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Motivation

Flink ETL job consumes data from Source and produces result to Sink. Source creates relationship with Sink through Flink ETL job. Flink needs a mechanism for users to report these relationships to external systems, such as meta system Datahub [1], Atlas [2] and meta store we mentioned in FLIP-276 [3].

This FLIP aims to introduce listener interfaces in Flink, users can implement them to report the meta data to external systems. The main information is as follows

1. Source and Sink information, such as table name, fields, partition keys, primary keys, watermarks, configurations

2. Job information, such as job id/name, job type, logical plan

3. Relationship between Source/Sink and jobs, such as source and sink and their column lineages.

4. Job execution information, such as job status

Public Interfaces

CatalogModificationListener

DDL operations such as create/alter/drop tables will generate different events and notify CatalogModificationListener . All events for CatalogModificationListener extend the basic CatalogEvent and listeners can get catalog from it. Some general events for database/table are defined as follows and more events can be implemented based on the requirements in the future.

/** The basic listener for lineage in Flink, all lineage related listener should extend this interface. */
public interface LineageListener {}

/**
 * Different events will be fired when a catalog/database/table is modified. The customized listener can get and report specific information from the event according to the event type.
 */
@PublicEvolving
public interface CatalogModificationListener extends LineageListener {
    /* Event fired after a catalog/database/table is modified. */
    void onEvent(CatalogEvent catalogEvent);

    /* The basic class for catalog related event. */
    @PublicEvolving
    public abstract class CatalogEvent {
        /* The catalog of the event. */
        Catalog catalog();
        /* The name of catalog. */
        String catalogName();
    }

    /* The basic class for database related event. */
    public abstract class BaseDatabaseEvent extends CatalogEvent {
        String databaseName();  
    }

    /* Event for database creation. */
    @PublicEvolving
    public class CreateDatabaseEvent extends BaseDatabaseEvent {
        CatalogDatabase database();
        boolean ignoreIfExists();
    }

    /* Event for alter database. */
    @PublicEvolving
    public class AlterDatabaseEvent extends BaseDatabaseEvent {
        CatalogDatabase newDatabase();
        boolean ignoreIfNotExists();
    }

    /* Event for dropping database. */
    @PublicEvolving
    public class DropDatabaseEvent extends BaseDatabaseEvent {
        boolean ignoreIfExists();
    }

    /* Base table event, provides column list, primary keys, partition keys, watermarks and properties in CatalogBaseTable. The table can be source or sink. */
    public abstract class BaseTableEvent extends CatalogEvent {
        ObjectIdentifier identifier();  
        CatalogBaseTable table();
    }

    /* Event for table creation. */
    @PublicEvolving
    public class CreateTableEvent extends BaseTableEvent {
        boolean ignoreIfExists();
    }

    /* Event for altering table, provides all information in old table and new table. */
    @PublicEvolving
    public class AlterTableEvent extends BaseTableEvent {
        List<TableChange> tableChanges();
        boolean ignoreIfExists();
    }

    /* Event for dropping table. */
    @PublicEvolving
    public class DropTableEvent extends BaseTableEvent {
        boolean ignoreIfExists();   
    }
}

/* Factory for catalog listener. */
@PublicEvolving
public interface CatalogModificationListenerFactory {
    public CatalogModificationListener createListener(Configuration configuration, ClassLoader classLoader);
}

/* Add listeners in the catalog context. */
@PublicEvolving
public interface CatalogFactory {
    /** Add listeners in the context. */
    @PublicEvolving
    interface Context {
        /* Get the listeners from context if they are exists. */
        List<CatalogModificationListener> listeners();
    }
}

Users may create different catalogs on the same physical catalog, for example, create two hive catalog named hive_catalog1  and hive_catalog2  for the same metastore. The tables hive_catalog1.my_database.my_table  and hive_catalog2.my_database.my_table  are the same table in hive metastore.

In addition, there are two table types: persistent table  and temporal table . The persistent table  can be identified by catalog and database above, while the temporal table  can only be identified by properties in ddl. Different temporal tables with the same connector type and related properties are the same physical table in external system, such as two tables for the same topic in Kafka.

