聊聊flink Table的Over Windows

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本文主要研究一下flink table的over windows

实例代码语言：javascript代码运行次数：0运行复制

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实例代码语言：javascript代码运行次数：0运行复制
​// 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UnboundedBounded Over Windows实例代码语言：javascript代码运行次数：0运行复制
// 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BoundedTable.window
flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/table.scala
代码语言：javascript代码运行次数：0运行复制
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OverWindowedTableOverWindow
flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/windows.scala
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/**  * 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
代码语言：javascript代码运行次数：0运行复制
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PartitionedOverPartitionedOver提供了orderBy方法，创建的是OverWindowWithOrderBy；OverWindowWithOrderBy提供了preceding方法，创建的是OverWindowWithPrecedingOverWindowWithPreceding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 offsetOverWindowedTable
flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/table.scala
代码语言：javascript代码运行次数：0运行复制
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 offsetOverWindowedTable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docOver Windows
以上就是聊聊flink Table的Over Windows的详细内容，更多请关注创想鸟其它相关文章！
                                                        
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