聊聊flink Table的Over Windows

序

本文主要研究一下flink Table的Over Windows

实例

Table table = input
  .window([OverWindow w].as("w"))           // define over window with alias w
  .select("a, b.sum over w, c.min over w"); // aggregate over the over window w

• Over Windows类似SQL的over子句，它可以基于event-time、processing-time或者row-count；具体可以通过Over类来构造，其中必须设置orderBy、preceding及as方法；它有Unbounded及Bounded两大类

Unbounded Over Windows实例

// Unbounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("unbounded_range").as("w"));

// Unbounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("unbounded_range").as("w"));

// Unbounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("unbounded_row").as("w"));
 
// Unbounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("unbounded_row").as("w"));

• 对于event-time及processing-time使用unbounded_range来表示Unbounded，对于row-count使用unbounded_row来表示Unbounded

Bounded Over Windows实例

// Bounded Event-time over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("1.minutes").as("w"))

// Bounded Processing-time over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("1.minutes").as("w"))

// Bounded Event-time Row-count over window (assuming an event-time attribute "rowtime")
.window(Over.partitionBy("a").orderBy("rowtime").preceding("10.rows").as("w"))
 
// Bounded Processing-time Row-count over window (assuming a processing-time attribute "proctime")
.window(Over.partitionBy("a").orderBy("proctime").preceding("10.rows").as("w"))

• 对于event-time及processing-time使用诸如1.minutes来表示Bounded，对于row-count使用诸如10.rows来表示Bounded

Table.window

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

class Table(
    private[flink] val tableEnv: TableEnvironment,
    private[flink] val logicalPlan: LogicalNode) {

  //......  

  @varargs
  def window(overWindows: OverWindow*): OverWindowedTable = {

    if (tableEnv.isInstanceOf[BatchTableEnvironment]) {
      throw new TableException("Over-windows for batch tables are currently not supported.")
    }

    if (overWindows.size != 1) {
      throw new TableException("Over-Windows are currently only supported single window.")
    }

    new OverWindowedTable(this, overWindows.toArray)
  }

  //......

}

• Table提供了OverWindow参数的window方法，用来进行Over Windows操作，它创建的是OverWindowedTable

OverWindow

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

/**
  * Over window is similar to the traditional OVER SQL.
  */
case class OverWindow(
    private[flink] val alias: Expression,
    private[flink] val partitionBy: Seq[Expression],
    private[flink] val orderBy: Expression,
    private[flink] val preceding: Expression,
    private[flink] val following: Expression)

• OverWindow定义了alias、partitionBy、orderBy、preceding、following属性

Over

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

object Over {

  /**
    * Specifies the time attribute on which rows are grouped.
    *
    * For streaming tables call [[orderBy 'rowtime or orderBy 'proctime]] to specify time mode.
    *
    * For batch tables, refer to a timestamp or long attribute.
    */
  def orderBy(orderBy: String): OverWindowWithOrderBy = {
    val orderByExpr = ExpressionParser.parseExpression(orderBy)
    new OverWindowWithOrderBy(Array[Expression](), orderByExpr)
  }

  /**
    * Partitions the elements on some partition keys.
    *
    * @param partitionBy some partition keys.
    * @return A partitionedOver instance that only contains the orderBy method.
    */
  def partitionBy(partitionBy: String): PartitionedOver = {
    val partitionByExpr = ExpressionParser.parseExpressionList(partitionBy).toArray
    new PartitionedOver(partitionByExpr)
  }
}

class OverWindowWithOrderBy(
  private val partitionByExpr: Array[Expression],
  private val orderByExpr: Expression) {

  /**
    * Set the preceding offset (based on time or row-count intervals) for over window.
    *
    * @param preceding preceding offset relative to the current row.
    * @return this over window
    */
  def preceding(preceding: String): OverWindowWithPreceding = {
    val precedingExpr = ExpressionParser.parseExpression(preceding)
    new OverWindowWithPreceding(partitionByExpr, orderByExpr, precedingExpr)
  }

}

class PartitionedOver(private val partitionByExpr: Array[Expression]) {

  /**
    * Specifies the time attribute on which rows are grouped.
    *
    * For streaming tables call [[orderBy 'rowtime or orderBy 'proctime]] to specify time mode.
    *
    * For batch tables, refer to a timestamp or long attribute.
    */
  def orderBy(orderBy: String): OverWindowWithOrderBy = {
    val orderByExpr = ExpressionParser.parseExpression(orderBy)
    new OverWindowWithOrderBy(partitionByExpr, orderByExpr)
  }
}

class OverWindowWithPreceding(
    private val partitionBy: Seq[Expression],
    private val orderBy: Expression,
    private val preceding: Expression) {

  private[flink] var following: Expression = _

  /**
    * Assigns an alias for this window that the following `select()` clause can refer to.
    *
    * @param alias alias for this over window
    * @return over window
    */
  def as(alias: String): OverWindow = as(ExpressionParser.parseExpression(alias))