StorageIdentifier  is introduced to address these issues, listeners can get it from CatalogTable . StorageIdentifierFactory creates StorageIdentifier for catalog table.

/* Storage identifier for different physical table. */
@PublicEvolving
public class StorageIdentifier {
    /* Storage type such as kafka, hive, iceberg or paimon. */
    String type();

    /* Properties for the storage identifier, users can get value from different keys for different storages, such as broker and topic for kafka. */
    Map<String, String> properties();
}

Different storages put their options in the properties according to dynamic source and sink, users get option value with them too. Flink Table/SQL jobs can get options from table properties automatically, and users need to add them manually for DataStream jobs. Flink has many connectors, and this FLIP provides the main connector kafka options here. More connectors and options can be added as needed in the future

/* Kafka storage identifier options. */
"properties.bootstrap.servers" for Kafka bootstrap servers
"topic" for Kafka Topic
"properties.group.id" for Kafka group id
"topic-pattern" for Kafka topic pattern

For some sensitive information, users can encode and desensitize it in their own implemented listeners.

JobStatusChangedListener 

Flink creates events and notify JobStatusChangedListener when status of job is changed after it is created. There are two types of job status event for the listener: JobCreatedEvent and JobExecutionStatusEvent. JobCreatedEvent has job lineage and listener can create lineages for source and sink, while JobExecutionStatusEvent has different job status and listener can delete the lineages when job goes to termination.

/**
 * When job status is changed, Flink will generate job event and notify job execution listener.
 */
@PublicEvolving
public interface JobExecutionListener {
    /* Event fired after job status has been changed. */ 
    void onEvent(JobStatusEvent jobStatusEvent);

    /** Basic job status event. */
    abstract class JobStatusEvent {
        JobID jobId();
        String jobName();
    }

    /** Job created event with job lineage. */
    @PublicEvolving
    class JobCreatedEvent extends JobStatusEvent {
		/* Lineage for the current job. */
        JobLineage lineage();

    	/* Job type, TableOrSQL or DataStream. */
    	String jobType();

    	/* Job execution type, BATCH or STREAMING. */
    	String executionType(); 
 
    	/* Job configuration. */
    	Map<String, String> config();  
    }

    /** Job status event with plan. */
	@PublicEvolving
    class JobExecutionStatusEvent extends JobStatusEvent {
        JobStatus oldStatus();
        JobStatus newStatus();
        @Nullable Throwable exception();
    }

/* Factory for job execution listener. */
@PublicEvolving
public interface JobExecutionListenerFactory {
    public JobExecutionListener createListener(Configuration configuration, ClassLoader classLoader);
}


The definition of job lineages is divided into two layers: the first layer is global abstraction for Flink jobs and connectors, and the second layer defines the lineages of Table/Sql jobs and DataStream based on the first one. 

/**
 * Job lineage is built according to StreamGraph. Users can get sources, sinks and the relationship between nodes from lineage.
 */
@PublicEvolvig
public interface JobLineage {
    /* Source lineage list. */
    List<SourceLineage> sources();

    /* Sink lineage list. */
    List<SinkLineage> sinks();
}

/** Base connector lineage interface. */
public interface ConnectorLineage {
    StorageIdentifier connector();
}

/** Base source lineages. */
public interface SourceLineage extends ConnectorLineage {}

/** Base sink lineages. */
public interface SinkLineage extends ConnectorLineage {
    List<SourceLineage> sources();
}

Job lineage for Table/SQL job

For Table/SQL jobs, we can create table lineages according to tables for source and sink. There're column lineages in table lineages, and Flink jobs can create the dependencies between source and sink columns. Flink creates these lineages for Table/SQL jobs from job planner, the entire processing has nothing to do with users.

