alex-balikov commented on code in PR #38288: URL: https://github.com/apache/spark/pull/38288#discussion_r1000989244
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WindowTime.scala: ########## @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.types._ + +// scalastyle:off line.size.limit line.contains.tab +@ExpressionDescription( + usage = """ + _FUNC_(window_column) - Extract the time value from time/session window column which can be used for event time value of window. + The extracted time is (window.end - 1) which reflects the fact that the the aggregating + windows have exclusive upper bound - [start, end) + See <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#window-operations-on-event-time";>'Window Operations on Event Time'</a> in Structured Streaming guide doc for detailed explanation and examples. + """, + arguments = """ + Arguments: + * window_column - The column representing time/session window. + """, + examples = """ + Examples: + > SELECT a, window.start as start, window.end as end, _FUNC_(window), cnt FROM (SELECT a, window, count(*) as cnt FROM VALUES ('A1', '2021-01-01 00:00:00'), ('A1', '2021-01-01 00:04:30'), ('A1', '2021-01-01 00:06:00'), ('A2', '2021-01-01 00:01:00') AS tab(a, b) GROUP by a, window(b, '5 minutes') ORDER BY a, window.start); + A1 2021-01-01 00:00:00 2021-01-01 00:05:00 2021-01-01 00:04:59.999999 2 + A1 2021-01-01 00:05:00 2021-01-01 00:10:00 2021-01-01 00:09:59.999999 1 + A2 2021-01-01 00:00:00 2021-01-01 00:05:00 2021-01-01 00:04:59.999999 1 + """, + group = "datetime_funcs", + since = "3.3.0") +// scalastyle:on line.size.limit line.contains.tab +case class WindowTime(windowColumn: Expression) + extends UnaryExpression + with ImplicitCastInputTypes + with Unevaluable + with NonSQLExpression { + + // FIXME: Should we check that windowColumn is a correct one - generated by + // a window or session_window aggregation? Do we need to check for the corresponding marker? + + override def child: Expression = windowColumn + override def inputTypes: Seq[AbstractDataType] = Seq(StructType) + + // FIXME: pick either "start" or "end" explicitly here Review Comment: removed. ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala: ########## @@ -4201,6 +4219,73 @@ object SessionWindowing extends Rule[LogicalPlan] { } } +/** + * Resolves the window_time expression which extracts the correct window time from the + * window column generated as the output of the window aggregating operators. The + * window column is of type struct { start: TimestampType, end: TimestampType }. + * The correct window time for further aggregations is window.end - 1. + * */ +object ResolveWindowTime extends Rule[LogicalPlan] { + override def apply(plan: LogicalPlan): LogicalPlan = plan.resolveOperatorsUp { + case p: LogicalPlan if p.children.size == 1 => + val child = p.children.head + val windowTimeExpressions = + p.expressions.flatMap(_.collect { case w: WindowTime => w }).toSet + + if (windowTimeExpressions.size == 1 && + windowTimeExpressions.head.windowColumn.resolved && + windowTimeExpressions.head.checkInputDataTypes().isSuccess) { + + val windowTime = windowTimeExpressions.head + + val metadata = windowTime.windowColumn match { + case a: Attribute => a.metadata + case _ => Metadata.empty + } + + if (!metadata.contains(TimeWindow.marker) && + !metadata.contains(SessionWindow.marker)) { + // FIXME: error framework? + throw new AnalysisException("The input is not a correct window column!") + } + + val newMetadata = new MetadataBuilder() + .withMetadata(metadata) + .remove(TimeWindow.marker) + .remove(SessionWindow.marker) + .build() + + val attr = AttributeReference( + "window_time", windowTime.dataType, metadata = newMetadata)() + + // NOTE: "window.end" is "exclusive" upper bound of window, so if we use this value as + // it is, it is going to be bound to the different window even if we apply the same window + // spec. Decrease 1 microsecond from window.end to let the window_time be bound to the + // correct window range. + val subtractExpr = + PreciseTimestampConversion( + Subtract(PreciseTimestampConversion( + // FIXME: better handling of window.end Review Comment: removed ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WindowTime.scala: ########## @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.types._ + +// scalastyle:off line.size.limit line.contains.tab +@ExpressionDescription( + usage = """ + _FUNC_(window_column) - Extract the time value from time/session window column which can be used for event time value of window. + The extracted time is (window.end - 1) which reflects the fact that the the aggregating + windows have exclusive upper bound - [start, end) + See <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#window-operations-on-event-time";>'Window Operations on Event Time'</a> in Structured Streaming guide doc for detailed explanation and examples. + """, + arguments = """ + Arguments: + * window_column - The column representing time/session window. + """, + examples = """ + Examples: + > SELECT a, window.start as start, window.end as end, _FUNC_(window), cnt FROM (SELECT a, window, count(*) as cnt FROM VALUES ('A1', '2021-01-01 00:00:00'), ('A1', '2021-01-01 00:04:30'), ('A1', '2021-01-01 00:06:00'), ('A2', '2021-01-01 00:01:00') AS tab(a, b) GROUP by a, window(b, '5 minutes') ORDER BY a, window.start); + A1 2021-01-01 00:00:00 2021-01-01 00:05:00 2021-01-01 00:04:59.999999 2 + A1 2021-01-01 00:05:00 2021-01-01 00:10:00 2021-01-01 00:09:59.999999 1 + A2 2021-01-01 00:00:00 2021-01-01 00:05:00 2021-01-01 00:04:59.999999 1 + """, + group = "datetime_funcs", + since = "3.3.0") +// scalastyle:on line.size.limit line.contains.tab +case class WindowTime(windowColumn: Expression) + extends UnaryExpression + with ImplicitCastInputTypes + with Unevaluable + with NonSQLExpression { + + // FIXME: Should we check that windowColumn is a correct one - generated by Review Comment: ResolveWindowTime does check the marker. Removed. ########## sql/core/src/main/scala/org/apache/spark/sql/functions.scala: ########## @@ -3777,6 +3777,23 @@ object functions { window(timeColumn, windowDuration, windowDuration, "0 second") } + /** + * Extracts the event time from the window column of a record produced by window aggregation Review Comment: Removed 'operator'. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
