Talk about the definition of flink Table Schema

  flink

Order

This article mainly studies the definition of flink Table Schema.

Example

Define fields and types

.withSchema(
  new Schema()
    .field("MyField1", Types.INT)     // required: specify the fields of the table (in this order)
    .field("MyField2", Types.STRING)
    .field("MyField3", Types.BOOLEAN)
)
  • Field name and field type are defined by field

Define field properties

.withSchema(
  new Schema()
    .field("MyField1", Types.SQL_TIMESTAMP)
      .proctime()      // optional: declares this field as a processing-time attribute
    .field("MyField2", Types.SQL_TIMESTAMP)
      .rowtime(...)    // optional: declares this field as a event-time attribute
    .field("MyField3", Types.BOOLEAN)
      .from("mf3")     // optional: original field in the input that is referenced/aliased by this field
)
  • Proctime defines processing-time and rowtime defines event-time and from defines reference or alias.

Define Rowtime property

// Converts an existing LONG or SQL_TIMESTAMP field in the input into the rowtime attribute.
.rowtime(
  new Rowtime()
    .timestampsFromField("ts_field")    // required: original field name in the input
)

// Converts the assigned timestamps from a DataStream API record into the rowtime attribute
// and thus preserves the assigned timestamps from the source.
// This requires a source that assigns timestamps (e.g., Kafka 0.10+).
.rowtime(
  new Rowtime()
    .timestampsFromSource()
)

// Sets a custom timestamp extractor to be used for the rowtime attribute.
// The extractor must extend `org.apache.flink.table.sources.tsextractors.TimestampExtractor`.
.rowtime(
  new Rowtime()
    .timestampsFromExtractor(...)
)
  • Rowtime is defined by timestampsFromField, timestampsFromSource, timestampsFromExtractor.

Defining watermark strategies

// Sets a watermark strategy for ascending rowtime attributes. Emits a watermark of the maximum
// observed timestamp so far minus 1. Rows that have a timestamp equal to the max timestamp
// are not late.
.rowtime(
  new Rowtime()
    .watermarksPeriodicAscending()
)

// Sets a built-in watermark strategy for rowtime attributes which are out-of-order by a bounded time interval.
// Emits watermarks which are the maximum observed timestamp minus the specified delay.
.rowtime(
  new Rowtime()
    .watermarksPeriodicBounded(2000)    // delay in milliseconds
)

// Sets a built-in watermark strategy which indicates the watermarks should be preserved from the
// underlying DataStream API and thus preserves the assigned watermarks from the source.
.rowtime(
  new Rowtime()
    .watermarksFromSource()
)
  • Watermark strategies are defined by watermarksPeriodicAscending, watermarksPeriodicBounded watermarksFromSource.

StreamTableEnvironment.connect

flink-table_2.11-1.7.0-sources.jar! /org/apache/flink/table/api/StreamTableEnvironment.scala

abstract class StreamTableEnvironment(
    private[flink] val execEnv: StreamExecutionEnvironment,
    config: TableConfig)
  extends TableEnvironment(config) {

  //......

  def connect(connectorDescriptor: ConnectorDescriptor): StreamTableDescriptor = {
    new StreamTableDescriptor(this, connectorDescriptor)
  }

  //......
}
  • The connect method of StreamTableEnvironment creates StreamTableDescriptor

StreamTableDescriptor

flink-table_2.11-1.7.0-sources.jar! /org/apache/flink/table/descriptors/StreamTableDescriptor.scala

class StreamTableDescriptor(
    tableEnv: StreamTableEnvironment,
    connectorDescriptor: ConnectorDescriptor)
  extends ConnectTableDescriptor[StreamTableDescriptor](
    tableEnv,
    connectorDescriptor)
  with StreamableDescriptor[StreamTableDescriptor] {