/** Basic table lineage and listeners can get catalog and table from it. */
public abstract class TableLineage implements ConnectorLineage {
    /* The catalog of the table lineage. */
    public Catalog catalog();

    /* The table of the table lineage. */
    public CatalogBaseTable table();
}

/** Source lineage for table. */
@PublicEvolving
public class TableSourceLineage extends TableLineage implements SourceLineage {
    public StorageIdentifier connector();

    /* Output columns for the source and the detailed column information such as data type are in the table. */
    public List<String> columns();
}

/** Sink lineage for table. */
@PublicEvolving
public class TableSinkLineage extends TableLineage implements SinkLineage {
    public StorageIdentifier connector(); 

    /* Modify type, INSERT/UPDATE/DELETE. */
    String modifyType();
 
    /* Update mode, APPEND/RETRACT/UPSERT. */
    String updateMode();
    boolean overwrite();

    /* The source lineages for the sink table. */
    public List<SourceLineage> sources();

    /* The output columns for the sink table. */
    public List<String> columns();

    /* The source column lineages for each target column in sink table. */
    public Map<String, List<TableColumnLineage>> columnLineages();    

    /* Source table and columns for the target column in sink lineage. Multiple source table columns would generate one sink column. */
	@PublicEvolving
    public class TableColumnLineage {
        /* The source table for column lineage. */
        public TableSourceLineage source();

        /* The columns in source lineage for column lineage. */
        public List<String> columns();
    }
}

Job lineage for DataStream job


Config Customized Listener

Users should add their listeners to the classpath of client and flink cluster, and config the listener factory in the following options

# Config catalog event listeners.
table.catalog.listeners: {job catalog listener factory1},{job catalog listener factory2}

# Existing config job submission listeners.
execution.job-submission-listeners: {job submission listener factory1},{job submission listener factory2}

# Config job execution listeners.
jobmanager.execution.listeners: {job execution listener factory1},{job execution listener factory2}

Proposed Changes

Use case of job lineage


Changes for CatalogModificationListener

TableEnvironmentImpl creates customized CatalogModificationListener according to the option table.catalog.listeners , and build CatalogManager with the listeners. Some other components such as Sql-Gateway can create CatalogManager with the listeners themselves. The database related operations are in Catalog , then the listeners are added in AbstractCatalog  and users can notify them after database related operations in their customized catalog.

/* Listeners and related operations in the catalog manager. */
public final class CatalogManager {
    private final List<CatalogModificationListener> listeners;

    /* Create catalog manager with listener list. */
    private CatalogManager(
            String defaultCatalogName,
            Catalog defaultCatalog,
            DataTypeFactory typeFactory,
            ManagedTableListener managedTableListener,
            List<CatalogModificationListener> listeners);

    /* Notify the listeners with given catalog event. */
    private void notify(CatalogEvent event) {
        listeners.forEach(listener -> listener.onEvent(event));
    }

    /* Notify listener for tables. */
    public void createTable/dropTable/alterTable(...) {
        ....;
        notify(Create Different Table Event);
    }

    /* Builder for catalog manager. */
    public static final class Builder {
        Builder listeners(List<CatalogModificationListener> listeners);
    }
}

/* Listeners and related operations in AbstractCatalog. */
public abstract class AbstractCatalog implements Catalog {
    private final List<CatalogModificationListener> listeners;

    /* Create the catalog with listeners. */
    public AbstractCatalog(String name, String defaultDatabase, List<CatalogModificationListener> listeners); 

    /**
     * Notify the listeners with given database event, after the customized implementation of AbstractCatalog create/alter/drop a database,
     * it can create the specific event and call the notify method.
     */
    protected void notify(BaseDatabaseEvent event) {
        for (CatalogModificationListener listener : listeners) {
            listener.onEvent(event);
        }
    }
}

/* Create default catalog context with listeners. */
public DefaultCatalogContext {
    public DefaultCatalogContext(
            String name,
            Map<String, String> options,
            ReadableConfig configuration,
            ClassLoader classLoader,
            List<CatalogModificationListener> listeners);

    /* Get catalog event listeners from the context. */
    public List<CatalogModificationListener> listeners() {
        return listeners;
    }
}

Changes for JobSubmissionListener

Build JobLogicalPlan in StreamGraph

Flink creates Planner for sql and table jobs which contains exec nodes, then the planner will be converted Transformation and StreamGraph. DataStream jobs are similar with SQL, Flink creates datastream from environment and converted it to Transformation and StreamGraph. The job conversion is shown as followed. 

