Checklist
- User Stories Documented
- User Stories Reviewed
- Design Reviewed
- APIs reviewed
- Release priorities assigned
- Test cases reviewed
- Blog post
Introduction
CDAP pipeline is composed of various CDAP plugins. These plugins handle error situations in case of invalid inputs or configurations. While developing CDAP pipelines, pipeline developer can provide invalid plugin configurations. For example, the BigQuery sink plugin can have schema which does not match with an underlying BigQuery table. In such situations, providing clear error message is helpful to guide user in right direction. Also, when such situation happens, providing all the error messages at once is important for better user experience.
Wrangler provides interactive way for users to apply directives to the data. However, while applying these directives, user may run into error situations. For example, the input json file may be corrupted which can fail parse-as-json directive. In such error situations, user should be provided clear error message so that further actions can be taken.
Goals
There are four goals which needs to be achieved to improve error handling:
Have a guideline on how an error message should be formulated that makes it easier for end user to interpret the error situation.
- Improve error messages in Wrangler directives.
To fail fast, validate fields and plugin schema at the deploy time.
- Improve error messages in CDAP plugins.
User Stories
As a CDAP pipeline developer, if a pipeline contains plugin configurations which are invalid, I will like it to fail early with appropriate error message.
As an ETL engineer, if I run into error situation while applying directives, I will like to see appropriate error message which clearly indicates the error.
Guidelines for Error Messages
An error message is the text used to provide information about error situations. Poorly written error messages can increase support costs and can be a source of frustration for users. Well-written error messages are very important for better user experience. Below is the guideline on writing better error messages.
1. Error messages should be contextual.
Contextual error messages provide specific information particular to error situation. Error messages without any context are very hard to interpret by users. Contextual information may include information such as why the error happened, what is the error value, what is the expected value, how user can fix the error etc.
For example, if there is a mismatch in data type of a field, providing more contextual information to user in error message would help user understand the problem and fix it if needed.
Error Message: Data type mismatch for field 'X'. Better Error Message: Field 'X' is expected to be of data type 'int'. However, provided data type is 'string'.
2. Do not include implementation details in user facing error messages.
Exposing implementation details to end users can be confusing and users may not be able to take any action to solve error situation. Thats why user facing error messages should not include implementation details. Below are some of the cases where we can avoid exposing implementation details:
Avoid using class hashes in error messages. For example:
Error Message: co.cask.directives.language.SetCharset@781ecbac : Invalid type 'java.lang.String' of column 'body'. Should be of type String. Better Error Message: Error executing 'set-charset' directive: The 'body' column was expected to be a byte array or ByteBuffer, but is of type 'String'.
Avoid using exception class names in user facing error messages. For example:
Error Message: java.lang.IllegalArgumentException: Database driver 'cloudsql-postgresql' not found. Better Error Message: Database driver 'cloudsql-postgresql' not found. Please make sure correct database driver is deployed.
Avoid using technical implementation details in user facing error messages. For example:
Error Message: Failed to configure pipeline: valueOf operation on abc failed. Better Error Message: Failed to configure pipeline: Expected type of field 'X' is either int/double but found 'abc'.
3. Error message should provide direction to user if action is needed from user.
An error message has 3 parts, problem identification, cause details if helpful, and a solution if possible. Whenever error situation occurs, users would like to fix it immediately. The error message should have enough information to guide the user in right direction.
4. Provide complete concise error message to user and avoid ambiguity.
An error message should be a complete sentence which provides clear message. User should be able to understand the problem by reading the error message. For example:
Error Message: io.cdap.directives.transformation.Decode@c2e00f5 : Failed to decode hex value. Better Error Message: Error while decoding field 'X' as hex value. Please make sure the provided field is encoded as hex.
5. Always prefer error message specific to the error situation instead of generic error messages
Whenever possible, use specific error message instead of generic error message. For example:
Error Message: Failed to decode hex value. Better Error Message: Error while decoding field 'X' as hex value. Please make sure the provided field is encoded as hex.
