dilipbiswal commented on code in PR #55629: URL: https://github.com/apache/spark/pull/55629#discussion_r3177352080
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteNearestByJoin.scala: ########## @@ -0,0 +1,125 @@ +/* + * 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.optimizer + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate._ +import org.apache.spark.sql.catalyst.plans._ +import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.rules._ + +/** + * Replaces a logical [[NearestByJoin]] operator with a `Generate(Inline(...))` over an + * `Aggregate` that tags each left row with a unique id, cross-joins with the right side, and + * groups by the unique id to compute the top-K matches via `MAX_BY`/`MIN_BY` (K-overload). + * + * Input Pseudo-Query: + * {{{ + * SELECT * FROM left [INNER | LEFT OUTER] JOIN right + * {APPROX | EXACT} NEAREST k BY {DISTANCE | SIMILARITY} expr + * }}} + * + * Rewritten Plan (SIMILARITY, INNER join type): + * {{{ + * Generate inline(_matches), [N], outer=false, [right.col1, right.col2, ...] + * +- Aggregate [__qid], + * [first(left.col0) AS left.col0, ..., first(left.colN-1) AS left.colN-1, + * max_by(struct(right.*), expr, k) AS _matches] + * +- Join Inner + * :- Project [left.*, monotonically_increasing_id() AS __qid] + * : +- left + * +- right + * }}} + * + * For `DISTANCE`, `MIN_BY` is used instead of `MAX_BY`. For `LEFT OUTER`, the `Generate` is + * constructed with `outer = true` so left rows with no matches (empty/null `_matches`) are + * preserved with `NULL` right-side columns. + * + * In this initial implementation both `APPROX` and `EXACT` take the same brute-force rewrite + * path. `APPROX` establishes the contract for future indexed-ANN strategies. + */ +object RewriteNearestByJoin extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan.transformUpWithNewOutput { + case j @ NearestByJoin(left, right, joinType, _, numResults, rankingExpression, direction) => + // 1. Tag each left row with a unique id so that rows from the same left row can later be + // grouped together after the cross-join with `right`. + val qidAlias = Alias(MonotonicallyIncreasingID(), "__qid")() + val taggedLeft = Project(left.output :+ qidAlias, left) + val qidAttr = qidAlias.toAttribute + + // 2. LEFT OUTER-join the tagged left with right (no join condition). LEFT OUTER + // (rather than INNER) preserves left rows even when `right` is empty, so that a + // `LEFT OUTER NEAREST BY` query still returns those rows with `NULL` right-side + // columns after the aggregate + inline below. When `right` is non-empty every left + // row already has right-row pairings, so LEFT OUTER and INNER are equivalent. + // + // Tag the join so `CheckCartesianProducts` skips it: the rewrite intentionally + // materializes a cross product bounded by the downstream `MaxMinByK` aggregate, so + // `spark.sql.crossJoin.enabled = false` should not reject user queries written as + // `NEAREST BY`. + val join = Join(taggedLeft, right, LeftOuter, None, JoinHint.NONE) + join.setTagValue(NearestByJoin.SYNTHETIC_JOIN_TAG, ()) + + // 3. Aggregate grouped by `__qid`: + // - first(col) for every left column so it flows to the output. + // - max_by/min_by(struct(right.*), ranking, k) as `_matches`. + // The ranking expression references left and right columns directly; no outer + // reference is needed because both sides are present in the joined input. + val rightStruct = CreateStruct(right.output) + // reverse = true -> MIN_BY (smallest ranking value first, for DISTANCE) + // reverse = false -> MAX_BY (largest ranking value first, for SIMILARITY) + val reverse = direction match { + case NearestByDistance => true + case NearestBySimilarity => false + } + val topK = MaxMinByK( + rightStruct, + rankingExpression, + Literal(numResults), + reverse = reverse).toAggregateExpression() + val matchesAlias = Alias(topK, "__nearest_matches__")() + + // Carry left columns through with `First`. Within a `__qid` group every row has the same + // left values (each group corresponds to one left row), so `First` is effectively a no-op. + // We use `First` rather than adding all left columns to the GROUP BY because grouping by + // `__qid` alone keeps the shuffle key small. + val firstLeftAggs = left.output.map { attr => + Alias( + First(attr, ignoreNulls = false).toAggregateExpression(), Review Comment: @zhidongqu-db I tried to do this i.e packing struct(left.* ++ right.*) into MaxMinByK's value struct and dropping the per-column First aggregates for INNER. It does produce a cleaner plan, but unfortunately it loses left-side predicate pushdown: once left columns live only as fields inside nearest_matches, PushPredicateThroughNonJoin's subset check fails (left columns aren't in the Aggregate's output. Would like to address this in a follow-up. I am thinking we may need a dedicated rule that recognizes the rewrite shape ? ########## sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/RewriteNearestByJoinSuite.scala: ########## @@ -0,0 +1,115 @@ +/* + * 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.optimizer + +import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.expressions.{Alias, AttributeReference, CreateStruct, Inline, Literal, MonotonicallyIncreasingID} +import org.apache.spark.sql.catalyst.expressions.aggregate.{First, MaxMinByK} +import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, NearestByDistance, NearestBySimilarity, PlanTest} +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Generate, Join, JoinHint, LocalRelation, NearestByJoin, Project} + +class RewriteNearestByJoinSuite extends PlanTest { Review Comment: done -- This is an automated message from the Apache Git Service. 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