  //......
}
  • StreamTableDescriptor inherits ConnectTableDescriptor

ConnectTableDescriptor

flink-table_2.11-1.7.0-sources.jar! /org/apache/flink/table/descriptors/ConnectTableDescriptor.scala

abstract class ConnectTableDescriptor[D <: ConnectTableDescriptor[D]](
    private val tableEnv: TableEnvironment,
    private val connectorDescriptor: ConnectorDescriptor)
  extends TableDescriptor
  with SchematicDescriptor[D]
  with RegistrableDescriptor { this: D =>

  private var formatDescriptor: Option[FormatDescriptor] = None
  private var schemaDescriptor: Option[Schema] = None

  /**
    * Searches for the specified table source, configures it accordingly, and registers it as
    * a table under the given name.
    *
    * @param name table name to be registered in the table environment
    */
  override def registerTableSource(name: String): Unit = {
    val tableSource = TableFactoryUtil.findAndCreateTableSource(tableEnv, this)
    tableEnv.registerTableSource(name, tableSource)
  }

  /**
    * Searches for the specified table sink, configures it accordingly, and registers it as
    * a table under the given name.
    *
    * @param name table name to be registered in the table environment
    */
  override def registerTableSink(name: String): Unit = {
    val tableSink = TableFactoryUtil.findAndCreateTableSink(tableEnv, this)
    tableEnv.registerTableSink(name, tableSink)
  }

  /**
    * Searches for the specified table source and sink, configures them accordingly, and registers
    * them as a table under the given name.
    *
    * @param name table name to be registered in the table environment
    */
  override def registerTableSourceAndSink(name: String): Unit = {
    registerTableSource(name)
    registerTableSink(name)
  }

  /**
    * Specifies the format that defines how to read data from a connector.
    */
  override def withFormat(format: FormatDescriptor): D = {
    formatDescriptor = Some(format)
    this
  }

  /**
    * Specifies the resulting table schema.
    */
  override def withSchema(schema: Schema): D = {
    schemaDescriptor = Some(schema)
    this
  }

  // ----------------------------------------------------------------------------------------------

  /**
    * Converts this descriptor into a set of properties.
    */
  override def toProperties: util.Map[String, String] = {
    val properties = new DescriptorProperties()

    // this performs only basic validation
    // more validation can only happen within a factory
    if (connectorDescriptor.isFormatNeeded && formatDescriptor.isEmpty) {
      throw new ValidationException(
        s"The connector '$connectorDescriptor' requires a format description.")
    } else if (!connectorDescriptor.isFormatNeeded && formatDescriptor.isDefined) {
      throw new ValidationException(
        s"The connector '$connectorDescriptor' does not require a format description " +
          s"but '${formatDescriptor.get}' found.")
    }

    properties.putProperties(connectorDescriptor.toProperties)
    formatDescriptor.foreach(d => properties.putProperties(d.toProperties))
    schemaDescriptor.foreach(d => properties.putProperties(d.toProperties))

    properties.asMap()
  }
}
  • ConnectTableDescriptor provides the withSchema method to return the Schema.

Schema

flink-table_2.11-1.7.0-sources.jar! /org/apache/flink/table/descriptors/Schema.scala

class Schema extends Descriptor {

  // maps a field name to a list of properties that describe type, origin, and the time attribute
  private val tableSchema = mutable.LinkedHashMap[String, mutable.LinkedHashMap[String, String]]()

  private var lastField: Option[String] = None

  def schema(schema: TableSchema): Schema = {
    tableSchema.clear()
    lastField = None
    schema.getFieldNames.zip(schema.getFieldTypes).foreach { case (n, t) =>
      field(n, t)
    }
    this
  }

  def field(fieldName: String, fieldType: TypeInformation[_]): Schema = {
    field(fieldName, TypeStringUtils.writeTypeInfo(fieldType))
    this
  }

  def field(fieldName: String, fieldType: String): Schema = {
    if (tableSchema.contains(fieldName)) {
      throw new ValidationException(s"Duplicate field name $fieldName.")
    }

    val fieldProperties = mutable.LinkedHashMap[String, String]()
    fieldProperties += (SCHEMA_TYPE -> fieldType)

    tableSchema += (fieldName -> fieldProperties)