Scope
Dataprep
- Improve error messages in all Directives
- Remove usages of object hashes in the error messages. It happens because of usage of toString() in error messages
- Update error messages to be more contextual
- In Http Handlers, add cause to the thrown exception for debugging so that the cause is not lost
Plugins
- Plugin Validation
- Instrument plugins so that all the invalid config and schema fields are reported to the user at once when a plugin is validated
- Deploy time validation for reference name
- Improve error messages in plugins (core-plugins, google-cloud plugins to start with)
Platform
- Improve user facing error messages from HttpHandlers
- When a mapreduce job/pipeline fails actual cause of error are logged under WARN level instead of ERROR log level.
Bug Fixes
- Error rendering macro 'jira' : Unable to locate Jira server for this macro. It may be due to Application Link configuration.
API Changes
Plugin Validation
Plugin validation endpoint is used to surface all the stage level errors at once. To collect multiple stage validation errors from the stage, StageConfigurer, MultiInputStageConfigurer and MultiOutputStageConfigurer can be modified as below. If there are one or more errors added to stage configurer, the pipeline deployment will fail.
public interface StageConfigurer {
/**
* get the input schema for this stage, or null if its unknown
*
* @return input schema
*/
@Nullable
Schema getInputSchema();
/**
* set the output schema for this stage, or null if its unknown
*
* @param outputSchema output schema for this stage
*/
void setOutputSchema(@Nullable Schema outputSchema);
/**
* set the error schema for this stage, or null if its unknown.
* If no error schema is set, it will default to the input schema for the stage. Note that since source
* plugins do not have an input schema, it will default to null for sources.
*
* @param errorSchema error schema for this stage
*/
void setErrorSchema(@Nullable Schema errorSchema);
/**
* add errors for this stage to the configurer if pipeline stage is invalid.
*
* @param error {@link InvalidStageException} when a pipeline stage is invalid for any reason.
*/
void addStageError(InvalidStageException error);
}
Plugins can use this api as below:
@Override
public void configurePipeline(PipelineConfigurer pipelineConfigurer) {
pipelineConfigurer.createDataset(conf.destinationFileset, FileSet.class);
StageConfigurer stageConfigurer = pipelineConfigurer.getStageConfigurer();
try {
Pattern.compile(conf.filterRegex);
} catch (Exception e) {
stageConfigurer.addStageError(new InvalidConfigPropertyException(e.getMessage(), "filterRegex"));
}
if (conf.sourceFileset.equals(conf.destinationFileset)) {
stageConfigurer.addStageError(new InvalidStageException("source and destination filesets must be different"));
}
}
Sources and sinks can have schema mismatch with underlying storage. New type of exception can be introduced so that invalid schema fields can be highlighted when schema mismatch occurs:
public class InvalidSchemaFieldException extends InvalidStageException {
private final String field;
public InvalidSchemaFieldException(String message, String field) {
super(message);
this.field = field;
}
public InvalidSchemaFieldException(String message, Throwable cause, String field) {
super(message, cause);
this.field = field;
}
public String getField() {
return field;
}
}
Validation error will have corresponding INVALID_SCHEMA type for UI to identify schema field errors.
public class ValidationError {
protected final Type type;
protected final String message;
/**
* Types of validation errors
*/
public enum Type {
ERROR,
STAGE_ERROR,
INVALID_FIELD,
PLUGIN_NOT_FOUND,
INVALID_SCHEMA
}
...
}
/**
* An error that occurred due to field schema mismatch in a specific pipeline stage.
*/
public class InvalidSchemaFieldError extends StageValidationError {
private final String field;
public InvalidSchemaFieldError(String stage, InvalidSchemaFieldException cause) {
this(cause.getMessage(), stage, cause.getField());
}
public InvalidConfigPropertyError(String message, String stage, String field) {
super(Type.INVALID_SCHEMA, message, stage);
this.field = field;
}
public String getField() {
return field;
}
@Override
public boolean equals(Object o) {
....
}
@Override
public int hashCode() {
...
}
}
Impact on UI
UI changes will be needed for invalid schema type errors returned from validation endpoint.
Test Scenarios
Test ID | Test Description | Expected Results |
|---|---|---|
Releases
Release 6.1.0