  /**
    * Assigns an alias for this window that the following `select()` clause can refer to.
    *
    * @param alias alias for this over window
    * @return over window
    */
  def as(alias: Expression): OverWindow = {

    // set following to CURRENT_ROW / CURRENT_RANGE if not defined
    if (null == following) {
      if (preceding.resultType.isInstanceOf[RowIntervalTypeInfo]) {
        following = CURRENT_ROW
      } else {
        following = CURRENT_RANGE
      }
    }
    OverWindow(alias, partitionBy, orderBy, preceding, following)
  }

  /**
    * Set the following offset (based on time or row-count intervals) for over window.
    *
    * @param following following offset that relative to the current row.
    * @return this over window
    */
  def following(following: String): OverWindowWithPreceding = {
    this.following(ExpressionParser.parseExpression(following))
  }

  /**
    * Set the following offset (based on time or row-count intervals) for over window.
    *
    * @param following following offset that relative to the current row.
    * @return this over window
    */
  def following(following: Expression): OverWindowWithPreceding = {
    this.following = following
    this
  }
}

• Over类是创建over window的帮助类，它提供了orderBy及partitionBy两个方法，分别创建的是OverWindowWithOrderBy及PartitionedOver
• PartitionedOver提供了orderBy方法，创建的是OverWindowWithOrderBy；OverWindowWithOrderBy提供了preceding方法，创建的是OverWindowWithPreceding
• OverWindowWithPreceding则包含了partitionBy、orderBy、preceding属性，它提供了as方法创建OverWindow，另外还提供了following方法用于设置following offset

OverWindowedTable

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

class OverWindowedTable(
    private[flink] val table: Table,
    private[flink] val overWindows: Array[OverWindow]) {

  def select(fields: Expression*): Table = {
    val expandedFields = expandProjectList(
      fields,
      table.logicalPlan,
      table.tableEnv)

    if(fields.exists(_.isInstanceOf[WindowProperty])){
      throw new ValidationException(
        "Window start and end properties are not available for Over windows.")
    }

    val expandedOverFields = resolveOverWindows(expandedFields, overWindows, table.tableEnv)

    new Table(
      table.tableEnv,
      Project(
        expandedOverFields.map(UnresolvedAlias),
        table.logicalPlan,
        // required for proper projection push down
        explicitAlias = true)
        .validate(table.tableEnv)
    )
  }

  def select(fields: String): Table = {
    val fieldExprs = ExpressionParser.parseExpressionList(fields)
    //get the correct expression for AggFunctionCall
    val withResolvedAggFunctionCall = fieldExprs.map(replaceAggFunctionCall(_, table.tableEnv))
    select(withResolvedAggFunctionCall: _*)
  }
}

• OverWindowedTable构造器需要overWindows参数；它只提供select操作，其中select可以接收String类型的参数，也可以接收Expression类型的参数；String类型的参数会被转换为Expression类型，最后调用的是Expression类型参数的select方法；select方法创建了新的Table，其Project的projectList为expandedOverFields.map(UnresolvedAlias)，而expandedOverFields则通过resolveOverWindows(expandedFields, overWindows, table.tableEnv)得到

小结

• Over Windows类似SQL的over子句，它可以基于event-time、processing-time或者row-count；具体可以通过Over类来构造，其中必须设置orderBy、preceding及as方法；它有Unbounded及Bounded两大类(对于event-time及processing-time使用unbounded_range来表示Unbounded，对于row-count使用unbounded_row来表示Unbounded；对于event-time及processing-time使用诸如1.minutes来表示Bounded，对于row-count使用诸如10.rows来表示Bounded)
• Table提供了OverWindow参数的window方法，用来进行Over Windows操作，它创建的是OverWindowedTable；OverWindow定义了alias、partitionBy、orderBy、preceding、following属性；Over类是创建over window的帮助类，它提供了orderBy及partitionBy两个方法，分别创建的是OverWindowWithOrderBy及PartitionedOver，而PartitionedOver提供了orderBy方法，创建的是OverWindowWithOrderBy；OverWindowWithOrderBy提供了preceding方法，创建的是OverWindowWithPreceding；OverWindowWithPreceding则包含了partitionBy、orderBy、preceding属性，它提供了as方法创建OverWindow，另外还提供了following方法用于设置following offset
• OverWindowedTable构造器需要overWindows参数；它只提供select操作，其中select可以接收String类型的参数，也可以接收Expression类型的参数；String类型的参数会被转换为Expression类型，最后调用的是Expression类型参数的select方法；select方法创建了新的Table，其Project的projectList为expandedOverFields.map(UnresolvedAlias)，而expandedOverFields则通过resolveOverWindows(expandedFields, overWindows, table.tableEnv)得到

doc

• Over Windows