests for only certain types of errors, you need to call the validator directly.

Request validation before resource handling

The RestLiValidationFilter rejects all invalid requests automatically. It sends a 422 (Unprocessable Entity) error response back to the client if the data is invalid. A sample error message is:

ERROR :: /stringA :: ReadOnly field present in a create request
ERROR :: /stringB :: field is required but not found and has no default value

Response validation after resource handling

The RestLiValidationFilter also discards all invalid responses. The filter sends a 500 error response back to the client if the response is invalid. A sample error message is:

ERROR :: /stringA :: length of “Lorem ipsum dolor sit amet” is out of range 1...10
ERROR :: /stringB :: field is required but not found and has no default value Filters explains how to install the filter.

Request validation during resource handling

To use the data validator explicitly, it needs to be declared as a method parameter using the @ValidatorParam annotation. For example, to validate the input of a create request,

public CreateResponse create(final Fortune entity, @ValidatorParam RestLiDataValidator validator)
  ValidationResult result = validator.validateInput(entity);  
  if (!result.isValid())  
    throw new RestLiServiceException(HttpStatus.S_422_UNPROCESSABLE_ENTITY, result.getMessages().toString());  

Batch requests have to be validated one by one:

public BatchUpdateResult<Integer, Fortune> batchUpdate(BatchPatchRequest<Integer, Fortune> updates, @ValidatorParam
RestLiDataValidator validator)
  for (Map.Entry<Integer, PatchRequest<Fortune>> entry : entityUpdates.getData().entrySet())
    Integer key = entry.getKey();
    PatchRequest<Fortune> patch = entry.getValue();
    ValidationResult result = validator.validateInput(patch);
    if (result.isValid())
      // update entity
      errors.put(key, new RestLiServiceException(HttpStatus.S_422_UNPROCESSABLE_ENTITY, result.getMessages().toString()));

The ValidationDemoResource class shows how to use the validator for each resource method type.

Response validation during resource handling

Similar to request validation, the data validator needs to be declared as a method parameter.
For example:

public Map<Integer, Fortune> batchGet(Set<Integer> ids, @ValidatorParam RestLiDataValidator validator)
  Map<Integer, Fortune> resultMap = new HashMap<Integer, Fortune>();
  for (Fortune entity : resultMap.values())
    ValidationResult result = validator.validateOutput(entity);
    if (!result.isValid())
      // fix the entity

Using the Data Validator For Clients

Request validation

Clients can validate requests before sending it to the server, to ensure that the request wouldn’t be rejected by the server. Request builders for create, update, partial update and their respective batch operations have validateInput() methods.

Photo newPhoto = new Photo().setTitle(New Photo).setFormat(PhotoFormats.PNG).setExif(newExif);
ValidationResult validationResult = PhotosCreateRequestBuilder.validateInput(newPhoto);
if (validationResult.isValid())
  // send request
  // fix photo

Input data for batch requests have to be validated one by one:

for (PatchRequest<Photo> patch : patches)
  ValidationResult validationResult = PhotosPartialUpdateRequestBuilder.validateInput(patch);
  if (!validationResult.isValid())
    // fix patch

Response validation

When validating data returned by the server, clients have to use the ValidateDataAgainstSchema class as explained below.

Validating Data Without Context

When ReadOnly or CreateOnly annotations are used, context (method type, request vs response) is necessary to validate the data. Otherwise the data and the schema information is enough. Data to Schema Validation explains how to validate data using the ValidateDataAgainstSchema class. If it is used with DataSchemaAnnotationValidator, it will consider the first two types of rules out of three listed in Specifying Validation Rules.

For example:

// Send the request to the server and get the response
final Photo photo = restClient.sendRequest(request).getResponse().getEntity();
DataSchemaAnnotationValidator validator = new DataSchemaAnnotationValidator(photo.schema());
ValidationResult result = ValidateDataAgainstSchema.validate(, photo.schema(), new ValidationOptions(), validator);
if (!result.isValid())
  // handle the error

Backwards Compatibility

Adding or removing a @ReadOnly or @CreateOnly annotation for a field is considered backwards incompatible. When an annotation is added, old client’s create or partial update requests may fail validation because they still contain the field in the request. When an annotation is removed, clients may have to send fields that they didn’t have to before.