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本文主要研究一下flink table的over windows
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 wOver 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.windowflink-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OverWindowedTableOverWindowflink-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
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 offsetOverWindowedTableflink-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