    lastField = Some(fieldName)
    this
  }

  def from(originFieldName: String): Schema = {
    lastField match {
      case None => throw new ValidationException("No field previously defined. Use field() before.")
      case Some(f) =>
        tableSchema(f) += (SCHEMA_FROM -> originFieldName)
        lastField = None
    }
    this
  }

  def proctime(): Schema = {
    lastField match {
      case None => throw new ValidationException("No field defined previously. Use field() before.")
      case Some(f) =>
        tableSchema(f) += (SCHEMA_PROCTIME -> "true")
        lastField = None
    }
    this
  }

  def rowtime(rowtime: Rowtime): Schema = {
    lastField match {
      case None => throw new ValidationException("No field defined previously. Use field() before.")
      case Some(f) =>
        tableSchema(f) ++= rowtime.toProperties.asScala
        lastField = None
    }
    this
  }

  final override def toProperties: util.Map[String, String] = {
    val properties = new DescriptorProperties()
    properties.putIndexedVariableProperties(
      SCHEMA,
      tableSchema.toSeq.map { case (name, props) =>
        (Map(SCHEMA_NAME -> name) ++ props).asJava
      }.asJava
    )
    properties.asMap()
  }
}
  • Schem provides the field, from, proctime, rowtime methods to define the relevant properties of the Schema.

Rowtime

flink-table_2.11-1.7.0-sources.jar! /org/apache/flink/table/descriptors/Rowtime.scala

class Rowtime extends Descriptor {

  private var timestampExtractor: Option[TimestampExtractor] = None
  private var watermarkStrategy: Option[WatermarkStrategy] = None

  def timestampsFromField(fieldName: String): Rowtime = {
    timestampExtractor = Some(new ExistingField(fieldName))
    this
  }

  def timestampsFromSource(): Rowtime = {
    timestampExtractor = Some(new StreamRecordTimestamp)
    this
  }

  def timestampsFromExtractor(extractor: TimestampExtractor): Rowtime = {
    timestampExtractor = Some(extractor)
    this
  }

  def watermarksPeriodicAscending(): Rowtime = {
    watermarkStrategy = Some(new AscendingTimestamps)
    this
  }

  def watermarksPeriodicBounded(delay: Long): Rowtime = {
    watermarkStrategy = Some(new BoundedOutOfOrderTimestamps(delay))
    this
  }

  def watermarksFromSource(): Rowtime = {
    watermarkStrategy = Some(PreserveWatermarks.INSTANCE)
    this
  }

  def watermarksFromStrategy(strategy: WatermarkStrategy): Rowtime = {
    watermarkStrategy = Some(strategy)
    this
  }

  final override def toProperties: java.util.Map[String, String] = {
    val properties = new DescriptorProperties()

    timestampExtractor.foreach(normalizeTimestampExtractor(_)
      .foreach(e => properties.putString(e._1, e._2)))
    watermarkStrategy.foreach(normalizeWatermarkStrategy(_)
      .foreach(e => properties.putString(e._1, e._2)))

    properties.asMap()
  }
}
  • Rowtime provides timestamps; FromField, timestampsFromSource, timestampsFromExtractor methods to define timestamps; The Watermark Spearheading, watermarksPeriodicBounded, watermarksFromSource, and watermarksFromStrategy methods are provided for defining watermark strategies.

Summary

  • The connect method of StreamTableEnvironment creates StreamTableDescriptor; ; StreamTableDescriptor inherits ConnectTableDescriptor; ; ConnectTableDescriptor provides the withSchema method to return the Schema.
  • Schem provides the field, from, proctime, rowtime methods to define the relevant attributes of the Schema; Proctime defines processing-time and rowtime defines event-time and from defines reference or alias.
  • Rowtime provides timestamps; FromField, timestampsFromSource, timestampsFromExtractor methods to define timestamps; The Watermark Spearheading, watermarksPeriodicBounded, watermarksFromSource, and watermarksFromStrategy methods are provided for defining watermark strategies.

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