vranes commented on code in PR #57086: URL: https://github.com/apache/spark/pull/57086#discussion_r3578981715
########## sql/core/src/main/scala/org/apache/spark/sql/execution/BinByExec.scala: ########## @@ -0,0 +1,213 @@ +/* + * 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.execution + +import java.time.ZoneOffset + +import org.apache.spark.SparkException +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeSet, BindReferences, BoundReference, Cast, Expression, GenericInternalRow, JoinedRow, Literal, Multiply, UnsafeProjection} +import org.apache.spark.sql.catalyst.util.{DateTimeUtils, TimestampFormatter} +import org.apache.spark.sql.errors.QueryExecutionErrors +import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.types.DoubleType + +/** + * Physical node for the `BIN BY` relation operator. For each input row it emits one output row per + * bin overlapping `[rangeStart, rangeEnd)`. + * + * Per output sub-row: the DISTRIBUTE UNIFORM columns are scaled by the overlap fraction (the bin's + * half-open intersection with `[rangeStart, rangeEnd)`); the other forwarded columns, including the + * range columns, are replicated unchanged; `bin_start`, `bin_end`, `bin_distribute_ratio` are + * appended. Bin boundaries reuse [[DateTimeUtils.timeBucketDTInterval]] / + * [[DateTimeUtils.timestampAddDayTime]], so they match `time_bucket`: sub-day widths use UTC + * microsecond arithmetic, multi-day widths use civil-time arithmetic in the session zone (UTC for + * TIMESTAMP_NTZ). + * + * DISTRIBUTE UNIFORM columns are FLOAT or DOUBLE only (enforced by `ResolveBinBy`); scaling is IEEE + * multiplication by the ratio, no rounding. Per-row edge cases: a NULL in either range column emits + * one row with all computed columns NULL (scaled DISTRIBUTE values and all three appended columns); + * a zero-length range emits one row with ratio 1.0; an inverted range raises + * `BIN_BY_INVALID_RANGE`. + * + * Output shape mirrors the logical `BinBy`: the child columns with each DISTRIBUTE slot swapped to + * its scaled produced attribute, then the three appended columns. + */ +case class BinByExec( + binWidthMicros: Long, + originMicros: Long, + rangeStart: Attribute, + rangeEnd: Attribute, + distributeColumns: Seq[Attribute], + scaledDistributeColumns: Seq[Attribute], + appendedAttributes: Seq[Attribute], + timeZoneId: Option[String], + child: SparkPlan) + extends UnaryExecNode { + + override lazy val metrics: Map[String, SQLMetric] = Map( + "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows")) + + private val distributeReplacements: AttributeMap[Attribute] = + AttributeMap(distributeColumns.zip(scaledDistributeColumns)) + + // Appended columns are [bin_start, bin_end, bin_distribute_ratio]; the ratio is the last one. + private val numAppended: Int = appendedAttributes.length + assert(numAppended == 3, + s"BinBy appends exactly 3 columns (bin_start, bin_end, bin_distribute_ratio), got $numAppended") + + override def output: Seq[Attribute] = + child.output.map(a => distributeReplacements.getOrElse(a, a)) ++ appendedAttributes + + override def producedAttributes: AttributeSet = Review Comment: I'll address this by extending PartitioningPreservingUnaryExecNode in a follow up PR. Thanks! ########## sql/core/src/main/scala/org/apache/spark/sql/execution/BinByExec.scala: ########## @@ -0,0 +1,213 @@ +/* + * 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.execution + +import java.time.ZoneOffset + +import org.apache.spark.SparkException +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeSet, BindReferences, BoundReference, Cast, Expression, GenericInternalRow, JoinedRow, Literal, Multiply, UnsafeProjection} +import org.apache.spark.sql.catalyst.util.{DateTimeUtils, TimestampFormatter} +import org.apache.spark.sql.errors.QueryExecutionErrors +import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.types.DoubleType + +/** + * Physical node for the `BIN BY` relation operator. For each input row it emits one output row per + * bin overlapping `[rangeStart, rangeEnd)`. + * + * Per output sub-row: the DISTRIBUTE UNIFORM columns are scaled by the overlap fraction (the bin's + * half-open intersection with `[rangeStart, rangeEnd)`); the other forwarded columns, including the + * range columns, are replicated unchanged; `bin_start`, `bin_end`, `bin_distribute_ratio` are + * appended. Bin boundaries reuse [[DateTimeUtils.timeBucketDTInterval]] / + * [[DateTimeUtils.timestampAddDayTime]], so they match `time_bucket`: sub-day widths use UTC + * microsecond arithmetic, multi-day widths use civil-time arithmetic in the session zone (UTC for + * TIMESTAMP_NTZ). + * + * DISTRIBUTE UNIFORM columns are FLOAT or DOUBLE only (enforced by `ResolveBinBy`); scaling is IEEE + * multiplication by the ratio, no rounding. Per-row edge cases: a NULL in either range column emits + * one row with all computed columns NULL (scaled DISTRIBUTE values and all three appended columns); + * a zero-length range emits one row with ratio 1.0; an inverted range raises + * `BIN_BY_INVALID_RANGE`. + * + * Output shape mirrors the logical `BinBy`: the child columns with each DISTRIBUTE slot swapped to + * its scaled produced attribute, then the three appended columns. + */ +case class BinByExec( + binWidthMicros: Long, + originMicros: Long, + rangeStart: Attribute, + rangeEnd: Attribute, + distributeColumns: Seq[Attribute], + scaledDistributeColumns: Seq[Attribute], + appendedAttributes: Seq[Attribute], + timeZoneId: Option[String], + child: SparkPlan) + extends UnaryExecNode { + + override lazy val metrics: Map[String, SQLMetric] = Map( + "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows")) + + private val distributeReplacements: AttributeMap[Attribute] = + AttributeMap(distributeColumns.zip(scaledDistributeColumns)) + + // Appended columns are [bin_start, bin_end, bin_distribute_ratio]; the ratio is the last one. + private val numAppended: Int = appendedAttributes.length + assert(numAppended == 3, + s"BinBy appends exactly 3 columns (bin_start, bin_end, bin_distribute_ratio), got $numAppended") + + override def output: Seq[Attribute] = + child.output.map(a => distributeReplacements.getOrElse(a, a)) ++ appendedAttributes + + override def producedAttributes: AttributeSet = Review Comment: I'll address this by extending PartitioningPreservingUnaryExecNode in a follow up PR, with the appropriate tests. 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