There is a graph structure in StreamGraph , we can create JobLogicalPlan based on StreamGraph easily. 

For table and SQL jobs, Flink translates planner exec node to Transformation in method ExecNodeBase.translateToPlanInternal for source and sink. There's resolved table in source and sink nodes, Flink can create source and sink lineage based on the table and save them in source/sink transformations.

For DataStream  jobs, DataStreamSource set the source lineage to source transformation in setLineage method, and DataStreamSink does the same thing.

Finally source and sink transformations are translated and added in StreamGraph by SourceTransformationTranslator.translateInternal  and SinkTransformationTranslator.translateInternal , where source and sink lineages information can be added to StreamGraph too.

Create and notify listeners in RestClusterClient

RestClusterClient  can read option execution.job-submission-listeners from Configuration and create JobSubmissionListener . But there is no StreamGraph in RestClusterClient. RestClusterClient.submitJob is used in AbstractSessionClusterExecutor , which will convert Pipeline to JobGraph and submit the job. AbstractSessionClusterExecutor can set StreamGraph in RestClusterClient before calling its submitJob method, then the RestClusterClient can get the StreamGraph and notify the listener before JobGraph is submitted.

/* Set pipeline to client before it submit job graph.  */
public class AbstractSessionClusterExecutor {
    public CompletableFuture<JobClient> execute(
            @Nonnull final Pipeline pipeline,
            @Nonnull final Configuration configuration,
            @Nonnull final ClassLoader userCodeClassloader)
            throws Exception {
        ....

        final ClusterClientProvider<ClusterID> clusterClientProvider =
                    clusterDescriptor.retrieve(clusterID);
        ClusterClient<ClusterID> clusterClient = clusterClientProvider.getClusterClient();

        // Set pipeline to the cluster client before submit job graph
        clusterClient.setPipeline(pipeline); 

        return clusterClient
                    .submitJob(jobGraph)
                    ....  
    }
}

/* Create job submission event and notify listeners before it submit job graph. */
public class RestClusterClient {
    private final List<JobSubmissionListener> listeners;
    private Pipeline pipeline;

    @Override
    public CompletableFuture<JobID> submitJob(@Nonnull JobGraph jobGraph) {
        // Create event and notify listeners before the job graph is submitted.
        JobSubmissionEvent event = createEventFromPipeline(pipeline);
        if (event != null) {
            listeners.forEach(listener -> listener.onEvent(event));
        }
        
        ....;
    }
}

Changes for JobExecutionListener

JobManager  can create JobExecutionListener in DefaultExecutionGraph according to option jobmanager.execution.listeners in job configuration. Currently JobManager will call DefaultExecutionGraph.transitionState when the job status changes, JobExecutionListener can be notified in the method as follows.

/* Set pipeline to client before it submit job graph.  */
public class DefaultExecutionGraph {
    private final List<JobExecutionListener> executionListeners;

    private boolean transitionState(JobStatus current, JobStatus newState, Throwable error) {
        ....;
        notifyJobStatusHooks(newState, error);
        // notify job execution listeners
        notifyJobExecutionListeners(current, newState, error);
        ....;
    }

    private void notifyJobExecutionListeners(JobStatus current, JobStatus newState, Throwable error) {
        JobStatusEvent event = create job status event;
        executionListeners.forEach(listener -> listener.onEvent(event));
    }
}

Listener Execution

Multiple listeners are independent, and client/JobManager will notify the listeners synchronously. It is highly recommended NOT to perform any blocking operation inside the listeners. If blocked operations are required, users need to perform asynchronous processing in their customized listeners.

Plan For The Future

  1. We add column relationships between job vertex in JobLogicalPlan, but it is not supported in Flink at present. We'd like to implement them in the next FLIP. 
  2. Currently we only supports scan source, lookup join source should be supported later.

  3. Add Job vertex listener for batch mode, such as scheduling and execution status of vertex, execution status of subtask, etc.


[1] https://datahub.io/

[2] https://atlas.apache.org/#/

[3] FLIP-276: Data Consistency of Streaming and Batch ETL in Flink and Table Store



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